Dr Gaojie Chen

In this letter, we first incorporate the concept of index modulation (IM) into simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided non-orthogonal multiple access (NOMA) system to improve the spectral efficiency. Specifically, the proposed IM aided STAR-RIS-NOMA system enables extra information bits to be transmitted by allocating subsurfaces to different users in a pre-defined subsurface allocation pattern. Furthermore, an approximate closed form expression on bit error rate (BER) is derived. Simulation results demonstrate that the proposed IM aided STAR-RIS-NOMA system is able to acquire transmission rate improvement compared to the conventional STAR-RIS NOMA.

This paper investigates the amalgamation of affine frequency division multiplexing (AFDM) with sparse code multiple access (SCMA), termed as AFDM-SCMA, to facilitate massive connectivity in high-mobility scenarios. We start by introducing the basic principles of SCMA and AFDM systems and then present the proposed AFDM-SCMA system with multiple input and multiple output (MIMO) for both downlink and uplink channels. A two stage detector is proposed for the multi-user detection of the downlink channels. Additionally, to reduce the detection complexity and exploit the channel sparsity, we propose an expectation propagation algorithm (EPA)-aided low complexity receiver for uplink channels. Through numerical simulations, we validate the enhanced performance of the proposed AFDMSCMA systems compared to conventional orthogonal frequency division multiplexing-empowered SCMA (OFDM-SCMA) systems in terms of error rate performance.

Physical layer security (PLS) technologies are expected to play an important role in the next-generation wireless networks, by providing secure communication to protect critical and sensitive information from illegitimate devices. In this paper, we propose a novel secure communication scheme where the legitimate receiver use full-duplex (FD) technology to transmit jamming signals with the assistance of simultaneous transmitting and reflecting reconfigurable intelligent surface (STARRIS) which can operate under the energy splitting (ES) model and the mode switching (MS) model, to interfere with the undesired reception by the eavesdropper. We aim to maximize the secrecy capacity by jointly optimizing the FD beamforming vectors, amplitudes and phase shift coefficients for the ESRIS, and mode selection and phase shift coefficients for the MS-RIS. With above optimization, the proposed scheme can concentrate the jamming signals on the eavesdropper while simultaneously eliminating the self-interference (SI) in the desired receiver. To tackle the coupling effect of multiple variables, we propose an alternating optimization algorithm to solve the problem iteratively. Furthermore, we handle the non-convexity of the problem by the the successive convex approximation (SCA) scheme for the beamforming optimizations, amplitudes and phase shifts optimizations for the ES-RIS, as well as the phase shifts optimizations for the MS-RIS. In addition, we adopt a semi-definite relaxation (SDR) and Gaussian randomization process to overcome the difficulty introduced by the binary nature of mode optimization of the MS-RIS. Simulation results validate the performance of our proposed schemes as well as the efficacy of adapting both two types of STAR-RISs in enhancing secure communications when compared to the traditional selfinterference cancellation technology.

The full-duplex (FD) communication can achieve higher spectrum efficiency than conventional half-duplex (HD) communication; however, self-interference (SI) is the key hurdle. This paper is the first work to propose the intelligent Omni surface (IOS)-assisted FD multi-input single-output (MISO) FD communication systems to mitigate SI, which solves the frequency-selectivity issue. In particular, two types of IOS are proposed, energy splitting (ES)-IOS and mode switching (MS)-IOS. We aim to maximize data rate and minimize SI power by optimizing the beamforming vectors, amplitudes and phase shifts for the ES-IOS and the mode selection and phase shifts for the MS-IOS. However, the formulated problems are non-convex and challenging to tackle directly. Thus, we design alternative optimization algorithms to solve the problems iteratively. Specifically, the quadratic constraint quadratic programming (QCQP) is employed for the beamforming optimizations, amplitudes and phase shifts optimizations for the ES-IOS and phase shifts optimizations for the MS-IOS. Nevertheless, the binary variables of the MS-IOS render the mode selection optimization intractable, and then we resort to semidefinite relaxation (SDR) and Gaussian randomization procedure to solve it. Simulation results validate the proposed algorithms' efficacy and show the effectiveness of both the IOSs in mitigating SI compared to the case without an IOS.

The low earth orbit (LEO) satellite network is undergoing rapid development with the maturing of satellite communications and rocket launch technologies, and the demand for a global coverage network. However, current satellite communication networks are constrained by limited transmitting signal power, resulting in the use of large-size and energy-consuming ground terminals to provide additional gain. This paper proposes a novel technology called distributed beamforming to address such challenges and support direct communications from LEO satellites to smartphones. The proposed distributed beamforming technique is based on the superposition of electromagnetic (EM) waves and aims to enhance the received signal strength. Furthermore, we utilize EM wave superposition to increase the link budget and provide the coverage pattern formed by the distributed antenna array, which will be affected by the array structure and the transmitter parameters. In addition, the impact of Doppler frequency shift and time misalignment on the performance of distributed beamforming is investigated. Numerical results show that the enhancement of the received power depends on the angle formed by those radiated beams and can be up to the square of the number of beams; namely, a maximum enhancement of 6 dB could be obtained by using two satellites and a maximum of 12 dB increase through four satellites, which provide a clear guideline for the design of distributed beamforming for future satellite communications.

In space-air-ground integrated networks (SAGIN), receivers experience diverse interference from both the satellite and terrestrial transmitters. The heterogeneous structure of SAGIN poses challenges for traditional interference management (IM) schemes to effectively mitigate interference. To address this, a novel UAV-RIS-aided IM scheme is proposed for SAGIN, where different types of channel state information (CSI) including no CSI, instantaneous CSI, and delayed CSI, are considered. According to the types of CSI, interference alignment, beamforming, and space-time precoding are designed at the satellite and terrestrial transmitter side, and meanwhile, the UAV-RIS is introduced for cooperating interference elimination process. Additionally, the degrees of freedom (DoF) obtained by the proposed IM scheme are discussed in depth when the number of antennas on the satellite side is insufficient. Simulation results show that the proposed IM scheme improves the system capacity in different CSI scenarios, and the performance is better than the existing IM benchmarks without UAV-RIS.

Small satellites in Low Earth Orbit (LEO) attract much attention from both industry and academia. The latest production and launch technologies constantly drive the development of LEO constellations. However, the wideband signal, except text messages, cannot be transmitted directly from an LEO satellite to a standard mobile cellular phone due to the insufficient link budget. The current LEO constellation network has to use an extra ground device to receive the signal from the satellite first and then forward the signal to the User Equipment (UE). To achieve direct network communications between LEO satellites and UE, we propose a novel distributed beamforming technology based on the superposition of electromagnetic (EM) waves radiated from multiple satellites that can significantly enhance the link budget in this paper. EM full-wave simulation and Monte Carlo simulation results are provided to verify the effectiveness of the proposed method. The simulation results show a nearly 6 dB enhancement using two radiation sources and an almost 12 dB enhancement using four sources. The received power enhancement could be doubled compared to the diversity gain in Multiple-Input and Single-Output (MISO). Furthermore, other practical application challenges, such as the synchronization and Doppler effect, are also presented.

The evolution of network technologies has witnessed a paradigm shift toward open and intelligent networks, with the Open Radio Access Network (O-RAN) architecture emerging as a promising solution. O-RAN introduces disaggregation and virtualization, enabling network operators to deploy multi-vendor and interoperable solutions. However, managing and automating the complex O-RAN ecosystem presents numerous challenges. To address this, machine learning (ML) techniques have gained considerable attention in recent years, offering promising avenues for network automation in O-RAN. This paper presents a comprehensive survey of the current research efforts on network automation usingML in O-RAN.We begin by providing an overview of the O-RAN architecture and its key components, highlighting the need for automation. Subsequently, we delve into O-RAN support forML techniques. The survey then explores challenges in network automation usingML within the O-RAN environment, followed by the existing research studies discussing application of ML algorithms and frameworks for network automation in O-RAN. The survey further discusses the research opportunities by identifying important aspects whereML techniques can benefit.

This paper proposes a new framework for reconfigurable intelligent surface (RIS)-equipped unmanned aerial vehicles (UAVs) in free-space optical (FSO) communication. To ensure practicality, we consider atmospheric loss caused by fog, which leads to an inhomogeneous medium for laser propagation. In addition, we incorporate the pointing error loss caused by the power fraction on the photodetector (PD) into the system and derive a closed-form expression for the elliptical beam footprint in the pointing error loss. We then propose a leading angle assisted particle swarm optimization (PSO) method to efficiently optimize the numerical results of pointing error loss. Furthermore, after obtaining these numerical results as a precondition, the UAV trajectory is optimized using the proximal policy optimization (PPO) method to achieve the maximum average capacity. Numerical simulations demonstrate that the proposed optimization method achieves greater efficiency and accuracy compared to the decode-and-forward (DF) relay and deep Q-learning (DQN) methods.

Low power consumption and high spectrum efficiency as the great challenges for multi-device access to Internet-of-Things (IoT) have put forward stringent requirements on the future intelligent network. Ambient backscatter communication (ABcom) is regarded as a promising technology to cope with the two challenges, where backscatter device (BD) can reflect ambient radio frequency (RF) signals without additional bandwidth. However, minimalist structural design of BD makes ABcom security vulnerable in wireless propagation environments. By virtue of this fact, this paper considers the physical layer security (PLS) of a wireless-powered ambient backscatter cooperative communication network threatened by an eavesdropper, where the BD with nonlinear energy harvesting model cooperates with decode-and-forward (DF) relay for secure communication. The PLS performance is investigated by deriving the secrecy outage probability (SOP) and secrecy energy efficiency (SEE). Specifically, the closed-form and asymptotic expressions of SOP are derived as well as the secrecy diversity order for the first time. As an energy-constrained device, balancing power consumption and security is major concern for BD, thus the SEE of the proposed network is studied. The results from numerical analysis show that the performance improvement of SOP and SEE is impacted by system parameters, including transmit power, secrecy rate threshold, reflection efficiency and distance between the source and BD, which provide guidance on balancing security and energy efficiency in ambient backscatter cooperative relay networks.

This letter exploits reconfigurable intelligent surface (RIS) in the heterogeneous networks (HetNets) with wireless backhaul to improve the task computing energy efficiency. To minimize the overall energy consumption of all users, we formulate the problem as a combinatorial optimization problem by jointly optimizing the transmit power of users and small cell base stations, computational capacity of users and base stations, task offloading rate of users, and passive beamforming matrix at RIS. The problem is NP-hard and difficult to solve directly. We decompose the problem into two sub-problems of energy consumption optimization at users and base stations, respectively, taking into account of wireless backhaul capacity. The two sub-problems are solved iteratively to find the final solution. Simulation results demonstrate that the proposed scheme can greatly reduce the energy consumption compared with the baselines.

This article proposes a wireless key generation solution for secure low-latency communications with active jamming attack prevention in wireless networked control systems (WNCSs) of Industrial Internet of Things (IIoT) applications. We first identify a new vulnerability in physical-layer key generation schemes using wireless channel and random pilots (RPs) in static environments. We derive a closed-form expression for the probability that the RP-based key is successfully attacked by a long-term eavesdropper at a fixed location. To prevent such attacks, we propose a one-time pad (OTP) encrypted transmission solution assisted by one-way self-interference (SI), which has low-latency, high-security benefits, and active attack detection capability. The performance of the proposed scheme is analytically compared with two benchmark RP-based schemes, and its advantages are verified in a ray-tracing-based simulation environment. We further investigate the impact of critical design parameters, which reveal fundamental insights for the deployment and implementation of our proposed secure communications scheme.

In this paper, we propose a transmission mechanism for fluid antennas (FAs) enabled multiple-input multiple-output (MIMO) communication systems based on index modulation (IM), named FA-IM, which incorporates the principle of IM into FAs-assisted MIMO system to improve the spectral efficiency (SE) without increasing the hardware complexity. In FA-IM, the information bits are mapped not only to the modulation symbols, but also the index of FA position patterns. Additionally, the FA position pattern codebook is carefully designed to further enhance the system performance by maximizing the effective channel gains. Then, a low-complexity detector, referred to efficient sparse Bayesian detector, is proposed by exploiting the inherent sparsity of the transmitted FA-IM signal vectors. Finally, a closed-form expression for the upper bound on the average bit error probability (ABEP) is derived under the finite-path and infinite-path channel condition. Simulation results show that the proposed scheme is capable of improving the SE performance compared to the existing FAs-assisted MIMO and the fixed position antennas (FPAs)-assisted MIMO systems while obviating any additional hardware costs. It has also been shown that the proposed scheme outperforms the conventional FA-assisted MIMO scheme in terms of error performance under the same transmission rate.

This paper investigates a learning-based approach autonomously and jointly optimizing the trajectory of unmanned aerial vehicle (UAV), phase shifts of reconfigurable intelligent surfaces (RIS), and aggregation weights for federated learning (FL) in wireless communications, forming an autonomous RIS-assisted UAV-enabled network. The proposed network considers practical RIS reflection models and FL transmission errors in wireless communications. To optimize the RIS phase shifts, a double cascade correlation network (DCCN) is introduced. Additionally, the deep deterministic policy gradient (DDPG) algorithm is employed to address the optimization problem of UAV trajectory and FL aggregation weights based on the results obtained from DCCN. Simulation results demonstrate the substantial improvement in FL performance within the autonomous RIS-assisted UAV-enabled network setting achieved by the proposed algorithms compared to the benchmarks.

—In recent years, the amalgamation of satellite communications and aerial platforms into space-air-ground integrated network (SAGINs) has emerged as an indispensable area of research for future communications due to the global coverage capacity of low Earth orbit (LEO) satellites and the flexible Deployment of aerial platforms. This paper presents a deep reinforcement learning (DRL)-based approach for the joint optimization of offloading and resource allocation in hybrid cloud and multi-access edge computing (MEC) scenarios within SAGINs. The proposed system considers the presence of multiple satellites, clouds and unmanned aerial vehicles (UAVs). The multiple tasks from ground users are modeled as directed acyclic graphs (DAGs). With the goal of reducing energy consumption and latency in MEC, we propose a novel multi-agent algorithm based on DRL that optimizes both the offloading strategy and the allocation of resources in the MEC infrastructure within SAGIN. A hybrid action algorithm is utilized to address the challenge of hybrid continuous and discrete action space in the proposed problems, and a decision-assisted DRL method is adopted to reduce the impact of unavailable actions in the training process of DRL. Through extensive simulations, the results demonstrate the efficacy of the proposed learning-based scheme, the proposed approach consistently outperforms benchmark schemes, highlighting its superior performance and potential for practical applications. Index Terms—Space-air-ground integrated networks, edge computing , resource allocation, unmanned aerial vehicle, deep reinforcement learning.

An active reconfigurable intelligent surface (RIS) has been shown to be able to enhance the sum-of-degrees-of-freedom (DoF) of a two-user multiple-input multiple-output (MIMO) interference channel (IC) with equal number of antennas at each transmitter and receiver. However, for any number of receive and transmit antennas, when and how an active RIS can help to improve the sum-DoF are still unclear. This paper studies the sum-DoF of an active RIS-assisted two-user MIMO IC with arbitrary antenna configurations. In particular, RIS beamforming, transmit zero-forcing, and interference decoding are integrated together to combat the interference problem. In order to maximize the achievable sum-DoF, an integer optimization problem is formulated to optimize the number of eliminating interference links by RIS beamforming. As a result, the derived achievable sum-DoF can be higher than the sum-DoF of two-user MIMO IC, leading to a RIS gain. Furthermore, a sufficient condition of the RIS gain is given as the relationship between the number of RIS elements and the antenna configuration.

We analyze the outage probability of an intelligent reflecting surface (IRS)-assisted communication network. An upper bound on the outage probability is formulated based on the Chernoff inequality. Furthermore, through an exact asymptotic (a large number of reflecting elements) analysis based on a saddlepoint approximation, we derive closed-form expressions of the outage probability for systems with and without a direct link and obtain the corresponding diversity orders. Simulation results corroborate our theoretical analysis and show the inaccuracies inherent in using the central limit theorem (CLT) to analyze system performance. Our analysis is accurate even for a small number of IRS elements in the high signal-to-noise ratio (SNR) regime.

With delayed and imperfect current channel state information at the transmitter (CSIT), namely mixed CSIT, the sum degrees-of-freedom (sum-DoF) in the two-user multiple-input multiple-output (MIMO) broadcast channel and the K-user multiple-input single-output (MISO) broadcast channel with not-less-than-K transmit antennas have been obtained. However, the case of the three-user broadcast channel with two transmit antennas and mixed CSIT is still unexplored. In this paper, we investigate the sum-DoF upper bound of three-user MISO broadcast channel with two transmit antennas and mixed CSIT. By exploiting genie-aided signaling and extremal inequalities, we derive the sum-DoF upper bound as (1 - 0:)3/2+ 90:/4, which is at most 12.5% larger than the expected sum-DoF, given by (1 - 0:) 3/2 + 20:. This indicates that the gap may mitigate by better bounding the imperfect current CSIT counterpart.

This paper proposes an intelligent reflecting surface (IRS) carried by an Unmanned Aerial Vehicle (UAV) for laser path controllable free-space optical (FSO) communication system. To quantify the physical impacts of this system, the generalized atmospheric turbulence is modelled as Gamma-Gamma distribution, and specified pointing error loss is modelled as Hoyt distribution, these channel coefficients participate in the performance assessment by form of the ergodic capacity. A closed-form expression of the asymptotic ergodic capacity is derived. Finally, the model is verified by the Monte Carlo simulation successfully.

With a massive amount of wireless sensor nodes in Internet of Things (IoT), it is difficult to establish key distribution and management mechanism for traditional encryption technology. Alternatively, the physical layer key generation technology is promising to implement in IoT, since it is based on the principle of information-theoretical security and has the advantage of low complexity. Most existing key generation schemes assume that eavesdropping channels are independent of legitimate channels, which may not be practical especially when eavesdropper nodes are near to legitimate nodes. However, this paper investigates key generation problems for a multi-relay wireless network in IoT, where the correlation between eavesdropping and legitimate channels are considered. Key generation schemes are proposed for both non-colluding and partially colluding eavesdroppers situations. The main idea is to divide the key agreement process into three phases: 1) we first generate a secret key by exploiting the difference between the random channels associated with each relay node and the eavesdropping channels; 2) another key is generated by integrating the residual common randomness associated with each relay pair; 3) the two keys generated in the first two phases are concatenated into the final key. The secrecy key performance of the proposed key generation schemes is also derived with closed-forms.

Among the recent advances and innovations in wireless technologies, reconfigurable intelligent surfaces (RISs) have received much attention and are envisioned to be one of the enabling technologies for beyond 5G (B5G) networks. On the other hand, active (or classical) cooperative relays have played a key role in providing reliable and power-efficient communications in previous wireless generations. In this article, we focus on hybrid network architectures that amalgamate both active relays and RISs. The operation concept and protocols of each technology are first discussed. Subsequently, we present multiple use cases of cooperative hybrid networks where both active relays and RISs can coexist harmoniously for enhanced rate performance. Furthermore, a case study is provided which demonstrates the achievable rate performance of a communication network assisted by either an active relay, an RIS, or both, and with different relaying protocols. Finally, we provide the reader with the challenges and key research directions in this area.

The paper proposes a novel design of simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) in a wireless powered Internet of Things (IoT) network, where two sensor node groups (SNGs) harvest energy from a power station (PS) and transmit their message to an access point (AP) with the harvested energy. The STAR-RIS, which is deployed in the middle of the SNGs and adopts the time splitting (TS) working mode, can help the energy transfer in the wireless energy transfer (WET) phase and the information transfer in the wireless information transfer (WIT) phase. The paper aims to maximize the sum throughput from the two SNGs to the AP by jointly designing the phase shifts of the STAR-RIS and the working time allocated to the two SNGs in the WET and WIT phases, respectively. To solve the formulated non-convex optimization problem, we propose a low-complexity algorithm where we first derive the optimal phase shifts of the STAR-RIS in the WIT phase. Then, we adopt the Lagrange dual method to simplify the optimization problem and optimize the phase shifts of the STAR-RIS in the WET phase by the Majorization-Minimization (MM) algorithm and the complex circle manifold (CCM) algorithm. Next, a two-layer iterative algorithm is used to obtain the optimal values of time allocated to the two SNGs. Finally, we evaluate the improvement of the proposed scheme by the simulation results compared with other benchmark schemes.

This paper conceives a novel sparse code multiple access (SCMA) codebook design which is motivated by the strong need for providing ultra-low decoding complexity and good error performance in downlink Internet-of-things (IoT) networks, in which a massive number of low-end and low-cost IoT communication devices are served. By focusing on the typical Rician fading channels, we analyze the pair-wise error probability of superimposed SCMA codewords and then deduce the design metrics for multi-dimensional constellation construction and sparse codebook optimization. For significant reduction of the decoding complexity, we advocate the key idea of projecting the multi-dimensional constellation elements to a few overlapped complex numbers in each dimension, called low projection (LP). An emerging modulation scheme, called golden angle modulation (GAM), is considered for multi-stage LP optimization, where the resultant multi-dimensional constellation is called LP-GAM. Our analysis and simulation results show the superiority of the proposed LP codebooks (LPCBs) including one-shot decoding convergence and excellent error rate performance. In particular, the proposed LPCBs lead to decoding complexity reduction by at least 97% compared to that of the conventional codebooks, whilst owning large minimum Euclidean distance. Some examples of the proposed LPCBs are available at https://github.com/ethanlq/SCMA-codebook.

We propose two novel antenna selection (AS) and discrete phase-shifts design (PSD) schemes for use in intelligent reflecting surface (IRS) assisted multiuser massive multiple-input multiple-output (mMIMO) networks. The first AS and PSD method aims at maximizing the gain of the channels; while the second method is an iterative sum-rate maximization (ISM) scheme that aims at maximizing the total achievable rate. For the AS part, we demonstrate that the ISM method achieves near optimal performance with much lower complexity compared to benchmark AS schemes, and can be utilized with any precoder at the mMIMO base station. For the PSD, our proposed successive-refinement optimization methods are not only efficient, but their complexities scale linearly with the number of elements at the IRS, making them highly attractive when dealing with large surfaces. A thorough complexity analysis for the proposed methods is carried out in terms of the number of floating point operations required for their implementations. Finally, extensive numerical results are provided and some key points are highlighted on the performance of the proposed schemes with both conjugate beamforming and zero-forcing precoders.

This paper investigates joint transmit beampattern and phase shifts optimization techniques for a reconfigurable intelligent surface (RIS)-assisted multiple-input multiple-output (MIMO) radar in the presence of an eavesdropping target. We propose an optimization technique to maximize the signal-to-interference plus noise ratio (SINR) at the MIMO radar. However, the problem is non-convex due to the non-concavity of the secrecy rate function. To tackle this issue, we apply the block coordinate descent (BCD) algorithm to update the transmit power and the phase shifts of the RIS alternately. Specifically, we utilize the majorization-minimization (MM) algorithm to optimize the phase shifts for a given transmit power and utilize the first-order Taylor expansion to reformulate the problem as a convex problem to optimize the transmit power for a given set of phase shifts. Two transmit beamforming vectors are designed to detect the target and convey information safely to the legitimate receiver. Simulation results show that the RIS-assisted MIMO radar can significantly enhance the SINR compared to an ordinary MIMO radar.

Power consumption and hardware cost are two of the main challenges for realizing beyond fifth generation (B5G) and sixth generation (6G) wireless communications. Recently, the emerging reconfigurable intelligent surface (RIS) has been recognized as a promising tool for enhancing the propagation environment and improving the spectral efficiency of wireless communications by controlling low-cost passive reflecting elements. However, current cellular communications were designed on the basis of conventional communication theories, significantly restricting the development of RIS-assisted B5G/6G technologies and leading to severe limitations. In this article, we discuss RIS-assisted channel estimation issues involved in B5G/6G communications including channel state information (CSI) acquisition, imperfect cascade CSI for beamforming design, and co-channel interference coordination, and develop a few possible solutions or visionary technologies to promote the development of B5G/6G. Finally, potential research opportunities are discussed. Chen , Z , Chen , G , Tang , J , Zhang , S , So , D K C , Dobre , O A , Wong , K & Chambers , J 2023 , ' Reconfigurable-Intelligent-Surface-Assisted B5G/6G Wireless Communications: Challenges, Solution, and Future Opportunities ' , IEEE Communications Magazine , vol. 61 , no. 1 , pp. 16-22 . https://doi.org/10.1109/MCOM.002.2200047

Intelligent omni surface (IOS) technology that can provide both reflection and refraction for signals impinging on its surface, was recently proposed to overcome the coverage area limitation of reconfigurable intelligent surfaces (RISs). This letter investigates the efficacy of IOS in the context of physical layer security (PLS) over wireless channels. In particular, we consider a scenario where the IOS is utilized to enhance the secrecy performance of a legitimate receiver in the presence of a multi-antenna eavesdropper. In addition, artificial noise (AN) aided beamforming is implemented to provide additional security robustness. The resultant optimization problem is non-convex and difficult to solve. Accordingly, the block coordinate descent (BCD) optimization approach is adopted, and the Lagrangian dual method is applied to reduce the complexity of the AN-aided beamforming design. Furthermore, the reflecting and refracting phase shifts are optimized via the quadratically constrained quadratic programming (QCQP) method. Simulation results validate the efficacy of the proposed algorithm and confirm the superiority of IOS over traditional RIS.

In automotive field, the term Internet of Vehicles (IoV) is a sub-application of Internet of Things (IoT).The communication scenario of IoV usually changes in the space-time dimension. Unfortunately, vehicles can not select the optimal routing policy when facing the dynamic environment. Thus, in this paper, we present a V2X Communication protocol based on Nakagami-m Outage Probability (VCNOP) to improve Packet Delivery Ratio (PDR) and Average-End-to-End-Delay (AE2ED). We consider Road-Side-Units (RSU)-assisted communication system to help find the best routing path, and the outage probability to measure the impact of the physical layer on routing protocol. Meanwhile, the dynamic broadcasting mechanism considering the various vehicle velocity and density are applied to increase the accuracy of routing decisions. We utilize vehicle flow model combining realistic Harbin map by SUMO to provision a realistic scenario. Following this, simulation results in NS3 show the advantage of VCNOP compared with other protocols in terms of PDR and AE2ED.

As one of the key features in 5G network, Millimeter wave (mmWave) technology can provide the ultra-wide bandwidth to support higher data rate. However, for high frequency band, mmWave signal still suffers from the high pathloss, the multipath fading and the signal blockage issue, especially in the indoor environment. For different application scenarios, the channel conditions and quality of services (QoS) are quite different. Therefore, it is essential to investigate the impact of the mmWave channel on the system performance. This paper investigates and measures the performance of a 60GHz mmWave module that is exploited for the downlink and uplink high data rate transmission in the Internet of Radio-Light (IoRL) project. The coverage area and the throughput of the mmWave module is estimated by measuring the error vector magnitude (EVM) of received signals with different transmitter (TX) and receiver (RX) angles and at different locations in a laboratory. In this paper, the measurement environment and system setup are introduced. After that, the waveform design for the measurement is also discussed. The measurement results show that this 60GHz mmWave module can provide an acceptable performance only in some cases, which restricts its application scenarios.

Mobile edge caching (MEC) and device-to-device (D2D) communications are two potential technologies to resolve traffic overload problems in the Internet of Things. Previous works usually investigate them separately with MEC for traffic offloading and D2D for information transmission. In this article, a joint framework consisting of MEC and cache-enabled D2D communications is proposed to minimize the energy cost of systematic traffic transmission, where file popularity and user preference are the critical criteria for small base stations (SBSs) and user devices, respectively. Under this framework, we propose a novel caching strategy, where the Markov decision process is applied to model the requesting behaviors. A novel scheme based on reinforcement learning (RL) is proposed to reveal the popularity of files as well as users' preference. In particular, a Q-learning algorithm and a deep Q-network algorithm are, respectively, applied to user devices and the SBS due to different complexities of status. To save the energy cost of systematic traffic transmission, users acquire partial traffic through D2D communications based on the cached contents and user distribution. Taking the memory limits, D2D available files, and status changing into consideration, the proposed RL algorithm enables user devices and the SBS to prefetch the optimal files while learning, which can reduce the energy cost significantly. Simulation results demonstrate the superior energy saving performance of the proposed RL-based algorithm over other existing methods under various conditions.

This letter proposes a novel hybrid relay and Intelligent Reflecting Surface (IRS) assisted system for future wireless networks. We demonstrate that for practical scenarios where the amount of radiated power and/or the number of reflecting elements are/is limited, the performance of an IRS-supported system can be significantly enhanced by utilizing a simple Decode-and-Forward (DF) relay. Tight upper bounds for the ergodic capacity are derived for the proposed scheme under different channel environments, and shown to closely match Monte-Carlo simulations.

This paper investigates asynchronous reinforcement learning algorithms for joint buffer-aided relay selection and power allocation in the non-orthogonal-multiple-access (NOMA) relay network. With the hybrid NOMA/OMA transmission, we investigate joint relay selection and power allocation to maximize the throughput with the delay constraint. To solve this complicated high-dimensional optimization problem, we propose two asynchronous reinforcement learning-based schemes: the asynchronous deep Q-Learning network (ADQN)-based scheme and the asynchronous advantage actor-critic (A3C)-based scheme, respectively. The A3C-based scheme achieves better performance and robustness when the action space is large, while the ADQN-based scheme converges faster with a small action space. Moreover, a-prior information is exploited to improve the convergence of the proposed schemes. The simulation results show that the proposed asynchronous learning-based schemes can learn from the environment and achieve good convergence.

Since the intra-group co-channel interference is introduced intentionally by the non-orthogonal multiple access (NOMA) technique, the reliability of NOMA based networks is limited by far users, while the sum-rate is mainly contributed by near users. In this paper, a NOMA based multiband scheduling policy is proposed to overcome this problem. Unlike conventional NOMA where all users are served in a single frequency band, the proposed scheme divides the entire band into multiple sub-bands, and accommodates the near and far users in different manners by exploring the sub-bands. Particularly, the far users employ repetition-based transmission in all sub-bands, and the maximum ratio combination (MRC) is integrated with successive interference cancellation (SIC) to jointly decode their intended symbols. Closed-form expression of the achievable sum-rate of the proposed scheme is derived, and the outage performance is characterized by the derived upper-bounded outage probability. Numerical results show that the proposed scheme enables NOMA systems to achieve both high reliability and high sum-rate.

Nonlinear post equalization (NPE) based on Volterra series (VS) is considered as an effective way to mitigate the severe light emitting diode (LED) nonlinearity and multipath effect in a visible light communication (VLC) system. However, it is restricted by kernel complexity in practical applications. In this paper, we formulate the kernel extraction of VS-based NPE to be a sparse recovery problem, and propose an efficient sparsity-aware approach, using combined sparse Bayesian learning (SBL) and Kalman filtering (KF) to extract the active VS kernels and thus to reduce the redundancy of NPE. First, from the view of probability, a Bayesian strategy is applied to select the dominant regressors from the original measurement matrices by exploiting the learning of hyperparameters, which encourages the sparseness of VS kernels with an imposed prior. Then, based on the specified regression matrix, the improved KF iteration is used in the estimation of the kernel coefficients to overcome the system instability in a dynamic noise environment. With this methodology, the active VS kernels can be effectively extracted and the corresponding kernel quantity is significantly reduced at least by 65%. Moreover, the system can still work effectively in the case of a lower size of training samples. The simulation results show that the proposed scheme is beneficial to both the nonlinearity compensation and multipath interference mitigation, and exhibits better overall performance than some existing methods, which demonstrates the potential and validity of kernel extraction in VS-based NPE.

Reconfigurable intelligent surface (RIS) has been developed as a promising approach to enhance the performance of fifth-generation (5G) systems through intelligently reconfiguring the reflection elements. However, RIS-assisted beamforming design highly depends on the channel state information (CSI) and RIS's location, which could have a significant impact on system performance. In this paper, the robust beamforming design is investigated for a RIS-assisted multiuser millimeter wave system with imperfect CSI, where the weighted sum-rate maximization problem (WSM) is formulated to jointly optimize transmit beamforming of the BS, RIS placement and reflect beamforming of the RIS. The considered WSM maximization problem includes CSI error, phase shifts matrices, transmit beamforming as well as RIS placement variables, which results in a complicated nonconvex problem. To handle this problem, the original problem is divided into a series of subproblems, where the location of RIS, transmit/reflect beamforming and CSI error are optimized iteratively. Then, a multiobjective evolutionary algorithm is introduced to gradient projection-based alternating optimization, which can alleviate the performance loss caused by the effect of imperfect CSI. Simulation results reveal that the proposed scheme can potentially enhance the performance of existing wireless communication, especially considering a desirable trade-off among beamforming gain, user priority and error factor.

An aim of Internet-of-Things (IoT) networks is to enable smart cities by connecting billions of devices for various applications. Such a massive connectivity can face many vital challenges, one of which is security. Accordingly, we propose a Cooperative Jamming (CJ) scheme for multihop IoT networks, where each transmitted symbol is protected by two hybrid-duplex jamming nodes from randomly located colluding eavesdroppers. Our proposed scheme provides a significant performance enhancement compared to a conventional Single Jamming (SJ) approach, at no additional power cost. In particular, to achieve a secrecy outage probability of 10 −1 , and assuming the number of hops is 8, the proposed CJ scheme outperforms the SJ case by 3.5 dB of jamming Signal-to-Noise Ratio (SNR). Integral closed-form expressions are derived for the secrecy outage probability and verified via Monte Carlo simulations.

Grant-free sparse code multiple access (GF-SCMA) is considered to be a promising multiple access candidate for future wireless networks. In this article, we focus on characterizing the performance of uplink GF-SCMA schemes in a network with ubiquitous connections, such as the Internet-of-Things (IoT) networks. To provide a tractable approach to evaluate the performance of GF-SCMA, we first develop a theoretical model taking into account the property of multiuser detection (MUD) in the SCMA system. Then, the error rate performance of GF-SCMA in the case of codebook collision is analyzed to investigate the reliability of GF-SCMA when reusing codebook in massive IoT networks. For performance evaluation, accurate approximations for both success probability and average symbol error probability (ASEP) are derived. To elaborate further, the analytical results are utilized to discuss the impact of codeword sparse degree in GF-SCMA. After that, we conduct a comparative study between SCMA and its variant, dense code multiple access (DCMA), with GF transmission to offer insights into the effectiveness of these two schemes. This facilitates the GF-SCMA system design in practical implementation. Simulation results show that denser codebooks can help to support more user equipments (UEs) and increase the reliability of data transmission in a GF-SCMA network. Moreover, a higher success probability can be achieved by GF-SCMA with denser UE deployment at low detection thresholds since SCMA can achieve overloading gain.

The high spectrum efficiency of nonorthogonal multiple access (NOMA) is attractive to solve the massive number of connections in the Internet of Things (IoT). This article investigates a buffer-aided cooperative NOMA (C-NOMA) system in the IoT, where the intended users are equipped with buffers for cooperation. The direct transmission from the access point to the users and the buffer-aided cooperative transmission between the intended users are coordinated. In particular, a novel buffer-aided C-NOMA scheme is proposed to adaptively select a direct or cooperative transmission mode, based on the instantaneous channel state information and the buffer state. Then, the performance of the proposed scheme, in terms of the system outage probability and average delay, is theoretically derived with closed-form expressions. Furthermore, the full diversity order of three is demonstrated to be achieved for each user pair if the buffer size is not less than three, which is larger than conventional nonbuffer-aided C-NOMA schemes whose diversity order is only two in the considered C-NOMA system in the IoT.

In this work, two efficient low complexity Antenna Selection (AS) algorithms are proposed for downlink Multi-User (MU) Massive Multiple-Input Multiple-Output (M-MIMO) systems with Matched Filter (MF) precoding. Both algorithms avoid vector multiplications during the iterative selection procedure to reduce complexity. Considering a system with N antennas at the Base Station (BS) serving K single-antenna users in the same time-frequency resources, the first algorithm divides the available antennas into K groups, with the kth group containing the N/K antennas that have the maximum channel norms for the kth user. Therefore, the Signal-to-Interference plus Noise Ratio (SINR) for the kth user can be maximized by selecting a subset of the antennas from only the kth group, thereby resulting in a search space reduction by a factor of K. The second algorithm is a semiblind interference rejection method that relies only on the signs of the interference terms, and at each iteration the antenna that rejects the maximum number of interference terms will be selected. The performance of our proposed methods is evaluated under perfect and imperfect Channel State Information (CSI) and compared with other low complexity AS schemes in terms of the achievable sum rate as well as the energy efficiency. In particular, when the Signal-to-Noise Ratio (SNR) is 10 dB, and for a system with 20 MHz of bandwidth, the proposed methods outperform the case where all the antennas are employed by 108.8 and 49.2 Mbps for the first and second proposed algorithms, respectively, given that the BS has perfect CSI knowledge and is equipped with 256 antennas, out of which 64 are selected to serve 8 single-antenna users.

Free-space optical communication brings large-capacity communication with excellent confidentiality, though fatal obstacles are set by atmospheric turbulence that causes phase shifting in laser links. Therefore, we derived a novel, to the best of our knowledge, iterative wavefront correction algorithm based on a complete second-order deformable mirror (DM) Shack-Hartmann wavefront sensor model as a solution to it. For correcting static wavefront aberration, the proposed algorithm possesses a converging speed faster than the traditional one. In terms of correcting dynamic atmospheric turbulence, it can achieve convergence within two iterations with a residual wavefront root mean square value of less than 1/8 wavelength. The input wavefront under 1.5 wavelength can be corrected on our testbed due to the deformability of the micromachined membrane DM. The research result offers a solution for atmospheric turbulence in the adaptive optics field and may contribute to the development of free-space optical communication. (C) 2021 Optical Society of America

In this paper, we investigate a hybrid multicast/unicast scheme for a multiple-input single-output cache-aided non-orthogonal multiple access (NOMA) vehicular scenario in the face of rapidly fluctuating vehicular wireless channels. Considering a more practical situation, imperfect channel state information is taking into account. In this paper, we formulate an optimization problem to maximize the unicast sum rate under the constraints of the peak power, the peak backhaul, the minimum unicast rate, and the maximum multicast outage probability. To solve the formulated non-convex problem, a lower bound relaxation method is proposed, which enables a division of the original problem into two convex sub-problems. Computer simulations show that the proposed caching-aided NOMA is superior to the orthogonal multiple access counterpart.

In this letter, we investigate the performance of a communication link assisted by a reconfigurable intelligent surface (RIS) over line-of-sight (LOS) channels. The phase configuration of the RIS is imperfect and the phase error for each reflective element is modeled by a Von Mises distribution. First, we derive novel closed-form approximations for the lower tail distribution of the magnitude of the equivalent channel coefficient. Then, we use the obtained expressions to evaluate the outage probability and diversity order of the proposed system. Approximation errors of the proposed method are analyzed in detail. Simulation results validate the theoretical analysis is accurate at a high signal-to-noise ratio (SNR) regime.

In this article, the sum secure degrees-of-freedom (SDoF) of the multiple-input multiple-output (MIMO) X channel with confidential messages (XCCM) and arbitrary antenna configurations is studied, where there is no channel state information (CSI) at two transmitters and only delayed CSI at a multiple-antenna, full-duplex, and decode-and-forward relay. We aim at establishing the sum-SDoF lower and upper bounds. For the sum-SDoF lower bound, we design three relay-aided transmission schemes, namely, the relay-aided jamming scheme, the relay-aided jamming and one-receiver interference alignment scheme, and the relay-aided jamming and two-receiver interference alignment scheme, each corresponding to one case of antenna configurations. Moreover, the security and decoding of each scheme are analyzed. The sum-SDoF upper bound is proposed by means of the existing SDoF region of two-user MIMO broadcast channel with confidential messages (BCCM) and delayed channel state information at the transmitter (CSIT). As a result, the sum-SDoF lower and upper bounds are derived, and the sum-SDoF is characterized when the relay has sufficiently large antennas. Furthermore, even assuming no CSI at two transmitters, our results show that a multiple-antenna full-duplex relay with delayed CSI can elevate the sum-SDoF of the MIMO XCCM. This is corroborated by the fact that the derived sum-SDoF lower bound can be greater than the sum-SDoF of the MIMO XCCM with output feedback and delayed CSIT.

Unmanned aerial vehicle (UAV) will be an essential carrier for future wireless communications due to its flexible deployment and low cost. As such, the information security of UAV communications is of paramount concern. In this paper, a novel physical layer secret key generation scheme is proposed for air-to-ground (A2G) UAV multiple-input-multiple-output (MIMO) communications, which is applicable in frequency division duplex (FDD) systems. In UAV communications, line-of-sight (LoS) propagation is a distinctive feature, which significantly weakens the performance of channel state information (CSI) based keys. Therefore, a novel channel parameter, three-dimension (3D) spatial angle, is employed to combat against a novel active eavesdropping method, which is termed as Environment Reconstruction based Attack for SEcret keys (ERASE). Compared to the existing plane-angle-based method, our scheme can efficiently utilize spatial resources and provide a higher key generation rate (KGR). The advantages of the proposed scheme are shown through both theoretical analysis and simulations.

In fifth generation networks (5G), beamforming technique is widely used to obtain higher system capacity, but it cannot eliminate inter-user interference (IUI) of networks due to excessive number of users. To handle this problem, interference alignment (IA) schemes attract great attention as they can effectively restrain IUI. However, the existing IA schemes cannot achieve antenna adaptation and the obtained degree of freedom (DoF) may be not optimal. In this paper, a novel antenna adaptation based interference elimination and regeneration (AA-IER) scheme is proposed for cooperative networks, where a relay with hybrid antenna array structure is adopted to assist the communication. The proposed transmission process is completed in two phases, including interference elimination phase (IEP) and interference regeneration phase (IRP). For the former, the IUI is eliminated and the redundant symbols are erased so that the received signal of multiple users can be decoded simultaneously. For the latter, the redundant symbols of all users are regenerated where the space resources are fully utilized. The simulation results show that AA-IER scheme obtains higher DoF than that of three benchmark schemes. Meanwhile, it requires fewer antennas of relay than HAA-CIE-RIA scheme.

In this paper, we study the simultaneous wireless information and power transfer (SWIPT) cooperative system, where one source forwards information to one destination with the assistance of multiple relays. Each relay is equipped with a finite data buffer and a finite energy buffer storing the harvested energy by radio-frequency (RF). An optimization problem is formulated for throughput maximization of the SWIPT cooperative system, taking into consideration the strict delay constraint, dynamic channel conditions, time-varying discrete data buffer states and time-varying continuous energy buffer states. A discrete-time Markov decision process (MDP) is adopted to model the relay selection process referring to data buffer states and energy buffer states. Two deep Q-network (DQN)-based methods named invalid action penalty (IAP) and invalid action mask (IAM) are proposed. The simulation results show that the proposed IAM method can achieve better convergence and throughput performance than the IAP method.

In this Letter, a deep reinforcement learning‐based approach is proposed for the delay‐constrained buffer‐aided relay selection in a cooperative cognitive network. The proposed learning algorithm can efficiently solve the complicated relay selection problem, and achieves the optimal throughput when the buffer size and number of relays are large. In particular, the authors use the deep‐Q‐learning to design an agent to estimate a specific action for each state of the system, which is then utilised to provide an optimum trade‐off between throughput and a given delay constraint. Simulation results are provided to demonstrate the advantages of the proposed scheme over conventional selection methods. More specifically, compared to the max‐ratio selection criteria, where the relay with the highest signal‐to‐interference ratio is selected, the proposed scheme achieves a significant throughput gain with higher throughput‐delay balance.

Cognitive multihop relaying has been widely considered for device-to-device (D2D) communications for applications in the physical layer of the Internet of Things. In this article, we construct a multihop cellular D2D communications system model with energy harvesting (EH) in underlay cognitive radio networks. The locations of primary user equipments (PUEs) and cellular base stations are considered as a Poisson point process in this model. The transmit power of secondary devices is collected from the power beacon with time-switching EH policy. Two charging policies for different applications are considered in this article. Then, the end-to-end outage probability analysis expressions of these two scenarios for the transmission scheme subject to interferences from PUEs are derived. The optimal harvesting time ratio is obtained to get the maximum capacity for end-to-end D2D communications. The analytical results are validated by performing the Monte Carlo simulation of the end-to-end outage probability, which is based on the half-duplex transmission scheme. The results of this article provide a potential pathway to reduce reliance on grid or battery energy supplies and, hence, further strengthen the benefits for the environment and deployment of future smart devices.

Wireless-powered communication networks (WPCNs) are a promising technology supporting resource-intensive devices in the Internet of Things (IoT). However, their transmission efficiency is very limited over long distances. The newly emerged intelligent reflecting surface (IRS) can effectively mitigate the propagation-induced impairment by controlling the phase shifts of passive reflection elements. In this article, we integrate IRS into WPCNs to assist both the energy and information transmission. We aim to maximize the uplink (UL) sum rate of all IoT devices by jointly optimizing the time allocation variable, energy beam matrix at the power transmitting base station (PTBS), receive beamforming matrix at the information receiving base station, and the phase shifts of the IRS both in the UL and downlink (DL) subject to time allocation constraint, together with transmit power constraint for the PTBS and unit modulus constraints. This problem is very difficult to solve directly due to the highly coupled variables, which results in the optimization problem taking neither linear nor convex form. Hence, we decouple this problem into three subproblems by using the block coordinate descent method. The UL receive beamforing matrix and phase shift are alternatively optimized in the UL optimization subproblem with fixed time allocation and the DL variables. The DL optimization subproblem is solved by the proposed successive convex approximation algorithm. Simulation results demonstrate that the performance of integrating IRS and WPCNs outperforms traditional WPCNs. Besides, the results show that IRS is an effective method to preserve the tradeoff of energy efficiency and transmission efficiency in the IoT.

In this letter, we propose a novel encrypted data transmission scheme using an intelligent reflecting surface (IRS) to generate secret keys in wireless communication networks. We show that perfectly secure one-time pad (OTP) communications can be established by using a simple random phase shifting of the IRS elements. To maximize the secure transmission rate, we design an optimal time slot allocation algorithm for the IRS secret key generation and the encrypted data transmission phases. Moreover, a theoretical expression of the key generation rate is derived based on Poisson point process (PPP) for the practical scenario when eavesdroppers' channel state information (CSI) is unavailable. Simulation results show that employing our IRS-based scheme can significantly improve the encrypted data transmission performance for a wide-range of wireless channel gains and system parameters.

Energy-limited devices and connectivity in complicated environments are two main challenges for Internet of Things (IoT)-enabled mobile networks, especially when IoT devices are distributed in a disaster area. Unmanned aerial vehicle (UAV)-enabled simultaneous wireless information and power transfer (SWIPT) is emerging as a promising technique to tackle the above problems. In this article, we establish an emergency communications framework of UAV-enabled SWIPT for IoT networks, where the disaster scenarios are classified into three cases, namely, dense areas, wide areas and emergency areas. First, to realize wireless power transfer for IoT devices in dense areas, a UAV-enabled wireless power transfer system is considered where a UAV acts as a wireless charger and delivers energy to a set of energy receivers. Then, a joint trajectory planning and resource scheduling scheme for a multi-UAVs system is discussed to provide wireless services for IoT devices in wide areas. Furthermore, an intelligent prediction mechanism is designed to predict service requirements (i.e., data transmission and battery charging) of the devices in emergency areas, and accordingly, a dynamic path planning scheme is established to improve the energy efficiency (EE) of the system. Simulation results demonstrate the effectiveness of the above schemes. Finally, potential research directions and challenges are also discussed.

Multi-hop route optimization in large-scale inhomogeneous networks is typically NP-hard, for most problem formulations, requiring the application of heuristics which, despite their relatively low processing complexity, find suboptimal solutions. Where optimal solutions can be determined by Lagrangian based constrained optimization techniques for example, the processing complexity typically scales like O(N 3 ), N being the number of relays employed. Here, we propose an alternative approach to route optimization by considering the limit of infinite relay node density to develop a continuum model, which yields an optimized equivalent continuous relay path. The model is carefully constructed to maintain a constant connection density even though the node density scales without bound. This leads to a formulation for minimizing the end-to-end outage probability that can be solved using methods from the calculus of variations. With the continuum model, we show that the processing complexity scales linearly with the number of points that sample the continuous path, which can be lower than the number of relay nodes in a large scale network. We demonstrate the effectiveness of this new approach and its potential by considering a network subjected to point sources of interference.

Due to hardware limitations, the phase shifts of the reflecting elements of reconfigurable intelligent surfaces (RISs) need to be quantized into discrete values. This letter aims to unveil the minimum required number of phase quantization levels {L} in order to achieve the full diversity order in RIS-assisted wireless communication systems. With the aid of an upper bound of the outage probability, we first prove that the full diversity order is achievable provided that {L} is not less than three. If {L}\,\,= 2, on the other hand, we prove that the achievable diversity order cannot exceed ( {N}\,\,+ 1)/2, where {N} is the number of reflecting elements. This is obtained with the aid of a lower bound of the outage probability. Therefore, we prove that the minimum required value of {L} for achieving the full diversity order is {L}\,\,= 3. Simulation results verify the theoretical analysis and the impact of phase quantization levels on RIS-assisted communication systems.

This paper considers secure simultaneous wireless information and power transfer (SWIPT) in cell-free massive multiple-input multiple-output (MIMO) systems. The system consists of a large number of randomly (Poisson-distributed) located access points (APs) serving multiple information users (IUs) and an information-untrusted dual-antenna active energy harvester (EH). The active EH uses one antenna to legitimately harvest energy and the other antenna to eavesdrop information. The APs are networked by a centralized infinite backhaul which allows the APs to synchronize and cooperate via a central processing unit (CPU). Closed-form expressions for the average harvested energy (AHE) and a tight lower bound on the ergodic secrecy rate (ESR) are derived. The obtained lower bound on the ESR takes into account the IUs' knowledge attained by downlink effective precoded-channel training. Since the transmit power constraint is per AP, the ESR is nonlinear in terms of the transmit power elements of the APs and that imposes new challenges in formulating a convex power control problem for the downlink transmission. To deal with these nonlinearities, a new method of balancing the transmit power among the APs via relaxed semidefinite programming (SDP) which is proven to be rank-one globally optimal is derived. A fair comparison between the proposed cell-free and the colocated massive MIMO systems shows that the cell-free MIMO outperforms the colocated MIMO over the interval in which the AHE constraint is low and vice versa. Also, the cell-free MIMO is found to be more immune to the increase in the active eavesdropping power than the colocated MIMO.

Multi-carrier non-orthogonal multiple access (MC-NOMA) system has been considered as a promising candidate in future wireless networks. In a MC-NOMA system, the available bandwidth of transmission is divided into several sub-bands, such that multiple users in each sub-band are served based on power-domain NOMA. Unlike the equal sub-band allocations, we propose a sum-rate maximization technique that jointly allocates the available power and bandwidth with opportunistic sharing between the sub-bands. A second-order cone program approach is exploited to deal with the non-convexity issues of the corresponding optimization problem. Simulation results reveal that the MC-NOMA system with opportunistic bandwidth allocation outperforms the scheme with the equal bandwidth allocation in terms of achieved sum-rate.

Millimeter-wave (mmWave) and ultra-dense networks are two key technologies for the fifth-generation (5G) and beyond communication system. However, the ultra-dense deployment of small base stations (SBSs) might introduce severe interference to users that connect to SBSs. This paper analyzes the performance of 5G communication networks where the SBSs with coordinated beamforming, operating at mmWave frequency band and macro base stations (MBSs) operating at sub-6 GHz coexist. First, by utilizing a stochastic geometry approach, we obtain the cell association probability expressions in terms of different cell association biases, base station density ratios and probabilities of line of sight (LoS) link. Furthermore, we propose a clustering method to choose some SBSs to eliminate intra-cell interference. Then, we put forward an average distance from the Kth SBS to a user to obtain signal-to-interference-ratio (SINR) and rate coverage probability expressions. The simulation results validate the correctness of the expressions, and indicate that the optimal cardinality of coordinated SBSs increases with the density of SBSs. In addition, the relationship between the cluster size K and the average energy efficiency is obtained, which can be used to guide the coordination principle in 5G and beyond communication systems.

As the number of connected devices is exponentially increasing, security in Internet of Things (IoT) networks presents a major challenge. Accordingly, in this work we investigate the secrecy performance of multihop IoT networks assuming that each node is equipped with only two antennas, and can operate in both Half-Duplex (HD) and Full-Duplex (FD) modes. Moreover, we propose an FD Cooperative Jamming (CJ) scheme to provide higher security against randomly located eavesdroppers, where each information symbol is protected with two jamming signals by its two neighbouring nodes, one of which is the FD receiver. We demonstrate that under a total power constraint, the proposed FD-CJ scheme significantly outperforms the conventional FD Single Jamming (FD-SJ) approach, where only the receiving node acts as a jammer, especially when the number of hops is larger than two. Moreover, when the Channel State Information (CSI) is available at the transmitter, and transmit beamforming is applied, our results demonstrate that at low Signal-to-Noise Ratio (SNR), higher secrecy performance is obtained if the receiving node operates in HD and allocates both antennas for data reception, leaving only a single jammer active; while at high SNR, a significant secrecy enhancement can be achieved with FD jamming. Our proposed FD-CJ scheme is found to demonstrate a great resilience over multihop networks, as only a marginal performance loss is experienced as the number of hops increases. For each case, an integral closed-form expression is derived for the secrecy outage probability, and verified by Monte Carlo simulations.

In this paper, we analyze the coverage probability of a reconfigurable intelligent surface (RIS) aided cellular network with the theory of stochastic geometry. A Poisson cluster process (PCP) is applied to model the positions of transmitters (TXs) and RISs, capturing their spatial correlations. Considering the general Nakagami-m fading channel model, we derive the approximate distributions of the composite channel gains with RIS-assisted transmission, representing the desired signal channel and the interference channel, respectively. The coverage probability of the typical user is then obtained. The derived coverage probability is in a closed form, which can be evaluated efficiently. Simulation results are presented to show that the presented analysis is effective, demonstrate the significant performance gains brought by the passive beamforming of a RIS with a large number of elements, and show the impact of TX density on the performance of the proposed system.

This letter investigates machine learning approach for the joint optimal phase shift and beamforming in the reconfigurable intelligent surface (RIS) assisted multiple-input and multiple-output (MIMO) network, consisting of one source node, one RIS panel and one destination node. If individual source-to-RIS and RIS-to-destination channels are known, the joint optimization is similar to that in the traditional MIMO network, which has been well studied. However, the channel estimation for the individual channels is complicated and often inaccurate. On the other hand, while estimating the cascaded channels for the source-RIS-destination links are more accessible, the corresponding joint optimization is complicated. In this letter, we propose a novel double deep learning network model which is superior to the conventional reinforcement learning in the RIS joint optimization. Numerical simulations are given to verify the proposed algorithm.

In this paper, we propose a novel reconfigurable intelligent surface (RIS)-based modulation scheme, named RIS-aided receive quadrature reflecting modulation (RIS-RQRM), by resorting to the concept of spatial modulation. In RIS-RQRM, the whole RIS is virtually partitioned into two halves to create signals with only in-phase (I-) and quadrature (Q-) components, respectively, and each half forms a beam to a receive antenna whose index carries the bit information. Furthermore, we design a low-complexity and non-coherent detector for RIS-RQRM, which measures the maximum power and polarities of the I- and Q- components of received signals. Approximate bit error rate (BER) expressions are derived for RIS-RQRM over Rician fading channels. Simulation results show that RIS-RQRM outperforms the existing counterparts without I/Q index modulation in terms of BER in the low signal-to-noise ratio region.

This paper considered an energy-harvesting based secure two-way relay (EH-STWR) network, where two users exchanged information with the assistance of one buffer-aided relay that harvested energy from two users. To realize the confidential message exchange between two users in the presence of a potential eavesdropper, a secure bidirectional relaying scheme based on time division broadcast (TDBC) was proposed, where one user sent artificial noise to suppress the eavesdropper and another user transmitted data to the relay. A secure sum-rate maximization problem was formulated subject to average and peak transmit power constraints, data buffer and energy storage causality, and transmission mode constraints. By employing the Lyapunov optimization framework, a security-aware adaptive transmission scheme was proposed to jointly adapt transmission mode selection, power allocation, and security rate allocation according to channel/buffer/energy state information (CSI/BSI/ESI). Analysis results showed that the average achievable secrecy rate region can be significantly improved and there exists an inherent trade-off among transmission delay, requirement of transmit power consumption, and achievable secure sum-rate. Moreover, the channel condition between the energy-constrained relay and the potential eavesdropper is a critical factor on the achievable long-term average secrecy rate performance.

In the era of big data and the Internet of Things (IoT), data owners need to share a large amount of data with the intended receivers in an insecure environment, posing a trade-off issue between user privacy and data utility. The privacy utility trade-off was facilitated through a privacy funnel based on mutual information. In this article, we propose a privacy funnel which is using mutual information neural estimator (MINE) to optimize the privacy utility trade-off by estimating mutual information. Firstly, we estimate mutual information in a training way for data with unknown distributions and make the result a measure of privacy and utility. Secondly, we optimize the privacy utility trade-off by optimizing the mutual information added noise as an encoding process and minimizing cross-entropy mutual information between published data and non-sensitive data as a decoding process. Finally, simulations are conducted comparing our methodology to the Kraskov, Stogbauer, and Grassberger (KSG) estimation obtained by k-nearest neighbor as well as information bottleneck in the traditional method. Our results clearly demonstrate that the designed framework has better performance and attains convergence quicker in the scenario where enormous volumes of data are handled, and the largest data utility obtained by the MINE for a given privacy threshold is even better.

In this paper, we consider a wireless power transfer assisted uplink non-orthogonal multiple access (WPT-NOMA) network in Internet of things (IoT), where the battery-less IoT devices harvest the energy from the dedicated access point, based on linear and non-linear energy harvesting (EH) models. Furthermore, a novel hybrid successive interference cancellation (SIC) scheme is proposed so that the non-energy constrained IoT device can adaptively adjust the transmit power according to its channel fading gain. For the non-linear EH model, closed-form expressions of outage performance are derived, with the observation of the error outage floor at high signal-to-noise ratio (SNR) region, and the asymptotic behavior of the error floor is derived by using the extreme value theory through increasing the number of IoT devices. For the linear EH model, the proposed hybrid SIC scheme can guarantee the uplink WPT-NOMA networks achieve full diversity gain for any target data rate of IoT devices. Simulation and analytical results verify that the proposed hybrid SIC scheme can overcome the existing works of the hybrid SIC scheme, where the error outage floor appears if the data rate of IoT devices is higher than a threshold value.

Buffer-aided relay networks provide more reliability and coverage in future wireless communications. Therefore, this paper investigates a buffer-aided cooperative relaying system with K relays and a direct link from the source to the destination, providing a general scenario different from other existing state-of-the-art techniques. In particular, we propose a novel link selection scheme, which adaptively coordinates the selection priorities of the direct and cooperative relay link according to the instantaneous buffer state. The performance of the proposed link selection scheme is analyzed, in terms of outage probability, average packet delay (APD) and diversity order by providing closed-form expressions. For asymptotic analysis, a theoretical framework is presented by dividing all buffer states into different sets, which verifies that the minimum buffer size is just two for achieving the full diversity order of 2K + 1. We also provide the relationship between the asymptotic APD and diversity order by adjusting predefined target queue lengths, which shows that the diversity order ranges from K + 1 to 2K + 1 as the asymptotic APD ranges from 0 to K time slots per packet. Both theoretical and simulation results demonstrate that direct transmission significantly improves the outage and delay performance simultaneously.

This paper investigates the outage performance of a reconfigurable intelligent surface (RIS)-assisted communication system with statistical channel state information (CSI) over Rician fading channels. An approximate closed-form expression of the outage probability is derived by determining the distribution of the RIS-based composite channel. To obtain more insights into the system performance, we derive an asymptotic outage probability expression at high signal-to-noise ratio (SNR) region and scaling laws for the coding gain with both continuous and discrete phase shifts. Analytical and simulation results show that the diversity gain is not affected by the number of reflecting elements, but the coding gain exponentially and linearly grows with the number of reflecting elements if the Rician channel factors are larger than and equal to zero, respectively.

In this article, we investigate the intelligent reflecting surface (IRS)-assisted downlink nonorthogonal multiple access (NOMA) networks, where the users are randomly deployed in a disk. The randomly deployed users are divided into two groups, named center group and edge group, and then two distance-dependent user selection schemes are proposed. The developed closed-form expressions of outage probability and average rate for the proposed two user selection schemes in IRS-assisted NOMA networks are derived, as well as the asymptotic results at high signal-to-noise ratio (SNR) and the number of the reflecting number of the IRS trends to infinity, respectively. The asymptotic analysis results of outage probability for cell-edge user decay as \frac{\ln \mathrm{SNR}}{\mathrm{SNR} ^{K+1}}, and the achieved diversity gain is K+1, at high SNR region, where K denotes the number of elements of the IRS. Furthermore, as K \rightarrow \infty, the asymptotic average rate of cell-edge user only depends on the power allocation factor of cell-center user. In addition, compared to IRS-assisted orthogonal multiple access networks, the IRS-assisted NOMA networks can always realize lower outage performance and higher average rate. Finally, Monte-Carlo simulation results are provided to verify the accuracy of the developed analytical results for the proposed two user selection schemes.

Conventionally, the sensing and communication stages for edge intelligence systems are executed sequentially, leading to an excessive time of dataset generation and uploading. To combat the weakness, this paper proposes to accelerate edge intelligence via integrated sensing and communication (ISAC), where the sensing and communication stages are merged to make the best use of the wireless signals for the dual purpose of dataset generation and uploading. For the proposed ISAC-accelerated edge intelligence system, the resource allocation and beamforming should be jointly optimized to exploit the underlying ISAC benefits. We formulate a joint resource allocation and beamforming optimization problem. Despite the non-convexity, we obtain globally optimal solutions assuming that the constant maximal transmits power, and devise an alternating optimization algorithm for the original problem without such assumption. Furthermore, we analyze the ISAC acceleration gain of the proposed system over that of the conventional edge intelligence system. Both theoretic analysis and simulation results show that ISAC accelerates the conventional edge intelligence system when the duration of generating a sample is more than that of uploading a sample. Otherwise, the ISAC acceleration gain vanishes or even is negative. In this case, we derive a sufficient condition for positive ISAC acceleration gain.

The cooperative non-orthogonal multiple access (NOMA) networks with one pair primary user and one pair cognitive user share the same spectrum resource via a common relay is considered in this paper. We propose a dynamic power transmission scheme for both uplink and downlink NOMA transmission in cognitive relay networks, which preserves the quality of service for the primary user. The closed-form expressions of overall outage probability and average sum rate for the proposed dynamic power transmission scheme of cognitive relay NOMA networks are derived. Both developed analytical results and Monte Carlo simulations show that the proposed dynamic power control scheme can dramatically enhance performance gain for the proposed networks, compared to other existing NOMA power allocation schemes.

This paper proposes a novel zero-forcing (ZF) beam-forming strategy that can simultaneously cope with active and passive eavesdroppers (EDs) in visible light communication systems. A related optimization problem is formulated to maximize the signal-to-noise ratio (SNR) of the legitimate user (UE) while suppressing the SNR of active ED to zero and constraining the average SNR of passive EDs. The proposed beam-forming directs the transmission along a particular eigenmode related to the null space of the active ED channel and the intensity of the passive ED point process. An inverse free preconditioned Krylov subspace projection method is used to find the eigenmode. The numerical results show that the proposed ZF beamforming scheme yields better performance relative to a traditional ZF beamforming scheme in the sense of increasing the SNR of the CIE and reducing the secrecy outage probability.

In this paper, we investigate a Deep Reinforcement Learning based delay-constrained relay selection for secure buffer-aided Cognitive Relay Networks (CRNs). We model the relay selection problem in secure buffer-aided CRNs as a Markov Decision Process (MDP) problem, and introduce Deep Q-Learning to solve this MDP problem. In the proposed scheme, delay constraint is considered when the packets arriving at the receiver in CRNs. Moreover, we consider the security of data transmissions in buffer-aided CRNs with an eavesdropper which can intercept the signals from the source and relays. Furthermore, we introduce r-greedy strategy to balance the exploitation and exploration. The result shows compared with Max-Ratio scheme, the proposed scheme enhances the throughput with both delay and security constrained significantly in secure CRNs.

This paper investigates a cooperative non-orthogonal multiple access (C-NOMA) system, where the NOMA and buffer-aided cooperative transmission modes between the users are integrated. Two novel mode selection schemes are proposed, which adaptively select the NOMA and cooperative modes according to different buffer states and communication environments. These two proposed schemes are termed single-core state (SCS) and dual-core state (DCS) schemes since they correspond to single and dual-core buffer states. These core states are carefully chosen, which ensure not only a sufficient amount of available transmission modes or links but also a small number of stored packets at each buffer. The closed-form expressions of the outage probabilities and average delays of the proposed schemes are derived and verified by simulation results. Asymptotic performance analysis is also performed, revealing that both proposed schemes achieve the full diversity within the minimum required buffer size of two. Analytical and simulation results show that the proposed SCS and DCS schemes ensure favourable outage performance and the lowest delay, respectively.

A downlink buffer-aided cooperative non-orthogonal multiple access (C-NOMA) system is investigated in this paper. Both the direct transmission from the base station to the users and the buffer-aided cooperative transmission between two users are considered. In particular, a novel buffer-aided C-NOMA scheme is proposed to adaptively select a direct or cooperative transmission mode, based on the instantaneous channel state information and the buffer state. Then, the system outage probability of the proposed scheme is theoretically derived with a closed-form expression. Furthermore, the full diversity order of three is demonstrated to be achieved if the buffer size is not less than three, which is larger than conventional non-buffer-aided C-NOMA schemes whose diversity order is only two in the considered C-NOMA system.

This article proposes a novel machine-learning-based routing optimization for the multiple reconfigurable intelligent surfaces (M-RIS)-assisted multihop cooperative networks, in which a practical phase model for reconfigurable intelligent surface (RIS) with the amplitude variation based on the corresponding discrete phase shift is considered. We aim to maximize the end-to-end data rate in the proposed network by jointly optimizing the data transmission path, the passive beamforming design of RIS, and transmit power allocation. To tackle this complicated nonconvex problem, we divide it into two subtasks: 1) the passive beamforming design of the RIS and 2) joint routing and power allocation optimization. First, for the passive beamforming design of RIS, we develop a distributed learning algorithm that employs a cascade forward backpropagation network in each relay node to solve the RIS coefficients optimization problem by directly using the optimization target to train the cascade networks. This solution can avoid the curse of dimensionality of traditional reinforcement learning algorithms in the RIS optimization problem. Then, based on the result of RIS optimization, we introduce the proximal policy optimization (PPO) algorithm with the clipping method to find solutions for joint optimization of routing and power allocation via achieving the long-term benefit in the Markov decision process (MDP). Simulation results show that the proposed learning-based scheme can learn from the environment to improve its policy stability and efficiency in the iterative training process for optimizing routing and RIS and significantly outperform the benchmark schemes.

This paper investigates the sum-secure degrees-of-freedom (SDoF) of three-user multiple-input multiple-output (MIMO) broadcast channel with confidential messages (BCCM) and delayed channel state information at the transmitter (CSIT). Specifically, we obtain non-trivial sum-SDoF upper and lower bounds. Firstly, we derive the sum-SDoF upper bound by means of statistical equivalence property, security constraints, and permutations. Then, for the sum-SDoF lower bound, we leverage the artificial noise transmission and interference re-transmission to design two transmission schemes, which have holistic and sequential higher-order symbol generation, respectively. For these two schemes, we propose the redundancy reduction approach for security analysis, by which the minimal duration of artificial noise transmission phase of the scheme is obtained. To eliminate the redundant equations in security analysis, this approach first identifies the constituent equations, and then analyzes the rank of assemble of them. As a result, both the proposed sum-SDoF upper and lower bounds are tighter than the existing sum-SDoF upper and lower bounds, respectively. Furthermore, the proposed lower bound showcases a three-user coding gain.

This paper considers the secrecy performance of several schemes for multi-antenna transmission to single-antenna users with full-duplex (FD) capability against randomly distributed single-antenna eavesdroppers (EDs). These schemes and related scenarios include transmit antenna selection (TAS), transmit antenna beamforming (TAB), artificial noise (AN) from the transmitter, user selection based their distances to the transmitter, and colluding and non-colluding EDs. The locations of randomly distributed EDs and users are assumed to be distributed as Poisson Point Process (PPP). We derive closed form expressions for the secrecy outage probabilities (SOP) of all these schemes and scenarios. The derived expressions are useful to reveal the impacts of various environmental parameters and user's choices on the SOP, and hence useful for network design purposes. Examples of such numerical results are discussed.

Local differential privacy federated learning (LDP-FL) is a framework to achieve high local data privacy protection while training the model in a decentralized environment. Currently, LDP-FL's trainings are suffering from efficiency problems due to many existing researches combine LDP and FL without looking deep into the relationships between the two most important parameters, i.e., privacy budget for privacy protection and gradients for model training. In this work, we propose a novel LDP-FL under multi-privacy regimes to combat the above problems. Firstly, unlike the existing multiple privacy regimes-based LDP-FL to compute the non -unbiased global gradient, we propose an unbiased mean estimator using maximum likelihood estimation (MLE) to obtain small variance global gradients with a higher training accuracy. Secondly, to improve the efficiency of model training for multi-privacy scenarios, we design two different dynamic privacy budget allocation approaches for users to choose from. The first approach allocates the privacy budget based on the training model's accuracy, and the second approach's privacy budget grows linearly, avoiding the computational effort caused by the comparison operation. Finally, since directly perturbing the high-dimensional local gradients in traditional methods would lead to considerable utility loss, we propose a layered dimension selection strategy by randomly selecting the layers of gradients that take part in the noise perturbation while others remain untouched. In simulations using the handwritten MNIST and Fashion-MNIST datasets, we compare our framework with the traditional LDP-FL, simple personalized mean estimation (S-PME), and PLU-FedOA. The results demonstrate the training efficiency of our framework.

This letter investigates the ergodic secrecy rate (ESR) of a reconfigurable intelligent surface (RIS)-assisted communication system in the presence multiple eavesdroppers (Eves), and by assuming discrete phase shifts at the RIS. In particular, a closed-form approximation of the ESR is derived for both non-colluding and colluding Eves. The analytical results are shown to be accurate when the number of reflecting elements of the RIS {N} is large. Asymptotic analysis is provided to investigate the impact of {N} on the ESR, and it is proved that the ESR scales with \log \,_{2} N for both non-colluding and colluding Eves. Numerical results are provided to verify the analytical results and the obtained scaling laws.

In this paper, we propose a lightweight and adaptable trust mechanism for the issue of trust evaluation among Internet of Things devices, considering challenges such as limited device resources and trust attacks. Firstly, we propose a trust evaluation approach based on Bayesian statistics and Josang's belief model to quantify a device's trustworthiness, where evaluators can freely initialize and update trust data with feedback from multiple sources, avoiding the bias of a single message source. It balances the accuracy of estimations and algorithm complexity. Secondly, considering that a trust estimation should reflect a device's latest status, we propose a forgetting algorithm to ensure that trust estimations can sensitively perceive changes in device status. Compared with conventional methods, it can automatically set its parameters to gain good performance. Finally, to prevent trust attacks from misleading evaluators, we propose a tango algorithm to curb trust attacks and a hypothesis testing-based trust attack detection mechanism. We corroborate the proposed trust mechanism's performance with simulation, whose results indicate that even if challenged by many colluding attackers that can exploit different trust attacks in combination, it can produce relatively accurate trust estimations, gradually exclude attackers, and quickly restore trust estimations for normal devices.

In this letter, we investigate the outage probability of a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted downlink non-orthogonal multiple access (NOMA) network over spatially correlated channels. To evaluate the impact of channel correlations on the system performance, we first approximate the distribution of the composite channel gain as a gamma random variable via a moment-matching approach and then derive new close-form outage probability expressions for a pair of NOMA users. Based on the approximate results, the diversity of each user is studied. Numerical results are provided to validate the effectiveness of the theoretical analysis and illustrate the performance loss due to channel correlations.

This paper proposes a multi-agent deep reinforcement learning-based buffer-aided relay selection scheme for an intelligent reflecting surface (IRS)-assisted secure cooperative network in the presence of an eavesdropper. We consider a practical phase model where both phase shift and reflection amplitude are discrete variables to vary the reflection coefficients of the IRS. Furthermore, we introduce the buffer-aided relay to enhance the secrecy performance, but the use of the buffer leads to the cost of delay. Thus, we aim to maximize either the average secrecy rate with a delay constraint or the throughput with both delay and secrecy constraints, by jointly optimizing the buffer-aided relay selection and the IRS reflection coefficients. To obtain the solution of these two optimization problems, we divide each of the problems into two sub-tasks and then develop a distributed multi-agent reinforcement learning scheme for the two cooperative sub-tasks, each relay node represents an agent in the distributed learning. We apply the distributed reinforcement learning scheme to optimize the IRS reflection coefficients, and then utilize an agent on the source to learn the optimal relay selection based on the optimal IRS reflection coefficients in each iteration. Simulation results show that the proposed learning-based scheme uses an iterative approach to learn from the environment for approximating an optimal solution via the exploration of multiple agents, which outperforms the benchmark schemes.

This article investigates the reinforcement learning for the relay selection in the delay-constrained buffer-aided networks. The buffer-aided relay selection significantly improves the outage performance but often at the price of higher latency. On the other hand, modern communication systems such as the Internet of Things often have strict requirement on latency. It is thus necessary to find relay selection policies to achieve good throughput performance in the buffer-aided relay network while stratifying the delay constraint. With the buffers employed at the relays and delay constraints imposed on the data transmission, obtaining the best relay selection becomes a complicated high-dimensional problem, making it hard for the reinforcement learning to converge. In this article, we propose the novel decision-assisted deep reinforcement learning to improve the convergence. This is achieved by exploring the a priori information from the buffer-aided relay system. The proposed approaches can achieve high throughput subject to delay constraints. Extensive simulation results are provided to verify the proposed algorithms.

In this letter, a buffer-state-based probabilistic relay selection (RS) scheme is proposed for buffer-aided cooperative relay networks with delay constraints. In particular, we first select the source-to-relay (S2R) and relay-to-destination (R2D) links with the smallest and the largest numbers of packets in the corresponding buffer queues, among all S2R and R2D links whose fading channel gains exceed a predefined threshold, respectively. Then, we utilize the outcome of an auxiliary random variable to decide which one between these two links is selected. Furthermore, the outage probability and average packet delay are analyzed, respectively. In addition, a useful definition of the probability mass function for the auxiliary random variable is proposed to enhance the outage performance under delay constraints. Simulation results show that the proposed scheme significantly outperforms existing buffer-aided RS schemes under different delay constraints.

This letter proposes a novel secure intelligent reflecting surface (IRS)-assisted unmanned aerial vehicle (UAV) system, where the UAV sends confidential messages to a legitimate receiver, in the presence of a passive eavesdropper. We aim to maximize the secrecy rate by jointly designing the trajectory, the power control of UAV and the phase shifters of the IRS from the physical layer security perspective. Because the formulated problem is non-convex and intractable, we propose an iterative algorithm based on successive convex approximation (SCA) to solve this problem. Simulation results validate the effectiveness of the proposed algorithm and show that the secrecy rate is significantly improved with the assistance of the IRS.

We analyse the performance of a communication link assisted by an intelligent reflective surface (IRS) positioned in the far field of both the source and the destination. A direct link between the transmitting and receiving devices is assumed to exist. Perfect and imperfect phase adjustments at the IRS are considered. For the perfect phase configuration, we derive an approximate expression for the outage probability in closed form. For the imperfect phase configuration, we assume that each element of the IRS has a one-bit phase shifter (0 degrees, 180 degrees) and an expression for the outage probability is obtained in the form of an integral. Our formulation admits an exact asymptotic (high SNR) analysis, from which we obtain the diversity orders for systems with and without phase errors. We show these are N + 1 and 1/2 (N + 3), respectively. Numerical results confirm the theoretical analysis and verify that the reported results are more accurate than methods based on the central limit theorem (CLT).

In this work, we propose a novel hybrid communication network that utilizes both a Full-Duplex (FD) Decode-and-Forward (DF) relay and an Intelligent Reflecting Surface (IRS) to support data transmission over wireless channels. We design the reflecting coefficients at the IRS to maximize the minimum achievable rate of the two hops for the proposed hybrid network. To that end, we utilize a change-of-variables with Semi-Definite Relaxation (SDR) approach to overcome the non-concave objective function and the non-convex optimization constraints. Our results demonstrate that the proposed hybrid IRS with FD relay scheme is able to achieve a significant performance gain over both the hybrid IRS with Half-Duplex (HD) relay as well as the IRS-only scheme, given that the self-interference at the relay is sufficiently suppressed.

In this paper, we investigate the performance of an intelligent omni-surface (IOS) assisted downlink non-orthogonal multiple access (NOMA) network with phase quantization errors and channel estimation errors, where the channels related to the IOS are spatially correlated. First, upper bounds on the average achievable rates of the two users are derived. Then, channel hardening is shown to occur in the proposed system, based on which we derive approximations of the average achievable rates of the two users. The analytical results illustrate that the proposed upper bound and approximation on the average achievable rates are asymptotically equivalent in the number of elements. Furthermore, it is proved that the asymptotic equivalence also holds for the average achievable rates with correlated and uncorrelated channels. Additionally, we extend the analysis by evaluating the average achievable rates for IOS assisted orthogonal multiple access (OMA) and IOS assisted multi-user NOMA scenarios. Simulation results corroborate the theoretical analysis and demonstrate that: i) low-precision elements with only two-bit phase adjustment can achieve the performance close to the ideal continuous phase shifting scheme; ii) The average achievable rates with correlated channels and uncorrelated channels are asymptotically equivalent in the number of elements; iii) IOS-assisted NOMA does not always perform better than OMA due to the reconfigurability of IOS in different time slots.

Massive multi-input multi-output (MIMO) has aroused extensive interest in communication industry, as it can effectively increase communication system capacity and reduce transmit power. For massive MIMO-orthogonal frequency division multiplexing (OFDM) systems, traditional pilot assisted channel estimation methods need to obtain the channel response of data subcarriers by interpolation. However, when the channel response of continuous data subcarriers changes dramatically, the accuracy of channel estimation will be decreased. In this article, therefore, a novel channel prediction framework that integrates the imperfect channel estimation of the massive MIMO-OFDM into the deep neural network scheme is proposed, thus replacing the interpolation method and achieving higher accuracy of channel estimation. From algorithm analysis and simulation results, the performance of proposed deep neural network channel prediction method based on the imperfect channel estimation outperforms the conventional least square method based on interpolation. Furthermore, compared with minimum mean square error, there are no need for channel autocorrelation matrix, prior statistics of noise variance, and the complex matrix inversion operations. In summary, deep neural network is an efficient method for the imperfect channel estimation in massive MIMO-OFDM systems.

This paper proposes a novel cooperative beamforming and jamming scheme to deal with passive and active eavesdroppers (EDs) in indoor visible light communication (VLC) networks. An ED in VLC systems can augment its front-end receiver by implementing possible device modifications; thus, jamming is very useful for curbing such an enhanced ED since it would be impossible to distinguish between the information and jamming signals. In contrast to the traditional artificial noise strategies for VLC that can only deal with either passive or active EDs, we propose a combined scheme of beamforming and jamming that significantly improves secrecy performance when both types of EDs exist. The proposed scheme is designed to maximize the signal-to-interference-plus-noise ratio (SINR) of the legitimate receiver, entirely suppress the SINRs of the active EDs, and restrict the average SINR of the passive EDs. We apply an inverse free preconditioned Krylov subspace projection method and the convex-concave procedure to obtain the suboptimal beamforming weight and jamming intensity vectors. Also, an optimal power splitter coefficient is found through the golden section search method. The numerical results verify that the proposed scheme shows superior performance compared to the three benchmarks: 1) zero-forcing beamforming; 2) artificial noise scheme; and 3) enhanced zero-forcing beamforming.

This article proposes zero-forcing (ZF) beamforming strategies that can simultaneously deal with active and passive eavesdroppers in visible light communication (VLC) systems. First, we propose a ZF beamforming scheme that steers a transmission beam to the null space of active eavesdroppers' (AEDs) channel, while simultaneously considering the SNRs for a legitimate user (UE) and passive eavesdroppers (PEDs) residing at unknown locations. To find an eigenmode related to the optimal beamforming vector, we adopt an inverse free preconditioned Krylov subspace projection method. For unfavorable VLC secrecy environments, the proposed ZF beamformer appears to be incapable of effectively coping with the PEDs due to the strict condition that the data transmission must be in the null space of the AEDs' channel matrix. Hence, an alternative beamforming scheme is proposed by relaxing the constraint on the SNRs of the AEDs. The related optimization problem is formulated to reduce the secrecy outages caused by PEDs, while simultaneously satisfying the target constraints on the SNRs of the UE and the AEDs. To simplify the mathematical complexity of the approach, Lloyd's algorithm is employed to sample the SNR field, which in turn discretizes the problem, thus making it tractable for practical implementation. The numerical results show that both the exact and relaxed ZF beamforming methods achieve superior performance in the sense of secrecy outage relative to a benchmark ZF scheme. Moreover, the proposed relaxed ZF beamforming method is shown to cope with PEDs better than the exact ZF beamforming approach for unfavorable VLC environments.

This letter proposes a deep reinforcement learning (DRL) based relay selection scheme for cooperative networks with the intelligent reflecting surface (IRS). We consider a practical phase-dependent amplitude model in which the IRS reflection amplitudes vary with the discrete phase-shifts. Furthermore, we apply the relay selection to reduce the signal loss over distance in IRS-assisted networks. To solve the complicated problem of joint relay selection and IRS reflection coefficient optimization, we introduce DRL to learn from the environment to obtain the solution and reduce the computational complexity. Simulation results show that the throughput is significantly improved with the proposed DRL-based algorithm compared to random relay selection and random reflection coefficients methods.

We propose and analyze secret key generation using intelligent reflecting surface (IRS) assisted wireless communication networks. To this end, we first formulate the minimum achievable secret key capacity for an IRS acting as a passive beamformer in the presence of multiple eavesdroppers. Next, we develop an optimization framework for the IRS reflecting coefficients based on the secret key capacity lower bound. To derive a tractable and efficient solution, we design and analyze a semidefinite relaxation (SDR) and successive convex approximation (SCA) based algorithm for the proposed optimization. Simulation results show that employing our IRS-based algorithm can significantly improve the secret key generation capacity for a wide-range of wireless channel parameters.

Codeword position index based sparse code multiple access (CPI-SCMA) is a novel variant of SCMA that employs the idea of index modulation (IM), which has been proposed recently as an effective alternative to the conventional SCMA system. In contrast to the conventional SCMA that directly maps the information bits of each user to a predefined M-point codebook, the data in CPI-SCMA is not only conveyed by the M-point codebook as in SCMA, but also by the indexes of the codeword positions. In this paper, we first propose a simplified detector termed as message passing algorithm aided index reliability detection (MPA-IRD) that is more suitable for practical implementation. Moreover, we investigate the spectral efficiency (SE) in CPI-SCMA, and two important features are identified to facilitate the design of the system. A tight approximation of the average block error probability (ABLEP) under the maximum likelihood detector (MLD) is also derived from studying the error rate performance of CPI-SCMA. Finally, to further improve the SE of CPI-SCMA, an enhanced version of CPI-SCMA termed as hybrid CPI-SCMA (HCPI-SCMA) is proposed. It is shown via both simulation, and analytical results that the proposed scheme not only achieves better error rate performance in the high signal-to-noise ratio (SNR) region but also improves the SE, and the level of robustness under channel estimation error compares to SCMA.

The UAV is emerging as one of the greatest technology developments for rapid network coverage provisioning at affordable cost. The aim of this paper is to outsource network coverage of a specific area according to a desired quality of service requirement and to enable various entities in the network to have intelligence to make autonomous decisions using blockchain and auction mechanisms. In this regard, by considering a multiple-UAV network where each UAV is associated to its own controlling operator, this paper addresses two major challenges: the selection of the UAV for the desired quality of network coverage and the development of a distributed and autonomous real-time monitoring framework for the enforcement of service level agreement (SLA). For a suitable UAV selection, we employ a reputation-based auction mechanism to model the interaction between the business agent who is interested in outsourcing the network coverage and the UAV operators serving in closeby areas. In addition, theoretical analysis is performed to show that the proposed auction mechanism attains a dominant strategy equilibrium. For the SLA enforcement and trust model, we propose a permissioned blockchain architecture considering Support Vector Machine (SVM) for real-time autonomous and distributed monitoring of UAV service. In particular, smart contract features of the blockchain are invoked for enforcing the SLA terms of payment and penalty, and for quantifying the UAV service reputation. Simulation results confirm the accuracy of theoretical analysis and efficacy of the proposed model.

This paper applies the reinforcement learning in the joint relay selection and power allocation in the secure cognitive radio (CR) relay network, where the data buffers and full-duplex jamming are applied at the relay nodes. Two cases are considered: maximizing the throughput with the delay and secrecy constraints, and maximizing the secrecy rate with the delay constraint, respectively. In both cases, the optimization relies on the buffer states, the interference to/from the primary user, and the constraints on the delay and/or secrecy. This makes it mathematically intractable to apply the traditional optimization methods. In this paper, the double deep Q-network (DDQN) is used to solve the above two optimization problems. We also apply the a-priori information in the CR network to improve the DDQN learning convergence. Simulation results show that the proposed scheme outperforms the traditional algorithm significantly.

Distributed cloud storage (DCS) has been undergoing fast development to provide customers with multiple storage resources in a mix of locations and environments that best meet the service and performance requirements. Trust is widely regarded as one of the top obstacles to the adoption and growth of DCS. Nonetheless, the existing works did not take the storage resource capacity of cloud service providers (CSPs) into the reputation evaluation, and most rely on a centralized reputation management framework. We bridge this gap by proposing a blockchain-assisted reputation mechanism for DCS. First, a blockchain-assisted DCS model is proposed to ensure the traceability and tamper proof of reputation-related data. Second, we propose a service credibility quantification method based on rating screening to mitigate the impact of outliers. Third, we design a stochastic process to characterize the storage resource change to quantify CSPs survival probability. Finally, we derive a reputation calculation algorithm based on the above two metrics that protects the authenticity of CSPs' reputations in the presence of attacks. The security analysis and simulation results show that the proposed mechanism is reliable and effective in promoting the service success rate and improving the efficiency and security of the service of DCS.

In this paper, a novel index and composition modulation (ICM) transmission scheme, termed as grouped generalized composition and spatial modulation (G-GCSM), is proposed for massive multiple-input multiple-output (MIMO) systems. Specifically , it amalgamates the concepts of composition modulation (CM), generalized spatial modulation (GSM) and spatial multi-plexing to attain high spectral efficiency (SE) and low implementation complexity. In the G-GCSM scheme, transmit antennas are divided into several groups and the GCSM transmission structure is employed independently in each group, facilitating the bit-to-index mapping issue in massive MIMO scenarios. Additionally, at the receiver side, an improved expectation propagation (EP) detector is designed for the proposed G-GCSM scheme, which exploits the inner sparsity of the transmitted vector in G-GCSM. Simulation results demonstrate the superiority of the proposed scheme over the existing GSM schemes in terms of bit error rate (BER) performance under the same SE conditions. Moreover, the proposed improved EP detector is able to provide a significant performance gain over the conventional minimum-mean-squared error (MMSE) detector in both determined and under-determined massive MIMO systems.

This paper investigates a deep learning-based algorithm to optimize the unmanned aerial vehicle (UAV) trajectory and reconfigurable intelligent surface (RIS) reflection coefficients in UAV-RIS-aided cell-free (CF) hybrid non-orthogonal multiple-access (NOMA)/orthogonal multiple-access (OMA) networks. The practical RIS reflection model and user grouping optimization are considered in the proposed network. A double cascade correlation network (DCCN) is proposed to optimize the RIS reflection coefficients , and based on the results from DCCN, an inverse-variance deep reinforcement learning (IV-DRL) algorithm is introduced to address the UAV trajectory optimization problem. Simulation results show that the proposed algorithms significantly improve the performance in UAV-RIS-assisted CF networks.

The full-duplex (FD) communication can achieve higher spectrum efficiency than conventional half-duplex (HD) communication; however, self-interference (SI) is the key hurdle. This paper is the first work to propose the intelligent omni surface (IOS)-assisted FD multi-input single-output (MISO) FD communication systems to mitigate SI, which solves the frequency-selectivity issue. In particular, two types of IOS are proposed, energy splitting (ES)-IOS and mode switching (MS)-IOS. We aim to maximize data rate and minimize SI power by optimizing the beamforming vectors, amplitudes and phase shifts for the ES-IOS and the mode selection and phase shifts for the MS-IOS. However, the formulated problems are non-convex and challenging to tackle directly. Thus, we design alternative optimization algorithms to solve the problems iteratively. Specifically, the quadratic constraint quadratic programming (QCQP) is employed for the beamforming optimizations, amplitudes and phase shifts optimizations for the ES-IOS and phase shifts optimizations for the MS-IOS. Nevertheless, the binary variables of the MS-IOS render the mode selection optimization intractable, and then we resort to semidefinite relaxation (SDR) and Gaussian randomization procedures to solve it. Simulation results validate the proposed algorithms' efficacy and show the effectiveness of both the IOSs in mitigating SI compared to the case without an IOS.

—Non-orthogonal multiple access (NOMA) is a promising candidate radio access technology for future wireless communication systems, which can achieve improved connectivity and spectral efficiency. Without sacrificing error rate performance , link adaptation combining with adaptive modulation and coding (AMC) and hybrid automatic repeat request (HARQ) can provide better spectral efficiency and reliable data transmission by allowing both power and rate to adapt to channel fading and enabling re-transmissions. However, current AMC or HARQ schemes may not be preferable for NOMA systems due to the imperfect channel estimation and error propagation during successive interference cancellation (SIC). To address this problem , a reinforcement learning based link adaptation scheme for downlink NOMA systems is introduced in this paper. Specifically, we first analyze the throughput and spectrum efficiency of NOMA system with AMC combined with HARQ. Then, taking into account the imperfections of channel estimation and error propagation in SIC, we propose SINR and SNR based corrections to correct the modulation and coding scheme selection. Finally, reinforcement learning (RL) is developed to optimize the SNR and SINR correction process. Comparing with a conventional fixed look-up table based scheme, the proposed solutions achieve superior performance in terms of spectral efficiency and packet error performance. Index Terms—Non-orthogonal multiple access (NOMA), adap-tive modulation and coding (AMC), hybrid automatic repeat request (HARQ), reinforcement learning (RL).

This letter proposes a hybrid beamforming design for an intelligent transmissive surface (ITS)-assisted transmitter wireless network. We aim to suppress the sidelobes and optimize the mainlobes of the transmit beams by minimizing the proposed cost function based on the least squares (LS) for the digital beamforming vector of the base station (BS) and the phase shifts of the ITS. To solve the minimization problem, we adopt an efficient algorithm based on the alternating optimization (AO) method to design the digital beamforming vector and the phase shifts of the ITS in an alternating manner. In particular, the alternating direction method of multipliers (ADMM) algorithm is utilized to obtain the optimal phase shifts of the ITS. Finally, we verify the improvement achieved by the proposed algorithm in terms of the beam response compared to the benchmark schemes by the simulation results.

—Grant-free non-orthogonal multiple access (GF-NOMA) technique is considered as a promising solution to address the bottleneck of ubiquitous connectivity in massive machine type communication (mMTC) scenarios. One of the challenging problems in uplink GF-NOMA systems is how to efficiently perform user activity detection and data detection. In this paper, a novel complexity-reduction weighted block coordinate descend (CR-WBCD) algorithm is proposed to address this problem. To be specific, we formulate the multiuser detection (MUD) problem in uplink GF-NOMA systems as a weighted l2 minimization problem. Based on the block coordinate descend (BCD) framework, a closed-form solution involving dynamic user-specific weights is derived to adaptively identify the active users with high accuracy. Furthermore, a complexity reduction mechanism is developed for substantial computational cost saving. Simulation results demonstrate that the proposed algorithm enjoys bound-approaching detection performance with more than three-order of magnitude computational complexity reduction. Index Terms—Grant-free non-orthogonal multiple access (GF-NOMA), block coordinate descend (BCD), compressed sensing (CS), multiuser detection (MUD).