This paper studies the impact of an intelligent reflecting surface (IRS) on computational performance in a mobile edge computing (MEC) system. Specifically, an access point (AP) equipped with an edge server provides MEC services to multiple internet of thing (IoT) devices that choose to offload a portion of their own computational tasks to the AP with the remaining portion being locally computed. We deploy an IRS to enhance the computational performance of the MEC system by intelligently adjusting the phase shift of each reflecting element. A joint design problem is formulated for the considered IRS assisted MEC system, aiming to optimize its sum computational bits and taking into account the CPU frequency, the offloading time allocation, transmit power of each device as well as the phase shifts of the IRS. To deal with the non-convexity of the formulated problem, we conduct our algorithm design by finding the optimized phase shifts first and then achieving the jointly optimal solution of the CPU frequency, the transmit power and the offloading time allocation by considering the Lagrange dual method and Karush-Kuhn-Tucker (KKT) conditions. Numerical evaluations highlight the advantage of the IRS-assisted MEC system in comparison with the benchmark schemes.
This paper investigates the impact of intelligent reflecting surface (IRS) enabled wireless secure transmission. Specifically, an IRS is deployed to assist multiple-input multiple-output (MIMO) secure system to enhance the secrecy performance, and artificial noise (AN) is employed to introduce interference to degrade the reception of the eavesdropper. To improve the secrecy performance, we aim to maximize the achievable secrecy rate, subject to the transmit power constraint, by jointly designing the precoding of the secure transmission, the AN jamming, and the reflecting phase shift of the IRS. We first propose an alternative optimization algorithm (i.e., block coordinate descent (BCD) algorithm) to tackle the non-convexity of the formulated problem. This is made by deriving the transmit precoding and AN matrices via the Lagrange dual method and the phase shifts by the Majorization-Minimization (MM) algorithm. Our analysis reveals that the proposed BCD algorithm converges in a monotonically non-decreasing manner which leads to guaranteed optimal solution. Finally, we provide numerical results to validate the secrecy performance enhancement of the proposed scheme in comparison to the benchmark schemes.
In this paper, we aim to unlock the potential of intelligent reflecting surfaces (IRSs) in cognitive internet of things (IoT). Considering that the secondary IoT devices send messages to the secondary access point (SAP) by sharing the spectrum with the primary network, the interference is introduced by the IoT devices to the primary access point (PAP) which profits from the IoT devices by pricing the interference power charged by them. A practical path loss model is adopted such that the IRSs deployed between the IoT devices and SAP serve as diffuse scatterers, but each reflected signal can be aligned with its own desired direction. Moreover, two transmission policies of the secondary network are investigated without/with a successive interference cancellation (SIC) technique. The signal-to-interference plus noise ratio (SINR) balancing is considered to overcome the near-far effect of the IoT devices so as to allocate the resource fairly among them. We propose a Stackelberg game strategy to characterize the interaction between primary and secondary networks. For the proposed game, the Stackelberg equilibrium is analytically derived to optimally obtain the closed-form solution of the power allocation and interference pricing. Numerical results are demonstrated to validate the performance of the theoretical derivations.
In this paper, we propose intelligent reflecting surface (IRS) aided multi-antenna physical layer security. We present a power efficient scheme to design the secure transmit power allocation and the surface reflecting phase shift. It aims to minimize the transmit power subject to the secrecy rate constraint at the legitimate user. Due to the non-convex nature of the formulated problem, we propose an alternative optimization algorithm and the semidefinite programming (SDP) relaxation to deal with this issue. Also, the closed-form expression of the optimal secure beamformer is derived. Finally, simulation results are presented to validate the proposed algorithm, which highlights the performance gains of the IRS to improve the secure transmission.
In this letter, we study the beamforming design in a lens-antenna array-based joint multicast-unicast millimeter wave massive MIMO system, where the simultaneous wireless information and power transfer at users is considered. First, we develop a beam selection scheme based on the structure of the lens-antenna array and then, the zero forcing precoding is adopted to cancel the inter-unicast interference among users. Next, we formulate a sum rate maximization problem by jointly optimizing the unicast power, multicast beamforming and power splitting ratio. Meanwhile, the maximum transmit power constraint for the base station and the minimum harvested energy for each user are imposed. By employing the successive convex approximation technique, we transform the original optimization problem into a convex one, and propose an iterative algorithm to solve it. Finally, simulation results are conducted to verify the effectiveness of the proposed schemes.
This paper investigates a secure wireless powered integrated service system with full duplex self-energy recycling. Specifically, an energy-constrained information transmitter (IT), powered by a power station (PS) in a wireless fashion, broadcasts two types of services to all users: a multicast service intended for all users, and a confidential unicast service subscribed to by only one user while protecting it from any other unsubscribed users and an eavesdropper. Our goal is to jointly design the optimal input covariance matrices for the energy beamforming, the multicast service, the confidential unicast service, and the artificial noises from the PS and the IT, such that the secrecy-multicast rate region (SMRR) is maximized subject to the transmit power constraints. Due to the non-convexity of the SMRR maximization (SMRRM) problem, we employ a semidefinite programmingbased two-level approach to solve this problem and find all of its Pareto optimal points. In addition, we extend the SMRRM problem to the imperfect channel state information case where a worst-case SMRRM formulation is investigated. Moreover, we exploit the optimized transmission strategies for the confidential service and energy transfer by analyzing their own rank-one profile. Finally, numerical results are provided to validate our proposed schemes.
In this paper, we consider multigroup multicast transmissions with different types of service messages in an overloaded multicarrier system, where the number of transmitter antennas is insufficient to mitigate all inter-group interference. We show that employing a rate-splitting based multiuser beamforming approach enables a simultaneous delivery of the multiple service messages over the same time-frequency resources in a non-orthogonal fashion. Such an approach, taking into account transmission power constraints which are inevitable in practice, outperforms classic beamforming methods as well as current standardized multicast technologies, in terms of both spectrum efficiency and the flexibility of radio resource allocation.
Chu Zheng, Yu Wenjuan, Xiao Pei, Zhou Fuhui, Al-Dhahir Naofal, ul Quddus Atta, Tafazolli Rahim (2019)Opportunistic Spectrum Sharing for D2D-Based URLLC, In: IEEE Transactions on Vehicular Technologypp. 1-1
Institute of Electrical and Electronics Engineers (IEEE)
A device-to-device (D2D) ultra reliable low latency communications (URLLC) network is investigated in this paper. Specifically, a D2D transmitter opportunistically accesses the radio resource provided by a cellular network and directly transmits short packets to its destination. A novel performance metric is adopted for finite block-length code. We quantify the maximum achievable rate for the D2D network, subject to a probabilistic interference power constraint based on imperfect channel state information (CSI). First, we perform a convexity analysis which reveals that the finite block-length rate for the D2D pair in short-packet transmission is not always concave. To address this issue, we propose two effective resource allocation schemes using the successive convex approximation (SCA)-based iterative algorithm. To gain more insights, we exploit the mono- tonicity of the average finite block-length rate. By capitalizing on this property, an optimal power control policy is proposed, followed by closed-form expressions and approximations for the optimal average power and the maximum achievable average rate in the finite block-length regime. Numerical results are provided to confirm the effectiveness of the proposed resource allocation schemes and validate the accuracy of the derived theoretical results.
In this article, we investigate a resource allocation problem for multicarrier multiuser MISO (multiple-input-Single-output) downlink systems, where multiple co-channel multicast groups are served simultaneously. We consider a rate-splitting transmission scheme to address the inevitable inter-group interference under an overloaded multigroup multicast scenario, where the insufficient number of transmit antennas prevents the conventional schemes from neutralizing the interference. We first formulate an optimization problem for maximizing the minimum multicast group rate among all groups on all available subcarriers. This problem involves a joint power and subcarrier allocation optimization, and is non-convex. We apply an iterative scheme based on successive convex approximation (SCA) to find the locally optimal solution. Simulation results demonstrate the performance gain of the proposed scheme compared to the state-of-the-art transmission schemes.
Chu Zheng, Hao Wanming, Xiao Pei, Zhou Fuhui, Mi De, Zhu Zhengyu, Leung Victor C.M. (2018)Energy Efficient Hybrid Precoding in Heterogeneous Networks with Limited Wireless Backhaul Capacity, In: Proceedings of the IEEE Global Communications Conference, Abu Dhabi, UAE, 9-13 Dec 2018
Institute of Electrical and Electronics Engineers (IEEE)
This paper investigates a two-tier heterogeneous networks (HetNets) with wireless backhaul, where millimeter wave (mmWave) frequency is adopted at the macro base station (MBS), and the cellular frequency is considered at small cell BS (SBS) with orthogonal frequency division multiple access (OFDMA). Subarray structure based hybrid analog/digital precoding scheme is investigated to reduce the hardware cost and energy consumption. Our goal is to maximize the energy efficiency (EE) of the HetNets with limited wireless backhaul capacity and all users’ quality of service (QoS) constraints. The formulated problem is non-convex mixed integer nonlinear fraction programming (MINLFP), which is non-trivial to solve directly. In order to circumvent this issue, we propose a two-loop iterative resource allocation algorithm. Specifically, we transform the outer-loop problem into a difference of convex programming (DCP) by employing integer relaxation and Dinkelback method. In addition, the first-order approximation is considered to linearize this inner-loop DCP problem into a convex optimization framework. Lagrange dual method is adapted to achieve the optimal closed-form power allocation. Furthermore, we analyze the convergence of the proposed iterative algorithm. Numerical results are presented to demonstrate our proposed schemes.
Physical layer security (PLS) technologies have attracted much attention in recent years for their potential to provide information-theoretically secure communications. Artificial Noise (AN)-aided transmission is considered as one of the most practicable PLS technologies, as it can realize secure transmission independent of the eavesdropper’s channel status. In this paper, we reveal that AN transmission has the dependency of eavesdropper’s channel condition by introducing our proposed attack method based on a supervised-learning algorithm which utilizes the modulation scheme, available from known packet preamble and/or header information, as supervisory signals of training data. Numerical simulation results with the comparison to conventional clustering methods show that our proposed method improves the success probability of attack from 4.8% to at most 95.8% for the QPSK modulation. It implies that the transmission to the receiver in the cell-edge with low order modulation will be cracked if the eavesdropper’s channel is good enough by employing more antennas than the transmitter. This work brings new insights into the effectiveness of AN schemes and provides useful guidance for the design of robust PLS techniques for practical wireless systems.
Non-orthogonal multiple-access (NOMA) and simultaneous wireless information and power transfer (SWIPT) are promising techniques to improve spectral efficiency and energy efficiency. However, the security of NOMA SWIPT systems has not received much attention in the literature. In this paper, an artificial noise-aided beamforming design problem is studied to enhance the security of a multiple-input single-output NOMA SWIPT system where a practical non-linear energy harvesting model is adopted. The problem is non-convex and challenging to solve. Two algorithms are proposed to tackle this problem based on semidefinite relaxation (SDR) and successive convex approximation. Simulation results show that a performance gain can be obtained by using NOMA compared to the conventional orthogonal multiple access. It is also shown that the performance of the algorithm using a cost function is better than the algorithm using SDR at the cost of a higher computation complexity.
Low latency and energy efficiency are two important performance requirements in various fifth-generation (5G) wire-less networks. In order to jointly design the two performance requirements, in this paper a new performance metric called effective energy efficiency (EEE) is defined as the ratio of the effective capacity (EC) to the total power consumption in a cellular network with underlaid device to device (D2D) communications. We aim to maximize the EEE of the D2D network subject to the D2D device power constraints and the minimum rate constraint of the cellular network. Due to the non-convexity of the problem, we propose a two-stage difference-of-two-concave (DC) function approach to solve this problem. Towards that end, we first introduce an auxiliary variable to transfer the fractional objective function into a subtractive form. We then propose a successive convex approximation (SCA) algorithm to iteratively solve the resulting non-convex problem. The convergence and the global optimality of the proposed SCA algorithm are both analyzed. The numerical results are presented to demonstrate the effectiveness of the proposed algorithm.
Abstract—Millimeter wave (mmWave) communication is a promising technology in future wireless networks because of its wide bandwidths that can achieve high data rates. However, high beam directionality at the transceiver is needed due to the large path loss at mmWave. Therefore, in this paper, we investigate the beam alignment and power allocation problem in a nonorthogonal multiple access (NOMA) mmWave system. Dierent from the traditional beam alignment problem, we consider the NOMA scheme during the beam alignment phase when two users are at the same or close angle direction from the base station. Next, we formulate an optimization problem of joint beamwidth selection and power allocation to maximize the sum rate, where the quality of service (QoS) of the users and total power constraints are imposed. Since it is dicult to directly solve the formulated problem, we start by fixing the beamwidth. Next, we transform the power allocation optimization problem into a convex one, and a closed-form solution is derived. In addition, a one-dimensional search algorithm is used to find the optimal beamwidth. Finally, simulation results are conducted to compare the performance of the proposed NOMA-based beam alignment and power allocation scheme with that of the conventional OMA scheme.
In this paper, we investigate the hybrid precoding design for joint multicast-unicast millimeter wave (mmWave) system, where the simultaneous wireless information and power transform is considered at receivers. The subarray-based sparse radio frequency chain structure is considered at base station (BS). Then, we formulate a joint hybrid analog/digital precoding and power splitting ratio optimization problem to maximize the energy efficiency of the system, while the maximum transmit power at BS and minimum harvested energy at receivers are considered. Due to the difficulty in solving the formulated problem, we first design the codebook-based analog precoding approach and then, we only need to jointly optimize the digital precoding and power splitting ratio. Next, we equivalently transform the fractional objective function of the optimization problem into a subtractive form one and propose a two-loop iterative algorithm to solve it. For the outer loop, the classic Bi-section iterative algorithm is applied. For the inner loop, we transform the formulated problem into a convex one by successive convex approximation techniques, which is solved by a proposed iterative algorithm. Finally, simulation results are provided to show the performance of the proposed algorithm.
This paper exploits a generic downlink symbiotic radio (SR) system, where a Base Station (BS) establishes a direct (primary) link with a receiver having an integrated backscatter device (BD). In order to accurately measure the backscatter link, the backscattered signal packets are designed to have ﬁnite block length. As such, the backscatter link in this SR system employs the ﬁnite block-length channel codes. According to different types of the backscatter symbol period and transmission rate, we investigate the non-cooperative and cooperative SR (i.e., NSR and CSR) systems, and derive their average achievable rate of the direct and backscatter links, respectively. We formulate two optimization problems, i.e., transmit power minimization and energy efﬁciency maximization. Due to the non-convex property of these formulated optimization problems, the semideﬁnite programming (SDP) relaxation and the successive convex approximation (SCA) are considered to design the transmit beamforming vector. Moreover, a low-complexity transmit beamforming structure is constructed to reduce the computational complexity of the SDP relaxed solution. Finally, the simulation results are demonstrated to validate the proposed schemes.
Cognitive satellite-terrestrial networks (CSTNs) have been recognized as a promising network architecture for addressing spectrum scarcity problem in next-generation communication networks. In this paper, we investigate the secure transmission for CSTNs where the terrestrial base station (BS) serving as a green interference resource is introduced to enhance the security of the satellite link. Adopting a stochastic model for the channel state information (CSI) uncertainty, we propose a secure and robust beamforming framework to minimize the transmit power, while satisfying a range of outage (probabilistic) constraints concerning the signal-to-interference-plus-noise ratio (SINR) recorded at the satellite user and the terrestrial user, the leakage-SINR recorded at the eavesdropper, as well as the interference power recorded at the satellite user. The resulting robust optimization problem is highly intractable and the key observation is that the highly intractable probability constraints can be equivalently reformulated as the deterministic versions with Gaussian statistics. In this regard, we develop two robust reformulation methods, namely S-Procedure and Bernstein-type inequality restriction techniques, to obtain a safe approximate solution. In the meantime, the computational complexities of the proposed schemes are analyzed. Finally, the effectiveness of the proposed schemes are demonstrated by numerical results with different system parameters.
This paper investigates a wireless powered sensor network (WPSN), where multiple sensor nodes are deployed to monitor a certain external environment. A multi-antenna power station (PS) provides the power to these sensor nodes during wireless energy transfer (WET) phase, and consequently the sensor nodes employ the harvested energy to transmit their own monitoring information to a fusion center (FC) during wireless information transfer (WIT) phase. The goal is to maximize the system sum throughput of the sensor network, where two different scenarios are considered, i.e., PS and the sensor nodes belong to the same or different service operator(s). For the first scenario, we propose a global optimal solution to jointly design the energy beamforming and time allocation. We further develop a closed-form solution for the proposed sum throughput maximization. For the second scenario in which the PS and the sensor nodes belong to different service operators, energy incentives are required for the PS to assist the sensor network. Specifically, the sensor network needs to pay in order to purchase the energy services released from the PS to support WIT. In this case, the paper exploits this hierarchical energy interaction, which is known as energy trading. We propose a quadratic energy trading based Stackelberg game, linear energy trading based Stackelberg game, and social welfare scheme, in which we derive the Stackelberg equilibrium for the formulated games, and the optimal solution for the social welfare scheme. Finally, numerical results are provided to validate the performance of our proposed schemes.