Dr Gaojie Chen

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.

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.

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).

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 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).