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In this paper, we investigate an intelligent reflecting surface (IRS) assisted multi-user multiple-input multiple-output (MIMO) full-duplex (FD) system. We jointly optimize the active beamforming matrices at the access point (AP) and uplink users, and the passive beamforming matrix at the IRS to maximize the weighted sum-rate of the system. Since it is practically difficult to acquire the channel state information (CSI) for IRS-related links due to its passive operation and large number of elements, we conceive a mixed-timescale beamforming scheme. Specifically, the high-dimensional passive beamforming matrix at the IRS is updated based on the channel statistics while the active beamforming matrices are optimized relied on the low-dimensional real-time effective CSI at each time slot. We propose an efficient stochastic successive convex approximation (SSCA)-based algorithm for jointly designing the active and passive beamforming matrices. Moreover, due to the high computational complexity caused by the matrix inversion computation in the SSCA-based optimization algorithm, we further develop a deep-unfolding neural network (NN) to address this issue. The proposed deep-unfolding NN maintains the structure of the SSCA-based algorithm but introduces a novel non-linear activation function and some learnable parameters induced by the first-order Taylor expansion to approximate the matrix inversion. In addition, we develop a black-box NN as a benchmark. Simulation results show that the proposed mixed-timescale algorithm outperforms the existing single-timescale algorithm and the proposed deep-unfolding NN approaches the performance of the SSCA-based algorithm with much reduced computational complexity when deployed online. [ABSTRACT FROM AUTHOR]

In this letter, we investigate an intelligent reflecting surface (IRS) aided device-to-device (D2D) offloading system, where an IRS is employed to assist in computation offloading from a group of users with intensive tasks to another group of idle users. To minimize the system latency while cutting down the heavy overhead in exchange of channel state information (CSI), we study the joint design of beamforming and resource allocation on mixed timescales. Specifically, the high-dimensional passive beamforming vector at the IRS is updated in a frame-based manner based on the channel statistics, where each frame consists of a number of time slots, while the offloading ratio and user matching strategy are optimized relied on the low-dimensional real-time effective channel coefficients in each time slot. A novel mixed-integer stochastic successive convex approximation (MISSCA) algorithm is proposed to tackle the challenging problem. The convergence property and the computational complexity of the proposed algorithm are then examined. Simulation results show that our proposed algorithm significantly outperforms the conventional benchmarks. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) have been applied in many different communication scenarios due to their mobility and manipuility. In this paper, we investigate a UAV-aided relay network, where a number of ground users in the urban area with many obstructions need to collect data from a base station (BS), and a UAV could fly around above the users and serve as a decode-and-forward (DF) mobile relay to improve the transmission coverage and performance. In this situation, channel can be represented by the segmented ray-tracing model. To ensure fairness, we aim to maximize the minimum throughput among all the users by jointly optimizing the three-dimensional (3D) UAV trajectory, user scheduling, and bandwidth allocation. To tackle the non-convex objective function and coupling constraints, we first construct surrogate functions, and then approximate the problem into a convex one and develop a constrained successive convex approximation (CSCA) algorithm. In particular, through insightful auxiliary variables and linearly coupled equality (LCE) constraints, we propose a low-complexity algorithm based on the alternating direction method of multipliers (ADMM) to solve the approximated convex problem in the iteration of the proposed CSCA algorithm. Furthermore, we prove the convergence of the proposed algorithm and analyze its complexity. The proposed algorithm can be easily extended to the multi-UAV scenario. Simulation results show that the proposed design significantly outperforms the existing schemes. [ABSTRACT FROM AUTHOR]

Presents the table of contents for this issue of the publication. [ABSTRACT FROM AUTHOR]