An intelligent reflecting surface (IRS) consists of a large number of low-cost reflecting elements, which can steer the incident signal collaboratively by passive beamforming. This way, IRS reconfigures the wireless environment to boost the system performance. In this paper, we consider an IRS-assisted uplink non-orthogonal multiple access (NOMA) system. The objective is to maximize the sum rate of all users under individual power constraint. The considered problem requires a joint power control at the users and beamforming design at the IRS, and is non-convex. To handle it, semidefinite relaxation is employed, which provides a near-optimal solution. Presented numerical results show that the proposed NOMA-based scheme achieves a larger sum rate than orthogonal multiple access (OMA)-based one. Moreover, the impact of the number of reflecting elements on the sum rate is revealed.

Mobile-edge computing (MEC) and intelligent reflecting surface (IRS) are envisioned as two promising technologies that enable massive connectivity in the future Internet of Things (IoT) networks. MEC allows IoT devices (IDs) to offload their computation intensive tasks and, thus, can prolong their lifespan. In contrast, the IRS can enhance the channel condition between IDs and the access points (APs), which are co-located with the MEC server. Wireless power transfer technique enabling energy harvesting for IDs helps realizing sustainable IoT network. This article applies IRS in a multi-ID MEC system for better latency performance. We first propose a multiple access scheme with hybrid frequency-division and nonorthogonal access technologies and then design a timing protocol for the IDs. Based on the above design, we study the latency optimization problem with the joint optimization of power allocation, the IRS phase shift matrix, and uplink and downlink beamformer under maximum power constraint for the IDs and AP. To tackle the formulated multivariable nonconvex problem, we split the target problem into several subproblems and provide a near-optimal low-complexity ID clustering scheme. Afterward, we derive optimal solutions to these subproblems, and a low-complexity fast-convergence alternating algorithm is proposed to minimize the overall latency. Presented simulation results verify the convergence of the alternating algorithm, and its superiority over the benchmarks.

In future wireless communication systems, intelligent reflecting surface (IRS) plays an important role since it is capable of dynamically reconfiguring the channel conditions. In this paper, we apply IRS to facilitate the communication of an uplink multiple-input multiple-output non-orthogonal multiple access system. The objective is to maximize the system energy-efficiency (EE), requiring a joint optimization of the active beamforming at the base station, passive beamforming at the IRS, and power allocation. An iterative method is proposed to solve the formulated multi-variable non-convex optimization problem. Moreover, a low-complexity user ordering method is provided. Numerical results verify the rapid convergence of the iterative optimization scheme and the effectiveness of the user ordering. In addition, a substantial gain can be obtained by employing the proposed scheme over the benchmark schemes without IRS and with orthogonal multiple access.

Multiple-input multiple-output (MIMO) is a favorable technique that can improve system capacity and performance through spatial multiplexing. However, the performance gets degraded over the correlated wireless channels. In this letter, we consider a point-to-point MIMO system and jointly optimize both the precoding matrix and the difference between two transmit vectors to resist transmit correlation. The simulation results demonstrate that the proposed method can get average 75% gain comparing with the existing method in terms of the minimum pairwise error probability.