This study focuses on an intelligent reflecting surface (IRS) enabled simultaneous wireless information and power transfer (SWIPT) system with the coexistence of legitimate users (LUs) and Eavesdroppers (Eves). The main objective is to jointly optimize the transmit beamforming and artificial noise covariance matrix at the access point, the phase shift matrix at the IRS, and the power splitting ratio at the LUs, to maximize the system’s min-secrecy rate. Due to the imperfect channel state information of Eves, an outage rate constraint is contained. The formulated problem is a challenging nonconvex optimization problem since it involves nonconvex objective function and constraints, and the outage rate constraint does not have simple closed form expression. To address this problem, an algorithm based on the alternating optimization method is proposed, which breaks down the nonconvex problem into three subproblems. The algorithm employs several techniques to solve these subproblems. Specifically, the outage rate constraint is approximated using the Bernstein-type inequality. And the Taylor formula, semi-definite relaxation, and successive convex approximation methods are employed to transform the nonconvex subproblems into convex ones. Simulation results demonstrate the effectiveness of the proposed algorithm compared to baseline algorithms under different conditions.

Intelligent reflecting surface (IRS) and rate-splitting multiple access (RSMA) technologies are at the forefront of enhancing spectrum and energy efficiency in the next generation multi-antenna communication systems. This paper explores a RSMA system with multiple IRSs, and proposes two purpose-driven scheduling schemes, i.e., the exhaustive IRS-aided (EIA) and opportunistic IRS-aided (OIA) schemes. The aim is to optimize the system weighted energy efficiency (EE) under the above two schemes, respectively. Specifically, the Dinkelbach, branch and bound, successive convex approximation, and the semidefinite relaxation methods are exploited within the alternating optimization framework to obtain effective solutions to the considered problems. The numerical findings indicate that the EIA scheme exhibits better performance compared to the OIA scheme in diverse scenarios when considering the weighted EE, and the proposed algorithm demonstrates superior performance in comparison to the baseline algorithms.

UAV-based wireless powered communication network is a promising method of power supply for battery-free IoT devices, but the limited wireless transmission capability of the UAV constrains the coverage area and transmission throughput. This paper aims to address this issue by exploring intelligent reflecting surfaces with optimized configurations, including the number of reflective elements, transmission power, and the UAV’s altitude. The scheme design is challenging because such a throughput maximization problem is essentially a non-convex optimization problem due to the random wireless channel state and the unknown probability distribution of the objective function. By sequentially applying alternating optimization, successive convex approximation, penalty function, and difference-convex optimization, the schemes proposed in this paper can transform the original non-convex optimization problem into a convex one. Extensive evaluation proves the efficacy of the proposed scheme. The paper further compares two settings of the intelligent reflecting surface, namely dynamic phase shift and static phase shift, and provides their performance gap.

In this paper, we investigate the resource optimization algorithm design in a multicarrier relay system with simultaneous wireless information and power transfer (SWIPT). The relay is capable of harvesting energy from the source’s signals by using the power splitting method. The non-ideal energy consumption including both the non-ideal power amplifier and non-ideal circuit power consumption is considered. Firstly, we study the transmission rate maximization problem (TRMP) in an asymmetric decode-and-forward (DF) relay transmission, where the transmission power at the source, the transmission power at the relay, the power splitting ratio, and the transmission time are jointly optimized. The formulated problem is a non-convex problem, and it is generally quite difficult to solve it. By exploiting the structure of the problem, we propose two methods (logarithmic operation on constraints and logarithmic change of variables) to transform it into corresponding difference of convex (DC) optimization problems. Then, we extend the TRMP to an amplify-and-forward (AF) relay transmission. Further, we propose an effective algorithm to solve the DC optimization problem and prove that the algorithm can converge to a stationary point. Finally, extensive simulations are conducted to verify the performance of the proposed algorithm. Simulation results show that the asymmetric DF relay transmission achieves the highest sum rate, and the AF relay transmission achieves much lower sum rate than both the asymmetric and symmetric DF relay transmissions under different conditions.

This paper explores the computation enhancement problem in an intelligent reflecting surface (IRS) enabled wireless powered mobile edge computing system serving the intelligent transportation relying on powerful computational support. Initially, in the downlink, the base station (BS) with edge server transmits energy signals to the battery-powered roadside units (RSUs) with computing capabilities, which are grouped into distinct clusters. Subsequently, RSUs leverage the harvested energy for local computing and offloading their tasks to the BS by using a hybrid rate splitting multiple access (RSMA) and time division multiple access (TDMA) strategy. Finally, the computed outcomes from the edge server are transmitted for integration with the computations carried out locally at the RSUs. The objective is to maximize the minimal computation rate of RSU clusters, where the transmit power of the BS and RSUs, the downlink and uplink beamforming of the IRS, the CPU frequency of the RSUs, and the time slot assignment are jointly optimized. To address the bottleneck issues constraining computation rate, this paper proposes an iterative algorithm that combines sequential rank-one constraint relaxation and block coordinate descent methods. Ultimately, the simulation results confirm the effectiveness of the proposed algorithm in enhancing the weakest link constraining the system computation rate when contrasted with the baseline algorithms.