Sum-Rate Maximization in IRS-Assisted Wireless Power Communication Networks

Abstract

Wireless powered communication networks (WPC-Ns) are a promising technology supporting resource-intensive devices in the Internet of Things (IoT). However, their transmission efficiency is very limited over long distances. The newly emerged intelligent reflecting surface (IRS) can effectively mitigate the propagation-induced impairment by controlling the phase shifts of passive reflection elements. In this paper, we integrate IRS into WPCNs to assist both the energy and information transmission. We aim to maximize the uplink (UL) sum rate of all IoT devices (IoTDs) by jointly optimizing the time allocation variable, energy beam matrix at the power transmitting base station (PTBS), receive beamforming matrix at the information receiving base station (IRBS), and the phase shifts of the IRS both in the UL and downlink (DL) subject to time allocation constraint, together with transmit power constraint for the PTBS and unit modulus constraints. This problem is very difficult to solve directly due to the highly coupled variables, which results in the optimization problem taking neither linear nor convex form. Hence, we decouple this problem into three subproblems by using the block coordinate descent (BCD) method. The UL receive beamforing matrix and phase shift are alternatively optimized in the UL optimization subproblem with fixed time allocation and the DL variables. The DL optimization subproblem is solved by the proposed successive convex approximation (SCA) algorithm. Simulation results demonstrate that the performance of integrating IRS and WPCNs outperforms traditional WPCNs. Besides, the results show that IRS is an effective method to preserve the tradeoff of energy efficiency and transmission efficiency in the IoT.

Intelligent reflecting surface empowered WPCN system over generalized fading channels: Performance analysis and optimization

“…The average throughput performance under different system settings is demonstrated in Figures 4-7. The exact and asymptotic average throughput curves are plotted based on the equations ( 19) and (20), respectively. In particular fig.…”

“…On the other hand, the authors in Reference 19 exploited a nonorthogonal multiple access‐ (NOMA) based IRS‐WPCN system, where the UL data collection can be carried out through the NOMA mechanism. The application of IRS in wireless powered sensor networks (WPSNs) is reported in Reference 20, in which the authors examine the sum‐rate maximization problem through jointly optimizing the EH time, energy/information beamforming variables, and phase shifts of the IRS both in the DL and UL. In contrast to the above works, the authors in Reference 21, investigate the performance of IRS‐ WPCN system in presence of system uncertainties, where a nonlinear EH and channel impairments have been taken into account.…”

Design and Application of Intelligent Reflecting Surface (IRS) for Beyond 5G Wireless Networks: A Review

“…Refs. [ 38 , 110 , 111 ] and references therein propose iterative solutions to tackle this problem. Furthermore, RIS-assisted integrated UAV-satellite communication for terrestrial networks has also been recently explored and is an active area for future research [ 39 ].…”

Joint Beamforming Aided Over-the-Air Computation Systems Relying on Both BS-Side and User-Side Reconfigurable Intelligent Surfaces

“…Lemma 2: By applying the sum-MMSE receive beamforming at the FC, the computation MSE in the SRIS-assisted and DRIS-assisted AirComp systems, i.e., (29) and (31), can approximately be rewritten as…”

“…According to (31) and the derivation of the receive beamforming at the FC in Section III, the sum-MMSE receive beamforming to minimize the computation MSE in the SRISassisted AirComp system is given by…”