4.8 Article

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

期刊

IEEE INTERNET OF THINGS JOURNAL
卷 8, 期 19, 页码 14959-14970

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JIOT.2021.3072987

关键词

Wireless communication; Resource management; Optimization; Array signal processing; Internet of Things; OFDM; Information processing; Intelligent reflecting surface (IRS); sum rate; wireless-powered communication networks (WPCNs)

资金

  1. Shenzhen Overseas High-Level Talents Innovation and Entrepreneurship [KQJSCX20180328093835762]
  2. Shenzhen Basic Research Program [JCYJ20190808122409660]
  3. Guangdong Basic Research Program [2019A1515110358, 2021A1515012097, 2020ZDZX1037, 2020ZDZX1021]
  4. National Natural Science Foundation of China [61801302]
  5. EPSRC of the U.K. [EP/R006377/1]

向作者/读者索取更多资源

This article introduces the integration of intelligent reflecting surfaces (IRS) into wireless-powered communication networks (WPCNs) to optimize the transmission efficiency of IoT devices. By using the block coordinate descent method to decouple the optimization problem into three subproblems, simulation results demonstrate that the performance of integrating IRS and WPCNs outperforms traditional WPCNs. Additionally, the results show that IRS is an effective method to preserve the tradeoff of energy efficiency and transmission efficiency in the IoT.
Wireless-powered communication networks (WPCNs) 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 article, 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 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, 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 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 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.

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