4.7 Article

RIS-Enhanced WPCNs: Joint Radio Resource Allocation and Passive Beamforming Optimization

期刊

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
卷 70, 期 8, 页码 7980-7991

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TVT.2021.3096603

关键词

Wireless communication; Protocols; Array signal processing; Simulation; Reconfigurable intelligent surfaces; Downlink; Throughput; Reconfigurable intelligent surface; wireless-powered communication; energy efficiency; passive beamforming; radio resource allocation

资金

  1. National Natural Science Foundation of China [61601071, 62071078]
  2. Natural Science Foundation of Chongqing [cstc2019jcyj-xfkxX0002, cstc2019jcyj-msxmX0666]
  3. Open Funding of Shaanxi Key Laboratory of Information Communication Network and Security [ICNS201904]
  4. Graduate Scientific research innovation Project of Chongqing [CYS21292]
  5. Fundamental Research Funds for the Central Universities [2021FZZX001-21]

向作者/读者索取更多资源

This paper proposes a joint optimization scheme for RIS-assisted wireless-powered communication networks, leading to improved energy efficiency compared to conventional schemes.
Wireless-powered communication and reconfigurable intelligent surface (RIS) can complement each other for increasing energy utilization and spectrum efficiency by reconfiguring the surrounding radio environment, however, which has not been sufficiently studied by the existing works. In this paper, we propose a joint radio resource and passive beamforming optimization scheme for a downlink RIS-assisted wireless-powered communication network with a harvest-then-transmit protocol to improve system energy efficiency (EE). In the considered model, the single-antenna wireless devices (WDs) harvest wireless energy from a multi-antenna dedicated power station (PS) through the RIS in the downlink and transmit their independent information to a single-antenna receiver in the uplink by a time-division-multiple-access mode. Our goal is to maximize the total EE of all WDs. To make full use of the beamforming gain provided by both the PS and the RIS, we jointly optimize the active beamforming of the PS and the passive beamforming of the RIS. To deal with the challenging non-convex optimization problem with multiple coupled variables, we first consider fixing the passive beamforming, and converting the remaining radio resource allocation problem into an equivalent convex problem which is solved by using Lagrange dual theory. Then, we fix the optimized resource allocation parameters and optimize the passive beamforming of the RIS by using a semidefinite programming method. Simulation results demonstrate that the proposed algorithm achieves higher EE compared to the conventional schemes.

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