RIS-enhanced WPCNs: Joint radio resource allocation and passive beamforming optimization

Y Xu, Z Gao, Z Wang, C Huang… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
IEEE Transactions on Vehicular Technology, 2021ieeexplore.ieee.org
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 …
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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