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Reflecting Surface Assisted Energy Harvesting with Optimized NOMA Downlink Transmissions

This paper studies an intelligent reflecting surface (IRS) assisted wireless communication system with multiple downlink data and energy harvesting users. We assume that base station uses non-orthogonal multiple access (NOMA) for transmission and downlink users employ successive interference cancellation to decode their information from the received signal. With this setting, our goal is to maximize the harvested energy at the energy harvesting users while guaranteeing the minimum rate requirements of the individual data users. We propose an alternating optimization based algorithm, where semidefinite relaxation is used to obtain the optimal beamforming design at the base station and the IRS. Specifically, an iterative rank minimization approach is used to obtain the optimal reflection phase vector at the IRS. The convergence of the proposed algorithm is also proved. Finally, the efficacy of the proposed algorithm is demonstrated with the help of simulation results.