Joint robust beamforming design for WPT-assisted D2D communications in MISO-NOMA: Fractional programming and deep reinforcement learning

This paper introduces a framework aimed at aiding the development of sixth-generation (6G) ultra-massive machine type communications (um-MTC). Precisely, the deployment of wireless power transfer (WPT) supported device-to-device (D2D) communication occurs within multiple-input single-output non-orthogonal multiple access (MISONOMA) downlink networks to facilitate spectrum and energy collaboration. A pure fractional programming (PFP) algorithm is proposed to maximize the WPT-assisted device's energy efficiency. An optimal closed-form solution for determining the time-switching coefficient of the WPT device is provided. For the robust beamforming design, the complex multi-dimension quadratic transform is applied. Moreover, the paper applies the deep deterministic policy gradient (DDPG)-based approach to directly address the problem and compares it with the proposed algorithm. Simulation outcomes highlight two key insights: 1) The PFP algorithm surpasses the performance of the DRL-based algorithm when the acquired channel state information (CSI) is accurate or contains negligible errors, while the opposite is true for imperfect CSI 2) The higher energy efficiency gains can be achieved in NOMA scheme than that in Orthogonal Multiple Access (OMA) scheme.