  This paper proposes a scheme for the envisioned sixth-generation (6G)
ultra-massive Machine Type Communications(umMTC). In particular, wireless power
transfer (WPT) assisted communication is deployed in non-orthogonal multiple
access (NOMA) downlink networks to realize spectrum and energy cooperation.
This paper focuses on joint robust beamforming design to maximize the energy
efficiency of WPT-assisted D2D communications in multiple-input single-output
(MISO)-NOMA downlink networks. To efficiently address the formulated
non-concave energy efficiency maximization problem, a pure fractional
programming (PFP) algorithm is proposed, where the time switching coefficient
of the WPT device and the beamforming vectors of the base station are
alternatively optimized by applying the Dinkelbach method and quadratic
transform respectively. To prove the optimality of the proposed algorithm, the
partial exhaustive search algorithm is proposed as a benchmark. A deep
reinforcement learning (DRL)-based method is also applied to directly solve the
non-concave problem. The proposed PFP algorithm and the DDPG-based algorithm
are compared in the presence of different channel estimation errors. Simulation
results show that the proposed PFP algorithm outperforms the DDPG-based
algorithm if perfect channel state information (CSI) can be obtained or just
have minor errors, while the DDPG-based algorithm is more robust when the
channel estimation accuracy is unsatisfactory. On the other hand, one can
conclude that the NOMA scheme can provide a higher gain than OMA on the energy
efficiency of the WPT-assisted D2D communication in legacy multi-user downlink
networks.
