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This paper proposes a scheme for the envisioned sixth-generation (6G)ultra-massive Machine Type Communications(umMTC). In particular, wireless powertransfer (WPT) assisted communication is deployed in non-orthogonal multipleaccess (NOMA) downlink networks to realize spectrum and energy cooperation.This paper focuses on joint robust beamforming design to maximize the energyefficiency of WPT-assisted D2D communications in multiple-input single-output(MISO)-NOMA downlink networks. To efficiently address the formulatednon-concave energy efficiency maximization problem, a pure fractionalprogramming (PFP) algorithm is proposed, where the time switching coefficientof the WPT device and the beamforming vectors of the base station arealternatively optimized by applying the Dinkelbach method and quadratictransform respectively. To prove the optimality of the proposed algorithm, thepartial exhaustive search algorithm is proposed as a benchmark. A deepreinforcement learning (DRL)-based method is also applied to directly solve thenon-concave problem. The proposed PFP algorithm and the DDPG-based algorithmare compared in the presence of different channel estimation errors. Simulationresults show that the proposed PFP algorithm outperforms the DDPG-basedalgorithm if perfect channel state information (CSI) can be obtained or justhave minor errors, while the DDPG-based algorithm is more robust when thechannel estimation accuracy is unsatisfactory. On the other hand, one canconclude that the NOMA scheme can provide a higher gain than OMA on the energyefficiency of the WPT-assisted D2D communication in legacy multi-user downlinknetworks.

Joint Robust Beamforming Design for WPT-assisted D2D Communications in MISO-NOMA: Fractional Programming and Deep Reinforcement Learning