The combination of non-orthogonal multiple access (NOMA) and wireless power
transfer (WPT) is a promising solution to enhance the energy efficiency of
Device-to-Device (D2D) enabled wireless communication networks. In this paper,
we focus on maximizing the energy efficiency of a WPT-D2D pair in a
multiple-input single-output (MISO)-NOMA downlink network, by alternatively
optimizing the beamforming vectors of the base station (BS) and the time
switching coefficient of the WPT assisted D2D transmitter. The formulated
energy efficiency maximization problem is non-convex due to the highly coupled
variables. To efficiently address the non-convex problem, we first divide it
into two subproblems. Afterwards, an alternating algorithm based on the
Dinkelbach method and quadratic transform is proposed to solve the two
subproblems iteratively. To verify the proposed alternating algorithm's
accuracy, partial exhaustive search algorithm is proposed as a benchmark. We
also utilize a deep reinforcement learning (DRL) method to solve the non-convex
problem and compare it with the proposed algorithm. To demonstrate the
respective superiority of the proposed algorithm and DRL-based method,
simulations are performed for two scenarios of perfect and imperfect channel
state information (CSI). Simulation results are provided to compare NOMA and
orthogonal multiple access (OMA), which demonstrate the superior performance of
energy efficiency of the NOMA scheme