  We propose a multiple-input multiple-output (MIMO)-based full-duplex (FD)
scheme that enables wireless devices to simultaneously transmit information and
harvest energy using the same time-frequency resources. In this scheme, for a
MIMO point-to-point set up, the energy transmitting device simultaneously
receives information from the energy harvesting device. Furthermore, the
self-interference (SI) at the energy harvesting device caused by the FD mode of
operation is utilized as a desired power signal to be harvested by the device.
For implementation-friendly antenna selection and MIMO precoding at both the
devices, we propose two methods: (i) a sub-optimal method based on relaxation,
and (ii) a hybrid deep reinforcement learning (DRL)-based method, specifically,
a deep deterministic policy gradient (DDPG)-deep double Q-network (DDQN)
method. Finally, we study the performance of the proposed system under the two
implementation methods and compare it with that of the conventional time
switching-based simultaneous wireless information and power transfer (SWIPT)
method.
  Findings show that the proposed system gives a significant improvement in
spectral efficiency compared to the time switching-based SWIPT. In particular,
the DRL-based method provides the highest spectral efficiency. Furthermore,
numerical results show that, for the considered system set up, the number of
antennas in each device should exceed three to mitigate self-interference to an
acceptable level.
