2020/02/13 by Yasser Al-Eryani, Mohamed Akrout, Al-Eryani, Yasser +3
Engineering · #Advanced MIMO Systems Optimization #Energy Harvesting in Wireless Networks #FOS: Computer and information sciences #FOS: Electrical engineering #Full-Duplex Wireless Communications #Information Theory (cs.IT) #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2002.06193
openalex publication_date 2020/02/13 · openalex created_date 2020/02/24 · openalex updated_date 2026/07/28
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.