Simultaneous Energy Harvesting and Information Transmission in a MIMO Full-Duplex System: A Machine Learning-Based Design
Yasser Al-Eryani, Mohamed Akrout, and Ekram Hossain

TL;DR
This paper introduces a MIMO full-duplex system that enables simultaneous wireless information and energy transfer, utilizing machine learning for antenna selection and precoding, resulting in improved spectral efficiency over traditional methods.
Contribution
It presents a novel MIMO FD scheme with a DRL-based antenna selection and precoding approach, enhancing spectral efficiency in simultaneous wireless information and power transfer.
Findings
DRL-based method achieves highest spectral efficiency.
System performance improves with more than three antennas per device.
Significant spectral efficiency gains over time switching-based SWIPT.
Abstract
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…
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Taxonomy
TopicsEnergy Harvesting in Wireless Networks · Full-Duplex Wireless Communications · Advanced MIMO Systems Optimization
