Joint Beamforming, Power Allocation, and Splitting Control for SWIPT-Enabled IoT Networks with Deep Reinforcement Learning and Game Theory

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 6; p. 2328
• Main Authors: Liu, JainShing, Lin, Chun-Hung Richard, Hu, Yu-Chen, Donta, Praveen Kumar
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 17.03.2022; MDPI
• Subjects: Algorithms; beamforming; Business metrics; Communication; Convex analysis; Deep learning; deep reinforcement learning; Energy consumption; Game theory; Internet of Things; joint optimization; Methods; multi-resource allocation; Optimization; power control; Wireless networks; joint optimization; multi-resource allocation; game theory; energy harvesting; power control; deep reinforcement learning; beamforming; IoT

Future wireless networks promise immense increases on data rate and energy efficiency while overcoming the difficulties of charging the wireless stations or devices in the Internet of Things (IoT) with the capability of simultaneous wireless information and power transfer (SWIPT). For such networks,...