Deep Reinforcement Learning for Joint Time and Power Management in SWIPT-EH CIoT

TL;DR

This paper introduces a deep reinforcement learning method for optimizing time and power management in SWIPT-enabled CIoT systems, improving throughput and energy efficiency through autonomous decision-making.

Contribution

It proposes a novel DDQN-based approach for joint time and power control in CIoT with SWIPT, incorporating realistic constraints and outperforming existing DRL methods.

Findings

Superior throughput and energy efficiency in simulations

Effective handling of fading and interference constraints

Outperforms existing DRL approaches

Abstract

This letter presents a novel deep reinforcement learning (DRL) approach for joint time allocation and power control in a cognitive Internet of Things (CIoT) system with simultaneous wireless information and power transfer (SWIPT). The CIoT transmitter autonomously manages energy harvesting (EH) and transmissions using a learnable time switching factor while optimizing power to enhance throughput and lifetime. The joint optimization is modeled as a Markov decision process under small-scale fading, realistic EH, and interference constraints. We develop a double deep Q-network (DDQN) enhanced with an upper confidence bound. Simulations benchmark our approach, showing superior performance over existing DRL methods.