Deep Reinforcement Learning for Backscatter Communications: Augmenting Intelligence in Future Internet of Things

TL;DR

This paper explores how deep reinforcement learning can enhance backscatter communication systems in IoT networks, focusing on system principles, recent advancements, and potential future research directions.

Contribution

It provides a comprehensive overview of DRL techniques applied to BC systems, including a detailed case study on RIS-aided NOMA BC systems, and discusses future challenges.

Findings

DRL can optimize backscatter communication performance.

RIS-aided NOMA BC systems benefit from DRL-based control.

Identifies key challenges and future research directions in DRL-BC.

Abstract

Backscatter communication (BC) technology offers sustainable solutions for next-generation Internet-of-Things (IoT) networks, where devices can transmit data by reflecting and adjusting incident radio frequency signals. In parallel to BC, deep reinforcement learning (DRL) has recently emerged as a promising tool to augment intelligence and optimize low-powered IoT devices. This article commences by elucidating the foundational principles underpinning BC systems, subsequently delving into the diverse array of DRL techniques and their respective practical implementations. Subsequently, it investigates potential domains and presents recent advancements in the realm of DRL-BC systems. A use case of RIS-aided non-orthogonal multiple access BC systems leveraging DRL is meticulously examined to highlight its potential. Lastly, this study identifies and investigates salient challenges and proffers prospective avenues for future research endeavors.