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

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.