IRS-Assisted Ambient Backscatter Communications Utilizing Deep Reinforcement Learning

We consider an ambient backscatter communication (AmBC) system aided by an intelligent reflecting surface (IRS). The optimization of the IRS to assist AmBC is extremely difficult when there is no prior channel knowledge, for which no design solutions are currently available. We utilize a deep reinforcement learning-based framework to jointly optimize the IRS and reader beamforming, with no knowledge of the channels or ambient signal. We show that the proposed framework can facilitate effective AmBC communication with a detection performance comparable to several benchmarks under full channel knowledge.

Automated summary

This study utilizes a framework based on deep reinforcement learning to jointly optimize the intelligent reflecting surface and reader beamforming, achieving effective communication in ambient backscatter communication (AmBC) systems. The proposed framework shows comparable detection performance to several benchmarks under full channel knowledge.