IRS-Assisted Ambient Backscatter Communications Utilizing Deep Reinforcement Learning

2021/03/12 by Xiaolun Jia, Jia, Xiaolun, Xiangyun Zhou +1 · 2 citations
Engineering · #Advanced Wireless Communication Technologies #Energy Harvesting in Wireless Networks #FOS: Computer and information sciences #FOS: Electrical engineering #Information Theory (cs.IT) #Signal Processing (eess.SP) #Underwater Vehicles and Communication Systems #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2103.07083

openalex publication_date 2021/03/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• Continuous control with deep reinforcement learning

Cited by

• Deep Learning-Enabled Multi-Tag Detection in Ambient Backscatter Communications
• Channel Estimation for Full-duplex Multi-tag Ambient Backscatter Communication Systems with I/Q Imbalance

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