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.

中文翻译：

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利用深度强化学习的 IRS 辅助环境反向散射通信

我们考虑由智能反射面 (IRS) 辅助的环境反向散射通信 (AmBC) 系统。在没有先验信道知识的情况下，优化 IRS 以协助 AmBC 是极其困难的，目前还没有可用的设计解决方案。我们利用基于深度强化学习的框架来联合优化 IRS 和阅读器波束成形，而无需了解通道或环境信号。我们表明，所提出的框架可以促进有效的 AmBC 通信，其检测性能可与全信道知识下的几个基准相媲美。