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 efficient AmBC communication with a detection performance comparable to several benchmarks under full channel knowledge.

翻译：我们认为,在智能反射表面(IRS)的帮助下,可以建立一个环境反向散射通信系统(AMBC)。在没有事先具备设计解决方案的频道知识的情况下,最优化IRC协助AMBC的IRC系统极为困难。我们利用一个深层强化学习框架,共同优化IRS和读者光束,对频道或环境信号一无所知。我们表明,拟议的框架可以促进高效的AMBC通信,其探测性能可与在全频道知识下的若干基准相比。