Reconfigurable intelligent surfaces (RISs) have been recently considered as a promising candidate for energy-efficient solutions in future wireless networks. Their dynamic and low-power configuration enables coverage extension, massive connectivity, and low-latency communications. Due to a large number of unknown variables referring to the RIS unit elements and the transmitted signals, channel estimation and signal recovery in RIS-based systems are the ones of the most critical technical challenges. To address this problem, we focus on the RIS-assisted wireless communication system and present two joint channel estimation and signal recovery schemes based on message passing algorithms in this paper. Specifically, the proposed bidirectional scheme applies the Taylor series expansion and Gaussian approximation to simplify the sum-product procedure in the formulated problem. In addition, the inner iteration that adopts two variants of approximate message passing algorithms is incorporated to ensure robustness and convergence. Two ambiguities removal methods are also discussed in this paper. Our simulation results show that the proposed schemes show the superiority over the state-of-art benchmark method. We also provide insights on the impact of different RIS parameter settings on the proposed schemes.

翻译：最近,人们认为重新配置的智能表面(RIS)是未来无线网络节能解决方案的一个有希望的候选对象,其动态和低功率配置能够扩大覆盖面、大规模连通和低纬度通信。由于在RIS单元元素和以RIS为基础的系统中传送的信号、频道估计和信号恢复方面有许多未知变量,这是最重要的技术挑战之一。为了解决这一问题,我们侧重于RIS辅助的无线通信系统,并根据本文件的信息传递算法提出了两个联合频道估计和信号恢复计划。具体地说,拟议的双向方案应用泰勒系列扩展和高斯近似法简化所拟订的问题中的总产品程序。此外,采用了两种大致信息传输算法的内在迭代法,以确保稳健性和趋同性。本文也讨论了两种模糊的清除方法。我们的模拟结果表明,拟议的计划显示优于最新基准方法。我们还就不同的RIS参数设置对拟议方案的影响提供了见解。