Reconfigurable Intelligent Surfaces (RISs) have been recently considered as an energy-efficient solution for future wireless networks due to their fast and low-power configuration, which has increased potential in enabling massive connectivity and low-latency communications. Accurate and low-overhead channel estimation in RIS-based systems is one of the most critical challenges due to the usually large number of RIS unit elements and their distinctive hardware constraints. In this paper, we focus on the uplink of a RIS-empowered multi-user Multiple Input Single Output (MISO) uplink communication systems and propose a channel estimation framework based on the parallel factor decomposition to unfold the resulting cascaded channel model. We present two iterative estimation algorithms for the channels between the base station and RIS, as well as the channels between RIS and users. One is based on alternating least squares (ALS), while the other uses vector approximate message passing to iteratively reconstruct two unknown channels from the estimated vectors. To theoretically assess the performance of the ALS-based algorithm, we derived its estimation Cramér-Rao Bound (CRB). We also discuss the downlink achievable sum rate computation with estimated channels and different precoding schemes for the base station. Our extensive simulation results show that our algorithms outperform benchmark schemes and that the ALS technique achieves the CRB. It is also demonstrated that the sum rate using the estimated channels always reach that of perfect channels under various settings, thus, verifying the effectiveness and robustness of the proposed estimation algorithms.

中文翻译：

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RIS 支持的多用户 MISO 无线通信的信道估计

由于其快速和低功耗的配置，可重构智能表面 (RIS) 最近被认为是未来无线网络的节能解决方案，这增加了实现大规模连接和低延迟通信的潜力。由于通常有大量 RIS 单元元素及其独特的硬件限制，在基于 RIS 的系统中准确和低开销的信道估计是最关键的挑战之一。在本文中，我们关注基于 RIS 的多用户多输入单输出 (MISO) 上行链路通信系统的上行链路，并提出了一种基于并行因子分解的信道估计框架，以展开由此产生的级联信道模型。我们提出了两种用于基站和 RIS 之间信道的迭代估计算法，以及RIS与用户之间的渠道。一种基于交替最小二乘法 (ALS)，而另一种使用向量近似消息传递来从估计的向量中迭代重建两个未知通道。为了从理论上评估基于 ALS 的算法的性能，我们导出了它的估计 Cramér-Rao Bound (CRB)。我们还讨论了使用估计信道和基站不同预编码方案的下行链路可实现总速率计算。我们广泛的模拟结果表明，我们的算法优于基准方案，并且 ALS 技术达到了 CRB。还证明了在各种设置下使用估计信道的总和率始终达到完美信道的总和率，从而验证了所提出的估计算法的有效性和鲁棒性。