Within the context of massive machine-type communications+, reconfigurable intelligent surfaces (RISs) represent a promising technology to boost system performance in scenarios with poor channel conditions. Considering single-antenna sensors transmitting short data packets to a multiple-antenna collector node, we introduce and design an RIS to maximize the weighted sum rate (WSR) of the system working in the finite blocklength regime. Due to the large number of reflecting elements and their passive nature, channel estimation errors may occur. In this letter, we then propose a robust RIS optimization to combat such a detrimental issue. Based on concave bounds and approximations, the nonconvex WSR problem for the RIS response is addressed via successive convex optimization (SCO). Numerical experiments validate the performance and complexity of the SCO solutions.

翻译：在大规模机器类通信+（mMTC+）背景下，可重构智能表面（RIS）技术为信道条件恶劣场景下的系统性能提升提供了极具前景的解决方案。本文考虑单天线传感器向多天线收集节点传输短数据包的应用场景，引入并设计了一种RIS，以最大化系统在有限块长机制下的加权和速率（WSR）。由于RIS反射单元数量庞大且其本身具有无源特性，信道估计误差可能发生。为此，本通信提出一种鲁棒的RIS优化方案以应对这一不利影响。基于凹界与近似方法，针对RIS响应的非凸WSR问题通过逐次凸优化（SCO）进行求解。数值实验验证了所提SCO方案的性能与复杂度。