Reconfigurable intelligent surface (RIS) is considered as one of the key technologies for future 6G communications. To fully unleash the performance of RIS, accurate channel state information (CSI) is crucial. Beam training is widely utilized to acquire the CSI. However, before aligning the beam correctly to establish stable connections, the signal-to-noise ratio (SNR) at UE is inevitably low, which reduces the beam training accuracy. To deal with this problem, we exploit the coded beam training framework for RIS systems, which leverages the error correction capability of channel coding to improve the beam training accuracy under low SNR. Specifically, we first extend the coded beam training framework to RIS systems by decoupling the base station-RIS channel and the RIS-user channel. For this framework, codewords that accurately steer to multiple angles is essential for fully unleashing the error correction capability. In order to realize effective codeword design in RIS systems, we then propose a new codeword design criterion, based on which we propose a relaxed Gerchberg-Saxton (GS) based codeword design scheme by considering the constant modulus constraints of RIS elements. In addition, considering the two dimensional structure of RIS, we further propose a dimension reduced encoder design scheme, which can not only guarentee a better beam shape, but also enable a stronger error correction capability. Simulation results reveal that the proposed scheme can realize effective and accurate beam training in low SNR scenarios.

翻译：可重构智能表面（RIS）被认为是未来6G通信的关键技术之一。为充分释放RIS性能，准确的信道状态信息至关重要。波束训练被广泛用于获取信道状态信息。然而，在波束正确对准以建立稳定连接前，用户终端的信噪比不可避免地处于较低水平，这会降低波束训练精度。为解决该问题，我们为RIS系统开发了编码波束训练框架，该框架利用信道编码的纠错能力来提升低信噪比下的波束训练精度。具体而言，我们首先通过解耦基站-RIS信道与RIS-用户信道，将编码波束训练框架扩展至RIS系统。在此框架下，能够精确指向多个角度的码字对于充分发挥纠错能力至关重要。为实现RIS系统中有效的码字设计，我们提出了一种新的码字设计准则，并基于该准则考虑RIS单元的恒模约束，提出了一种基于松弛Gerchberg-Saxton算法的码字设计方案。此外，针对RIS的二维结构，我们进一步提出了降维编码器设计方案，该方案不仅能保证更优的波束形状，还能实现更强的纠错能力。仿真结果表明，所提方案能够在低信噪比场景下实现有效且精确的波束训练。