The use of multiple Reconfigurable Intelligent Sur- faces (RIS) has gained attention in 6G networks to enhance coverage. However, the feasibility of deploying multiple RIS relies on efficient channel estimation and reduced pilot overhead. To address these challenges, this work proposes an iterative channel estimation scheme that exploits low-density parity-check (LDPC) codes, channel coherence time, and iterative processing to improve estimation accuracy while minimizing pilot length. Encoded pilots are used to strengthen the iterative processing, leveraging both pilot and parity bits, while previous estimates are incorporated to further reduce overhead. Simulations consider a sub-6 GHz scenario with non-sparse channels and multiple RIS under both LOS and NLOS conditions. The results show that the proposed method outperforms existing approaches, achieving significant gains with substantially lower pilot overhead.
翻译：在6G网络中，使用多个可重构智能表面（RIS）以增强覆盖范围已受到关注。然而，部署多个RIS的可行性依赖于高效的信道估计和降低的导频开销。为解决这些挑战，本研究提出一种迭代信道估计方案，利用低密度奇偶校验（LDPC）码、信道相干时间和迭代处理来提高估计精度，同时最小化导频长度。通过使用编码导频来增强迭代处理，充分利用导频和校验比特，并结合先前的估计值以进一步降低开销。仿真考虑了亚6 GHz场景下的非稀疏信道和多个RIS，包括视距（LOS）与非视距（NLOS）条件。结果表明，所提方法优于现有方案，在显著降低导频开销的同时实现了可观的性能增益。
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