🤖 AI Summary
To address the low channel estimation accuracy and high pilot overhead in multi-RIS-aided MIMO systems, this paper proposes a joint iterative detection, decoding, and channel estimation framework. The method innovatively integrates LDPC-coded pilots with data packet parity bits for channel estimation, enabling mutual enhancement between channel information and code constraints across iterations—thereby significantly improving estimation accuracy while reducing reliance on pilot resources. Unlike conventional approaches, it imposes no sparsity assumption on the channel and is applicable to realistic Sub-6 GHz LOS/NLOS propagation environments. Simulation results demonstrate that, under identical pilot overhead, the proposed scheme achieves substantial reductions in both bit error rate and channel estimation mean-square error compared to state-of-the-art methods, exhibiting superior efficiency and robustness.
📝 Abstract
This work proposes an iterative detection, decoding and channel estimation scheme for multiple-antenna systems assisted by multiple reflective intelligent surfaces (RIS). A novel channel estimation technique that exploits low-density parity-check (LDPC) codes and iterative processing is developed to enhance estimation accuracy while reducing the number of required pilot symbols. The key idea is to exploit encoded pilots to improve the iterative process, enabling the use of not only pilot bits but also parity bits from the coded packet to refine channel estimation. Simulations analyze a sub-6 GHz scenario where the channel propagation is not sparse and multiple RIS are deployed, considering both LOS and NLOS conditions. Numerical results show significant performance gains for the proposed scheme and estimator over competing approaches.