Decoding-Aided Channel Estimation Based on the GC-LDPC Codes in Time-Varying MIMO Systems

Massive multiple-input multiple-output (MIMO) has been widely adopted in next-generation wireless systems to enhance spectral efficiency and reliability. However, channel aging poses a significant challenge, leading to the outdated channel state information and degraded system performance. To address this issue, we propose a decoding-aided Kalman filter channel tracking algorithm based on the globally-coupled low-density parity-check (GC-LDPC) codes. The proposed approach leverages locally decoded symbols to optimize channel estimation and employs global decoding to further reduce the error floor. Additionally, protograph-based extrinsic information transfer analysis and the error minimization progressive edge-growth algorithm are utilized to optimize the compatible GC-LDPC codes. Simulation results demonstrate that the proposed algorithm significantly improves channel tracking performance in time-varying MIMO systems.