Convex Optimization-Based OFDM Channel Estimation Algorithm

This program implements a convex optimization-based OFDM channel estimation algorithm. The Smooth SLO algorithm, functioning as an iterative solver for sparse linear equation systems, is adapted here for OFDM channel estimation, demonstrating excellent signal estimation accuracy and processing speed. Our implementation employs gradient descent with adaptive step-size control and incorporates sparsity constraints through L1-norm regularization. During experimental validation, we utilized extensive datasets and compared the algorithm's performance against conventional methods like LS and LMMSE estimators. The results indicate clear advantages in both estimation accuracy and computational efficiency. Furthermore, we analyzed potential optimization directions including parallel computation implementations and adaptive threshold selection, while exploring potential applications in massive MIMO systems and 5G/6G communications, providing valuable references for future research developments.