Improved compressed sensing channel estimation algorithm for MIMO-OFDM systems

The signals in MIMO-OFDM systems have good sparsity characteristics, and the channel estimation problem can be transformed into a sparse signal recovery problem using compressed sensing theory. This paper proposes an adaptive compressed sampling matching and tracking algorithm based on zebra optimization (ZOA-CoSaMP), which utilizes the ZOA algorithm to optimize the atomic matching process of the CoSaMP algorithm. The generalized Jaccard coefficient is used for atomic secondary screening to improve the recovery accuracy, Simultaneously introducing a power function type variable step size strategy to achieve sparsity adaptation. Finally, it was demonstrated through experimental simulation that compared to the CoSaMP algorithm, this algorithm has better estimation accuracy.