Sparsity Learning-Based CSI Feedback for FDD Massive MIMO Systems

In frequency-division duplexing mode, accurate channel state information (CSI) needs to be acquired at the base station by feedback from the terminals. However, the costly feedback overhead generated by large-scale arrays poses an arduous challenge to the traditional feedback scheme. In this letter, we propose a sparse learning-based CSI feedback method in a compressive sensing framework for massive MIMO systems. The key insights are to learn the sparse structure of CSI through the recursive least squares algorithm and process a continuous update of the sparse basis. Accordingly, a CSI sparse characteristic adaptive dictionary is constructed. The simulation results reveal that the proposed scheme achieves excellent performance in terms of both compression efficacy and recovery accuracy.