Compressive Sensing-based Differential Channel Feedback Scheme Using Subspace Matching Pursuit Algorithm for B5G Wireless Systems

Millimeter wave (mmWave) massive multi-input multi-output (MIMO) systems are the promising technology for next-generation 5G wireless systems and beyond. Sparse signal recovery and channel feedback are challenging and fundamental problems affecting downlink transmission due to the substantial increase in channel matrix size in mmWave systems. To overcome the overhead of the channel and improve CS recovery effectiveness, this article proposes the joint use of the subspace matching search algorithm and differential operation for channel impulse response (CIR). Here, the current CIR is converted to a differential CIR using operations between the current and previous CIRs. The differential CIR is then compressed and fed back to the base station. Subsequently, this differential CIR is recovered using the subspace matching search algorithm. Such a scheme leverages effective structural sparsity through a combination of subspace and differential operations. The adaptive algorithm adaptively selects relevant subspaces based on coefficients. The simulation results show that the proposed scheme reduces channel overhead by 36% and 24% at compression ratios of 25%and 45%, respectively, over different time slots in mmWave massive MIMO systems.