Channel Estimation using Approximate Message Passing based Hybrid-Field for Massive MIMO systems

A mere promising approach for next generation wireless technologies involves utilizing Massive Multiple Inputs and Multiple Outputs (MIMO) technology. However, conventional channel estimation methods are not applicable to estimate the hybrid-field Massive MIMO channelaccurately. To address this challenge, in this paper introduces an efficient scheme for hybrid-field channel estimation tailored for Massive MIMO systems. The existing hybrid-field that are on Orthogonal Matching Pursuit (OMP) algorithm, faces issues as insensitivity to the dictionary matrix, limitations with fixed signal dimensions, and increased computational complexity, pilot overhead, and channel estimation errors with dimension expansion. In response to these challenges, an iterative-based Compressed Sensing (CS) algorithm, specifically the Approximate Message Passing (AMP) based Hybrid-Field for Massive MIMO systems, is proposed. The AMP algorithm exhibits improved performance with lower computational complexity by avoiding matrix inversions. Results validates the superior performance of proposed scheme on par withavailable approaches.