The problem of compressed sensing (CS) is considered in two-dimensional (2D) sparse decomposition measurement model. Correspondingly, a novel recovery algorithm — modified 2D subspace pursuit (M-2DSP) algorithm is proposed with the available prior support and chunk sparse structure. The massive multi-input multi-out (MIMO) system show a concealed sparse structure and temporal correlation in the user (UE) channel matrix in virtue of the shared local scatterers in the physical propagation environment. Thereby the proposed scheme can be applied to sparse channel estimation in massive MIMO systems with temporal correlation. Furthermore its effectiveness is proved, both theoretical analysis and experiment simulations testify the usefulness and advantages of the new algorithm in recovery performance, especially in smaller overhead training pilot quantity and lower transmit signal noise ratio (SNR).
['Yingxiang Wang', 'Hairong Wang', 'Youhua Fu']
Modified two-dimensional compressed sensing scheme for massive MIMO channel estimation