Modified Two-dimensional Compressed Sensing Scheme For Massive MIMO Channel Estimation

Compressed sensing(CS),a principle might enable dramatically reduced measurement time,dramatically reduced sampling rates or reduced use of analog-to-digital converter resources,been applied extensively in contemporary communication systems.In the research community,the issue of recovering sparse signals from a great many of compressed measurements have been attracting a great deal of attention.With the research progress,the new method of exploiting the channel sparse and constant correlation,shows the great potential of using prior signal support in enhancing the performance of CS recovery.With the continuous development of the wireless communication,massive multiple-input multiple-output(MIMO)technology has become the key research areas of MIMO technology.Research on channel estimation for the Massive MIMO system is very significant.The problem of 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.Adaptively exploited the prior support information and using the chunk sparsity structure,the 2D CS model is considered and a new M-2DSP algorithm is designed to heighten the signal recovery performance.The massive multi-input multi-out(MIMO)system shows 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.The estimated channel state information at the transmitter(CSIT)is analyzed according to the NMSE of the estimated channel under the conditions of changing the length of the training pilot and transmit SNR respectively.It is obtained that,in massive MIMO systems,the 2D CS scheme is excellent,the channel chunk sparse structure and the prior channel support are reconstructed to strengthen the CSIT estimation quality.Furthermore,the effectiveness of M-2DSP algorithm 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).