Abstract:
In massive multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) systems, accurate channel state information (CSI) is essential to realize system performance gains such as high spectrum and energy efficiency. high-dimensional CSI acquisition requires prohibitively high pilot overhead, which leads to a significant reduction in spectrum efficiency and energy efficiency and more channel estimation complexity. In this thesis we proposed a efficient frequency channel estimation scheme in massive MIMO-OFDM systems are Sparsity Adaptive Matching pursuit SAMP and distributed Sparsity Adaptive Matching pursuit DSAMP that would be accurately estimate a large number of channels and reduce channel estimation complexity. It’s been concerting mathematical model to calculate performance such as MSE and spectral efficiency for number of repetition with SNR for the SAMP and DSAMP algorithms to make possible result to compare them, evaluation of SAMP and DSAMP algorithms in terms of different number of Antennas and Direction of Arrival DOA, second presents the evaluation metrics for different time slot length and different Virtual Angular Domain VAD Sparsity levels. All results are evaluated in term of Mean Square Error MSE in MATLAB Simulation system. results show that the proposed method achieves higher channel estimation accuracy while requiring lower pilot sequence overhead compared with other methods .Finally the comparison result shows that DSAMP has an advantage over SAMP with all perspective parameters used
