{'fraction_non_alphanumeric': 0.015748031496062992, 'fraction_numerical': 0.0, 'mean_word_length': 5.4963503649635035, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
MIMO-OFDM channel estimation using distributed compressed sensing

This paper proposes a method of sparse channel estimation using compressed sensing for MIMO-OFDM system. The channel estimation is formulated as a sparse recovery problem because of the maximum delay spread in the high data rate OFDM communication systems. The proposed Distributed Compressed Sensing (DCS) algorithm for channel estimation in MIMO-OFDM system exploits the join sparsity of the MIMO channel. It takes less number of iterations in solving the channel estimation problem and runs much faster than the existing Compressive Sampling Matching Pursuit (CoSaMP). Simulation results demonstrate the validity of the algorithm. For the MIMO channels of unknown sparse degrees, the proposed DCS algorithm gives good channel estimation performance with less number of subcarriers reducing the complexity of the system.