{'fraction_non_alphanumeric': 0.01804511278195489, 'fraction_numerical': 0.0, 'mean_word_length': 5.688442211055277, '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}}
Channel Estimation Based on Improved Compressive Sampling Matching Tracking for Millimeter-wave Massive MIMO

In the millimeter-wave (mmWave) massive multi-input multi-output (MIMO) systems, the channel has a certain degree of sparsity. The sparseness needs to be used as prior information when the compressive sampling matching pursuit (CoSaMP) algorithm is used for channel estimation, so that the selection of atoms during iteration is not flexible and large in number. Therefore, we propose an improved CoSaMP channel estimation algorithm called iCoSaMP. iCoSaMP uses a fuzzy threshold selection strategy to perform a second screening of the preselected atom index set after the preselection stage to ensure that the more relevant atoms constitute a new preselected atom index set. It can avoid the blind adjustment of the preselected atom set caused by the excessive adjustment of the sparseness, which leads to the increase of the algorithm calculation complexity, thereby improving the algorithm's reconstruction ability and reducing the algorithm calculation complexity, effectively reducing the redundancy of the preselected atom set. Simulation results show that the proposed algorithm has high reconstruction accuracy and low computational complexity, and can accurately recover mmWave massive MIMO channel information.