{'fraction_non_alphanumeric': 0.02423603793466807, 'fraction_numerical': 0.001053740779768177, 'mean_word_length': 5.506849315068493, '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}}
A hybrid compressed sensing algorithm for sparse channel estimation in MIMO OFDM systems

Due to multipath delay spread and relatively high sampling rate in OFDM systems, the channel estimation is formulated as a sparse recovery problem, where a hybrid compressed sensing algorithm as subspace orthogonal matching pursuit (SOMP) is proposed. SOMP first identifies the channel sparsity and then iteratively refines the sparse recovery result, which essentially combines the advantages of orthogonal matching pursuit (OMP) and subspace pursuit (SP). Since SOMP still belongs to greedy algorithms, its computational complexity is in the same order as OMP. With frequency orthogonal random pilot placement, the technique is also extend to MIMO OFDM systems. Simulation results based on 3GPP spatial channel model (SCM) demonstrate that SOMP performs better than OMP, SP and interpolated least square (LS) in terms of normalized mean square error (NMSE).