{'fraction_non_alphanumeric': 0.032059186189889025, 'fraction_numerical': 0.0, 'mean_word_length': 5.294573643410852, '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}}
Data-dependent superimposed training based channel estimation and symbol detection using Zero Correlation Zone (ZCZ) sequences

We address the problem of channel estimation and symbol detection using data-dependent superimposed training (DDST) in the presence of dc-offset for both single-input/single-output (SISO) and multiple-input/multiple-output (MIMO) systems using a set of Zero Correlation Zone (ZCZ) sequences. The use of ZCZ sequences in the proposed method not only results in dc-offset free channel estimation but also ensures that the equalisation output is completely independent of the dc-offset. The proposed approach is shown to perform significantly better than existing methods for any value of dc-offset and hence removes the computational burden of having to estimate the unknown dc-offset.