{'fraction_non_alphanumeric': 0.025839793281653745, 'fraction_numerical': 0.0, 'mean_word_length': 5.315217391304348, '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}}
Performance Comparison of Real and Complex Valued Neural Networks for Digital Self-Interference Cancellation

Digital Self-Interference Cancellation (SIC) is the key technique for enabling In-Band Full-Duplexing (IBFD). Commonly, a nonlinear model is required to approximate the circuity nonlinearity so as to achieve SIC. Artificial neural network (ANN) is a potential candidate due to its nature of nonlinear approximation. Although real-valued neural network (RVNN) is more widely used in the field of artificial intelligence, the counterpart, complex-valued neural network (CVNN) seems to be more appropriate for digital SIC since the communication baseband signals are essentially complex. With this hypothesis, we conduct comprehensive comparisons of RVNN and CVNN for digital SIC in this paper. It is interesting to find that CVNN does perform better but only when non-holomorphic activation functions are used, and the number of hidden layer neurons can be reduced by maximally a half. When the number of neurons is larger than a certain value, RVNN and CVNN both achieve their optimal performance and provide approximately the same cancellation result.