{'fraction_non_alphanumeric': 0.017663043478260868, 'fraction_numerical': 0.0, 'mean_word_length': 5.63963963963964, '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}}
An Adaptive Spectrum Sensing Algorithm Based on Eigenvalue Decomposition

In this letter, we analyze and optimize spectrum sensing algorithm based on signal statistical characteristics. We claim some key parameters of the algorithm, including oversampling factor, the length of statistical sampling sequence and the backward linear prediction order, and analyze how these parameters affect the performance of spectrum sensing algorithm. Based on the evaluation on simulation results, we propose an adaptive spectrum sensing strategy to adapt the algorithm to the wireless environment. The proposed adaptive algorithm can obtain a desired performance in low SNR and reduce the algorithm complexity when the signal-to-noise (SNR) is high.