{'fraction_non_alphanumeric': 0.022417153996101363, 'fraction_numerical': 0.0, 'mean_word_length': 5.339506172839506, '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}}
Learning Based Interference Cancellation for Cognitive Radio

This paper is concerned with a machine learning approach to cancel the interference for a cognitive radio (CR) system in the concurrent spectrum access (CSA) model, where the CR system is non-cooperative and has very limited knowledge on the interference. Our transceiver design uses a turbo structure, which consists of a linear estimator, a demodulation and decoding module, and a clustering module. In the clustering module, we employ machine leaning algorithms such as K-means and expectation maximization (EM) algorithms to estimate the interference. We show that the EM based receiver significantly outperforms the K-means receiver, since the former is able to generate a soft clustering result. We further improve the performance of the iterative receiver by introducing the extrinsic information technique with the resulting receiver referred to as Ext-EM. Considerable performance gain of the Ext-EM receiver is demonstrated over the original EM receiver.