{'fraction_non_alphanumeric': 0.015286624203821656, 'fraction_numerical': 0.0, 'mean_word_length': 5.442622950819672, '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}}
Distributed spectrum sensing in cognitive radios via graphical models

There is a recent interest in developing algorithms for the reconstruction of jointly sparse signals, which arises in a large number of applications. In this work, we study the problem of wide-band spectrum sensing for cognitive radio networks using compressed sensing to exploit the underlying joint sparsity structure in a distributed setting. In particular, we use the recently proposed Approximate Message Passing (AMP) framework and exploit the spatial correlation that exists locally between different CRs in a non-centralized fashion. We will show that with the suggested scheme, the nodes iteratively exploit the local spatial information and achieve the consensus on the spectrum in a distributed fashion.