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dc.contributor.authorFerreira, Gabriel de Carvalho-
dc.contributor.authorBarreto, Priscila América Solís Mendez-
dc.contributor.authorRocha, Geraldo P.-
dc.contributor.authorCaetano, Marcos Fagundes-
dc.contributor.authorKarvonen, Heikki-
dc.contributor.authorVartiainen, Johanna-
dc.identifier.citationFERREIRA, Gabriel et al. MHM: a novel collaborative spectrum sensing method based on markov-chains and harmonic mean for 5G networks. In: 2020 IFIP NETWORKING CONFERENCE (NETWORKING), 2020, Paris. Proceedings [...]. Paris: IEEE, 2020. Disponível em:
dc.rightsAcesso Restritopt_BR
dc.titleMHM : a novel collaborative spectrum sensing method based on markov-chains and harmonic mean for 5G networkspt_BR
dc.subject.keywordProcessos de Markovpt_BR
dc.subject.keywordAcesso dinâmico ao espectropt_BR
dc.description.abstract1Cognitive radios and spectrum sensing are considered fundamental for spectrum optimization in 5G networks. Collaborative spectrum sensing improves detection by collecting data from different nodes and increasing the amount of information available for accurate channel state detection. However, malicious nodes can report wrong information, disturbing the collaborative sensing results and network operation. This paper presents two techniques: (1) a Markov chain-based technique that improves spectrum sensing accuracy while reducing the reporting control traffic; (2) a harmonic mean-based technique that discards less relevant sensing reports, mitigating Byzantine attacks. The two techniques were evaluated in a simulation scenarios based on rural areas. The results show that the proposed techniques increase the accuracy of a classic hard-combining fusion technique, reducing false positives and reporting overhead while improving network resilience to malicious nodes.pt_BR
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