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Mobile Bayesian Spectrum Learning for Heterogeneous Networks

Citation Author(s):
Submitted by:
YIZHEN XU
Last updated:
19 April 2018 - 3:01pm
Document Type:
Poster
Document Year:
2018
Event:
Presenters Name:
YIZHEN XU
Paper Code:
2505

Abstract 

Abstract: 

Spectrum sensing in heterogeneous networks is very challenging as it usually requires a large number of static secondary users (SUs) to obtain the global spectrum states. In this paper, we tackle the spectrum sensing in heterogeneous networks from a new perspective. We exploit the mobility of multiple SUs to simultaneously collect spatial-temporal spectrum sensing data. Then, we propose a novel non-parametric Bayesian learning model, referred to as beta process hidden Markov model to capture the spatio-temporal correlation in the collected spectrum data. Finally, Bayesian inference is carried out to establish the global spectrum picture. Simulation results show that the proposed algorithm can achieve a significant spectrum sensing performance improvement in terms of receiver operating characteristic curve and detection accuracy compared with other existing spectrum sensing algorithms。

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