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Mobile Bayesian Spectrum Learning for Heterogeneous Networks

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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Paper Details

Authors:
Submitted On:
19 April 2018 - 3:01pm
Short Link:
Type:
Poster
Event:
Presenter's Name:
YIZHEN XU
Paper Code:
2505
Document Year:
2018
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Document Files

ICASSP Poster.pdf

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[1] , "Mobile Bayesian Spectrum Learning for Heterogeneous Networks", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3007. Accessed: Jun. 20, 2018.
@article{3007-18,
url = {http://sigport.org/3007},
author = { },
publisher = {IEEE SigPort},
title = {Mobile Bayesian Spectrum Learning for Heterogeneous Networks},
year = {2018} }
TY - EJOUR
T1 - Mobile Bayesian Spectrum Learning for Heterogeneous Networks
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3007
ER -
. (2018). Mobile Bayesian Spectrum Learning for Heterogeneous Networks. IEEE SigPort. http://sigport.org/3007
, 2018. Mobile Bayesian Spectrum Learning for Heterogeneous Networks. Available at: http://sigport.org/3007.
. (2018). "Mobile Bayesian Spectrum Learning for Heterogeneous Networks." Web.
1. . Mobile Bayesian Spectrum Learning for Heterogeneous Networks [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3007