Cooperative spectrum sensing and locationing: A sparse Bayesian learning approach

D. H.Tina Huang, Sau-Hsuan Wu, Peng Hua Wang

研究成果: Conference contribution同行評審

21 引文 斯高帕斯(Scopus)

摘要

Based on the concept of sparse Bayesian learning, an expectation and maximization algorithm is proposed for cooperative spectrum sensing and locationing of the primary transmitters in cognitive radio systems. Different from typical approaches, not only the signal strength, but also the number and the radio power profiles of the primary transmitters are estimated, which greatly facilitates resource management in cognitive radio. Furthermore, the proposed algorithm can still roughly reconstruct the power propagation map of the primary transmitters even when the measurement rate is below the lower bound for which compressive sensing (CS) can reconstruct signals with the ℓ1-norm optimization method. Compared with the typical CS and Bayesian CS algorithms, simulation results show that average mean squared errors (MSE) of the estimated power propagation map are lower with the proposed algorithm. Besides, the computational complexity is also lower owing to bases pruning. The MSE of the location estimation are also shown to demonstrate the capability of the proposed algorithm.

原文English
主出版物標題2010 IEEE Global Telecommunications Conference, GLOBECOM 2010
DOIs
出版狀態Published - 1 12月 2010
事件53rd IEEE Global Communications Conference, GLOBECOM 2010 - Miami, FL, United States
持續時間: 6 12月 201010 12月 2010

出版系列

名字GLOBECOM - IEEE Global Telecommunications Conference

Conference

Conference53rd IEEE Global Communications Conference, GLOBECOM 2010
國家/地區United States
城市Miami, FL
期間6/12/1010/12/10

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