TY - JOUR
T1 - Theoretical stopping criteria guided Greedy Algorithm for Compressive Cooperative Spectrum Sensing
AU - Liang, Wei Jie
AU - Chien, Tsung Hsun
AU - Lu, Chun Shien
N1 - Funding Information:
This work was supported by Ministry of Science and Technology Taiwan, ROC , under grants MOST 102-2221-E-001-002-MY2 , MOST 102-2221-E-001-022-MY2 , and MOST 104-2221-E-001-019-MY3 .
PY - 2017/10/1
Y1 - 2017/10/1
N2 - Cooperative spectrum sensing (CSS) in homogeneous cognitive radio networks conducts cooperation among sensing users to jointly sense the information of spectrum usage for recovery of spectrum status and utilization of available ones. Motivated by the fact that the number of occupied channels is sparse, the mechanism of greedy multiple measurement vectors (MMVs) in the context of compressive/compressed sensing can ideally model the wideband CSS scenario to efficiently solve the support detection problem for identification of occupied channels. Actually, the number of sparsity is unknown, and the existing greedy algorithms for MMVs lack for a robust stopping criterion of determining when the greedy algorithm should terminate. In this paper, we analyze and derive oracle stopping bounds that are independent of prior information such as sparsity for greedy algorithms. Simulations are provided to confirm that, in compressive cooperative spectrum sensing, the proposed stopping criteria for greedy algorithms can remarkably improve detection performance.
AB - Cooperative spectrum sensing (CSS) in homogeneous cognitive radio networks conducts cooperation among sensing users to jointly sense the information of spectrum usage for recovery of spectrum status and utilization of available ones. Motivated by the fact that the number of occupied channels is sparse, the mechanism of greedy multiple measurement vectors (MMVs) in the context of compressive/compressed sensing can ideally model the wideband CSS scenario to efficiently solve the support detection problem for identification of occupied channels. Actually, the number of sparsity is unknown, and the existing greedy algorithms for MMVs lack for a robust stopping criterion of determining when the greedy algorithm should terminate. In this paper, we analyze and derive oracle stopping bounds that are independent of prior information such as sparsity for greedy algorithms. Simulations are provided to confirm that, in compressive cooperative spectrum sensing, the proposed stopping criteria for greedy algorithms can remarkably improve detection performance.
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U2 - 10.1016/j.comcom.2017.08.007
DO - 10.1016/j.comcom.2017.08.007
M3 - Article
AN - SCOPUS:85029417603
VL - 111
SP - 165
EP - 175
JO - Computer Communications
JF - Computer Communications
SN - 0140-3664
ER -