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Transactions: WSEAS TRANSACTIONS ON COMMUNICATIONS
Transactions ID Number: 42-482
Full Name: Yipeng Liu
Position: Ph.D. Candidate
Age: ON
Sex: Male
Address: No. 2006, Xiyuan Avenue, Western High-Tech, Chengdu
Country: CHINA
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E-mail address: liuyipeng@uestc.edu.cn
Other E-mails: liuyipeng.uestc@gmail.com
Title of the Paper: mixed l2/l1 norm denoising operator for compressive fixed wideband spectrum sensing
Authors as they appear in the Paper: Yipeng Liu and Qun Wan
Email addresses of all the authors: liuyipeng@uestc.edu.cn,wanqun@uestc.edu.cn
Number of paper pages: 10
Abstract: Too high sampling rate is the bottleneck to wideband spectrum sensing for cognitive radio (CR). As the survey shows that the sensed signal has a sparse representation in frequency domain in the mass, compressed sensing (CS) can be used to transfer the sampling burden to the digital signal processor. An analog to information converter (AIC) can randomly sample the received signal with sub-Nyquist rate to obtained the random measurements. Considering that the static frequency spectrum allocation of primary radios means the bounds between different primary radios is known in advance, here we incorporate information of the spectrum boundaries between different primary user as a priori information to obtain a mixed l2/l1 norm denoising operator (MNDO). In the MNDO, the estimated power spectrum density (PSD) vector is divided into block sections with bounds corresponding different allocated primary radios. Different from previous standard l1-norm constraint on the whole !
PSD vector, a sum of the l2 norm of each section of the PSD vector is minimized to encourage the local grouping distribution while the sparse distribution in mass, while a relaxed constraint is used to improve the denoising performance. Simulation demonstrates that the proposed method outperforms standard sparse spectrum estimation in accuracy, denoising ability, etc.
Keywords: Cognitive radio, Dynamic spectrum access, Wideband spectrum sensing, Compressive sensing, Mixed l2/ l1 norm, Sparse signal recovery.
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