Acta Metallurgica Sinica(English letters) ›› 2011, Vol. 18 ›› Issue (5): 15-21.doi: 10.1016/S1005-8885(10)60097-0

• Wireless • 上一篇    下一篇

Probabilistic greedy pursuit for streaming compressed spectrum sensing

LU Yang , GUO Wen-bin, WANG Xing, WANG Wen-bo   

  1. Key Laboratory of Universal Wireless Communication, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing 100876, China
  • 收稿日期:2011-01-14 修回日期:2011-06-02 出版日期:2011-10-31 发布日期:2011-10-13
  • 通讯作者: LU Yang E-mail: luyangnnu@126.com
  • 基金资助:

    This work was supported by the Fundamental Research Funds for the Central Universities (BUPT2009RC0107), the National Basic Research Program of China (2009CB320400), the Important National Science and Technology Specific Projects (2009ZX03007-004), and the Joint Funds of NSFC-Guangdong (Grant U1035001).

Probabilistic greedy pursuit for streaming compressed spectrum sensing

LU Yang , GUO Wen-bin, WANG Xing, WANG Wen-bo   

  1. Key Laboratory of Universal Wireless Communication, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing 100876, China
  • Received:2011-01-14 Revised:2011-06-02 Online:2011-10-31 Published:2011-10-13
  • Contact: Yang LU E-mail: luyangnnu@126.com
  • Supported by:

    This work was supported by the Fundamental Research Funds for the Central Universities (BUPT2009RC0107), the National Basic Research Program of China (2009CB320400), the Important National Science and Technology Specific Projects (2009ZX03007-004), and the Joint Funds of NSFC-Guangdong (Grant U1035001).

摘要:

This paper presents a probabilistic greedy pursuit (PGP) algorithm for compressed wide-band spectrum sensing under cognitive radio (CR) scenario. PGP relies on streaming compressed sensing (CS) framework, which differs from traditional CS processing way that only focuses on fixed-length signal’s compressive sampling and reconstruction. It utilizes analog-to-information converter (AIC) to perform sub-Nyquist rate signal acquisition at the radio front-end (RF) of CR, the measurement process of which is carefully designed for streaming framework. Since the sparsity of wide-band spectrum is unavailable in practical situation, PGP introduces the probabilistic scheme by dynamically updating support confidence coefficient and utilizes greedy pursuit to perform streaming spectrum estimation, which gains sensing performance promotion progressively. The proposed algorithm enables robust spectrum estimation without the priori sparsity knowledge, and keeps low computational complexity simultaneously, which is more suitable for practical on-line applications. Various simulations and comparisons validate the effectiveness of our approach.

关键词:

cognitive radio, wide-band spectrum sensing, streaming compressed sensing, probabilistic greedy pursuit, support confidence coefficient

Abstract:

This paper presents a probabilistic greedy pursuit (PGP) algorithm for compressed wide-band spectrum sensing under cognitive radio (CR) scenario. PGP relies on streaming compressed sensing (CS) framework, which differs from traditional CS processing way that only focuses on fixed-length signal’s compressive sampling and reconstruction. It utilizes analog-to-information converter (AIC) to perform sub-Nyquist rate signal acquisition at the radio front-end (RF) of CR, the measurement process of which is carefully designed for streaming framework. Since the sparsity of wide-band spectrum is unavailable in practical situation, PGP introduces the probabilistic scheme by dynamically updating support confidence coefficient and utilizes greedy pursuit to perform streaming spectrum estimation, which gains sensing performance promotion progressively. The proposed algorithm enables robust spectrum estimation without the priori sparsity knowledge, and keeps low computational complexity simultaneously, which is more suitable for practical on-line applications. Various simulations and comparisons validate the effectiveness of our approach.

Key words:

cognitive radio, wide-band spectrum sensing, streaming compressed sensing, probabilistic greedy pursuit, support confidence coefficient