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