DocumentCode :
34868
Title :
Energy Detection Based Spectrum Sensing for Cognitive Radios Over Time-Frequency Doubly Selective Fading Channels
Author :
Bin Li ; Mengwei Sun ; Xiaofan Li ; Nallanathan, Arumugam ; Chenglin Zhao
Author_Institution :
Sch. of Inf. & Commun. Eng. (SICE), Beijing Univ. of Posts & Telecommun. (BUPT), Beijing, China
Volume :
63
Issue :
2
fYear :
2015
fDate :
Jan.15, 2015
Firstpage :
402
Lastpage :
417
Abstract :
Cognitive radios may operate in practice under various adverse environments. For typical mobile and short-range scenarios, wireless links may tend to be time and frequency selective, i.e., the multipath propagations with time-varying fading coefficients will be inevitable. To cope with the encountered doubly-selective channels, in this paper we present a new spectrum sensing algorithm for distributed applications. First, a dynamic discrete state-space model is established to characterize sensing process, where the occupancy state of primary band and the time-varying multipath channel are treated as two hidden states, while the summed energy is adopted as the observed output. With this new paradigm, spectrum sensing is realized by acquiring primary states and time-dependent multipath channel jointly. For the formulated problem, unfortunately, Bayesian statistical inference may be impractical due to the absence of likelihoods and involved non-stationary distributions. To remedy this problem, an iterative algorithm is further designed by resorting to sequential importance sampling techniques; thus, the dynamic non-Gaussian multipath channel and primary states are estimated recursively. Another critical challenge, e.g., the noise uncertainty, is also considered, which may be incorporated conveniently into this sensing diagram and, furthermore, addressed effectively by the designed algorithm. Simulations validate the proposed algorithm. While classical schemes fail to deal with doubly selective channels, the new sensing scheme can exploit the underlying channel memory and operate well, which provides a great promise to realistic applications.
Keywords :
Bayes methods; cognitive radio; fading channels; frequency selective surfaces; iterative methods; mobile radio; multipath channels; radio links; radio spectrum management; sampling methods; sequential estimation; signal detection; state-space methods; statistical distributions; time-frequency analysis; time-varying channels; Bayesian statistical inference; cognitive radio; doubly selective channel memory; doubly-selective channel; dynamic discrete state-space model; dynamic nonGaussian multipath channel; energy detection; frequency selective wireless link; iterative algorithm; mobile scenarios; multipath propagation; nonstationary distribution; sequential importance sampling technique; short-range scenarios; spectrum sensing algorithm; time selective wireless link; time-dependent multipath channel; time-frequency doubly selective fading channel; time-varying fading coefficient; time-varying multipath channel; Estimation; Fading; Sensors; Signal processing algorithms; Time-frequency analysis; Wireless communication; Wireless sensor networks; Dynamic state-space model; noise uncertainty; spectrum sensing; time-frequency doublely selective channels;
fLanguage :
English
Journal_Title :
Signal Processing, IEEE Transactions on
Publisher :
ieee
ISSN :
1053-587X
Type :
jour
DOI :
10.1109/TSP.2014.2368996
Filename :
6951456
Link To Document :
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