Joint signal detection algorithm of cognitive radio in UWB
Hongjun WANG, Guangguo BI
Joint signal detection algorithm of cognitive radio in UWB
With the progress of research on cognitive radio in ultra-wideband (UWB) open frequency-band, a joint detection algorithm integrating the energy and bi-spectrum detection is proposed in detail for non-Gaussian signal detection from Gaussian noise. The performance of the algorithm was evaluated by simulation, the result of which indicates that the joint detection not only solves the problem of the signal detection in low signal-to-noise ratio (SNR) but also improves the operational speed and the detection probability. Thus, the joint detection algorithm has definite prospect in practice.
ultra-wideband (UWB) / cognitive radio / joint detection / bi-spectrum estimation
[1] |
Sahai A, Hoven N, Tandra R. Some fundamental limits on cognitive radio. In: Proceedings of Allerton Conference on Communication, Control, and Computing, 2004
|
[2] |
Lansford J. Universal radio: making new spectrum (sort of). 2002 Fourth Annual International Symposium On Advanced Radio Technologies, 2002
|
[3] |
Soliman S. Cognitive Radio: Key Performance Indicators. Gualcomm Incorporated. Technical Report, 2004
|
[4] |
Broderson R W, Wolisz A, Cabric D, Mishra S M, Willkomm D. Corvus: a cognitive radio approach for usage of virtual unlicensed spectrum. Berkeley Wireless Research Center White Paper, 2004
|
[5] |
Lansford J. UWB coexistence and cognitive radio. In: Proceedings of IEEE Conference on Ultrawideband Systems and Technologies, 2004, 35-39
|
[6] |
Gardner W A. Signal interception: a unifying theoretical framework for feature detection. IEEE Transactions on Communications, 1988, 36(8): 897-906
CrossRef
Google scholar
|
[7] |
Golmie N, Chevrollier N, Rebala O. Bluetooth and WLAN coexistence: challenges and solutions. IEEE Wireless Communications, 2003, 10(6): 22-29
CrossRef
Google scholar
|
[8] |
Lunden J, Koivunen V, Huttunen A, Poor H V. Spectrum sensing in cognitive radios based on multiple cyclic frequencies. In: Proceedings of the 2nd International Conference on Cognitive Radio Oriented Wireless Networks and Communications, 2007, 37-43
|
[9] |
Robert N A. Detection theory. IEEE Transactions on Information Theory, 1961, 7: 135-139
CrossRef
Google scholar
|
[10] |
Middleton D. On the detection of stochastic signals in additive normal noise. IEEE Transactions on Information Theory, 1957, 3(2): 86-121
CrossRef
Google scholar
|
[11] |
Slepian D. Some comments on the detection of Gaussian signals in Gaussian noise. IEEE Transactions on Information Theory, 1958, 4(2): 65-68
CrossRef
Google scholar
|
[12] |
Tandra R. Fundamental limits on detection in low SNR. Dissertation for the Master’s Degree. Berkeley: University of California, 2005
|
[13] |
Urkowitz H. Energy detection of unknown deterministic signals. Proceedings of the IEEE, 1967, 55(4): 523-531
CrossRef
Google scholar
|
[14] |
Kostylev V I. Energy detection of a signal with random amplitude. In: Proceedings of the IEEE International Conference on Communications, 2002, 3: 1606-1610
|
[15] |
Eaddy D, Kadota T, Seery J. On the approximation of the optimum detector by the energy detector in detection of colored Gaussian signals in noise. IEEE Transactions on Acoustics, Speech and Signal Processing, 1984, 32(3): 661-664
CrossRef
Google scholar
|
[16] |
Fuemmeler J, Vaidya N H, Veeravalli V V. Selecting Transmit Powers and Carrier Sense Thresholds for CSMA Protocols. Technical Report, 2004
|
[17] |
Nikias C L, Mendel J M. Signal processing with high-order spectra. IEEE Signal Processing Magazine, 1993, 10(3): 10-37
CrossRef
Google scholar
|
/
〈 | 〉 |