Frontiers of Electrical and Electronic Engineering >
An improved cooperative spectrum detection algorithm for cognitive radio
Received date: 06 Sep 2012
Accepted date: 09 Oct 2012
Published date: 05 Dec 2012
Copyright
The ability to detect the primary user’s signal is one of the main performances for cognitive radio networks. Based on the multi-different-cyclic-frequency characteristics of the cyclostationary primary user’s signal and the cooperation detection advantage of the multi-secondary-user, the paper presents the weighted cooperative spectrum detection algorithm based on cyclostationarity in detail. The core of the algorithm is to detect the primary user’s signal by the secondary users’ cooperation detection to the multi-different-cyclic-frequency, and to make a final decision according to the fusion data of the independent secondary users’ detection results. Meanwhile, in order to improve the detection performance, the paper proposes a method to optimize the weight on basis of the deflection coefficient criterion. The result of simulation shows that the proposed algorithm has better performance even in low signal-to-noise ratio (SNR).
Lei CHEN , Hongjun WANG , Guangguo BI , Min ZHANG . An improved cooperative spectrum detection algorithm for cognitive radio[J]. Frontiers of Electrical and Electronic Engineering, 2012 , 7(4) : 367 -373 . DOI: 10.1007/s11460-012-0214-y
| 1 |
Mitola J, Maguire G Q. Cognitive radio: Making software radios more personal. IEEE Personal Communications, 1999, 6(4): 13–18
|
| 2 |
Haykin S. Cognitive radio: Brain-empowered wireless communications. IEEE Journal on Selected Areas in Communications, 2005, 23(2): 201–220
|
| 3 |
Dandawate A V, Giannakis G B. Statistical tests for presence of cyclostationarity. IEEE Transactions on Signal Processing, 1994, 42(9): 2355–2369
|
| 4 |
Mosquera C, Scalise S, Lopez-Valcarce R. Non-data-aided symbol rate estimation of linearly modulated signals. IEEE Transactions on Signal Processing, 2008, 56(2): 664–675
|
| 5 |
Mazet L, Loubaton P H. Cyclic correlation based symbol rate estimation. In: Proceedings of the 33rd Asilomar Conference on Signals, Systems an Computers. 1999, 2: 1008–1012
|
| 6 |
Quan Z, Cui S, Sayed A H. Optimal linear cooperation for spectrum sensing in cognitive radio networks. IEEE Journal of Selected Topics in Signal Processing, 2008, 2(1): 28–40
|
| 7 |
Letaief K B, Zhang W. Cooperative communications for cognitive radio networks. Proceedings of the IEEE, 2009, 97(5): 878–893
|
| 8 |
Sadeghi H, Azmi P. Cyclostationarity-based cooperative spectrum sensing for cognitive radio networks. In: Proceedings of International Symposium on Telecommunications. 2008, 429–434
|
| 9 |
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
|
| 10 |
Lunden J, Koivunen V, Huttunen A, Poor H V. Collaborative cyclostationary spectrum sensing for cognitive radio systems. IEEE Transactions on Signal Processing, 2009, 57(11): 4182–4195
|
| 11 |
Li J, Cui W, Jiang H, Wang N. Second-order cyclostationarity analysis of OQAM/OFDM signals. Journal of Rlectronics & Information Technology, 2011, 33(5): 1076–1081
|
| 12 |
Schott J R. Matrix Analysis for Statistics. Wiley-Interscience, 1997
|
| 13 |
Quan Z, Cui S, Sayed A H, Poor H V. Optimal multiband joint detection for spectrum sensing in cognitive radio networks. IEEE Transactions on Signal Processing, 2009, 57(3): 1128–1140
|
/
| 〈 |
|
〉 |