Weitere Artikel dieser Ausgabe durch Wischen aufrufen
Cognitive Radio (CR), a hierarchical Dynamic Spectrum Access (DSA) model, has been considered as a strong candidate for future communication systems improving spectrum efficiency utilizing unused spectrum of opportunity. However, to ensure the effectiveness of dynamic spectrum access, accurate signal classification in fading channels at low signal to noise ratio is essential. In this paper, a hierarchical cyclostationary-based classifier is proposed to reliably identify the signal type of a wide range of unknown signals. The proposed system assumes no a priori knowledge of critical signal statistics such as carrier frequency, carrier phase, or symbol rate. The system is designed with a multistage approach to minimize the number of samples required to make a classification decision while simultaneously ensuring the greatest reliability in the current and previous stages. The system performance is demonstrated in a variety of multipath fading channels, where several multiantenna-based combining schemes are implemented to exploit spatial diversity.
Mitola J: Cognitive radio: an integrated agent architecture for software defined radio, Ph.D. dissertation. KTH Royal Institute of Technology, Stockholm, Sweden; 2000.
Proceedings of the 1st IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks. , Baltimore, Md, USA; 2005.
Ma J, Li GY, Juang BH: Signal processing in cognitive radio. Proceedings of the IEEE 2009, 97(5):805-823. CrossRef
Haykin S, Thomson DJ, Reed JH: Spectrum sensing for cognitive radio. Proceedings of the IEEE 2009, 97(5):849-877. CrossRef
Chakravarthy V, Wu Z, Temple MA, Garber F, Kannan R, Vasilakos A: Novel overlay/underlay cognitive radio waveforms using SD-SMSE framework to enhance spectrum efficiency - part I: theoretical framework and analysis in AWGN channel. IEEE Transactions on Communications 2009., 57(12):
Dobre OA, Abdi A, Bar-Ness Y, Su W: Selection combining for modulation recognition in fading channels. Proceedings of IEEE Military Communications Conference (MILCOM '05), October 2005, Atlatnic City, NJ, USA
Swami A, Sadler B: Hierarchical digital modulation classification using cumulants. IEEE Transactions on Communication 2000, 48(3):416-429. 10.1109/26.837045 CrossRef
Dobre OA, Abdi A, Bar-Ness Y, Su W: Survey of automatic modulation classification techniques: classical approaches and new trends. IET Communications 2007, 1(2):137-156. 10.1049/iet-com:20050176 CrossRef
Azzouz E, Nandi A: Automatic Modulation Recognition of Communication Signals. Kluwer Academic Publishers, Dordrecht, The Netherlands; 1996. CrossRef
Marchard P, Lacoume J, Martret C: Multiple hypothesis modulation classification based on cyclic cumulants of different orders. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '98), May 1998, Seattle, Wash, USA 4: 2157-2160.
Gardner WA: Cyclostationarity in Communications and Signal Processing. IEEE Press, Piscataway, NJ, USA; 1993.
Like E, Chakravarthy V, Wu Z: Reliable modulation classification at low SNR using spectral correlation. Proceedings of the 4th Annual IEEE Consumer Communications and Networking Conference (CCNC '07), 2007 1134-1138.
Fehske A, Gaeddert J, Reed JH: A new approach to signal classification using spectral correlation and neural networks. Proceedings of the 1st IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks (DySPAN '05), 2005, Baltimore, Md, USA 144-150.
Spooner CM: On the utility of sixth-order cyclic cumulants for RF signal classification. Proceedings of the 35th Asilomar Conference on Signals, Systems and Computers, November 2001, Pacific Grove, Calif, USA 1: 890-897.
Dobre OA, Bar-Ness Y, Su W: Higher-order cyclic cumulants for high order modulation classification. Proceedings of IEEE Military Communications Conference (MILCOM '03), October 2003, Monterey, Calif, USA 1: 112-117.
Gardner WA: Measurement of spectral correlation. IEEE Transactions on Acoustics, Speech, and Signal Processing 1986, 34(5):1111-1123. 10.1109/TASSP.1986.1164951 CrossRef
Roberts RS, Brown WA, Loomis HH Jr.: Computationally efficient algorithms for cyclic spectral analysis. IEEE Signal Processing Magazine 1991, 8(2):38-49. 10.1109/79.81008 CrossRef
Vucic D, Obradovic M, Obradovic D: Spectral correlation of OFDM signals related to their PLC applications. Proceedings of IEEE International Symposium on Power-Line Communications and Its Applications (ISPLC '02), March 2002, Athens, Greece
Sutton PD, Nolan KE, Doyle LE: Cyclostationary signatures for rendezvous in OFDM-based dynamic spectrum access networks. Proceedings of the 2nd IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks (DySPAN '07), 2007, Dublin, Ireland 220-231. CrossRef
Han N, Zheng G, Sohn SH, Kim JM: Cyclic autocorrelation based blind OFDM detection and identification for cognitive radio. Proceedings of the 4th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM '08), 2008
Bouzegzi A, Jallon P, Ciblat P: A second order statistics based algorithm for blind recognition of OFDM based systems. Proceedings of IEEE Global Telecommunications Conference (GLOBECOM '08), 2008, New Orleans, La, USA 3257-3261.
Li H, Bar-Ness Y, Abdi A, Somekh OS, Su W: OFDM modulation classification and parameters extraction. Proceedings of the 1st International Conference on Cognitive Radio Oriented Wireless Networks and Communications (CROWNCOM '06), 2006
Pauluzzi D, Beaulieu N: A comparison of SNR estimation techniques for the AWGN channel. Proceedings of IEEE Pacific Rim Conference on Communications, Computers, and Signal Processing, 1995
Chen Z, Nowrouzian B, Zarowski CJ: An investigation of SNR estimation techniques based on uniform Cramer-Rao lower bound. Proceedings of the 48th Midwest Symposium on Circuits and Systems (MWSCAS '05), August 2005 1: 215-218.
Wiesel A, Goldberg J, Messer-Yaron H: SNR estimation in time-varying fading channels. IEEE Transactions on Communications 2006, 54(5):841-848. CrossRef
- Signal Classification in Fading Channels Using Cyclic Spectral Analysis
- Springer International Publishing
- EURASIP Journal on Wireless Communications and Networking
Elektronische ISSN: 1687-1499
Neuer Inhalt/© ITandMEDIA, Product Lifecycle Management/© Eisenhans | vege | Fotolia