MAC protocol classification in a cognitive radio network

WOCC2010 Technical Program - The 19th Annual Wireless and Optical Communications Conference: Converging Communications Around the Pacific

Volumn , Issue , 2010, Pages

  Yang, Zhuo ^a   Yao, Yu Dong ^a   Chen, Sheng ^a   He, Haibo ^a   Zheng, Di ^a  

^a Stevens Institute of Technology   (United States)

Author keywords

Capture effect; MAC protocol classification; Machine learning; Rayleigh fading

Indexed keywords

CAPTURE EFFECT; CLASS LABELS; COGNITIVE RADIO; COGNITIVE RADIO NETWORK; CONTENTION-BASED; MAC PROTOCOL; MACHINE-LEARNING; MEDIA ACCESS CONTROL PROTOCOL; NETWORK MODELS; RECEIVED POWER; SECONDARY NETWORKS; SLOTTED ALOHA; TRAINING PROCESS; TRANSMISSION MECHANISMS;

COMMUNICATION; OPTICAL COMMUNICATION; RADIO; RAYLEIGH FADING; SUPPORT VECTOR MACHINES; TIME DIVISION MULTIPLE ACCESS; WIRELESS SENSOR NETWORKS;

ACCESS CONTROL;

EID: 77955618040     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/WOCC.2010.5510617     Document Type: Conference Paper

References (10)

1. J. Mitola, Cognitive Radio: "An integrated Agent Architecture for Software Defined Radio,", Ph.D. dissertation, Royal Inst. Technology, Stockholm, Sweden, 2000.
2. K. Enda, "A study on the estimation of traffic quantity and MAC protocol in radio wave environmental monitoring," SDR Forum Technical Conference, Denver, November 5-9, 2007.
3. R. Hachemani, J. Palicot, C. Moy "A new standard recognition sensor for cognitive radio terminals," EURASIP, 2007.
4. R. Rom and M. Sidi, Multiple Access Protocols: Performance and Analysis, New York: Springer Verlag, 1990.
5. L. G. Roberts, "ALOHA packet system with and without slots and capture," Comput. Commun.Rev., no. 5, pp. 28-42, 1975.
6. F. Kuperbus and J. Arnbak, "Packet radio in a Rayleigh channel," Electron. Lett., vol. 18, pp. 506-507, June 1982.
7. J. Arnbak and W. Blitterswijk, "Capacity of slotted ALOHA in Rayleigh-fading channels," IEEE J. Select. Areas Commmun., vol. 5, pp. 685-692, Feb. 1987.
8. V. N. Vapnik, The Nature of Statistical Learning Theory, 2st ed. Springer, Nov. 1999.
9. B. E. Boser, I. M. Guyon, and V. N. Vapnik, "A training algorithm for optimal margin classifiers," in COLT '92: Proceedings of The Fifth Annual Workshop on Computational Learning Theory, Pittsburgh, Pennsylvania, 1992, pp. 144-152.
10. N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, "Smote: synthetic minority over-sampling technique," Journal of Artificial Intelligence Research, vol. 16, pp. 321-357, 2002.