Exact performance of cooperative spectrum sensing for cognitive radios with quantized information under imperfect reporting channels
Type
Conference PaperKAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionElectrical Engineering Program
Communication Theory Lab
Date
2013-09Permanent link to this record
http://hdl.handle.net/10754/564802
Metadata
Show full item recordAbstract
Spectrum sensing is the first and main step for cognitive radio systems to achieve an efficient use of the spectrum. Cooperation among cognitive radio users is a technique employed to improve the sensing performance by exploiting the diversity between the sensing channels to overcome the fading and shadowing effects which allows reduction of miss-detection and false alarm probabilities. Information can be exchanged between cooperating users in different formats from the binary hard information to the full soft information. Quantized information has shown its efficiency as a trade-off between binary hard and full soft for other cooperative schemes, in this paper, we investigate the use of quantized information between cooperating cognitive users. We derive closed-form expressions of the cooperative average false alarm and detection probabilities over fading channels for a generalized system model with not necessarily identical average sensing Signal-to-Noise Ratio (SNR) and imperfect reporting channels. Numerical simulations allow us to conclude a tradeoff between the quantization size and the reporting energy in order to achieve the optimal cooperative error probability. Copyright © 2013 by the Institute of Electrical and Electronic Engineers, Inc.Citation
Ben Ghorbel, M., Nam, H., & Alouini, M.-S. (2013). Exact Performance of Cooperative Spectrum Sensing for Cognitive Radios with Quantized Information under Imperfect Reporting Channels. 2013 IEEE 78th Vehicular Technology Conference (VTC Fall). doi:10.1109/vtcfall.2013.6692201Conference/Event name
2013 IEEE 78th Vehicular Technology Conference, VTC Fall 2013ISBN
9781467361873ae974a485f413a2113503eed53cd6c53
10.1109/VTCFall.2013.6692201