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Experimental detection using cyclostationary feature detectors for cognitive radios (2014)
Conference Proceeding
Zolfaghari, M., Fanan, A., Mehdawi, M., Riley, N. G., & Ammar, M. (2014). Experimental detection using cyclostationary feature detectors for cognitive radios. . https://doi.org/10.1109/telfor.2014.7034403

© 2014 IEEE. Signal detection is widely used in many applications. Some examples include Cognitive Radio (CR) and military intelligence. Without guaranteed signal detection, a CR cannot reliably perform its role. Spectrum sensing is currently one of... Read More about Experimental detection using cyclostationary feature detectors for cognitive radios.

Survey: A comparison of spectrum sensing techniques in cognitive radio (2014)
Conference Proceeding
Fanan, A., Riley, N., Mehdawi, M., Ammar, M., & Zolfaghari, M. (2014). Survey: A comparison of spectrum sensing techniques in cognitive radio. In International Conference Image Processing, Computers and Industrial Engineering (ICICIE'2014) (73-77). https://doi.org/10.15242/IIE.E0114560

In the present day Cognitive Radio has become a realistic option for solution of the spectrum scarcity problem in wireless communication. One of the most challenging issues in cognitive radio systems is spectrum sensing concepts, which is considered... Read More about Survey: A comparison of spectrum sensing techniques in cognitive radio.

A Spectrum Sensing Test Bed based on Matlab and USRP2 (2014)
Conference Proceeding
Ammar, M., Mehdawi, M., Riley, N., Fanan, A., Paulson, K., & Zolfaghari, M. (2014). A Spectrum Sensing Test Bed based on Matlab and USRP2. In International Conference Image Processing, Computers and Industrial Engineering (ICICIE'2014) (14-17). https://doi.org/10.15242/IIE.E0114

— The spectrum monitoring has gained new aspects with development of the software defined radio SDR as its very important issue in the spectrum management area. In this paper a spectrum monitoring technique with SDR is proposed, the setup of the prot... Read More about A Spectrum Sensing Test Bed based on Matlab and USRP2.