Efficient detection schemes for cooperative spectrum sensing infading channels using cognitive radio networks
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Date
2010
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University of Management and Technology
Abstract
Advancements in wireless technologies are a breakthrough in revolutionizing the communication paradigm by providing ubiquitous internetwork access to large number of users at incredibly high data rates. The ever increasing deployment of mobile applications, networks and diverse services are a true depiction to this fact. As this trend continues the phenomenon of frequency spectrum occupancy is mounting up leading to spectrum scarcity. Analysis shows that for most periods the spectrum in control of majority primary users remains vacant and underutilized. This eventually implied to the idea of Dynamic spectrum allocation to users by Federal Communication Commission (FCC) which worked as a preventive measure against the issue of spectrum scarcity and underutilization.
Cognitive Radios are sophisticated devices which can effectively sense the availability of frequency bands and can self modulate its parameters for dynamic transmission. In this thesis we have focused on Primary transmission detection techniques for spectrum sensing. Comprehensive analysis of energy detection, matched filter detection and cyclostationary feature detection is done with prime emphasis on their advantages and limitations. Based on these results we have proposed a two-step detection scheme which outperforms the existing models in terms of detection probability even at very low SNR value. The censoring capability of this detector leads to energy efficiency in cooperative environment. Analysis is done on Nakagami channel model for wireless communication which includes fading and multipath propagation effects. The system model discussed provides good agility for band shifting in case of interference or primary user presence because of reasonable sensing time which is very close to that of cyclostationary feature detector and can differentiate between desired signal and wideband noise.
Description
Advisor: Prof. Jameel Ahmed
Keywords
MS Thesis, Wireless Technologies, Cognitive radio networks, Energy Detection