Optimal waveform-based detection framework & convexity range estimation in cognitive radios

Abstract

A cognitive radio provides opportunistic underutilized spectrum exploitation without detrimental interference to licensed users. Cognitive radio addresses the issue of efficient spectrum utilization as it provides the unlicensed users with the capacity to adjust their operating parameters as require for transmission in the new wireless environment. In this work, a cognitive radio network is considered where wideband spectrum is to be sensed and analyzed to determine the channel availability for a cognitive user. Here waveform based spectrum sensing technique is explored. Unlike energy detection, it gives good performance even at low Signal-to-Noise ratio and performs similarly as energy detection at high Signal-to-Noise ratio increasing system efficiency and accuracy. A joint spectrum sensing technique is also investigated which sense the primary signals over multiple bands. The licensed user is assumed to use different type of modulated signals. The spectrum sensing problem, which is apparently in non-convex category, is given as an optimization problem and convexity range of the problem is determined under certain constraints and channel condition, maximizing the aggregate opportunistic throughput of the unlicensed user. From simulation results given in the thesis, it is evident that the proposed framework showed a better performance than the existing frameworks. Furthermore, to overcome uncertainty in decision making about presence or absence of licensed user, we propose the adoption of Dempster-Shafer theory along with weighted node selection. By removing redundant users, we may decrease network overhead and increase system transmission efficiency