Proposed Technique for Cooperative Spectrum Sensing Optimization through Maximizing the Network Utility and Minimizing the Error Probability

Spectrum Sensing is an emerging technology in the field of wireless communication. It is an essential functionality of Cognitive Radio (CR) where it is used to detect whether there are primary users currently using the spectrum. Energy Detection technique is the most commonly used method for spectrum sensing. Non-cooperative spectrum sensing i.e. signal detection by single user suffers from several drawbacks. These drawbacks include shadowing/fading and noise uncertainty of wireless channels. Hence, to overcome these disadvantages, a new methodology called Cooperative Spectrum Sensing (CSS) has been suggested in the literature.This thesis deals with the comparison of conventional spectrum sensing techniques. Here, we consider the optimization of conventional energy detection based CSS. In CSS, hard combining technique has gained importance due to its simplicity and it deals with three decision rules which are ‘AND rule’, ‘OR rule’ and ‘MAJORITY rule’.For optimization, we have considered the network utility function and error probability. The aim of the thesis is to maximize the network utility and minimize the error probability. In order to achieve the goal we have proposed that the optimum voting rule is half voting rule also known as majority rule in ‘n out of K’ rules and obtained optimal number of cognitive radios by applying the hard decision rules. A method of obtaining the optimal detection threshold, numerically, has been presented. The optimal conditions have been verified through simulation results over an AWGN channel and it is concluded that, in proposed optimization scheme ‘MAJORITY rule (half voting rule)’ outperforms the ‘AND rule’ and ‘OR rule’. It has been found that the suitable selection of CR can achieve better utility function with minimum error probability for any wireless environment

Enhancing Spectrum Utilization in Dynamic Cognitive Radio Systems

Cognitive radio (CR) is regarded as a viable solution to enabling flexible use of the frequency spectrum in future generations of wireless systems by allowing unlicensed secondary users (SU) to access licensed spectrum under the specific condition that no harmful interference be caused to the licensed primary users (PU) of the spectrum. In practical scenarios, the knowledge of PU activity is unknown to CRs and radio environments are mostly imperfect due to various issues such as noise uncertainty and multipath fadings. Therefore, important functionalities of CR systems are to efficiently detect availability of radio spectrum as well as to avoid generating interference to PUs, by missing detection of active PU signals. Typically, CR systems are expected to be equipped with smart capabilities which include sensing, adapting, learning, and awareness concerned with spectrum opportunity access, radio environments, and wireless communications operations, such that SUs equipped with CRs can efficiently utilize spectrum opportunities with high quality of services. Most existing researches working on CR focus on improving spectrum sensing through performance measures such as the probabilities of PU detection and false alarm but none of them explicitly studies the improvement in the actual spectrum utilization. Motivated by this perspective, the main objective of the dissertation is to explore new techniques on the physical layer of dynamic CR systems, that can enhance actual utilization of spectrum opportunities and awareness on the performance of CR systems. Specifically, this dissertation investigates utilization of spectrum opportunities in dynamic scenarios, where a licensed radio spectrum is available for limited time and also analyzes how it is affected by various parameters. The dissertation proposes three new methods for adaptive spectrum sensing which improve dynamic utilization of idle radio spectrum as well as the detection of active PUs in comparison to the conventional method with fixed spectrum sensing size. Moreover, this dissertation presents a new approach for optimizing cooperative spectrum sensing performance and also proposes the use of hidden Markov models (HMMs) to enabling performance awareness for local and cooperative spectrum sensing schemes, leading to improved spectrum utilization. All the contributions are illustrated with numerical results obtained from extensive simulations which confirm their effectiveness for practical applications