Random Medium Access Decision-Based Cooperative Spectrum Sensing for Cognitive Radio Vehicular Ad-hoc Networks

Abstract

In this thesis, the performance of decision-based Cooperative Spectrum Sensing (CSS) using random medium access sequential reporting assuming an imperfect reporting channel shared with a primary user is studied. Random medium access reporting using unacknowledged transmissions is the case of vehicular networks using IEEE 802.11p. This entails, as opposed to scheduled medium access, a non-negligible probability of collision and a variable reporting phase duration. Sequential reporting can reduce the reporting phase duration by employing different early termination schemes where the Fusion Centre (FC) terminates the reporting process once it receives enough evidence about the primary user status. A reduced reporting phase duration for a given detection accuracy performance enhances the overall network throughput. A centralized CSS scheme, where energy detection is employed at the secondary users, and hard-decision fusion is employed at the FC, is considered in this thesis. The objective is then to derive closed-form expressions for the overall probabilities of detection and false alarm at the FC as well as the average reporting phase duration. Analysis is performed first for the simple OR decision fusion rule, at the FC, and then generalized to the K-out-of-M decision fusion rule. Two early termination schemes are investigated for sequential reporting and then a heuristic energy-based reporting priority scheme is proposed. The proposed energy-based reporting scheme gives higher priority to secondary users more capable of detecting the primary user to access the common reporting channel. The proposed reporting scheme is compared to a base or control scheme where all secondary users have the same reporting priority.