Game theory has been a tool of choice for modeling dynamic interactions between autonomous systems. Cognitive radio ad hoc networks (CRAHNs) constituted of autonomous wireless nodes are a natural fit for game theory-based modeling. The game theory-based model is particularly suitable for “collaborative spectrum sensing” where each cognitive radio senses the spectrum and shares the results with other nodes such that the targeted sensing accuracy is achieved. Spectrum sensing in CRAHNs, especially when used in emergency scenarios such as disaster management and military applications, needs to be not only accurate and resource efficient, but also adaptive to the changing number of users as well as signal-to-noise ratios. In addition, spectrum sensing mechanism must also be proactive, fair, and tolerant to security attacks. Existing work in collaborative spectrum sensing has mostly been confined to resource efficiency in static systems using request-based reactive sensing resulting in high latencies. In this paper, evolutionary game theory (EGT) is used to model the behavior of the emergency CRAHNS, providing an efficient model for collaborative spectrum sensing. The resulting implementation model is adaptive to the changes in its environment such as signal-to-noise ratio and number of users in the network. The analytical and simulation models presented validate the system design and the desired performance.
Wideband Collaborative Spectrum Sensing Using Massive MIMO Decision Fusion
