Channel selection for simultaneous move game in cognitive radio ad hoc networks

In cognitive radio ad hoc networks, the nodes exhibit autonomous behavior and selfishly search for the available channels to make spectrum selection decisions. A natural competition among cognitive users arises that may result is chaos and serious degradation in spectrum utilization. In this competitive and hostile environment, game theoretic model can ease this rivalry and autonomously assist in creating a subdued environment. We introduce a repeated game to alleviate the spectrum allocation problem and facilitate the cognitive radio users to make spectrum selection decision simultaneously or asynchronously. In contrast to sequential games, the proposed simultaneous move multi-stage game model is appropriate for practical applications where paucity of central spectrum management resources is common. In order to avoid the conflicts arising from coinciding concurrent decisions, we incorporate learning via history statistics to attain a stable and efficient equilibrium point. Every player computes the feasibility of playing a strategy from the action of the opponents in the previous iterations by incorporating the proposed learning rule. This learning process assists in decision making for the next iteration and the Nash equilibrium is achieved.