Evolution Framework for Resource Allocation With Local Interaction: An Infection Approach
This paper presents an evolution framework of resource allocation by infection among Secondary Users (SUs) in OFDMA-based cognitive radio cellular networks. Each Primary User (PU) sells his extra sub-channels to SUs in his sensing range to achieve the highest payoff and each SU may come across another in his sensing range to make infection. Two different infection processes among SUs, the infection with and without local knowledge respectively, are considered. The authors prove the existence and convergence of Evolutionary Equilibrium (EE) for both cases, and show some interesting properties such as the impact of cheating of SUs in the above infection processes.