An Infection Approach for Resource Allocation in Cognitive Radio Networks

This paper presents an evolution framework of resource allocation by infection among secondary users (SUs) in an 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. We first analyze the sensing range to guarantee the connectivity of all SUs and then two different infection processes among SUs are considered. For the infection with local knowledge, each SU learns from the SU he meets with in his sensing range, who has larger bandwidth than that of himself, to achieve higher payoff. While in the case without local knowledge, each SU learns from another by probability which may not always help to achieve higher payoff each time. However, we 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. Besides, we proposed two algorithms for the two infection process to converge to EE in a distributed manner. Simulation results show that the algorithms with local knowledge can equally share the extra resources among SUs efficiently, which is actually the overall optimal solution (OOS). While for the algorithm without local knowledge, we find that though EE exists, OOS cannot always be achieved. Furthermore, we optimize the second algorithm to make EE approximate to OOS.