Relay node selection for spectrum leasing in cognitive radio networks

Abstract

In spectrum leasing, licensed users (or primary users, PUs) and unlicensed users (or secondary users, SUs) interact with each other to achieve mutual agreement on channel access in order to increase their respective network performance. The PUs must select suitable SUs as relay nodes which are expected to uphold the leasing agreement. General speaking, the SU's transmission power must fulfill the minimum and maximum power threshold levels imposed by PUs. The minimum power thresholds ensure that a satisfactory level of successful transmission can be achieved by SUs while helping to relay PUs' packets. On the other hand, the maximum power threshold ensures that SUs' interference to PUs is acceptable to PUs. In this paper, the PUs announce their requirements on minimum and maximum power threshold levels to SUs for the selection of relay nodes; while the SUs maintain their respective transmission power within the threshold level defined by PUs in order to increase their respective network performance (e.g. throughput and end-to-end delay performances). The functionalities are modeled and solved using Reinforcement Learning (RL), which determines the suitable SUs as relay nodes on the basis of the aforementioned power threshold criterion. Our preliminary simulation results show that the number of SUs that qualify as relay nodes increases with the maximum power level imposed by PU, and thus it is expected to provide PUs' and SUs' performance enhancement (e.g. throughput). It also shows that, the convergence rate of SUs' power level increases with the number of simulation iterations.