A Channel Allocation Framework Under Responsive Pricing in Heterogeneous Cognitive Radio Network

Abstract

For a virtual network operator (VNO) of a heterogeneous cognitive radio network (CRN), finding a channel allocation method that can maximize income while guaranteeing the quality of service (QoS) of secondary users (SU) is a complex but crucial problem. Aim to this problem, based on the probability distribution vector (PDV) designed to describe all possible channel allocation results, a queueing analysis framework capable of obtaining the performance metrics of the QoS, such as throughput, queue length, packets rejection rate and the VNO’s income, is proposed. The proposed framework, which is able to analyze each non-homogeneous SU with configurable parameters independently, can be applied to the real-world scenarios of CRN flexibly. To calculate income of the VNO accurately, the nonlinear function between the throughput and the pricing for each SU is set as a configurable parameter in the proposed framework. To maximize income of the VNO, a maximum price for throughput allocation (MPTA) protocol is designed. Numerical results of experiments show that the MPTA protocol can increase VNO’s income while maintaining the Qos of SUs compared with the existing protocols. And it is proved that the proposed framework is capable of helping design channel allocation protocols to increase VNO’s income.