An Adjustable Channel Bonding Strategy in Centralized Cognitive Radio Networks and its Performance Optimization

Abstract

One form of spectrum enhancement technologies for cognitive radio networks is a channel bonding strategy, with which a network user can utilize multiple idle channels as one bonding channel. In this paper, we propose an adjustable channel bonding strategy for centralized cognitive radio networks. The main idea is that the number of channels to be aggregated is set according to the number of packets remaining in the system dynamically. Based on the working principle of the proposed adjustable channel bonding strategy, we present a discrete-time priority queueing model with an adjustable transmission rate for this network system. By constructing a two-dimensional Markov chain, we give the transition probability matrix of the Markov chain and obtain the steady-state distribution of the system model. Accordingly, we derive the formulas for the blocking rate, the normalized throughput, the average latency of the Secondary Users (SUs) and the closed channel ratio. Moreover, we provide numerical experiments to investigate the influence of the buffer capacity of the SUs on different performance measures. Finally, with regard to the joining of an SU packet as a trial, we give the Nash equilibrium strategy and socially optimal strategy for the SUs, and propose a reasonable admission fee in order to oblige the SU packets to adopt the socially optimal strategy.