Link capacity based channel assignment (LCCA) for cognitive radio wireless mesh networks

Abstract

Cognitive radio wireless mesh network (CRWMN) is expected as an upcoming technology with the potential advantages of both cognitive radio (CR) and the wireless mesh networks (WMN). In CRWMN, co-channel interference is one of the key limiting factors that affect the reception capabilities of the client and reduce the achievable transmission rate. Furthermore, it increases the frame loss rate and results in underutilization of resources. To maximize the performance of such networks, interference related issues need to be considered. Channel assignment (CA) is one of the key techniques to overcome the performance degradation of a network caused by the interferences. To counter the interference issues, we propose a novel CA technique which is based on link capacity, primary user activity and secondary user activity. These three parameters are fed to the proposed weightage decision engine to get the weight for each of the stated parameters. Thus, the link capacity based channel assignment (LCCA) algorithm is based on the weightage decision engine. The end-to-end delay, packet delivery ratio and the throughput is used to estimate the performance of the proposed algorithm. The numerical results demonstrate that the proposed algorithm is closer to the optimum resource utilization