Channel assignment and optimal path selection for cognitive radio wireless mesh networks

Abstract

Cognitive Radio (CR) technology, secondary or CR users are allowed to occupy primary licensed spectrum in an opportunistic manner subsequently improving spectrum utilisation in telecommunication networks. Cognitive Radio Wireless Mesh Networks (CR-WMNs) have gained in popularity in recent years as a vivid and promising solution for efficient resource utilisation. Since the channels in CR-WMNs are dynamic and exhibit diverse characteristics, the channel assignment and path selection are two essential methods needed for the optimal network performance. These caused complexities for the CR-WMN secondary users, such as spectrum heterogeneity, unpredictable primary user activity, and interference constraints. If spectrum heterogeneity and primary user activity are considered while assigning the channel and selecting end to end path, then the network performance improvement can be achieved. The objective of this thesis is to develop a channel assignment algorithm and end to end path selection scheme to enhance the CR-WMN user channel and path reliability. For the optimal channel assignment, the Link Capacity based Channel Assignment (LCCA) algorithm is developed in which primary user activity, secondary user activity and link interference parameters are considered in decision-making. The proposed LCCA algorithm provides a 63% more reliable channel among the available channels in the network. Next, this thesis focuses on optimal end to end path selection analytical modelling. The results show that the developed analytical model has achieved about 18.9% improvement compared to the conventional Gateway Discovery method. The last objective of this thesis is to develop fuzzy logic based end to end path selection technique. The relation of path route with users, bandwidth, and mobility are considered in this intelligent algorithm. The proposed fuzzy based system delivers up to 62.1% improvement in providing optimal path in comparison to the analytical model and the Gateway Discovery method. Lastly, the intelligent end to end path selection has the potential to improve spectrum utilisation in multi-hop wireless networks such as 5G network and Internet of Things (IoT) applications.