Clustering-Based Channel Allocation Scheme for Neighborhood Area Network in a Cognitive Radio Based Smart Grid Communication

Cognitive radio ((CR))-based standards are apt to cater the diverse communication requirements for the humongous volume of data generated by Smart Grid applications. Although CR technology is one of the most promising techniques to increase the spectral efficiency of any wireless communication network, efficient spectrum allocation among secondary user (SUs) in different application scenarios remains an intriguing area for researchers. In this paper, we propose a clustering-based approach to deal with channel allocation ((CA)) problem among SUs considering practical constraints in SG environment. We first present a simple CR based SG communication network architecture by dividing the service area into groups of SUs called neighborhood area network clusters, depending upon the distance of Smart Meters from data concentrator unit. Then we formulate a multiple constraint NP-hard CA problem using interference avoidance strategy by considering two practical scenarios: fairness-based allocation and priority-based allocation. We then propose our CA algorithm based on cat swarm optimization to eliminate the severe integer constraints of the problem under consideration. We simulate the two above-mentioned practical scenarios to measure a number of allocations per SU, Jain's Fairness Index and per user average rewards. Moreover, conventional average user rewards are compared for a varying number of channels, SUs, and rounds to evaluate the performance of proposed CA scheme. The results indicate that our proposed CA algorithm works well, for both fairness-based and priority-based cases.