Dynamic resource allocation for cognitive radio based smart grid communication networks

Abstract

Cognitive Radio (CR) is a new technology to answer the spectrum shortage problem by dynamically allowing secondary (unlicensed) users to utilize the spectrum holes, avoiding interference with primary (licensed) users. Smart Grid (SG) is an enhancement of the conventional system of electricity distribution and management. Two-way communication, smart devices, and sensors are the core competencies of SG which result in increasing the efficiency and reliability of the SG system. An enormous amount of data in the range of thousands of Terabytes is expected to be generated due to various SG applications in a fully functional smart grid communication network (SGCN), requiring a fair share of spectrum resources. CR based SGCN is widely proposed in the literature to carry a major chunk of this data to increase spectral efficiency. Dynamic spectrum allocation on the basis of fairness using CR technology is proposed in this work, which ensures the fair distribution of spectrum resources among cognitive SG users. This optimization problem is solved using heuristic approach. A comparative analysis of three algorithms: genetic algorithm (GA), particle swarm optimization (PSO) and cat swarm optimization (CSO) is presented for evaluating fairness and max sum reward (MSR). It is shown that CSO outperforms both GA and PSO in terms of average fairness and MSR achieved by secondary users for a number of allocations.