Development and Performance Evaluation of Multi-Objective Approach-Based Spectrum Allocation in Cognitive Radio Network

Abstract

Abstract For effective utilization of radio frequency, Spectrum Sensing (SS), Spectrum Decision (SD), Spectrum Allocation (SA), and Spectrum Management(SM) are some recent research directions in Cognitive Radio Networks (CRNs). Out of these sub-areas, the SA is an important task in CRN. In the past, researchers have addressed SA as a Single Objective Optimization (SOO) problem and have solved it by using bio-inspired techniques. But in reality, the SA has a multi-objective optimization problem. In the recent past, the SA problem has been addressed as a two objectives optimization problem and has been solved by Multi–Objective Bio-inspired Approach (MOBA). In the present paper, we have also considered spectral and energy efficiencies of the SA task as two independent objectives and then solved this problem using two different Multi-Objective Optimization (MOO) techniques. These two are the Multi-Objective Ant Lion Optimization Algorithm (MOALO) and the Multi-Objective Grasshopper Optimization Algorithm (MOGOA). The spectrum utilization performances achieved by the two approaches have been compared. In addition, the results obtained by the already reported Superior Population Genetic Algorithm (SPGA) based technique have also been compared. The analysis of simulation-based experimental results obtained from the three methods reveals that the proposed MOALO algorithm provides superior performance in terms of Pareto front and network capacity compared to the other two approaches. The proposed multi-objective methods can also be applied when the SA task is viewed as a three-objective optimization problem.