Meta heuristic Based Performance Optimization in Energy Harvesting Cognitive Radio Network

Abstract

Sensing and harvesting are the two intertwined functions that have emerged as key enablers to make Energy Harvesting Cognitive Radio Network (EHCRN) efficient in terms of spectral efficiency and energy efficiency. These metrics are essential for improving the Cognitive Radio (CR) performance that are key enablers for 5G wireless networks. In an EHCRN, sensing and energy harvesting can be performed either separately or simultaneously, depending on the time frame structure. The duration allocated to each functionality is critical for overall performance and can be optimized by adjusting CR parameters. The improvement in metaheuristic-based optimization techniques as well as hybridizing with other game theory approaches for optimization in particular is effective in achieving optimal parameter values across various EHCRN scenarios. The basic EHCRN system model considered in this research includes a primary network and a secondary network with energy harvesting capabilities. The primary transmitter (PT) has licensed access to the spectrum, while the cognitive radio, acting as the secondary transmitter (ST), senses the spectrum and transmits data to the secondary receiver (SR) when the channel is free. Based upon the sensing and transmission three scenarios are formulated for this network. This research work includes proposing various metaheuristic techniques for optimization in three EHCRN scenarios. Firstly, a Rank-Based Multi-Objective Antlion Optimization (RMOALO) is proposed and the performance is tested on benchmark functions and also compared with Multi-Objective Antlion Optimization (MOALO), Multi-Objective Moth Flame Optimization (MOMFO), and Multi-Objective Particle Swarm Optimization (MOPSO). It is observed that RMOLAO provides better solutions and improved convergence when compared with these techniques. Further, the performance investigation of these algorithms is done for optimizing the sensing duration in Separated Spectrum Sensing and Energy Harvesting scenario (SSSEH) that improves the throughput