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 of the ST. RMOALO shows higher throughput across different levels of harvested energy while maintaining the optimal sensing duration. Additionally, it shows stable performance with the lowest variability compared to other algorithms. Secondly, the research work proposes the Unbiased PSO Grey Wolf Optimization (Unbiased PSOGWO) to the constrained optimization problem in Combined Spectrum Sensing and Energy Harvesting (CSSEH) scenario. In addition, a Shapley hybrid multi-objective optimization algorithm (Shapley PSOGWO) is also proposed where in the unbiased PSOGWO is hybridized with shapely value a game theoretic concept. The performance of the Shapley hybrid multi-objective optimization algorithm is evaluated against Unbiased PSOGWO, Particle Swarm Optimization Grey Wolf Optimization (PSOGWO), and Grey Wolf Optimization (GWO). The simulation results indicate that the Shapley PSOGWO converges more quickly as compared to the other algorithms. The proposed algorithm is applied for optimization problem formulated in CSSEH scenario and outperforms other approaches in finding the optimal set of solutions, maximizing both throughput and energy efficiency. It significantly improves energy efficiency and reduces sensing duration while increasing average throughput. Finally, Hybrid Cellular Genetic Algorithm Particle Swarm Optimization Shapley (CGAPSO Shapley) is proposed in this research work. The proposed algorithm uses strong concepts of game theory. The performance of the proposed algorithm is evaluated against traditional algorithms as PSO, CGAPSO, and Particle Swarm Optimization Genetic Search Algorithm (PSOGSA). The algorithms effectiveness is tested on different benchmark functions showing a good balance between exploration and exploitation and reaching global optimum with faster convergence. This research addresses a multi-objective optimization problem within the Relay-Assisted Energy-Harvesting Cognitive Radio Network (RA-EHCRN) scenario, aiming to maximize throughput and Signal-to-Interference-Plus-Noise Ratio (SINR) while minimizing outage probability.