Dynamic Spectrum Allocation: Unleashing the Power of DRL-RW Algorithm in UAV-Based Energy Harvesting Networks

Abstract

Abstract The improvement of energy and spectral efficiency in networks can be realized by seamlessly integrating energy harvesting, cognitive radio technologies, and NOMA techniques. These complementary strategies work together to optimize resource usage and address challenges related to energy consumption. Additionally, the adaptability and versatility of UAVs offer an innovative solution for enhancing coverage performance, not only improving connectivity but also overall efficiency and reliability. This study introduces a novel approach named a Deep Reinforcement Learning-Random Walrus (DRL-RW) algorithm, to enhance energy efficiency. The developed method combines Deep Reinforcement Learning and the Random Walrus optimization technique to efficiently allocate spectrum resources and manage energy harvesting in a dynamic environment. The DRL-RW algorithm empowers UAVs to learn optimal spectrum sharing strategies and energy harvesting policies. The random walrus optimization enhances the algorithm's adaptability and speed in exploring diverse solutions. Simulation results demonstrate the effectiveness of the DRL-RW algorithm, indicating improvements in various performance metrics, including reduced energy consumption, enhanced computation time, improved convergence, signal-to-noise ratio, increased throughput, network lifetime, harvested energy, and overall superior network performance compared to baseline techniques. These findings highlight the efficacy of the DRL-RW approach in effectively addressing challenges associated with energy management in cognitive radio networks. The integration of UAVs, NOMA networks, and the novel algorithm represents a promising direction for advancing energy-efficient communication systems.