Seerangan Koteeswaran, Nandagopal Malarvizhi, Govindaraju Tamilmani, Manogaran Nalini, Balusamy Balamurugan, Selvarajan Shitharth
Department of CSE (AI&ML), S.A. Engineering College (Autonomous), Chennai, Tamil Nadu, 600077, India.
Department of CSE, School of Computing, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Chennai, Tamil Nadu, 600062, India.
Sci Rep. 2024 Sep 27;14(1):22188. doi: 10.1038/s41598-024-71621-x.
In the air-to-ground transmissions, the lifespan of the network is based on the "unmanned aerial vehicle's (UAV)" life span because of the limited battery capacity. Thus, the enhancement of energy efficiency and the outage of the ground candidate's minimization are significant factors of the network functionality. UAV-aided transmission can highly enhance the spectrum efficacy and coverage. Because of their flexible deployment and the high maneuverability, the UAVs can be the best alternative for the situations where the "Internet of Things (IoT)" systems utilize more energy to attain the essential information rate, when they are far away from the terrestrial base station. Therefore, it is significant to win over the few troubles in the conventional UAV-aided efficiency approaches. Thus, this proposed work is aimed to design an innovative energy efficiency framework in the UAV-assisted network using a reinforcement learning mechanism. The energy efficiency optimization in the UAV offers better wireless coverage to the static and mobile ground user. Presently, reinforcement learning techniques effectively optimize the energy efficiency rate of the system by employing the 2D trajectory mechanism, which effectively removes the interference rate attained in the nearby UAV cells. The main objective of the recommended framework is to maximize the energy efficiency rate of the UAV network by performing the joint optimization using UAV 3D trajectory, with the energy utilized during interference accounting, and connected user counts. Hence, an efficient Adaptive Deep Reinforcement Learning with Novel Loss Function (ADRL-NLF) framework is designed to provide a better energy efficiency rate to the UAV network. Moreover, the parameter of ADRL is tuned using the Hybrid Energy Valley and Hermit Crab (HEVHC) algorithm. Various experimental observations are performed to observe the effectualness rate of the recommended energy efficiency model for UAV-based networks over the classical energy efficiency framework in UAV Networks.
在空对地传输中，由于电池容量有限，网络的寿命取决于“无人机（UAV）”的寿命。因此，提高能源效率和最小化地面候选节点的中断是网络功能的重要因素。无人机辅助传输可以显著提高频谱效率和覆盖范围。由于其灵活的部署和高机动性，当物联网（IoT）系统远离地面基站且需要更多能量来达到基本信息速率时，无人机可以成为最佳选择。因此，克服传统无人机辅助效率方法中的一些问题具有重要意义。因此，这项拟议的工作旨在使用强化学习机制设计无人机辅助网络中的创新能源效率框架。无人机中的能源效率优化为静态和移动地面用户提供了更好的无线覆盖。目前，强化学习技术通过采用二维轨迹机制有效地优化了系统的能源效率，该机制有效消除了附近无人机小区中获得的干扰率。推荐框架的主要目标是通过使用无人机三维轨迹进行联合优化，同时考虑干扰期间使用的能量和连接的用户数量，来最大化无人机网络的能源效率。因此，设计了一种具有新型损失函数的高效自适应深度强化学习（ADRL-NLF）框架，以提高无人机网络的能源效率。此外，使用混合能量谷和寄居蟹（HEVHC）算法对ADRL的参数进行了调整。进行了各种实验观察，以观察所推荐的基于无人机网络的能源效率模型相对于无人机网络中的经典能源效率框架的有效率。
