Intelligent UAV-Based Mobile Offloading: A Multi-Objective Optimization Approach

IF 6.7 2区 计算机科学 Q1 TELECOMMUNICATIONS
Farzad H. Panahi;Fereidoun H. Panahi
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引用次数: 0

Abstract

We explore the use of an uncrewed aerial vehicle (UAV) flying on a circular path to offload mobile data from a ground base station (GBS) to enhance cellular network capacity. The UAV’s performance is constrained by battery life and energy-intensive radio frequency communications. To address this, we jointly optimize energy efficiency (EE) and spectrum efficiency (SE) by adjusting the UAV’s trajectory, speed, and minimum user throughput. The multi-objective optimization problem we propose is complex and non-convex, presenting substantial challenges in finding an optimal solution. We develop a tailored deep reinforcement learning (DRL) approach to address this specific problem. Simulations show that our method effectively balances EE and SE, enhancing UAV-based cellular offloading while maintaining robust performance, even in uncertain and dynamic conditions.
基于智能无人机的移动卸载:多目标优化方法
我们探索使用在圆形路径上飞行的无人驾驶飞行器(UAV)从地面基站(GBS)卸载移动数据以增强蜂窝网络容量。无人机的性能受到电池寿命和能源密集型射频通信的限制。为了解决这个问题,我们通过调整无人机的轨迹、速度和最小用户吞吐量来共同优化能效(EE)和频谱效率(SE)。我们提出的多目标优化问题是复杂和非凸的,在寻找最优解方面提出了实质性的挑战。我们开发了一种定制的深度强化学习(DRL)方法来解决这个特定的问题。仿真表明,我们的方法有效地平衡了EE和SE,增强了基于无人机的蜂窝卸载,同时保持了鲁棒性,即使在不确定和动态条件下也是如此。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IEEE Transactions on Green Communications and Networking
IEEE Transactions on Green Communications and Networking Computer Science-Computer Networks and Communications
CiteScore
9.30
自引率
6.20%
发文量
181
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