Reinforcement Learning Based Technique for Interference Management in UAV Aided HetNets

Nehal Hamden, Ahmed Nasser, Mohamed Y. Selim, M. Elsabrouty
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引用次数: 1

Abstract

In this paper, we consider the sum rate maximization problem in a downlink unmanned aerial vehicles (UAV) assisted heterogeneous networks (HetNets) to mitigate existing interference. We propose to manage the interference by jointly optimizing the position of the UAV as well as power allocation. The proposed optimization problem is non-convex due to the non-linearity in the objective function and the constraints. We propose a near-optimal solution that breaks down the problem into two consecutive problems. First, we proposed a reinforcement learning-based technique to solve the UAV positioning problem. Then, a particle swarm optimization (PSO) based technique is applied to the power allocation problem. Simulation results demonstrate the efficiency of the proposed algorithm.
基于强化学习的无人机辅助HetNets干扰管理技术
在本文中,我们考虑了下行无人机(UAV)辅助异构网络(HetNets)中的和速率最大化问题,以减轻现有干扰。我们提出通过联合优化无人机的位置和功率分配来管理干扰。由于目标函数和约束条件的非线性,所提出的优化问题是非凸的。我们提出了一个接近最优的解决方案,将问题分解为两个连续的问题。首先,提出了一种基于强化学习的无人机定位方法。然后,将粒子群优化技术应用于电力分配问题。仿真结果验证了该算法的有效性。
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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