基于注意力机制的海基无人机网络资源分配方法

IF 2.6 3区 工程技术 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Zhongyang Mao, Zhilin Zhang, Faping Lu, Yaozong Pan, Tianqi Zhang, Jiafang Kang, Zhiyong Zhao, Yang You
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引用次数: 0

摘要

随着人类对海洋的不断开发,海上无人机节点的数量和对其服务的需求都在不断增加。鉴于海洋环境的动态特性,传统的资源分配方法会导致服务传输效率低下和乒乓效应。本研究通过引入关注机制和双深度 Q 学习(DDQN)算法,优化服务获取策略,抑制行动输出,提高服务与节点的兼容性,从而增强网络资源与节点服务之间的一致性,构成了一种新型的海洋环境下无人机网络资源分配方法。选择性抑制模块最大限度地减少了行动输出的变化,有效缓解了乒乓效应;设计的注意力感知模块加强了节点与服务的兼容性,从而显著提高了服务传输效率。仿真结果表明,与 DDQN、软行为批判(SAC)和深度确定性策略梯度(DDPG)算法相比,所提出的方法提高了已完成服务的数量,并增加了已完成服务的总价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Sea-Based UAV Network Resource Allocation Method Based on an Attention Mechanism
As humans continue to exploit the ocean, the number of UAV nodes at sea and the demand for their services are increasing. Given the dynamic nature of marine environments, traditional resource allocation methods lead to inefficient service transmission and ping-pong effects. This study enhances the alignment between network resources and node services by introducing an attention mechanism and double deep Q-learning (DDQN) algorithm that optimizes the service-access strategy, curbs action outputs, and improves service-node compatibility, thereby constituting a novel method for UAV network resource allocation in marine environments. A selective suppression module minimizes the variability in action outputs, effectively mitigating the ping-pong effect, and an attention-aware module is designed to strengthen node-service compatibility, thereby significantly enhancing service transmission efficiency. Simulation results indicate that the proposed method boosts the number of completed services compared with the DDQN, soft actor–critic (SAC), and deep deterministic policy gradient (DDPG) algorithms and increases the total value of completed services.
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来源期刊
Electronics
Electronics Computer Science-Computer Networks and Communications
CiteScore
1.10
自引率
10.30%
发文量
3515
审稿时长
16.71 days
期刊介绍: Electronics (ISSN 2079-9292; CODEN: ELECGJ) is an international, open access journal on the science of electronics and its applications published quarterly online by MDPI.
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