Optimizing deep-space DTN congestion control via deep reinforcement learning

IF 4.4 2区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE
Lei Yang , Juan A. Fraire , Kanglian Zhao , Ruhai Wang , Wenfeng Li , Hong Yang
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引用次数: 0

Abstract

This paper introduces an innovative congestion control mechanism for delay/disruption-tolerant networking (DTN) within deep-space communication systems, leveraging the nuanced capabilities of deep reinforcement learning (DRL). This approach significantly departs from traditional methods, addressing the unique challenges of deep-space data transmissions. The proposed DRL-based strategy demonstrates a superior balance of critical factors, including transmission delay, energy efficiency, and data reception integrity. We assess our approach through meticulous simulation and comparison with established benchmark schemes. The findings underscore the mechanism’s enhanced performance metrics, positing it as an appealing solution in the evolving landscape of non-terrestrial networking.
通过深度强化学习优化深空 DTN 拥塞控制
本文利用深度强化学习(DRL)的细微功能,为深空通信系统中的延迟/中断容忍网络(DTN)引入了一种创新的拥塞控制机制。这种方法大大不同于传统方法,可应对深空数据传输的独特挑战。所提出的基于 DRL 的策略在传输延迟、能效和数据接收完整性等关键因素之间实现了出色的平衡。我们通过细致的模拟和与既定基准方案的比较来评估我们的方法。研究结果强调了该机制增强的性能指标,并将其视为在不断发展的非地面网络环境中一种极具吸引力的解决方案。
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
Computer Networks
Computer Networks 工程技术-电信学
CiteScore
10.80
自引率
3.60%
发文量
434
审稿时长
8.6 months
期刊介绍: Computer Networks is an international, archival journal providing a publication vehicle for complete coverage of all topics of interest to those involved in the computer communications networking area. The audience includes researchers, managers and operators of networks as well as designers and implementors. The Editorial Board will consider any material for publication that is of interest to those groups.
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