NeuraLunaDTNet：面向容延迟月球通信网络的基于前馈神经网络的路由协议
NeuraLunaDTNet: Feedforward Neural Network-Based Routing Protocol for Delay-Tolerant Lunar Communication Networks
AI总结：
针对月球DTN通信中的高延迟、路由难预测和中断问题，本文提出NeuraLunaDTNet，利用前馈神经网络在动态时空图中学习接触计划以增强PRoPHET路由效率。
摘要：
空间通信面临严重延迟、难以预测的路由以及通信中断等挑战。容迟网络（Delay Tolerant Network，DTN）架构在设计时专门考虑了此类场景，因此适合应对其中一些挑战。由于空间通信固有的复杂性，传统DTN路由协议无法提供最佳性能。研究人员一直致力于利用人工智能的最新进展来缓解部分路由难题[9]。我们提出利用前馈神经网络开发一种新型协议NeuraLunaDTNet，该协议通过在动态变化的时空图中学习接触计划，提升用于月球通信的PRoPHET路由协议的效率。
英文摘要：
Space Communication poses challenges such as severe delays, hard-to-predict routes and communication disruptions. The Delay Tolerant Network architecture, having been specifically designed keeping such scenarios in mind, is suitable to address some challenges. The traditional DTN routing protocols fall short of delivering optimal performance, due to the inherent complexities of space communication. Researchers have aimed at using recent advancements in AI to mitigate some routing challenges [9]. We propose utilising a feedforward neural network to develop a novel protocol NeuraLunaDTNet, which enhances the efficiency of the PRoPHET routing protocol for lunar communication, by learning contact plans in dynamically changing spatio-temporal graph.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
