多跳无人机网络中流量自适应的逐跳多路径路由
Traffic-Adaptive Per-Hop Multipath Routing in Multi-Hop UAV Networks
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中文总结 AI 辅助
针对多跳无人机网络的链路波动与拓扑变化问题，提出MAPPO-DM算法实现逐跳多路径流量自适应路由，可提升分组按时交付率并降低丢失率，性能优于基线方法。
中文摘要 AI 辅助
在无人机（UAV）中继的移动边缘计算（MEC）网络中，计算任务会产生具有不同延迟要求和数据大小的流量，因此路由决策需要同时适配流量特性和不断变化的网络状况。与单路径路由相比，多路径路由更适合这类异构流量，因为它提供了多个转发选项并支持灵活的流量拆分。然而，传统多路径路由通常在预定义的端到端路径上拆分流量，难以快速响应无人机网络中的链路波动和拓扑变化。为解决该问题，本文提出一种适用于多跳无人机网络的流量自适应逐跳多路径路由方法，其中每架无人机在多个候选下一跳中动态分配流量。我们将路由问题建模为以提高分组按时交付率并降低分组丢失率为目标的去中心化部分可观测马尔可夫决策过程（Dec-POMDP），并开发了一种名为带狄利克雷建模的多智能体近端策略优化（MAPPO-DM）的多智能体强化学习（MARL）算法。MAPPO-DM遵循集中式训练与分布式执行框架，使用狄利克雷分布对连续的流量拆分动作进行建模。仿真结果表明，MAPPO-DM在各类网络条件下均优于基线方法，且性能保持稳健。
英文摘要
In uncrewed aerial vehicle (UAV)-relayed mobile edge computing (MEC) networks, computation tasks generate traffic with diverse latency requirements and data sizes. Routing decisions therefore need to adapt to both traffic characteristics and changing network conditions. Compared with single-path routing, multipath routing is better suited to such heterogeneous traffic because it provides multiple forwarding options and enables flexible traffic splitting. However, conventional multipath routing usually splits traffic over predefined end-to-end paths, making it difficult to respond quickly to link fluctuations and topology changes in UAV networks. To address this issue, we propose a traffic-adaptive per-hop multipath routing method for multi-hop UAV networks, in which each UAV dynamically distributes traffic among multiple candidate next hops. We formulate the routing problem to improve the on-time packet delivery ratio while reducing the packet loss ratio, and model it as a decentralized partially observable Markov decision process (Dec-POMDP). To solve this problem, we develop a multi-agent reinforcement learning (MARL) algorithm, termed Multi-Agent Proximal Policy Optimization with Dirichlet Modeling (MAPPO-DM). MAPPO-DM follows the centralized-training-and-decentralized-execution framework and models continuous traffic-splitting actions using a Dirichlet distribution. Simulation results show that MAPPO-DM outperforms the baseline methods and maintains robust performance under various network conditions.
