Multipath Routing for Multi-Hop UAV Networks

2026-01-15

AI Summary

This work addresses the challenge of high latency and local congestion in multi-hop UAV networks caused by single-path routing, which struggles to meet the stringent delay requirements of dynamic traffic. To overcome this limitation, the paper proposes a traffic-adaptive multipath routing approach that, for the first time, formulates traffic distribution in UAV networks as a decentralized partially observable Markov decision process (Dec-POMDP). A continuous stochastic policy based on the Dirichlet distribution is designed to ensure valid and efficient traffic splitting ratios. Building upon the independent proximal policy optimization (IPPO) framework, the authors develop a multi-agent reinforcement learning algorithm, termed IPPO-DM, to enable distributed traffic scheduling. Simulation results demonstrate that the proposed method significantly outperforms existing approaches in terms of end-to-end latency and packet loss rate.

Technology Category

• Planning, Routing, and Scheduling: Planning with Markov Models (MDPs, POMDPs)
• Multiagent Systems: Multiagent Planning
• Reasoning under Uncertainty: Sequential Decision Making

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Multi-hop uncrewed aerial vehicle (UAV) networks are promising to extend the terrestrial network coverage. Existing multi-hop UAV networks employ a single routing path by selecting the next-hop forwarding node in a hop-by-hop manner, which leads to local congestion and increases traffic delays. In this paper, a novel traffic-adaptive multipath routing method is proposed for multi-hop UAV networks, which enables each UAV to dynamically split and forward traffic flows across multiple next-hop neighbors, thus meeting latency requirements of diverse traffic flows in dynamic mobile environments. An on-time packet delivery ratio maximization problem is formulated to determine the traffic splitting ratios at each hop. This sequential decision-making problem is modeled as a decentralized partially observable Markov decision process (Dec-POMDP). To solve this Dec-POMDP, a novel multi-agent deep reinforcement leaning (MADRL) algorithm, termed Independent Proximal Policy Optimization with Dirichlet Modeling (IPPO-DM), is developed. Specifically, the IPPO serves as the core optimization framework, where the Dirichlet distribution is leveraged to parameterize a continuous stochastic policy network on the probability simplex, inherently ensuring feasible traffic splitting ratios. Simulation results demonstrate that IPPO-DM outperforms benchmark schemes in terms of both delivery latency guarantee and packet loss performance.

Problem

• Multipath Routing
• Multi-Hop UAV Networks
• Traffic Splitting
• Latency Requirements
• Network Congestion

Innovation

• multipath routing
• multi-hop UAV networks
• Dec-POMDP
• multi-agent deep reinforcement learning
• Dirichlet policy