Problem
Research questions and friction points this paper is trying to address.
Age of Information
UAV-assisted emergency networks
multi-agent decision making
heterogeneous emergency services
information freshness
MA-HEAD-Net: Adaptive Rule-Guided Multi-Agent DRL for AoI Minimization in UAV-Assisted Emergency Networks
🤖 AI Summary
This work addresses the coupled challenge of heterogeneous bursty traffic and Age of Information (AoI) optimization in post-disaster emergency communications by proposing a multi-agent deep reinforcement learning framework that integrates domain-specific communication rules as priors. The approach jointly optimizes unmanned aerial vehicle (UAV) trajectories, user scheduling, and adaptive checkpoint intervals, innovatively embedding domain rules into a multi-head policy network and employing a gating mechanism to dynamically balance rule-based guidance with learned policies. Bursty traffic is modeled via a Markov-modulated Poisson process, while finite blocklength information theory is leveraged to characterize the interplay among transmission duration, packet completion, and AoI evolution. Experimental results demonstrate that the proposed method significantly improves convergence efficiency and achieves superior AoI performance compared to existing learning-based and heuristic approaches.
📝 Abstract
In post-disaster scenarios, unmanned aerial vehicles (UAVs) are critical for establishing emergency communication networks. For time-critical rescue missions, information freshness is crucial because decisions based on outdated data may lead to ineffective control actions. This paper investigates age of information (AoI) minimization for UAV-assisted emergency communications with heterogeneous emergency services. We model bursty packet arrivals using a Markov-modulated Poisson process and adopt finite blocklength theory to capture the coupling among transmission duration, packet completion, and AoI evolution. To balance delay-tolerant long-packet transmission and urgent short-packet response, we propose a mini-slot-embedded scheduling mechanism with adaptive checkpoint-interval selection. We formulate the joint optimization of UAV trajectory control, user scheduling, and checkpoint-interval selection as a multi-agent decision problem, and develop MA-HEAD-Net, an adaptive rule-guided multi-agent deep reinforcement learning framework. MA-HEAD-Net incorporates communication-domain rule priors into a gated multi-head policy, where adaptive gates regulate the contributions of rule-prior and learned-policy logits for different subtasks. The policy and gating components are jointly optimized under multi-agent proximal policy optimization. Simulation results show that MA-HEAD-Net improves policy-formation efficiency compared with representative multi-agent deep reinforcement learning baselines and achieves lower AoI than both learning-based and heuristic methods in dynamic UAV-assisted emergency communication scenarios.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Age of Information (AoI)
Multi-Agent Deep Reinforcement Learning
Rule-Guided Policy
UAV-Assisted Emergency Networks
Finite Blocklength Theory
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