UAV-Assisted Cooperative Edge Inference for Low-Altitude Economy via MoE-based Hierarchical Deep Reinforcement Learning

TL;DR

This paper introduces a UAV-assisted cooperative edge inference framework for low-altitude economy applications, optimizing UAV trajectories and inference tasks using a hierarchical deep reinforcement learning approach with a mixture-of-experts architecture.

Contribution

It proposes a novel HDRL-MoE framework that jointly optimizes UAV trajectories and inference decisions, addressing mission constraints and throughput bottlenecks in LAE.

Findings

HDRL-MoE achieves significant inference accuracy improvements.

The framework demonstrates high scalability and efficiency.

Extensive simulations validate the approach's effectiveness.

Abstract

The low-altitude economy (LAE) is reshaping the industrial landscape by deploying unmanned aerial vehicles (UAVs) to facilitate a wide range of applications demanding flexible aerial mobility. Integrating edge artificial intelligence (AI) into LAE platforms creates a compelling paradigm where UAVs provide real-time AI-driven analysis while simultaneously executing their primary aerial mission duties. However, realizing this paradigm remains challenging due to the strict mission constraints imposed by these primary duties and the throughput bottlenecks of wireless links. To bridge this gap, we propose a UAV-assisted cooperative edge inference framework where UAVs execute mission-critical LAE duties, quantified by trajectory deviations from reference paths, while concurrently supporting ground devices via intermediate feature offloading. Within this framework, UAV trajectories, inference task offloading decisions, and feature compression ratios are jointly optimized to maximize the system performance. We cast this joint optimization task into a constrained partially observable Markov decision process (POMDP) framework. To efficiently solve it, we propose HDRL-MoE, a novel hierarchical deep reinforcement learning framework that decouples the optimization of slow-varying inference decisions from rapidly changing UAV trajectory control. Furthermore, HDRL-MoE integrates a mixture-of-experts (MoE) architecture, where a router network orchestrates discrete offloading decisions while expert networks independently optimize the feature compression ratios. Extensive simulations show that HDRL-MoE achieves significant inference accuracy gains over baselines and exhibits high scalability and efficiency through its MoE design.