Collaborative Intelligence for UAV-Satellite Network Slicing: Towards a Joint QoS-Energy-Fairness MADRL Optimization

TL;DR

This paper introduces a multi-agent deep reinforcement learning framework for joint UAV trajectory, power, and spectrum management in satellite-integrated networks, optimizing QoS, energy efficiency, and fairness.

Contribution

It presents a novel hierarchical network slicing framework with a decentralized DRL solution for complex UAV-satellite resource management tasks.

Findings

Outperforms existing methods by up to 33% in cumulative reward.

Achieves superior energy efficiency and fairness.

Effectively manages heterogeneous service demands in dynamic environments.

Abstract

Non terrestrial networks are critical for achieving global 6G coverage, yet efficient resource management in aerial and space environments remains challenging due to limited onboard power and dynamic operational conditions. Network slicing offers a promising solution for spectrum optimization in UAV based systems serving heterogeneous service demands. For that, this paper proposes a hierarchical network slicing framework for UAV satellite integrated networks supporting eMBB, URLLC, and mMTC services. Specifically, we formulate a joint optimization of UAV trajectory, transmission power, and spectrum allocation as a decentralized partially observable Markov decision process that ensures quality of service while minimizing energy consumption and maximizing resource fairness. To address the computational intractability and partial observability, we develop a multi agent deep reinforcement learning solution under the centralized training and decentralized execution paradigm. In the proposed system, UAV agents act as distributed actors coordinated by a shared critic operating with multi head attention mechanism at a low Earth orbit satellite. Experimental results then demonstrate that our approach outperforms existing methods by up to 33% in cumulative reward while achieving superior energy efficiency and fairness.