Cooperative Multi-Type Multi-Agent Deep Reinforcement Learning for Resource Management in Space-Air-Ground Integrated Networks

TL;DR

This paper proposes a cooperative multi-agent deep reinforcement learning approach to optimize resource management in the complex, heterogeneous Space-Air-Ground Integrated Network, improving data transmission and service quality for smart city applications.

Contribution

It introduces a novel CMT-MARL method tailored for SAGIN's multi-type, multi-link environment, enhancing resource management efficiency.

Findings

Improved overall transmission rate

Higher transmission success rate

Demonstrated feasibility of SAGIN deployment

Abstract

The Space-Air-Ground Integrated Network (SAGIN), integrating heterogeneous devices including low earth orbit (LEO) satellites, unmanned aerial vehicles (UAVs), and ground users (GUs), holds significant promise for advancing smart city applications. However, resource management of the SAGIN is a challenge requiring urgent study in that inappropriate resource management will cause poor data transmission, and hence affect the services in smart cities. In this paper, we develop a comprehensive SAGIN system that encompasses five distinct communication links and propose an efficient cooperative multi-type multi-agent deep reinforcement learning (CMT-MARL) method to address the resource management issue. The experimental results highlight the efficacy of the proposed CMT-MARL, as evidenced by key performance indicators such as the overall transmission rate and transmission success rate. These results underscore the potential value and feasibility of future implementation of the SAGIN.