Work

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

Year: 2023

Type: conference-paper

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 the 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 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.

Source: ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp)

DOI: 10.1145/3594739.3612912

Authors: Hengxi Zhang, Huaze Tang, Wenbo Ding, Xiao-Ping Zhang

Institutions: Tsinghua University, Tsinghua–Berkeley Shenzhen Institute

Language: English

Topic: Satellite Communication Systems

Subfield: Aerospace Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

Funder: Tsinghua Shenzhen International Graduate School