Distributed Coordination for Heterogeneous Non-Terrestrial Networks

TL;DR

This paper analyzes the challenges of distributed coordination in heterogeneous non-terrestrial networks (NTNs) like UAVs, HAPs, and satellites, proposing solutions and validating them through a case study using multi-agent deep reinforcement learning.

Contribution

It systematically characterizes NTN platforms, identifies communication challenges, and introduces delay-tolerant and delay-sensitive coordination solutions with a novel MADRL-based approach.

Findings

Proposed delay-tolerant and delay-sensitive coordination solutions.

Validated the effectiveness of TTS-MADDPG in a joint scheduling and trajectory optimization case study.

Demonstrated the feasibility of multi-agent deep reinforcement learning for heterogeneous NTN coordination.

Abstract

To achieve global coverage and ubiquitous connectivity, the non-terrestrial network (NTN) has been regarded as a key enabler in the sixth generation (6G) network, which includes uncrewed aerial vehicles (UAVs), high-altitude platforms (HAPs), and satellites. Since the unique characteristics of various NTN platforms strongly affect their implementation and lead to a highly dynamic and heterogeneous NTN scenario, achieving distributed coordination remains an important research direction. However, the explicit and systematic analysis of the individual layers' challenges and corresponding distributed coordination solutions in heterogeneous NTNs has not been proposed yet. Therefore, in this paper, we summarize the unique characteristics of each NTN platform, identify communication challenges within individual layers, and propose potential delay-tolerant or delay-sensitive coordinated solutions accordingly. We further analyse the feasibility of leveraging multi-agent deep reinforcement learning (MADRL) algorithms to achieve the proposed coordinated solutions. Finally, we present a case study of the joint scheduling and trajectory optimization problem in heterogeneous NTN, where a two-timescale multi-agent deep deterministic policy gradient (TTS-MADDPG) algorithm is developed to validate the effectiveness of distributed coordination.