Distributed Coordination for Heterogeneous Non-Terrestrial Networks

TL;DR

This paper addresses distributed coordination for heterogeneous non-terrestrial networks (NTN) comprising UAVs, HAPs, and satellites, arguing that the dynamic, multi-tier topology necessitates distributed learning-based solutions. It analyzes platform-specific characteristics, identifies cross-layer challenges, and proposes delay-tolerant and delay-sensitive MADRL frameworks within a CTDE setting to achieve scalable coordination. A case study using a two-timescale MADDPG algorithm demonstrates significant throughput gains by jointly optimizing user scheduling and trajectory for mixed UAVs, validating the proposed approach. The work highlights the potential of integrating MADRL with graph Neural networks and semantic communication to enhance scalability and performance in 6G NTN deployments.

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.

Figures (3)

• Figure 1: A typical structure of terrestrial cellular network
• Figure 2: Individual-layer challenges in heterogeneous non-terrestrial network.
• Figure 3: Average downlink throughput performance of the proposed TTS-MADDPG algorithm