Resilience Optimization in 6G and Beyond Integrated Satellite-Terrestrial Networks: A Deep Reinforcement Learning Approach

2026-02-01

AI Summary

This work addresses service continuity and resilience in 6G integrated space-air-ground networks under base station failures by proposing a joint optimization framework for user association, antenna tilt, transmit power, and LEO satellite access strategies. For the first time, deep reinforcement learning is introduced to this domain, employing a Deep Q-Network (DQN) to tackle the non-convex NP-hard problem arising from dynamic user distributions, heterogeneous traffic demands, and multidimensional resource coupling. The approach simultaneously satisfies user rate and Reference Signal Received Power (RSRP) requirements while maximizing aggregate network throughput and minimizing reliance on high-latency satellite links. Simulation results demonstrate that the proposed method significantly outperforms baseline schemes, achieving higher throughput while conserving satellite resources and thereby extending their operational lifespan.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Large language models for search

Abstract

Ensuring network resilience in 6G and beyond is essential to maintain service continuity during base station (BS) outages due to failures, disasters, attacks, or energy-saving operations. This paper proposes a novel resilience optimization framework for integrated satellite-terrestrial networks (ISTNs), leveraging low Earth orbit (LEO) satellites to assist users when terrestrial BSs are unavailable. Specifically, we develop a realistic multi-cell model incorporating user association, antenna downtilt adaptation, power control, heterogeneous traffic demands, and dynamic user distribution. The objective is to maximize of the total user rate in the considered area by optimizing the BS's antenna tilt, transmission power, user association to neighboring BS or to a LEO satellite with a minimum number of successfully served user satisfaction constraint, defined by rate and Reference Signal Received Power (RSRP) requirements. To solve the non-convex, NP-hard problem, we design a deep Q-network (DQN)-based algorithm to learn network dynamics to maximize throughput while minimizing LEO satellite usage, thereby limiting reliance on links with longer propagation delays and prolonging satellite operational lifetime. Simulation results confirm that our approach significantly outperforms the benchmark one.

Problem

• network resilience
• integrated satellite-terrestrial networks
• 6G
• base station outage
• user satisfaction

Innovation

• Integrated Satellite-Terrestrial Networks
• Resilience Optimization
• Deep Reinforcement Learning
• LEO Satellite Assistance
• Antenna Downtilt Adaptation