Connectivity Management in Satellite-Aided Vehicular Networks with Multi-Head Attention-Based State Estimation

TL;DR

This paper proposes MAAC-SAM, a multi-agent reinforcement learning framework with multi-head attention for autonomous connectivity management in satellite-aided vehicular networks, improving efficiency and robustness under dynamic conditions.

Contribution

It introduces a novel multi-head self-attention mechanism within a multi-agent RL framework for enhanced state estimation and connectivity management in satellite-vehicular networks.

Findings

Outperforms existing baselines by up to 14% in transmission utility.

Maintains high estimation accuracy across different vehicle densities.

Effective in dynamic and partially observable environments.

Abstract

Managing connectivity in integrated satellite-terrestrial vehicular networks is critical for 6G, yet is challenged by dynamic conditions and partial observability. This letter introduces the Multi-Agent Actor-Critic with Satellite-Aided Multi-head self-attention (MAAC-SAM), a novel multi-agent reinforcement learning framework that enables vehicles to autonomously manage connectivity across Vehicle-to-Satellite (V2S), Vehicle-to-Infrastructure (V2I), and Vehicle-to-Vehicle (V2V) links. Our key innovation is the integration of a multi-head attention mechanism, which allows for robust state estimation even with fluctuating and limited information sharing among vehicles. The framework further leverages self-imitation learning (SIL) and fingerprinting to improve learning efficiency and real-time decisions. Simulation results, based on realistic SUMO traffic models and 3GPP-compliant configurations, demonstrate that MAAC-SAM outperforms state-of-the-art terrestrial and satellite-assisted baselines by up to 14% in transmission utility and maintains high estimation accuracy across varying vehicle densities and sharing levels.