Journal Article

Multi-Radio Access Fusion with Contrastive Graph Message Passing Neural Networks for Intelligent Maritime Routing

Chen, Jin, Zhou, Xuan, Lin, Haitao

2026

Overview

Maritime heterogeneous wireless networks are characterized by dynamic topology and significant heterogeneity in bandwidth, latency, and coverage across communication paradigms, rendering traditional terrestrial routing protocols inadequate. To address these challenges, this paper proposes a unified multi-radio access fusion infrastructure featuring a gateway that enables protocol conversion and collaborative resource management across heterogeneous systems. Building upon this infrastructure, we introduce CMPGNN-DQN, an intelligent routing algorithm that integrates Contrastive Message Passing Graph Neural Networks with Deep Reinforcement Learning. Specifically, the algorithm employs k-hop neighbor aggregation to expand the receptive field for routing decisions, and utilizes a dual-view contrastive learning mechanism—encompassing both homogeneous and heterogeneous perspectives—to enhance representation robustness against dynamic topology perturbations. By deeply fusing network topology features with real-time state information, including bandwidth, delay, and queue length, the agent makes hop-by-hop routing decisions via an ε-greedy policy within the DQN framework. Extensive simulations conducted across various scales of dynamic maritime communication scenarios demonstrate that CMPGNN-DQN outperforms state-of-the-art benchmark algorithms, including AODV, DQN, and GCN, across key metrics such as packet delivery ratio, transmission latency, and bandwidth utilization. Quantitatively, compared to the best-performing alternative (MPNN-DQN), our algorithm achieves throughput improvements of 2.06–5.04% under standard traffic loads and 6.6–27.1% under partial link failure conditions, while converging within merely 25 training episodes. Notably, under heavy network loads (40% load rate) or partial link failures, the algorithm maintains stable communication performance, demonstrating strong adaptability to complex dynamic environments.

• Publisher: MDPI AG
• Subject: Adaptation / Algorithms / Bandwidths / Collaboration / Communication / Communications networks / Contrastive learning / Convergence / Decision making / Decisions / Deep learning / Distance learning / Geospatial data / Graph neural networks / Heterogeneity / Infrastructure / Infrastructure (Economics) / Machine learning / Message passing / Network latency / Network topologies / Neural networks / Optimization / Packet transmission / Protocol / Quality of service / Real time / Resource management / Routing (telecommunications) / Sensors / Software / Telecommunication systems / Traffic control / Wireless networks