Multi-RAT Fusion Architecture and Intelligent Routing Method for Marine Heterogeneous Wireless Networks
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摘要: 海上异构无线网络通信场景中多种通信体制并存，面临互联互通与资源协同双重挑战，而现有基于深度强化学习的路由算法对动态拓扑的表征能力不足，难以在网络拓扑频繁变化时做出高效的路由决策。这主要归因于现有主流框架多采用标准图神经网络(GNN)或全连接网络进行状态编码，难以有效捕捉高动态拓扑下的结构特征。为此，该文提出一种支持5G与自组网融合的多无线接入网关，并构建融合对比消息传递图神经网络与深度强化学习的路由方法，以提升网络服务质量与转发效率，实现资源的优化分配。实验结果表明，与最优基准方法相比，所提方法端到端时延降低20.8%～47.7%，丢包率降低0.3%～5.3%，吞吐量提升5.2%～14.2%，验证了其动态适应性与泛化能力。
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关键词:
- 海上异构无线网络 /
- 多无线接入网关 /
- 对比消息传递图神经网络 /
- 深度强化学习
Abstract:Objective Marine Heterogeneous Wireless Networks (MHWNs) deeply integrate multidimensional resources across space, air, and sea. Multiple communication systems coexist in these networks, creating challenges in both interconnection and resource coordination. Existing routing algorithms based on Deep Reinforcement Learning (DRL) have limited capability to represent dynamic network topologies and therefore cannot make efficient routing decisions when the topology changes frequently. This limitation mainly arises because mainstream frameworks typically use standard Graph Neural Networks (GNNs) or fully connected networks for state encoding, which cannot effectively capture the structural features of highly dynamic topologies. Methods A modular Multi-Radio Access Technology (Multi-RAT) gateway supporting the fusion of 5G and mesh ad hoc networks is designed, and an intelligent routing method integrating Contrastive Message Passing for Graph Neural Networks (CMPGNN) with DRL, termed CMPGNN-DRL, is proposed. The method is designed to improve Quality of Service (QoS), forwarding efficiency, and resource allocation. In the gateway, service data undergo IP encapsulation, protocol identification, and semantic conversion through communication interface modules before being forwarded through the target interface. The gateway periodically collects node features and link states to construct the input graph for routing decisions. Key indicators, including link bandwidth utilization, queue depth, packet loss rate, and end-to-end delay, are monitored in real time. To compensate for information delays caused by periodic reporting, short-term trends in key indicators are incorporated into the state vector, and an asynchronous decision-execution architecture is adopted. Under a centralized Software-Defined Networking (SDN) control plane, the network is modeled as a graph with continuously monitored node and link features. For each node, CMPGNN synchronously constructs homophily and heterophily views through two message-passing paths. The resulting embeddings are constrained by a contrastive loss to obtain discriminative node representations that are robust to edge perturbations. These representations are then input into a Double Deep Q-network (DDQN) agent, which makes hop-by-hop routing decisions using an ε-greedy exploration strategy. For each neighboring node, CMPGNN predicts a topology prior value. This value is combined with the corresponding DDQN Q-value estimate by weighted summation to obtain a joint action value. The topology prior can correct inaccurate Q-value estimates when model training is insufficient or observations are noisy. A normalized multiobjective reward function assigns negative contributions to end-to-end delay, packet loss rate, and link load and a positive contribution to throughput, while explicitly penalizing routing loops. Results and Discussions The proposed solution is evaluated through hardware prototype measurements and extensive simulations. Prototype tests show average CPU utilization rates of 14%, 25%, and 37% under mesh-only, 5G-only, and dual-mode operation, respectively. The aggregate throughput under dual-mode operation reaches 108 Mbps, compared with 32 Mbps under mesh-only operation and 84 Mbps under 5G-only uplink transmission. The average ping delays between the gateway and the application server are 6 ms for mesh and 16 ms for 5G. CMPGNN-DRL is compared with six baseline methods, namely OSPF, AODV, GNN, DQN, MPNN-DQN, and DAR-DRL, on the GEANT2, GBN, Germany, and Synth50 topologies. The evaluation covers dynamic traffic, random link failures at rates of 3%–24%, and large-scale topology changes. The training reward increases rapidly and then stabilizes, and ablation experiments confirm the effectiveness of the contrastive learning mechanism. Compared with the best-performing baseline, CMPGNN-DRL reduces the average end-to-end delay by 20.8%～47.7% and the packet loss rate by 0.3%～5.3%, while increasing the average throughput by 5.2%～14.2%. Additional MHWN scenarios are configured according to the environmental constraints of the Maritime Internet of Things (MIoT). A 1 500 m × 1 500 m maritime area containing 50～100 randomly deployed nodes is simulated, and each configuration is evaluated through repeated Monte Carlo simulations. CMPGNN-DRL maintains stable performance as the network scale and node mobility vary and outperforms MPNN-DQN and DAR-DRL in terms of Packet Delivery Ratio (PDR) and packet loss rate. When the network load increases from 20% to 50%, CMPGNN-DRL improves PDR by 4.2%～17.3% over MPNN-DQN and DAR-DRL while maintaining lower end-to-end delay, higher bandwidth utilization, and a lower packet retransmission rate under medium-to-high network loads. Conclusions For sea-air cross-domain heterogeneous networks, a Multi-RAT fusion gateway supporting 5G and mesh ad hoc networks is designed, and a CMPGNN-DRL intelligent multipath routing method is proposed. The contrastive learning mechanism improves topology representation and enhances the robustness of routing policies. Experimental results show that CMPGNN-DRL outperforms existing mainstream algorithms in key performance indicators, including PDR, end-to-end delay, and throughput, and achieves good generalization across different network topologies. Future work will focus on validation in real maritime environments and improvement of training efficiency to support the practical deployment of integrated sea-air communication systems. -
表 1 仿真参数
参数 数值 节点部署策略 均匀随机分布 节点数量 50～100 区域尺寸 1 500 m × 1 500 m 节点移动速度 4～16 m/s 负载率 20%～50% 数据包大小 1 Mbits 仿真时间 3 000 s 
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