Link State Awareness Enhanced Intelligent Routing Algorithm for Tactical Communication Networks

SHI Huaifeng; ZHOU Long; PAN Chengsheng; CAO Kangning; LIU Chaofan; LV Miao

doi:10.11999/JEIT241132

Volume 47 Issue 7

Jul. 2025

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Article Navigation > Journal of Electronics & Information Technology > 2025 > 47(7): 2127-2139

SHI Huaifeng, ZHOU Long, PAN Chengsheng, CAO Kangning, LIU Chaofan, LV Miao. Link State Awareness Enhanced Intelligent Routing Algorithm for Tactical Communication Networks[J]. Journal of Electronics & Information Technology, 2025, 47(7): 2127-2139. doi: 10.11999/JEIT241132

Citation:

SHI Huaifeng, ZHOU Long, PAN Chengsheng, CAO Kangning, LIU Chaofan, LV Miao. Link State Awareness Enhanced Intelligent Routing Algorithm for Tactical Communication Networks[J]. Journal of Electronics & Information Technology, 2025, 47(7): 2127-2139. doi: 10.11999/JEIT241132

Citation:

SHI Huaifeng, ZHOU Long, PAN Chengsheng, CAO Kangning, LIU Chaofan, LV Miao. Link State Awareness Enhanced Intelligent Routing Algorithm for Tactical Communication Networks[J]. Journal of Electronics & Information Technology, 2025, 47(7): 2127-2139. doi: 10.11999/JEIT241132

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Link State Awareness Enhanced Intelligent Routing Algorithm for Tactical Communication Networks

doi: 10.11999/JEIT241132 cstr: 32379.14.JEIT241132

1.
Key Laboratory of Intelligent Support Technology for Complex Environments, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing 210044, China
2.
63rd Research Institute of National University of Defense Technology, Nanjing 210007, China

Funds: The Foundation of Key Laboratory of Intelligent Support Technology for Complex Environments, Ministry of Education (B2202401)

Received Date: 2024-12-24
Rev Recd Date: 2025-04-07

Available Online: 2025-04-25

Publish Date: 2025-07-22

Abstract

Abstract

Objective Operational concept iteration, combat style innovation, and the emergence of new combat forces are accelerating the transition of warfare toward intelligent systems. In this context, tactical communication networks must establish end-to-end transmission paths through heterogeneous links, including ultra-shortwave and satellite communications, to meet differentiated routing requirements for multi-modal services sensitive to latency, bandwidth, and reliability. Existing Deep Reinforcement Learning (DRL)-based intelligent routing algorithms primarily use single neural network architectures, which inadequately capture complex dependencies among link states. This limitation reduces the accuracy and robustness of routing decisions under time-varying network conditions. To address this, a link state perception-enhanced intelligent routing algorithm (DRL-SGA) is proposed. By capturing spatiotemporal dependencies in link state sequences, the algorithm improves the adaptability of routing decision models to dynamic network conditions and enables more effective path selection for multi-modal service transmission. Methods The proposed DRL-SGA algorithm incorporates a link state perception enhancement module that integrates a Graph Neural Network (GNN) and an attention mechanism into a Proximal Policy Optimization (PPO) agent framework for collecting network state sequences. This module extracts high-order features from the sequences across temporal and spatial dimensions, thereby addressing the limited global link state awareness of the PPO agent’s Fully Connected Neural Network (FCNN). This enhancement improves the adaptability of the routing decision model to time-varying network conditions. The Actor-Critic framework enables periodic interaction between the agent and the network environment, while an experience replay pool continuously refines policy parameters. This process facilitates the discovery of routing paths that meet heterogeneous transmission requirements across latency-, bandwidth-, and reliability-sensitive services. Results and Discussions The routing decision capability of the DRL-SGA algorithm is evaluated in a simulated network comprising 47 routing nodes and 61 communication links. Its performance is compared with that of five other routing algorithms under varying traffic intensities. The results show that DRL-SGA provides superior adaptability to heterogeneous network environments. At a traffic intensity of 100 kbit/s, DRL-SGA reduces latency by 14.42～33.57% compared with the other algorithms (Figure 4). Network throughput increases by 2.51～23.41% (Figure 5). In scenarios characterized by resource constraints or topological changes, DRL-SGA consistently maintains higher service quality and greater adaptability to fluctuations in network state (Figures 7～12). Ablation experiments confirm the effectiveness of the individual components within the link state perception enhancement module in improving the algorithm’s perception capability (Table 3). Conclusions A link state perception-enhanced intelligent routing algorithm (DRL-SGA) is proposed for tactical communication networks. By extracting high-order features from link state sequences across temporal and spatial dimensions, the algorithm addresses the limited global link state awareness of the PPO agent’s FCNN. Through the Actor-Critic framework and periodic interactions between the agent and the network environment, DRL-SGA enables iterative optimization of routing strategies, improving decision accuracy and robustness under dynamic topology and link conditions. Experimental results show that DRL-SGA meets the differentiated transmission requirements of heterogeneous services—latency-sensitive, bandwidth-sensitive, and reliability-sensitive, while offering improved adaptability to variations in network state. However, the algorithm may exhibit delayed convergence when training samples are insufficient in rapidly changing environments. Future work will examine the integration of diffusion models to enrich training data and accelerate convergence.
- Intelligent routing,
- Link state,
- Proximal Policy Optimization (PPO),
- Quality of Service (QoS),
- Tactical communication networks

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References(17)

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