{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,18]],"date-time":"2025-10-18T00:11:34Z","timestamp":1760746294278,"version":"build-2065373602"},"reference-count":31,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T00:00:00Z","timestamp":1760659200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Commun. Netw."],"abstract":"<jats:p>In tactical communication networks, highly dynamic topologies and frequent data exchanges create complex spatiotemporal dependencies among link states. However, most existing intelligent routing algorithms rely on simplified model architectures and fail to capture these spatiotemporal correlations, resulting in limited situational awareness and poor adaptability under dynamic network conditions. To address these challenges, this study proposes an intelligent path selection method\u2014Deep Reinforcement Learning with Spatiotemporal-aware Link State Guidance Algorithm (DRLSGA). The algorithm builds upon the Proximal Policy Optimization (PPO) framework to develop an intelligent decision-making model and integrates a link state feature extraction module that combines Gated Recurrent Units (GRU) and a Graph Attention Network (GAT). This design enables the model to learn long-term temporal dependencies and spatial structural relationships from sequential link state data, thereby enhancing perception and decision-making capability. An attention mechanism is further introduced to highlight salient features within link state sequences, while an optimal routing strategy is derived through a deep reinforcement learning-based training process. Experimental results demonstrate that, compared with the existing DRL-ST algorithm, DRLSGA reduces average end-to-end latency by at least 2.07%, lowers the packet loss rate by 1.65%, and increases average throughput by up to 2.59% under high-traffic conditions. Moreover, the proposed algorithm exhibits stronger adaptability to highly dynamic network topologies.<\/jats:p>","DOI":"10.3389\/frcmn.2025.1635982","type":"journal-article","created":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T05:30:55Z","timestamp":1760679055000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Intelligent path selection algorithm for tactical communication networks enhanced by link state awareness"],"prefix":"10.3389","volume":"6","author":[{"given":"Yigang","family":"Shen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Xie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ming","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2025,10,17]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"827","DOI":"10.18196\/jrc.v3i6.16526","article-title":"Wireless sensor network optimization using genetic algorithm","volume":"3","author":"Alnajjar","year":"2022","journal-title":"J. Robotics Control (JRC)"},{"key":"B2","doi-asserted-by":"publisher","first-page":"83996","DOI":"10.1109\/access.2023.3303211","article-title":"5G and beyond private military communication: trend, requirements, challenges and enablers","volume":"11","author":"Bajracharya","year":"2023","journal-title":"IEEE Access"},{"key":"B3","first-page":"1","article-title":"Recent scientific achievements and developments in software defined networking: a survey","author":"Bale","year":"2023"},{"key":"B4","doi-asserted-by":"publisher","first-page":"4807","DOI":"10.1109\/tnsm.2021.3132491","article-title":"DRSIR: a deep reinforcement learning approach for routing in software-defined networking","volume":"19","author":"Casas-Velasco","year":"2021","journal-title":"IEEE Trans. Netw. Serv. Manag."},{"key":"B5","doi-asserted-by":"publisher","first-page":"1375","DOI":"10.1109\/tnsm.2020.3047623","article-title":"Efficient topology discovery for software-defined networks","volume":"18","author":"Chang","year":"2020","journal-title":"IEEE Trans. Netw. Serv. Manag."},{"key":"B6","doi-asserted-by":"publisher","first-page":"330","DOI":"10.14569\/ijacsa.2021.0120541","article-title":"Traffic engineering in software-defined networks using reinforcement learning: a review","volume":"12","author":"Dake","year":"2021","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"B7","doi-asserted-by":"publisher","first-page":"3032997","DOI":"10.1155\/2022\/3032997","article-title":"An ant colony optimization-based routing algorithm for load balancing in leo satellite networks","volume":"2022","author":"Deng","year":"2022","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"B8","doi-asserted-by":"publisher","first-page":"107401","DOI":"10.1016\/j.asoc.2021.107401","article-title":"FH-ACO: fuzzy heuristic-based ant colony optimization for joint virtual network function placement and routing","volume":"107","author":"Shokouhifar","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"B9","doi-asserted-by":"publisher","first-page":"103491","DOI":"10.1109\/access.2020.2995511","article-title":"Deep Q-learning for routing schemes in SDN-based data center networks","volume":"8","author":"Fu","year":"2020","journal-title":"IEEE Access"},{"key":"B10","doi-asserted-by":"publisher","first-page":"1444","DOI":"10.1109\/tmc.2023.3235446","article-title":"Routing optimization with deep reinforcement learning in knowledge defined networking","volume":"23","author":"He","year":"2023","journal-title":"IEEE Trans. Mob. Comput."},{"key":"B11","doi-asserted-by":"publisher","first-page":"1951","DOI":"10.3390\/app12041951","article-title":"Deep graph reinforcement learning based intelligent traffic routing control for software-defined wireless sensor networks","volume":"12","author":"Huang","year":"2022","journal-title":"Appl. Sci."},{"key":"B12","first-page":"287","article-title":"Intelligent node placement for improving traffic engineering in hybrid SDN","author":"Hussain","year":"2021"},{"key":"B13","doi-asserted-by":"publisher","first-page":"0163","DOI":"10.21123\/bsj.2021.18.1.0163","article-title":"Smart flow steering agent for end-to-end delay improvement in software-defined networks","volume":"18","author":"Hussain","year":"2021","journal-title":"Baghdad Sci. J."},{"key":"B14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/taes.2025.3571400","article-title":"Dynamic load-balancing routing strategy for LEO satellite networks based on spatio-temporal traffic prediction","author":"Ju","year":"2025","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"B15","doi-asserted-by":"publisher","first-page":"12873","DOI":"10.1109\/tnnls.2023.3265358","article-title":"Multiagent trust region policy optimization","volume":"35","author":"Li","year":"2023","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"B16","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1186\/s42400-024-00288-8","article-title":"Efficient network attack path optimization method based on prior knowledge-based PPO algorithm","volume":"8","author":"Li","year":"2025","journal-title":"Cybersecurity"},{"key":"B17","first-page":"25","article-title":"DRNet: QoS-aware routing for SDN using deep reinforcement learning","author":"Li","year":"2021"},{"key":"B18","doi-asserted-by":"publisher","first-page":"102865","DOI":"10.1016\/j.jnca.2020.102865","article-title":"DRL-R: deep reinforcement learning approach for intelligent routing in software-defined data-center networks","volume":"177","author":"Liu","year":"2021","journal-title":"J. Netw. Comput. Appl."},{"key":"B19","doi-asserted-by":"publisher","first-page":"120254","DOI":"10.1016\/j.eswa.2023.120254","article-title":"Path planning techniques for mobile robots: review and prospect","volume":"227","author":"Liu","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"B20","first-page":"1","article-title":"Spatial-temporal graph representation learning for tactical networks future state prediction","author":"Liu","year":"2024"},{"key":"B21","first-page":"792","article-title":"C-LLDP-monitoring: latency monitoring across large-scale software defined networks","author":"Lu","year":"2023"},{"key":"B22","first-page":"331","article-title":"The evolution of Ad hoc networks for tactical military communications: trends, technologies, and case studies","author":"Patel","year":"2023"},{"key":"B23","doi-asserted-by":"publisher","first-page":"8278","DOI":"10.1109\/tvt.2023.3243127","article-title":"Defense and offence strategies for tactical wireless networks using recurrent neural networks","volume":"72","author":"Pourranjbar","year":"2023","journal-title":"IEEE Trans. Veh. Technol."},{"key":"B24","volume-title":"Component-based software defined networking frame work","year":"2020"},{"key":"B25","doi-asserted-by":"publisher","first-page":"1590","DOI":"10.1109\/ojcoms.2021.3093529","article-title":"Securing public safety communications on commercial and tactical 5G networks: a survey and future research directions","volume":"2","author":"Suomalainen","year":"2021","journal-title":"IEEE Open J. Commun. Soc."},{"key":"B26","doi-asserted-by":"publisher","first-page":"e0265113","DOI":"10.1371\/journal.pone.0265113","article-title":"Provide energy-aware routing protocol in wireless sensor networks using bacterial foraging optimization algorithm and Mobile sink","volume":"17","author":"Tabatabaei","year":"2022","journal-title":"Plos one"},{"key":"B27","doi-asserted-by":"publisher","first-page":"103674","DOI":"10.1016\/j.jnca.2023.103674","article-title":"Energy-efficient and delay-guaranteed routing algorithm for software-defined wireless sensor networks: a cooperative deep reinforcement learning approach","volume":"217","author":"Wang","year":"2023","journal-title":"J. Netw. Comput. Appl."},{"key":"B28","doi-asserted-by":"publisher","first-page":"123089","DOI":"10.1016\/j.eswa.2023.123089","article-title":"WOAD3QN-RP: an intelligent routing protocol in wireless sensor networks\u2014A swarm intelligence and deep reinforcement learning based approach","volume":"246","author":"Yang","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"B29","doi-asserted-by":"publisher","first-page":"3605","DOI":"10.3233\/jifs-211787","article-title":"PQROM: to optimize software defined network QoS aware routing with proximal policy optimization","volume":"42","author":"Zhou","year":"2022","journal-title":"J. Intelligent and Fuzzy Syst."},{"key":"B30","doi-asserted-by":"publisher","first-page":"9955","DOI":"10.3390\/app13179955","article-title":"Path planning of rail-mounted logistics robots based on the improved dijkstra algorithm","volume":"13","author":"Zhou","year":"2023","journal-title":"Appl. Sci."},{"key":"B31","first-page":"404","article-title":"Routing algorithm for AD hoc networks of LEO satellites based on OSPF","author":"Zhu","year":"2023"}],"container-title":["Frontiers in Communications and Networks"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frcmn.2025.1635982\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T05:30:56Z","timestamp":1760679056000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frcmn.2025.1635982\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,17]]},"references-count":31,"alternative-id":["10.3389\/frcmn.2025.1635982"],"URL":"https:\/\/doi.org\/10.3389\/frcmn.2025.1635982","relation":{},"ISSN":["2673-530X"],"issn-type":[{"value":"2673-530X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,17]]},"article-number":"1635982"}}