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1.杭州电子科技大学通信工程学院,浙江 杭州 310018
2.浙江大学信息与电子工程学院,浙江 杭州 311121
3.中国电信股份有限公司义乌分公司,浙江 义乌 321000
4.台州学院,浙江 台州 318000
[ "章坚武(1961- ),男,博士,杭州电子科技大学信息工程学院特聘教授、创新创业学院院长,中国通信学会会士,主要研究方向为移动通信与信息安全、移动通信与AI融合技术。" ]
张天恒(2000- ),男,杭州电子科技大学通信工程学院硕士生,主要研究方向为移动通信与AI融合技术。
章谦骅(1990- ),男,浙江大学信息与电子工程学院博士生,主要研究方向为天基计算、激光通信、计算卸载等。
郭春生(1971- ),男,博士,杭州电子科技大学通信工程学院教授、博士生导师,主要研究方向为图像处理、移动通信与AI融合技术。
傅剑峰(1976- ),男,浙江大学信息与电子工程学院硕士生,中国电信股份有限公司义乌分公司副总经理、正高级工程师,主要研究方向为智慧城市、智慧教育(校园)、智慧安防等。
詹明(1975- ),男,博士,台州学院教授、博士生导师,主要研究方向为信道编码理论与技术、工业无线传感器网络、高可靠低时延通信和安全通信技术。
收稿日期:2024-12-01,
修回日期:2025-04-07,
纸质出版日期:2025-06-20
移动端阅览
章坚武,张天恒,章谦骅等.OSI-RM视角下的移动通信网络AI技术应用综述[J].电信科学,2025,41(06):1-28.
ZHANG Jianwu,ZHANG Tianheng,ZHANG Qianhua,et al.A review of artificial intelligence applications in mobile communication networks from the perspective of the OSI-RM[J].Telecommunications Science,2025,41(06):1-28.
章坚武,张天恒,章谦骅等.OSI-RM视角下的移动通信网络AI技术应用综述[J].电信科学,2025,41(06):1-28. DOI: 10.11959/j.issn.1000-0801.2025111.
ZHANG Jianwu,ZHANG Tianheng,ZHANG Qianhua,et al.A review of artificial intelligence applications in mobile communication networks from the perspective of the OSI-RM[J].Telecommunications Science,2025,41(06):1-28. DOI: 10.11959/j.issn.1000-0801.2025111.
人工智能(artificial intelligence,AI)凭借其强大的推理、学习及自我修正能力,已成为解决复杂问题的重要技术,并广泛应用于尖端科技领域。移动通信网络作为现代信息社会的核心基础设施,其复杂性对网络构建、管理和优化提出了更高要求。开放系统互连参考模型(open systems interconnection reference model,OSI-RM)通过分层结构实现网络功能的模块化和标准化,为移动通信网络的设计与实现提供了重要理论框架,并指导多协议、多设备的互联互通。首先从OSI-RM的视角,系统梳理了AI技术在移动通信网络各层中的应用现状,分析了AI在解决移动通信网络复杂问题中的作用。同时,指出部分AI技术具备跨层应用的潜力,突破了单一层次的限制,为AI技术在移动通信网络中的深入研究与应用提供了新的方向。
Artificial intelligence (AI)
with its powerful capabilities in reasoning
learning
and self-correction
has become a key technology for addressing complex problems and is widely applied in cutting-edge scientific and technological fields. As the core infrastructure of modern information society
mobile communication networks face increasing challenges in their construction
management
and optimization due to their complexity. The open systems interconnection reference model (OSI-RM)
with its layered structure
enables the modularization and standardization of network functions
providing a fundamental theoretical framework for the design and implementation of mobile communication networks and guiding the interoperability of multiple-protocols and multi-device systems. From the perspective of OSI-RM
the current applications of AI technologies across different layers of mobile communication networks were systematically reviewed
and the roles of AI in addressing complex issues in mobile communication networks were analyzed. Furthermore
it was highlighted that some AI technologies demonstrated potential for cross-layer applications
breaking the limitations of single-layer approaches
and offering new directions for advancing the research and application of AI in mobile communication networks.
张平 , 牛凯 , 田辉 , 等 . 6G移动通信技术展望 [J ] . 通信学报 , 2019 , 40 ( 1 ): 141 - 148 .
ZHANG P , NIU K , TIAN H , et al . Technology prospect of 6G mobile communications [J ] . Journal on Communications , 2019 , 40 ( 1 ): 141 - 148 .
ZIMMERMANN H . OSI reference model - the ISO model of architecture for open systems interconnection [J ] . IEEE Transactions on Communications , 1980 , 28 ( 4 ): 425 - 432 .
MATA J , DE MIGUEL I , DURÁN R J , et al . Artificial intelligence (AI) methods in optical networks: a comprehensive survey [J ] . Optical Switching and Networking , 2018 , 28 : 43 - 57 .
6GANA.6G网络原生AI技术需求白皮书 [R ] . 2023 .
6 GANA . 6G network native AI technical requirements white paper [R ] . 2023 .
YAZICI İ , SHAYEA I , DIN J . A survey of applications of artificial intelligence and machine learning in future mobile networks-enabled systems [J ] . Engineering Science and Technology, an International Journal , 2023 , 44 : 101455 .
OSPINA CIFUENTES B J , SUÁREZ Á , GARCÍA PINEDA V , et al . Analysis of the use of artificial intelligence in software-defined intelligent networks: a survey [J ] . Technologies , 2024 , 12 ( 7 ): 99 .
NACHMANI E , BE’ERY Y , BURSHTEIN D . Learning to decode linear codes using deep learning [C ] // Proceedings of the 2016 54th Annual Allerton Conference on Communication, Control, and Computing (Allerton) . Piscataway : IEEE Press , 2016 : 341 - 346 .
LUGOSCH L , GROSS W J . Neural offset min-sum decoding [C ] // Proceedings of the 2017 IEEE International Symposium on Information Theory (ISIT) . Piscataway : IEEE Press , 2017 : 1361 - 1365 .
GRUBER T , CAMMERER S , HOYDIS J , et al . On deep learning-based channel decoding [C ] // Proceedings of the 2017 51st Annual Conference on Information Sciences and Systems (CISS) . Piscataway : IEEE Press , 2017 : 1 - 6 .
CAMMERER S , GRUBER T , HOYDIS J , et al . Scaling deep learning-based decoding of polar codes via partitioning [C ] // Proceedings of the GLOBECOM 2017 - 2017 IEEE Global Communications Conference . Piscataway : IEEE Press , 2017 : 1 - 6 .
LYU W , ZHANG Z Y , JIAO C X , et al . Performance evaluation of channel decoding with deep neural networks [C ] // Proceedings of the 2018 IEEE International Conference on Communications (ICC) . Piscataway : IEEE Press , 2018 : 1 - 6 .
ZHANG W Y , WANG Y Z , SHEN C , et al . A regression approach to certain information transmission problems [J ] . IEEE Journal on Selected Areas in Communications , 2019 , 37 ( 11 ): 2517 - 2531 .
CAO C Z , LI D S , FAIR I . Deep learning-based decoding of constrained sequence codes [J ] . IEEE Journal on Selected Areas in Communications , 2019 , 37 ( 11 ): 2532 - 2543 .
RADUNOVIC B , PROUTIERE A , GUNAWARDENA D , et al . Dynamic channel, rate selection and scheduling for white spaces [C ] // Proceedings of the Seventh Conference on Emerging Networking Experiments and Technologies . New York : ACM Press , 2011 : 1 - 12 .
COMBES R , OK J , PROUTIERE A , et al . Optimal rate sampling in 802.11 systems: theory, design, and implementation [J ] . IEEE Transactions on Mobile Computing , 2019 , 18 ( 5 ): 1145 - 1158 .
LEE D G , SUN Y G , KIM S H , et al . DQN-based adaptive modulation scheme over wireless communication channels [J ] . IEEE Communications Letters , 2020 , 24 ( 6 ): 1289 - 1293 .
BRUNO R , MASARACCHIA A , PASSARELLA A . Robust adaptive modulation and coding (AMC) selection in LTE systems using reinforcement learning [C ] // Proceedings of the 2014 IEEE 80th Vehicular Technology Conference (VTC2014-Fall) . Piscataway : IEEE Press , 2014 : 1 - 6 .
MASHHADI S , GHIASI N , FARAHMAND S , et al . Deep reinforcement learning based adaptive modulation with outdated CSI [J ] . IEEE Communications Letters , 2021 , 25 ( 10 ): 3291 - 3295 .
YE X W , FU L Q . Joint MCS adaptation and RB allocation in cellular networks based on deep reinforcement learning with stable matching [J ] . IEEE Transactions on Mobile Computing , 2024 , 23 ( 1 ): 549 - 565 .
O’SHEA T J , KARRA K , CLANCY T C . Learning to communicate: channel auto-encoders, domain specific regularizers, and attention [C ] // Proceedings of the 2016 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT) . Piscataway : IEEE Press , 2016 : 223 - 228 .
O’SHEA T , HOYDIS J . An introduction to deep learning for the physical layer [J ] . IEEE Transactions on Cognitive Communications and Networking , 2017 , 3 ( 4 ): 563 - 575 .
O’SHEA T J , ERPEK T , CLANCY T C . Deep learning based MIMO communications [J ] . ArXiv Prints , 2017 : arXiv: 1707.07980 .
ZHU B H , WANG J T , HE L Z , et al . Joint transceiver optimization for wireless communication PHY using neural network [J ] . IEEE Journal on Selected Areas in Communications , 2019 , 37 ( 6 ): 1364 - 1373 .
CUI W , SHEN K M , YU W . Spatial deep learning for wireless scheduling [J ] . IEEE Journal on Selected Areas in Communications , 2019 , 37 ( 6 ): 1248 - 1261 .
FOUKAS X , MARINA M K , KONTOVASILIS K . Iris: deep reinforcement learning driven shared spectrum access architecture for indoor neutral-host small cells [J ] . IEEE Journal on Selected Areas in Communications , 2019 , 37 ( 8 ): 1820 - 1837 .
SUN W L , YUAN D , STRÖM E G , et al . Cluster-based radio resource management for D2D-supported safety-critical V2X communications [J ] . IEEE Transactions on Wireless Communications , 2016 , 15 ( 4 ): 2756 - 2769 .
LIANG L , YE H , LI G Y . Spectrum sharing in vehicular networks based on multi-agent reinforcement learning [J ] . IEEE Journal on Selected Areas in Communications , 2019 , 37 ( 10 ): 2282 - 2292 .
ALI A , AHMED M E , ALI F , et al . Non-parametric Bayesian channels clustering (NOBEL) scheme for wireless multimedia cognitive radio networks [J ] . IEEE Journal on Selected Areas in Communications , 2019 , 37 ( 10 ): 2293 - 2305 .
GENG Z , DENG H , HIMED B . Adaptive radar beamforming for interference mitigation in radar-wireless spectrum sharing [J ] . IEEE Signal Processing Letters , 2015 , 22 ( 4 ): 484 - 488 .
WANG J J , GUAN S H , JIANG C X , et al . Network association in machine-learning aided cognitive radar and communication co-design [J ] . IEEE Journal on Selected Areas in Communications , 2019 , 37 ( 10 ): 2322 - 2336 .
AVNER O , MANNOR S . Multi-user lax communications: a multi-armed bandit approach [C ] // Proceedings of the IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications . Piscataway : IEEE Press , 2016 : 1 - 9 .
BISTRITZ I , LESHEM A . Game of thrones: fully distributed learning for multiplayer bandits [J ] . Mathematics of Operations Research , 2021 , 46 ( 1 ): 159 - 178 .
ZAFARUDDIN S M , BISTRITZ I , LESHEM A , et al . Distributed learning for channel allocation over a shared spectrum [J ] . IEEE Journal on Selected Areas in Communications , 2019 , 37 ( 10 ): 2337 - 2349 .
DARAK S J , HANAWAL M K . Multi-player multi-armed bandits for stable allocation in heterogeneous ad-hoc networks [J ] . IEEE Journal on Selected Areas in Communications , 2019 , 37 ( 10 ): 2350 - 2363 .
KANG S , JOO C . Low-complexity learning for dynamic spectrum access in multi-user multi-channel networks [J ] . IEEE Transactions on Mobile Computing , 2020 , 20 ( 11 ): 3267 - 3281 .
SHI Y S , KUO W H , HUANG C W , et al . Cross-layer video synthesizing and antenna allocation scheme for multi-view video provisioning under massive MIMO networks [J ] . IEEE Transactions on Mobile Computing , 2024 , 23 ( 1 ): 327 - 340 .
LIN Y J , GAO H , XU W J , et al . Dynamic antenna configuration for 3D massive MIMO system via deep reinforcement learning [C ] // Proceedings of the 2020 IEEE 31st Annual International Symposium on Personal, Indoor and Mobile Radio Communications . Piscataway : IEEE Press , 2020 : 1 - 6 .
VANNELLA F , IAKOVIDIS G , AI HAKIM E , et al . Remote electrical tilt optimization via safe reinforcement learning [C ] // Proceedings of the 2021 IEEE Wireless Communications and Networking Conference (WCNC) . Piscataway : IEEE Press , 2021 : 1 - 7 .
BALEVI E , ANDREWS J G . Online antenna tuning in heterogeneous cellular networks with deep reinforcement learning [J ] . IEEE Transactions on Cognitive Communications and Networking , 2019 , 5 ( 4 ): 1113 - 1124 .
ZHAO Y , ZHANG K Q , HAN R . Multi-antenna tuning simulation platform by deep reinforcement learning [C ] // Proceedings of the 2019 IEEE International Conference on Signal, Information and Data Processing (ICSIDP) . Piscataway : IEEE Press , 2019 : 1 - 6 .
LIU X W , CHUAI G , WANG X , et al . QoE-driven antenna tuning in cellular networks with cooperative multi-agent reinforcement learning [J ] . IEEE Transactions on Mobile Computing , 2022 , 23 ( 2 ): 1186 - 1199 .
YAN H , DOMAE B W , CABRIC D . mmRAPID: machine learning assisted noncoherent compressive millimeter-wave beam alignment [C ] // Proceedings of the 4th ACM Press Workshop on Millimeter-Wave Networks and Sensing Systems . New York : ACM Press , 2020 : 1 - 6 .
NASIM I , SKRIMPONIS P , IBRAHIM A S , et al . Reinforcement learning of millimeter wave beamforming tracking over COSMOS platform [C ] // Proceedings of the 16th ACM Press Workshop on Wireless Network Testbeds , Experimental evaluation & Characterization . New York : ACM Press , 2022 : 40 - 44 .
MOORTHY S K , GUAN Z Y . Beam learning in mmWave/THz-band drone networks under in-flight mobility uncertainties [J ] . IEEE Transactions on Mobile Computing , 2022 , 21 ( 6 ): 1945 - 1957 .
ZOU J Q , CUI Y H , ZOU Z X , et al . Computer vision assisted mmWave beamforming for UAV-to-vehicle links [C ] // Proceedings of the 1st ACM Press MobiCom Workshop on Integrated Sensing and Communications Systems . New York : ACM Press , 2022 : 7 - 11 .
NIE J L , ZHOU Q , MU J S , et al . Vision and radar multimodal aided beam prediction: facilitating metaverse development [C ] // Proceedings of the 2nd Workshop on Integrated Sensing and Communications for Metaverse . New York : ACM Press , 2023 : 13 - 18 .
CUI Y H , NIE J L , YU T K , et al . Sensing-assisted communication beamforming based on multi-modal feature extraction for high-reliable IoV [C ] // Proceedings of the 3rd ACM Press MobiCom Workshop on Integrated Sensing and Communications Systems . New York : ACM Press , 2023 : 19 - 24 .
KRUNZ M , AYKIN I , SARKAR S , et al . Online reinforcement learning for beam tracking and rate adaptation in millimeter-wave systems [J ] . IEEE Transactions on Mobile Computing , 2024 , 23 ( 2 ): 1830 - 1845 .
ALI SHAH S H , SHARMA M , RANGAN S . LSTM-based multi-link prediction for mmWave and Sub-THz wireless systems [C ] // Proceedings of the ICC 2020 - 2020 IEEE International Conference on Communications (ICC) . Piscataway : IEEE Press , 2020 : 1 - 6 .
PALACIOS J , CASARI P , ASSASA H , et al . LEAP: location estimation and predictive handover with consumer-grade mmWave devices [C ] // Proceedings of the IEEE INFOCOM 2019 - IEEE Conference on Computer Communications . Piscataway : IEEE Press , 2019 : 2377 - 2385 .
NEEMA M , GOPI E S . Data driven approach for mmWave channel characteristics prediction using deep neural network [J ] . Wireless Personal Communications , 2021 , 120 ( 3 ): 2161 - 2177 .
LIU Y C , BLOUGH D M . Environment-aware link quality prediction for millimeter-wave wireless LANs [C ] // Proceedings of the 20th ACM Press International Symposium on Mobility Management and Wireless Access . New York : ACM Press , 2022 : 1 - 10 .
NAPARSTEK O , COHEN K . Deep multi-user reinforcement learning for distributed dynamic spectrum access [J ] . IEEE Transactions on Wireless Communications , 2019 , 18 ( 1 ): 310 - 323 .
WANG S X , LIU H P , GOMES P H , et al . Deep reinforcement learning for dynamic multichannel access in wireless networks [J ] . IEEE Transactions on Cognitive Communications and Networking , 2018 , 4 ( 2 ): 257 - 265 .
CHANG H H , SONG H , YI Y , et al . Distributive dynamic spectrum access through deep reinforcement learning: a reservoir computing-based approach [J ] . IEEE Internet of Things Journal , 2019 , 6 ( 2 ): 1938 - 1948 .
YU Y D , WANG T T , LIEW S C . Deep-reinforcement learning multiple access for heterogeneous wireless networks [C ] // Proceedings of the 2018 IEEE International Conference on Communications (ICC) . Piscataway : IEEE Press , 2018 : 1 - 7 .
YU Y D , LIEW S C , WANG T T . Multi-agent deep reinforcement learning multiple access for heterogeneous wireless networks with imperfect channels [J ] . IEEE Transactions on Mobile Computing , 2022 , 21 ( 10 ): 3718 - 3730 .
YU Y D , LIEW S C , WANG T T . Non-uniform time-step deep Q-network for carrier-sense multiple access in heterogeneous wireless networks [J ] . IEEE Transactions on Mobile Computing , 2021 , 20 ( 9 ): 2848 - 2861 .
TONI L , FROSSARD P . IRSA transmission optimization via online learning [J ] . ArXiv Prints , 2018 : arXiv: 1801.09060 .
NISIOTI E , THOMOS N . Decentralized reinforcement learning based MAC optimization [C ] // Proceedings of the 2018 IEEE 29th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC) . Piscataway : IEEE Press , 2018 : 1 - 5 .
NISIOTI E , THOMOS N . Robust coordinated reinforcement learning for MAC design in sensor networks [J ] . IEEE Journal on Selected Areas in Communications , 2019 , 37 ( 10 ): 2211 - 2224 .
JOSEPH I . Joint statistical and machine learning approach for practical data-driven assessment of user throughput quality in microcellular radio networks [J ] . Wireless Personal Communications , 2021 , 119 ( 2 ): 1661 - 1680 .
LIU X , ZHANG Y W . Application-aware fine-grained QoS framework for 5G and beyond [C ] // Proceedings of the 18th International Conference , BIC-TA 2023 . Singapore : Springer , 2024 : 342 - 356 .
ZHU H K , FAN H B , LUO X , et al . Intelligent timeout master: dynamic timeout for SDN-based data centers [C ] // Proceedings of the 2015 IFIP/IEEE International Symposium on Integrated Network Management (IM) . Piscataway : IEEE Press , 2015 : 734 - 737 .
ZHANG L L , WANG S , XU S Z , et al . TimeoutX: an adaptive flow table management method in software defined networks [C ] // Proceedings of the 2015 IEEE Global Communications Conference (GLOBECOM) . Piscataway : IEEE Press , 2015 : 1 - 6 .
HAQ F , NAAZ A , BANTUPALLI T V P K , et al . DRL-FTO: dynamic flow rule timeout optimization in SDN using deep reinforcement learning [C ] // Proceedings of the 16rh Asian Internet Engineering Conference . New York : ACM Press , 2021 : 41 - 48 .
FLEISCHER L K . Approximating fractional multicommodity flow independent of the number of commodities [C ] // Proceedings of the 40th Annual Symposium on Foundations of Computer Science . Piscataway : IEEE Press , 2002 : 24 - 31 .
XU Z Y , YAN F Y , SINGH R , et al . Teal: learning-accelerated optimization of WAN traffic engineering [C ] // Proceedings of the ACM Press SIGCOMM 2023 Conference . New York : ACM Press , 2023 : 378 - 393 .
CHEN L , LINGYS J , CHEN K , et al . AuTO: scaling deep reinforcement learning for datacenter-scale automatic traffic optimization [C ] // Proceedings of the 2018 Conference of the ACM Press Special Interest Group on Data Communication . New York : ACM Press , 2018 : 191 - 205 .
PÉREZ P F , FIANDRINO C , WIDMER J . Characterizing and modeling mobile networks user traffic at millisecond level [C ] // Proceedings of the 17th ACM Press Workshop on Wireless Network Testbeds , Experimental evaluation & Characterization . New York : ACM Press , 2023 : 64 - 71 .
ROY A , CHAPORKAR P , KARANDIKAR A . An on-line radio access technology selection algorithm in an LTE-WiFi network [C ] // Proceedings of the 2017 IEEE Wireless Communications and Networking Conference (WCNC) . Piscataway : IEEE Press , 2017 : 1 - 6 .
ROY A , BORKAR V , CHAPORKAR P , et al . Low complexity online radio access technology selection algorithm in LTE-WiFi HetNet [J ] . IEEE Transactions on Mobile Computing , 2020 , 19 ( 2 ): 376 - 389 .
CHINCHALI S , HU P , CHU T S , et al . Cellular network traffic scheduling with deep reinforcement learning [J ] . Thirty-Second AAAI Conference on Artificial Intelligence , 2018 , 32 ( 1 ): 766 - 774 .
SCIANCALEPORE V , SAMDANIS K , COSTA-PEREZ X , et al . Mobile traffic forecasting for maximizing 5G network slicing resource utilization [C ] // Proceedings of the IEEE INFOCOM 2017 - IEEE Conference on Computer Communications . Piscataway : IEEE Press , 2017 : 1 - 9 .
QIU C , ZHANG Y Y , FENG Z Y , et al . Spatio-temporal wireless traffic prediction with recurrent neural network [J ] . IEEE Wireless Communications Letters , 2018 , 7 ( 4 ): 554 - 557 .
ZHANG C T , ZHANG H X , YUAN D F , et al . Citywide cellular traffic prediction based on densely connected convolutional neural networks [J ] . IEEE Communications Letters , 2018 , 22 ( 8 ): 1656 - 1659 .
WANG X , ZHOU Z M , XIAO F , et al . Spatio-temporal analysis and prediction of cellular traffic in metropolis [J ] . IEEE Transactions on Mobile Computing , 2019 , 18 ( 9 ): 2190 - 2202 .
SUN F Y , WANG P H , ZHAO J Z , et al . Mobile data traffic prediction by exploiting time-evolving user mobility patterns [J ] . IEEE Transactions on Mobile Computing , 2022 , 21 ( 12 ): 4456 - 4470 .
YU L X , LI M , JIN W Q , et al . STEP: a spatio-temporal fine-granular user traffic prediction system for cellular networks [J ] . IEEE Transactions on Mobile Computing , 2021 , 20 ( 12 ): 3453 - 3466 .
YAO Y , GU B , SU Z , et al . MVSTGN: a multi-view spatial-temporal graph network for cellular traffic prediction [J ] . IEEE Transactions on Mobile Computing , 2023 , 22 ( 5 ): 2837 - 2849 .
POLESE M , JANA R , KOUNEV V , et al . Machine learning at the edge: a data-driven architecture with applications to 5G cellular networks [J ] . IEEE Transactions on Mobile Computing , 2021 , 20 ( 12 ): 3367 - 3382 .
LIU C , HE L T , XIONG G , et al . FS-Net: a flow sequence network for encrypted traffic classification [C ] // Proceedings of the IEEE INFOCOM 2019 - IEEE Conference on Computer Communications . Piscataway : IEEE Press , 2019 : 1171 - 1179 .
LIN K D , XU X L , GAO H H . TSCRNN: a novel classification scheme of encrypted traffic based on flow spatiotemporal features for efficient management of IIoT [J ] . Computer Networks , 2021 , 190 : 107974 .
ZHENG W B , GOU C , YAN L , et al . Learning to classify: a flow-based relation network for encrypted traffic classification [C ] // Proceedings of The Web Conference 2020 . New York : ACM Press , 2020 : 13 - 22 .
SONG Z X , ZHAO Z M , ZHANG F , et al . I 2 RNN: an incremental and interpretable recurrent neural network for encrypted traffic classification [J ] . IEEE Transactions on Dependable and Secure Computing (Early Access) , 2023 ( 99 ): 1 - 14 .
NASCITA A , CERASUOLO F , ACETO G , et al . Explainable mobile traffic classification: the case of incremental learning [C ] // Proceedings of the 2023 on Explainable and Safety Bounded, Fidelitous, Machine Learning for Networking . New York : ACM Press , 2023 : 25 - 31 .
ZHOU H , HU C M , YUAN Y , et al . Large language model (LLM) for telecommunications: a comprehensive survey on principles, key techniques, and opportunities [J ] . IEEE Communications Surveys & Tutorials (Early Access) , 2024 ( 99 ): 1 .
SHI Z L , LUKTARHAN N , SONG Y Y , et al . BFCN: a novel classification method of encrypted traffic based on BERT and CNN [J ] . Electronics , 2023 , 12 ( 3 ): 516 .
LIN X J , XIONG G , GOU G P , et al . ET-BERT: a contextualized datagram representation with pre-training transformers for encrypted traffic classification [C ] // Proceedings of the ACM Press Web Conference 2022 . New York : ACM Press , 2022 : 633 - 642 .
XU Z Y , TANG J , MENG J S , et al . Experience-driven networking: a deep reinforcement learning based approach [C ] // Proceedings of the IEEE INFOCOM 2018 - IEEE Conference on Computer Communications . Piscataway : IEEE Press , 2018 : 1871 - 1879 .
DI VALERIO V , LO PRESTI F , PETRIOLI C , et al . CARMA: channel-aware reinforcement learning-based multi-path adaptive routing for underwater wireless sensor networks [J ] . IEEE Journal on Selected Areas in Communications , 2019 , 37 ( 11 ): 2634 - 2647 .
SALEEM Y , YAU K A , MOHAMAD H , et al . Clustering and reinforcement-learning-based routing for cognitive radio networks [J ] . IEEE Wireless Communications , 2017 , 24 ( 4 ): 146 - 151 .
ZHAO L , WANG J D , LIU J J , et al . Routing for crowd management in smart cities: a deep reinforcement learning perspective [J ] . IEEE Communications Magazine , 2019 , 57 ( 4 ): 88 - 93 .
HE Q , WANG Y , WANG X W , et al . Routing optimization with deep reinforcement learning in knowledge defined networking [J ] . IEEE Transactions on Mobile Computing , 2024 , 23 ( 2 ): 1444 - 1455 .
HE B , WANG J Y , QI Q , et al . RTHop: real-time hop-by-hop mobile network routing by decentralized learning with semantic attention [J ] . IEEE Transactions on Mobile Computing , 2023 , 22 ( 3 ): 1731 - 1747 .
MONDAL R , TANG A L , BECKETT R , et al . What do LLMs need to synthesize correct router configurations? [C ] // Proceedings of the 22nd ACM Press Workshop on Hot Topics in Networks . New York : ACM Press , 2023 : 189 - 195 .
CHEN X , MÉRIAUX F , VALENTIN S . Predicting a user’s next cell with supervised learning based on channel states [C ] // Proceedings of the 2013 IEEE 14th Workshop on Signal Processing Advances in Wireless Communications (SPAWC) . Piscataway : IEEE Press , 2013 : 36 - 40 .
UNIYAL N , BRAVALHERI A , VASILAKOS X , et al . Intelligent mobile handover prediction for zero downtime edge application mobility [C ] // Proceedings of the 2021 IEEE Global Communications Conference (GLOBECOM) . Piscataway : IEEE Press , 2021 : 1 - 6 .
VIELHAUS C L , BUSCH J V S , GEUER P , et al . Handover predictions as an enabler for anticipatory service adaptations in next-generation cellular networks [C ] // Proceedings of the 20th ACM Press International Symposium on Mobility Management and Wireless Access . New York : ACM Press , 2022 : 19 - 27 .
XU Y , XU W J , WANG Z , et al . Load balancing for ultradense networks: a deep reinforcement learning-based approach [J ] . IEEE Internet of Things Journal , 2019 , 6 ( 6 ): 9399 - 9412 .
SIERRA FRANCO C A , DE MARCA J R B . Load balancing in self-organized heterogeneous LTE networks: a statistical learning approach [C ] // Proceedings of the 2015 7th IEEE Latin-American Conference on Communications (LATINCOM) . Piscataway : IEEE Press , 2015 : 1 - 5 .
ALSUHLI G , BANAWAN K , ATTIAH K , et al . Mobility load management in cellular networks: a deep reinforcement learning approach [J ] . IEEE Transactions on Mobile Computing , 2023 , 22 ( 3 ): 1581 - 1598 .
ALBANNA A , YOUSEFI’ZADEH H . Learning-constrained enhancement of cellular networks capacity [J ] . IEEE Transactions on Mobile Computing , 2019 , 18 ( 1 ): 153 - 164 .
YAJNANARAYANA V , RYDÉN H , HÉVIZI L . 5G handover using reinforcement learning [C ] // Proceedings of the 2020 IEEE 3rd 5G World Forum (5GWF) . Piscataway : IEEE Press , 2020 : 349 - 354 .
KARMAKAR R , KADDOUM G , CHATTOPADHYAY S . Mobility management in 5G and beyond: a novel smart handover with adaptive time-to-trigger and hysteresis margin [J ] . IEEE Transactions on Mobile Computing , 2022 , 22 ( 10 ): 5995 - 6010 .
SALHAB N , LANGAR R , RAHIM R , et al . Autonomous network slicing prototype using machine-learning-based forecasting for radio resources [J ] . IEEE Communications Magazine , 2021 , 59 ( 6 ): 73 - 79 .
TOSCANO M , GRUNWALD F , RICHART M , et al . Machine learning aided network slicing [C ] // Proceedings of the 2019 21st International Conference on Transparent Optical Networks (ICTON) . Piscataway : IEEE Press , 2019 : 1 - 4 .
LI R P , ZHAO Z F , SUN Q , et al . Deep reinforcement learning for resource management in network slicing [J ] . IEEE Access , 2018 , 6 : 74429 - 74441 .
YAN M , FENG G , ZHOU J H , et al . Intelligent resource scheduling for 5G radio access network slicing [J ] . IEEE Transactions on Vehicular Technology , 2019 , 68 ( 8 ): 7691 - 7703 .
YARKINA N , GAYDAMAKA A , MOLTCHANOV D , et al . Performance assessment of an ITU-T compliant machine learning enhancements for 5G RAN network slicing [J ] . IEEE Transactions on Mobile Computing , 2024 , 23 ( 1 ): 719 - 736 .
PAPAGIANNI C , MANGUES-BAFALLUY J , BERMUDEZ P , et al . 5Growth: AI-driven 5G for automation in vertical industries [C ] // Proceedings of the 2020 European Conference on Networks and Communications (EuCNC) . Piscataway : IEEE Press , 2020 : 17 - 22 .
WANG W L , LIANG C C , TANG L , et al . Federated multi-discriminator BiWGAN-GP based collaborative anomaly detection for virtualized network slicing [J ] . IEEE Transactions on Mobile Computing , 2023 , 22 ( 11 ): 6445 - 6459 .
WINSTEIN K , BALAKRISHNAN H . TCP ex machina: computer-generated congestion control [C ] // Proceedings of the ACM Press SIGCOMM 2013 conference on SIGCOMM . New York : ACM Press , 2013 : 123 - 134 .
YAN F Y , MA J , HILL G D , et al . Pantheon: the training ground for Internet congestion-control research [C ] // Proceedings of the 2018 USENIX Annual Technical Conference (USENIX ATC 18) . Berkeley : USENIX Association , 2020 : 731 - 743 .
JAY N , ROTMAN N H , GODFREY B , et al . A deep reinforcement learning perspective on Internet congestion control [C ] // Proceedings of the 36th International Conference on Machine Learning(ICML 2019) . ICML , 2019 : 5390 - 5399 .
ABBASLOO S , YEN C Y , CHAO H J . Classic meets modern: a pragmatic learning-based congestion control for the Internet [C ] // Proceedings of the Annual Conference of the ACM Press Special Interest Group on Data Communication on the Applications, Technologies, Architectures, and Protocols for Computer Communication . New York : ACM Press , 2020 : 632 - 647 .
ABBASLOO S , YEN C Y , CHAO H J . Wanna make your TCP scheme great for cellular networks? let machines do it for you! [J ] . IEEE Journal on Selected Areas in Communications , 2021 , 39 ( 1 ): 265 - 279 .
MA Y Q , TIAN H , LIAO X D , et al . Multi-objective congestion control [C ] // Proceedings of the Seventeenth European Conference on Computer Systems . New York : ACM Press , 2022 : 218 - 235 .
YEN C Y , ABBASLOO S , CHAO H J . Computers can learn from the heuristic designs and master Internet congestion control [C ] // Proceedings of the ACM Press SIGCOMM 2023 Conference . New York : ACM Press , 2023 : 255 - 274 .
NIE X H , ZHAO Y J , LI Z H , et al . Dynamic TCP initial windows and congestion control schemes through reinforcement learning [J ] . IEEE Journal on Selected Areas in Communications , 2019 , 37 ( 6 ): 1231 - 1247 .
AHMAD T , IYER S R , DIEZ L , et al . Learning congestion state for mmWave channels [C ] // Proceedings of the 3rd ACM Press Workshop on Millimeter-wave Networks and Sensing Systems . New York : ACM Press , 2019 : 19 - 25 .
LI W Z , ZHANG H , GAO S H , et al . SmartCC: a reinforcement learning approach for multipath TCP congestion control in heterogeneous networks [J ] . IEEE Journal on Selected Areas in Communications , 2019 , 37 ( 11 ): 2621 - 2633 .
XU Z Y , TANG J , YIN C X , et al . Experience-driven congestion control: when multi-path TCP meets deep reinforcement learning [J ] . IEEE Journal on Selected Areas in Communications , 2019 , 37 ( 6 ): 1325 - 1336 .
POKHREL S R , WALID A . Learning to harness bandwidth with multipath congestion control and scheduling [J ] . IEEE Transactions on Mobile Computing , 2023 , 22 ( 2 ): 996 - 1009 .
XU C Q , QIN J R , ZHANG P , et al . Reinforcement learning-based mobile AR/VR multipath transmission with streaming power spectrum density analysis [J ] . IEEE Transactions on Mobile Computing , 2022 , 21 ( 12 ): 4529 - 4540 .
HOOPER H . CCNP security VPN 642-648 official cert guide [M ] . 2nd ed . Indianapolis, Ind. : Cisco Press , 2012 .
BRUBAKER C , JANA S , RAY B , et al . Using frankencerts for automated adversarial testing of certificate validation in SSL/TLS implementations [C ] // Proceedings of the 2014 IEEE Symposium on Security and Privacy . Piscataway : IEEE Press , 2014 : 114 - 129 .
CHEN C , DIAO W R , ZENG Y P , et al . DRLgencert: deep learning-based automated testing of certificate verification in SSL/TLS implementations [C ] // Proceedings of the 2018 IEEE International Conference on Software Maintenance and Evolution (ICSME) . Piscataway : IEEE Press , 2018 : 48 - 58 .
ANDERSON B , PAUL S , MCGREW D . Deciphering malware’s use of TLS (without decryption) [J ] . Journal of Computer Virology and Hacking Techniques , 2018 , 14 ( 3 ): 195 - 211 .
ZHENG R F , LIU J Y , LI K , et al . Detecting malicious TLS network traffic based on communication channel features [C ] // Proceedings of the 2020 IEEE 8th International Conference on Information, Communication and Networks (ICICN) . Piscataway : IEEE Press , 2020 : 14 - 19 .
SHAIK T A , KATAOKA K . capsAEUL: Slow HTTP DoS attack detection using autoencoders through unsupervised learning [C ] // Proceedings of the Asian Internet Engineering Conference . New York : ACM Press , 2021 : 49 - 55 .
HIRAKAWA T , OGURA K , BISTA B B , et al . A defense method against distributed slow HTTP DoS attack [C ] // Proceedings of the 2016 19th International Conference on Network-Based Information Systems (NBiS) . Piscataway : IEEE Press , 2016 : 152 - 158 .
MURALEEDHARAN N , JANET B . A deep learning based HTTP slow DoS classification approach using flow data [J ] . ICT Express , 2021 , 7 ( 2 ): 210 - 214 .
BENTALEB A , TIMMERER C , BEGEN A C , et al . Bandwidth prediction in low-latency chunked streaming [C ] // Proceedings of the 29th ACM Press Workshop on Network and Operating Systems Support for Digital Audio and Video . New York : ACM Press , 2019 : 7 - 13 .
HUANG T Y , JOHARI R , MCKEOWN N , et al . A buffer-based approach to rate adaptation: evidence from a large video streaming service [C ] // Proceedings of the 2014 ACM Press conference on SIGCOMM . New York : ACM Press , 2014 : 187 - 198 .
KARAGKIOULES T , MEKURIA R , GRIFFIOEN D , et al . Online learning for low-latency adaptive streaming [C ] // Proceedings of the 11th ACM Press Multimedia Systems Conference . New York : ACM Press , 2020 : 315 - 320 .
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