A tenant-driven slicing enforcement scheme based on Pervasive Intelligence in the Radio Access Network

[1]  Lamia Chaari Fourati,et al.  An Overview of Interslice and Intraslice Resource Allocation in B5G Telecommunication Networks , 2022, IEEE Transactions on Network and Service Management.

[2]  Giuseppe Piro,et al.  Anticipatory Allocation of Communication and Computational Resources at the Edge Using Spatio-Temporal Dynamics of Mobile Users , 2021, IEEE Transactions on Network and Service Management.

[3]  Pantelis A. Frangoudis,et al.  Data-Driven RAN Slicing Mechanisms for 5G and Beyond , 2021, IEEE Transactions on Network and Service Management.

[4]  Giuseppe Piro,et al.  Deep reinforcement learning‐aided RAN slicing enforcement supporting latency sensitive services in B5G networks , 2021, Internet Technol. Lett..

[5]  Gary Boudreau,et al.  Intelligent Radio Access Network Slicing for Service Provisioning in 6G: A Hierarchical Deep Reinforcement Learning Approach , 2021, IEEE Transactions on Communications.

[6]  Ren-Hung Hwang,et al.  Security and Privacy for 6G: A Survey on Prospective Technologies and Challenges , 2021, IEEE Communications Surveys & Tutorials.

[7]  Weihua Zhuang,et al.  Joint RAN Slicing and Computation Offloading for Autonomous Vehicular Networks: A Learning-Assisted Hierarchical Approach , 2021, IEEE Open Journal of Vehicular Technology.

[8]  Michele Rossi,et al.  Mobile Traffic Classification Through Physical Control Channel Fingerprinting: A Deep Learning Approach , 2021, IEEE Transactions on Network and Service Management.

[9]  Andrea Zanella,et al.  Using Distributed Reinforcement Learning for Resource Orchestration in a Network Slicing Scenario , 2021, IEEE/ACM Transactions on Networking.

[10]  Christos V. Verikoukis,et al.  5G RAN Slicing: Dynamic Single Tenant Radio Resource Orchestration for eMBB Traffic within a Multi-Slice Scenario , 2021, IEEE Communications Magazine.

[11]  Baojia Li,et al.  You Calculate and I Provision: A DRL-Assisted Service Framework to Realize Distributed and Tenant-Driven Virtual Network Slicing , 2021, Journal of Lightwave Technology.

[12]  Shunliang Zhang,et al.  Towards artificial intelligence enabled 6G: State of the art, challenges, and opportunities , 2020, Comput. Networks.

[13]  Xu Li,et al.  Dynamic RAN Slicing for Service-Oriented Vehicular Networks via Constrained Learning , 2020, IEEE Journal on Selected Areas in Communications.

[14]  F. Yu,et al.  Utility Optimization for Resource Allocation in Edge Network Slicing Using DRL , 2020, Global Communications Conference.

[15]  Victor C. M. Leung,et al.  Slice Reconfiguration Based on Demand Prediction with Dueling Deep Reinforcement Learning , 2020, GLOBECOM 2020 - 2020 IEEE Global Communications Conference.

[16]  Xianbin Wang,et al.  An intelligent self-sustained RAN slicing framework for diverse service provisioning in 5G-beyond and 6G networks , 2020 .

[17]  Giuseppe Piro,et al.  Cascaded WLAN-FWA Networking and Computing Architecture for Pervasive In-Home Healthcare , 2020, IEEE Wireless Communications.

[18]  Tao Jiang,et al.  Pervasive intelligent endogenous 6G wireless systems: Prospects, theories and key technologies , 2020, Digit. Commun. Networks.

[19]  Giuseppe Piro,et al.  Multi-Task Learning at the Mobile Edge: An Effective Way to Combine Traffic Classification and Prediction , 2020, IEEE Transactions on Vehicular Technology.

[20]  Zhifeng Zhao,et al.  The LSTM-Based Advantage Actor-Critic Learning for Resource Management in Network Slicing With User Mobility , 2020, IEEE Communications Letters.

[21]  Ahmad Awada,et al.  Slice Management in Radio Access Network via Deep Reinforcement Learning , 2020, 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring).

[22]  Hiroshi Mineno,et al.  Flexible Resource Block Allocation to Multiple Slices for Radio Access Network Slicing Using Deep Reinforcement Learning , 2020, IEEE Access.

[23]  Marco Fiore,et al.  DeepCog: Optimizing Resource Provisioning in Network Slicing With AI-Based Capacity Forecasting , 2020, IEEE Journal on Selected Areas in Communications.

[24]  Kezhi Wang,et al.  Stacked Autoencoder-Based Deep Reinforcement Learning for Online Resource Scheduling in Large-Scale MEC Networks , 2020, IEEE Internet of Things Journal.

[25]  Andrew Hines,et al.  5G network slicing using SDN and NFV- A survey of taxonomy, architectures and future challenges , 2019, Comput. Networks.

[26]  Baojia Li,et al.  DRL-Based Network Orchestration to Realize Cooperative, Distributed and Tenant-Driven Virtual Network Slicing , 2019, 2019 Asia Communications and Photonics Conference (ACP).

[27]  Lena Wosinska,et al.  Reinforcement Learning for Slicing in a 5G Flexible RAN , 2019, Journal of Lightwave Technology.

[28]  Giuseppe Piro,et al.  Architecting RAN Slicing for URLLC: Design Decisions and Open Issues , 2019, 2019 IEEE/ACM 23rd International Symposium on Distributed Simulation and Real Time Applications (DS-RT).

[29]  Wansu Lim,et al.  Machine Learning for 5G/B5G Mobile and Wireless Communications: Potential, Limitations, and Future Directions , 2019, IEEE Access.

[30]  Zhu Han,et al.  Coverage Analysis of Integrated Sub-6GHz-mmWave Cellular Networks With Hotspots , 2019, IEEE Transactions on Communications.

[31]  Sameer Sharma,et al.  RAN Resource Usage Prediction for a 5G Slice Broker , 2019, MobiHoc.

[32]  Ahmed Alkhateeb,et al.  Deep Reinforcement Learning for 5G Networks: Joint Beamforming, Power Control, and Interference Coordination , 2019, IEEE Transactions on Communications.

[33]  Zhiguo Ding,et al.  A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art , 2019, IEEE Access.

[34]  Daochen Zha,et al.  Experience Replay Optimization , 2019, IJCAI.

[35]  Tommaso Melodia,et al.  Toward Operator-to-Waveform 5G Radio Access Network Slicing , 2019, IEEE Communications Magazine.

[36]  Xianfu Chen,et al.  GAN-Based Deep Distributional Reinforcement Learning for Resource Management in Network Slicing , 2019, 2019 IEEE Global Communications Conference (GLOBECOM).

[37]  Mihaela van der Schaar,et al.  Machine Learning in the Air , 2019, IEEE Journal on Selected Areas in Communications.

[38]  Shunliang Zhang,et al.  An Overview of Network Slicing for 5G , 2019, IEEE Wireless Communications.

[39]  Jordi Pérez-Romero,et al.  An Efficient RAN Slicing Strategy for a Heterogeneous Network With eMBB and V2X Services , 2019, IEEE Access.

[40]  Giuseppe Aceto,et al.  Mobile Encrypted Traffic Classification Using Deep Learning: Experimental Evaluation, Lessons Learned, and Challenges , 2019, IEEE Transactions on Network and Service Management.

[41]  Antonio Capone,et al.  Dynamic Resource Trading in Sliced Mobile Networks , 2019, IEEE Transactions on Network and Service Management.

[42]  Sana Ben Jemaa,et al.  5G RAN Slicing for Verticals: Enablers and Challenges , 2019, IEEE Communications Magazine.

[43]  Andres Garcia-Saavedra,et al.  Overbooking network slices through yield-driven end-to-end orchestration , 2018, CoNEXT.

[44]  Ying-Chang Liang,et al.  Applications of Deep Reinforcement Learning in Communications and Networking: A Survey , 2018, IEEE Communications Surveys & Tutorials.

[45]  Mehdi Bennis,et al.  Multi-Tenant Cross-Slice Resource Orchestration: A Deep Reinforcement Learning Approach , 2018, IEEE Journal on Selected Areas in Communications.

[46]  Xianfu Chen,et al.  Deep Reinforcement Learning for Resource Management in Network Slicing , 2018, IEEE Access.

[47]  Hamed Haddadi,et al.  Deep Learning in Mobile and Wireless Networking: A Survey , 2018, IEEE Communications Surveys & Tutorials.

[48]  Philip Bachman,et al.  Deep Reinforcement Learning that Matters , 2017, AAAI.

[49]  Honggang Zhang,et al.  Network slicing as a service: enabling enterprises' own software-defined cellular networks , 2016, IEEE Communications Magazine.

[50]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[51]  John G. Carney,et al.  The Epoch Interpretation of Learning , 1998 .

[52]  Bruce W. Schmeiser,et al.  Improving model accuracy using optimal linear combinations of trained neural networks , 1995, IEEE Trans. Neural Networks.

[53]  Amirhosein Taherkordi,et al.  Deep Learning for Network Traffic Monitoring and Analysis (NTMA): A Survey , 2021, Comput. Commun..

[54]  Dushantha Nalin K. Jayakody,et al.  A Survey on Security and Privacy of 5G Technologies: Potential Solutions, Recent Advancements, and Future Directions , 2020, IEEE Communications Surveys & Tutorials.