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Deep Learning in Mobile and Wireless Networking: A Survey [article]

Chaoyun Zhang, Paul Patras, Hamed Haddadi
2019 arXiv   pre-print
One potential solution is to resort to advanced machine learning techniques to help managing the rise in data volumes and algorithm-driven applications.  ...  We first briefly introduce essential background and state-of-the-art in deep learning techniques with potential applications to networking.  ...  Deep Q learning Xu et al. [388] Traffic engineering Reinforcement learning Deep policy gradient Liu et al. [389] Base station sleep control Reinforcement learning Deep Q learning Zhao et al.  ... 
arXiv:1803.04311v3 fatcat:awuvyviarvbr5kd5ilqndpfsde

Deep Learning in Mobile and Wireless Networking: A Survey

Chaoyun Zhang, Paul Patras, Hamed Haddadi
2019 IEEE Communications Surveys and Tutorials  
One potential solution is to resort to advanced machine learning techniques, in order to help manage the rise in data volumes and algorithm-driven applications.  ...  We first briefly introduce essential background and state-of-theart in deep learning techniques with potential applications to networking.  ...  Deep Q learning Xu et al. [391] Traffic engineering Reinforcement learning Deep policy gradient Liu et al. [392] Base station sleep control Reinforcement learning Deep Q learning Zhao et al.  ... 
doi:10.1109/comst.2019.2904897 fatcat:xmmrndjbsfdetpa5ef5e3v4xda

2021 Index IEEE Transactions on Intelligent Transportation Systems Vol. 22

2021 IEEE transactions on intelligent transportation systems (Print)  
The Author Index contains the primary entry for each item, listed under the first author's name.  ...  ., +, TITS Jan. 2021 416-429 Forecasting A Hybrid Deep Learning Model With Attention-Based Conv-LSTM Net-works for Short-Term Traffic Flow Prediction.  ...  ., +, TITS Dec. 2021 7904-7913 STNN: A Spatio-Temporal Neural Network for Traffic Predictions.  ... 
doi:10.1109/tits.2021.3139738 fatcat:p2mkawtrsbaepj4zk24xhyl2oa

Deep Neural Mobile Networking [article]

Chaoyun Zhang
2020 arXiv   pre-print
In particular, deep learning based solutions can automatically extract features from raw data, without human expertise.  ...  This makes monitoring and managing the multitude of network elements intractable with existing tools and impractical for traditional machine learning algorithms that rely on hand-crafted feature engineering  ...  Convolutional LSTM (ConvLSTM) is a dedicated model for spatio-temporal data forecasting [211] .  ... 
arXiv:2011.05267v1 fatcat:yz2zp5hplzfy7h5kptmho7mbhe

Graph Neural Networks in IoT: A Survey [article]

Guimin Dong, Mingyue Tang, Zhiyuan Wang, Jiechao Gao, Sikun Guo, Lihua Cai, Robert Gutierrez, Bradford Campbell, Laura E. Barnes, Mehdi Boukhechba
2022 arXiv   pre-print
Continuous sensing generates massive amounts of data and presents challenges for machine learning.  ...  Deep learning models (e.g., convolution neural networks and recurrent neural networks) have been extensively employed in solving IoT tasks by learning patterns from multi-modal sensory data.  ...  For example, Yu et al. [305] propose Spatio-Temporal Graph Convolutional Networks (STGCN) for traffic forecasting.  ... 
arXiv:2203.15935v2 fatcat:jkqg5ukg5fezbewu5mr5hqsp4e

When 5G Meets Deep Learning: A Systematic Review

Guto Leoni Santos, Patricia Takako Endo, Djamel Sadok, Judith Kelner
2020 Algorithms  
In this context, deep learning models could be seen as one of the main tools that can be used to process monitoring data and automate decisions.  ...  We also discuss the main research challenges when using deep learning models in 5G scenarios and identify several issues that deserve further consideration.  ...  Acknowledgments: The authors would like to thank the Fundação de Amparo a Ciência e Tecnologia de Pernambuco (FACEPE) for funding this work through grant IBPG-0059-1.03/19.  ... 
doi:10.3390/a13090208 fatcat:bw3evog5xbc5jjf3bbdorda7zq

A Survey on Applications of Deep Learning in Cloud Radio Access Network

Rehenuma Tasnim Rodoshi, Wooyeol Choi
2021 IEEE Access  
Deep learning (DL) appears to be a feasible approach for facilitating the data processing capability, resource management in the cloud, and predicting dynamic traffic in cellular communication.  ...  In C-RAN, the data processing unit can be centralized and virtualized in data centers and can be shared among distributed base stations.  ...  an accurate spatio-temporal pattern of user traffic.  ... 
doi:10.1109/access.2021.3074180 fatcat:pki3glqnafcnrg4blog42zlaee

A Survey on 5G Radio Access Network Energy Efficiency: Massive MIMO, Lean Carrier Design, Sleep Modes, and Machine Learning [article]

David Lopez-Perez, Antonio De Domenico, Nicola Piovesan, Harvey Bao, Geng Xinli, Song Qitao, Merouane Debbah
2021 arXiv   pre-print
Then, as a main contribution, we survey in detail -- from a theoretical and a practical viewpoint -- the main energy efficiency enabling technologies that 3GPP NR provides, together with their main benefits  ...  Special attention is paid to four key enabling technologies, i.e., massive multiple-input multiple-output (MIMO), lean carrier design, and advanced idle modes, together with the role of artificial intelligence  ...  Figure 22 . 22 Relationships among deep reinforcement learning, deep learning, reinforcement learning, supervised learning, unsupervised learning, machine learning, and AI. 1 ) 1 Statistical-based methods  ... 
arXiv:2101.11246v2 fatcat:rm6e5ubwdnf25kvgdufvtd5uam

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks [article]

Jingjing Wang and Chunxiao Jiang and Haijun Zhang and Yong Ren and Kwang-Cheng Chen and Lajos Hanzo
2020 arXiv   pre-print
Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning.  ...  Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making.  ...  [327] presented a hybrid deep learning aided structure for spatio-temporal traffic modeling and prediction in cellular networks by mining information from the China Mobile dataset.  ... 
arXiv:1902.01946v2 fatcat:7bveg6rmjfga5mftdkr3mst2qa

Collaborative duty cycling strategies in energy harvesting sensor networks

James Long, Oral Büyüköztürk
2019 Computer-Aided Civil and Infrastructure Engineering  
The deep reinforcement learning agent is shown to outperform baseline approaches on both seen and unseen data.  ...  We then train the proposed reinforcement learning agent to learn optimal node selection strategies through interaction with the simulation environment.  ...  must be collected at a base station.  ... 
doi:10.1111/mice.12522 fatcat:immc2xhz6rdrdnbafno5urenui

Resource Management in Cloud Radio Access Network: Conventional and New Approaches

Rehenuma Tasnim Rodoshi, Taewoon Kim, Wooyeol Choi
2020 Sensors  
Cloud radio access network (C-RAN) is a promising mobile wireless sensor network architecture to address the challenges of ever-increasing mobile data traffic and network costs.  ...  Then both of the techniques are further classified and analyzed based on the strategies used in the studies.  ...  Power Control Schemes Xu et al. proposed a two-step deep reinforcement learning (DRL)-based framework for dynamic allocation of resources in C-RAN, to minimize the total power consumption while satisfying  ... 
doi:10.3390/s20092708 pmid:32397540 fatcat:hwomwvzo4ngzzj3ci5bxs6atma

International Workshop on Agent-Based Modelling of Urban Systems (ABMUS) Proceedings: 2022 [article]

Nick Malleson, Le-Minh Kieu, Koen H. van Dam, JASON THOMPSON, Alison Heppenstall, Jiaqi Ge
2022 figshare.com  
Proceedings of the 2022 Agent-Based Modelling of Urban Systems (ABMUS) workshop. Part of the AAMAS conference. Further details: http://modelling-urban-systems.com/abmus2022/  ...  We are currently exploring BN, GAN and various other methods in synthetic data modelling and generation for sequential transport data. Acknowledgements.  ...  This structure expresses the individuality of each person in as much as their activities are associated with travelling.  ... 
doi:10.6084/m9.figshare.19733800.v1 fatcat:sb2ynno5vfd4ffrml46sngr6by

A Survey on Machine Learning-Based Performance Improvement of Wireless Networks: PHY, MAC and Network Layer

Merima Kulin, Tarik Kazaz, Eli De Poorter, Ingrid Moerman
2021 Electronics  
First, the related work and paper contributions are discussed, followed by providing the necessary background on data-driven approaches and machine learning to help non-machine learning experts understand  ...  This paper presents a systematic and comprehensive survey that reviews the latest research efforts focused on machine learning (ML) based performance improvement of wireless networks, while considering  ...  For instance, the functional base station sleeping mechanism may be adapted by utilizing knowledge about future traffic demands, which are in [266] predicted based on a NN model.  ... 
doi:10.3390/electronics10030318 fatcat:p6jslz26dvfvbpnqzmrpptloim

A survey on Machine Learning-based Performance Improvement of Wireless Networks: PHY, MAC and Network layer [article]

Merima Kulin, Tarik Kazaz, Ingrid Moerman, Eli de Poorter
2020 arXiv   pre-print
First, the related work and paper contributions are discussed, followed by providing the necessary background on data-driven approaches and machine learning for non-machine learning experts to understand  ...  This paper provides a systematic and comprehensive survey that reviews the latest research efforts focused on machine learning (ML) based performance improvement of wireless networks, while considering  ...  For instance, the functional base station sleeping mechanism may be adapted by utilizing knowledge about future traffic demands, which are in [256] predicted based on a NN model.  ... 
arXiv:2001.04561v2 fatcat:kbbvgechmjgwla6noolrf6ds7u

2021 Index IEEE Internet of Things Journal Vol. 8

2021 IEEE Internet of Things Journal  
The Author Index contains the primary entry for each item, listed under the first author's name.  ...  ., +, JIoT Nov. 15, 2021 16598-16612 Reliable Cybertwin-Driven Concurrent Multipath Transfer With Deep Reinforcement Learning.  ...  ., +, JIoT Jan. 15, 2021 684-694 CDDPG: A Deep-Reinforcement-Learning-Based Approach for Electric Vehicle Charging Control.  ... 
doi:10.1109/jiot.2022.3141840 fatcat:42a2qzt4jnbwxihxp6rzosha3y
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