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Distributed Intelligence in Wireless Networks [article]

Xiaolan Liu and Jiadong Yu and Yuanwei Liu and Yue Gao and Toktam Mahmoodi and Sangarapillai Lambotharan and Danny H. K. Tsang
2022 arXiv   pre-print
of native-AI wireless networks, on the AI-enabled edge computing, on the design of distributed learning architectures for heterogeneous networks, on the communication-efficient technologies to support  ...  A blend of edge computing and Artificial Intelligence (AI) techniques could optimally shift the resourceful computation servers closer to the network edge, which provides the support for advanced AI applications  ...  with the channel gain as the input and the output is the offloading decision [130] . 2) Imitation Learning for Computation Offloading: Another promising ML technique, imitation learning, has also been  ... 
arXiv:2208.00545v1 fatcat:znfxcsceifevvbccog4yz5mkji

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

Chaoyun Zhang, Paul Patras, Hamed Haddadi
2019 arXiv   pre-print
Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains.  ...  In this paper we bridge the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas.  ...  This framework further involves an optimizer, which instructs mobile devices to offload videos, in order to reduce query response time.  ... 
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  
Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains.  ...  In this paper we bridge the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas.  ...  , where we use a two-dimensional (2D) Convolutional Neural Network (CNN) as an example.  ... 
doi:10.1109/comst.2019.2904897 fatcat:xmmrndjbsfdetpa5ef5e3v4xda

Smart Anti-jamming Mobile Communication for Cloud and Edge-Aided UAV Network

2020 KSII Transactions on Internet and Information Systems  
In this article, we propose a novel cloud and edge-aided mobile communication scheme for low-cost UAV network against smart jamming.  ...  In the operation of this communication scheme, UAVs need to offload massive computing tasks to the cloud or the edge servers.  ...  It is worth noting that the research on anti-smart jamming is mainly concentrated in wireless networks, and there is almost no related research on UAV networks, except for UAV-aided networks.  ... 
doi:10.3837/tiis.2020.12.004 fatcat:osdb3en4freebgfxzzghzevxma

Green Internet of Vehicles (IoV) in the 6G Era: Toward Sustainable Vehicular Communications and Networking [article]

Junhua Wang, Kun Zhu, Ekram Hossain
2021 arXiv   pre-print
There has been an upsurge of interest to develop the green IoV towards sustainable vehicular communication and networking in the 6G era.  ...  With the emergence of the sixth generation (6G) communications technologies, massive network infrastructures will be densely deployed and the number of network nodes will increase exponentially, leading  ...  The authors in [113] propose an imitation learning-based online task scheduling algorithm for offloading end users' computation tasks to cooperative vehicles.  ... 
arXiv:2108.11879v1 fatcat:l3fidzspwbhi5mmw55dds7vddu

Edge Network Optimization Based on AI Techniques: A Survey

Mitra Pooyandeh, Insoo Sohn
2021 Electronics  
This paper describes the role of AI in a network edge. Moreover, this paper elaborates and discusses the optimization methods for an edge network based on AI techniques.  ...  The network edge is becoming a new solution for reducing latency and saving bandwidth in the Internet of Things (IoT) network.  ...  The SVM in a high dimensional space separates clearly two or more groups of data with a hyperplane.  ... 
doi:10.3390/electronics10222830 fatcat:kddhl7usq5aj5g3mr623lb24hm

Applications of Deep Reinforcement Learning in Communications and Networking: A Survey [article]

Nguyen Cong Luong, Dinh Thai Hoang, Shimin Gong, Dusit Niyato, Ping Wang, Ying-Chang Liang, Dong In Kim
2018 arXiv   pre-print
Furthermore, we present applications of deep reinforcement learning for traffic routing, resource sharing, and data collection.  ...  The issues include dynamic network access, data rate control, wireless caching, data offloading, network security, and connectivity preservation which are all important to next generation networks such  ...  The mobile user selects nearby cloudlets within D2D communication range for task offloading.  ... 
arXiv:1810.07862v1 fatcat:qc3mqk2norazvc2xnynau6bqzu

Applications of Deep Reinforcement Learning in Communications and Networking: A Survey

Nguyen Cong Luong, Dinh Thai Hoang, Shimin Gong, Dusit Niyato, Ping Wang, Ying-Chang Liang, Dong In Kim
2019 IEEE Communications Surveys and Tutorials  
Furthermore, we present applications of deep reinforcement learning for traffic routing, resource sharing, and data collection.  ...  The issues include dynamic network access, data rate control, wireless caching, data offloading, network security, and connectivity preservation which are all important to next generation networks such  ...  The mobile user selects nearby cloudlets within D2D communication range for task offloading.  ... 
doi:10.1109/comst.2019.2916583 fatcat:5owsswhhrbctnirdtxre6mhv24

Federated Learning in Mobile Edge Networks: A Comprehensive Survey [article]

Wei Yang Bryan Lim, Nguyen Cong Luong, Dinh Thai Hoang, Yutao Jiao, Ying-Chang Liang, Qiang Yang, Dusit Niyato, Chunyan Miao
2020 arXiv   pre-print
FL can serve as an enabling technology in mobile edge networks since it enables the collaborative training of an ML model and also enables DL for mobile edge network optimization.  ...  However, conventional enabling technologies for ML at mobile edge networks still require personal data to be shared with external parties, e.g., edge servers.  ...  learning at mobile edge networks.  ... 
arXiv:1909.11875v2 fatcat:a2yxlq672needkejenu4j3izyu

Edge Learning for B5G Networks with Distributed Signal Processing: Semantic Communication, Edge Computing, and Wireless Sensing [article]

Wei Xu, Zhaohui Yang, Derrick Wing Kwan Ng, Marco Levorato, Yonina C. Eldar, M'erouane Debbah
2022 arXiv   pre-print
In addition, from the viewpoint of information theory, we identify fundamental open problems of characterizing rate regions for communication networks supporting distributed learning-and-computing tasks  ...  In particular, typical performance metrics for dual-functional learning and communication networks are discussed.  ...  Typically, in an edge network, UAV with high mobility can assist mobile edge computing (MEC) in offloading computationally intensive tasks from IoT devices.  ... 
arXiv:2206.00422v1 fatcat:osp426emrngi3bvye6fmk7kqce

Application of deep learning algorithms and architectures in the new generation of mobile networks

Dejan Dasic, Miljan Vucetic, Nemanja Ilic, Milos Stankovic, Marko Beko
2021 Serbian Journal of Electrical Engineering  
The paper continues with an overview of applications and services related to the new generation of mobile networks that employ deep learning methods.  ...  Having firstly presented the background of deep learning and related technologies, the paper goes on to present the architectures used for deployment of deep learning in mobile networks.  ...  A novel deep learning framework is suggested in [61] in order to perform multi-task audio sensing.  ... 
doi:10.2298/sjee2103397d fatcat:n3hduljspfbt3mkq2zdzbae72u

Artificial Intelligence for UAV-Enabled Wireless Networks: A Survey

Mohamed-Amine Lahmeri, Mustafa A. Kishk, Mohamed-Slim Alouini
2021 IEEE Open Journal of the Communications Society  
Their mobility and their ability to establish line of sight (LOS) links with the users made them key solutions for many potential applications.  ...  We also highlight the limits of the existing works and outline some potential future applications of AI for UAVs networks.  ...  For example, the authors in [187] proposed a secure FL framework for a mobile crowdsensing application assisted by a UAV-network.  ... 
doi:10.1109/ojcoms.2021.3075201 fatcat:4q6cl2sz7nha5mijm6fmomv3fi

Distributed Resource Scheduling for Large-Scale MEC Systems: A Multi-Agent Ensemble Deep Reinforcement Learning with Imitation Acceleration [article]

Feibo Jiang and Li Dong and Kezhi Wang and Kun Yang and Cunhua Pan
2020 arXiv   pre-print
Finally, an imitation acceleration scheme is presented to pre-train all the agents, which can significantly accelerate the learning process of the proposed framework through learning the professional experience  ...  We consider the optimization of distributed resource scheduling to minimize the sum of task latency and energy consumption for all the Internet of things devices (IoTDs) in a large-scale mobile edge computing  ...  Imitation learning focuses on imitating human learning or expert demonstration for controlling the behaviour of the agent, which can help DRL reduce the time required to learn by an agent to a great extent  ... 
arXiv:2005.12364v1 fatcat:tnitv7tq7zgyfn3caenvrybu74

LORM: Learning to Optimize for Resource Management in Wireless Networks with Few Training Samples [article]

Yifei Shen, Yuanming Shi, Jun Zhang, Khaled B. Letaief
2019 arXiv   pre-print
To further address the task mismatch problem, we develop a transfer learning method via self-imitation in LORM, named LORM-TL, which can quickly adapt a pre-trained machine learning model to the new task  ...  In this paper, to reduce the sample complexity and address the feasibility issue, we propose a framework of Learning to Optimize for Resource Management (LORM).  ...  Then, we introduce an approach called self-imitation to address the problems for fine-tuning. Fine-tuning is the most frequently employed method for transfer learning in neural networks.  ... 
arXiv:1812.07998v2 fatcat:54v7y5f7lrablkgazuhejneuym

Deep Neural Mobile Networking [article]

Chaoyun Zhang
2020 arXiv   pre-print
in mobile networks.  ...  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  ...  Therefore, both lifelong learning and transfer learning are essential for ap- Learning Task 1 Learning task 2 Learning Task n ...  ... 
arXiv:2011.05267v1 fatcat:yz2zp5hplzfy7h5kptmho7mbhe
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