State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems

Fadlullah, Zubair Md.; Tang, Fengxiao; Mao, Bomin; Kato, Nei; Akashi, Osamu; Inoue, Takeru; Mizutani, Kimihiro

doi:10.1109/comst.2017.2707140

Cited by 717 publications

(302 citation statements)

References 172 publications

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“…Specifically, by deriving proper paths for data transmission, transmission delay and other types of performance can be optimized. Recently, machine learning has emerged as a breakthrough for providing efficient routing protocols to enhance the overall network performance [27]. In this vein, we provide a vivid summarization on novel machine learning based routing schemes.…”

Section: Machine Learning Based Routingmentioning

confidence: 99%

“…To facilitate future applications of machine learning, challenges and open issues are identified. Overall, this survey aims to fill the gaps found in the previous papers [13]- [27], and our contributions are threefold: 1) Popular machine learning techniques utilized in wireless networks are comprehensively summarized including their basic principles and general applications, which are classified into supervised learning, unsupervised learning, reinforcement learning, (deep) NNs and transfer learning. Note that (deep) NNs and transfer learning are separately highlighted because of their increasing importance to wireless communication systems.…”

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confidence: 99%

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Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues

Sun

Peng

Zhou

et al. 2019

IEEE Commun. Surv. Tutorials

516

290

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As a key technique for enabling artificial intelligence, machine learning (ML) is capable of solving complex problems without explicit programming. Motivated by its successful applications to many practical tasks like image recognition, both industry and the research community have advocated the applications of ML in wireless communication. This paper comprehensively surveys the recent advances of the applications of ML in wireless communication, which are classified as: resource management in the MAC layer, networking and mobility management in the network layer, and localization in the application layer. The applications in resource management further include power control, spectrum management, backhaul management, cache management, beamformer design and computation resource management, while ML based networking focuses on the applications in clustering, base station switching control, user association and routing. Moreover, literatures in each aspect is organized according to the adopted ML techniques. In addition, several conditions for applying ML to wireless communication are identified to help readers decide whether to use ML and which kind of ML techniques to use, and traditional approaches are also summarized together with their performance comparison with ML based approaches, based on which the motivations of surveyed literatures to adopt ML are clarified. Given the extensiveness of the research area, challenges and unresolved issues are presented to facilitate future studies, where ML based network slicing, infrastructure update to support ML based paradigms, open data sets and platforms for researchers, theoretical guidance for ML implementation and so on are discussed.

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Section: Machine Learning Based Routingmentioning

confidence: 99%

mentioning

confidence: 99%

Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues

Sun

Peng

Zhou

et al. 2019

IEEE Commun. Surv. Tutorials

516

290

View full text Add to dashboard Cite

show abstract

“…All indicators point towards an even wider use of deep learning in various fields. Deep learning has already found its application in transportation and greenhouse-gas emission control [68], traffic control [69], text classification [8,70], object detection [71], speech detection [72,73], translation [74] and in other fields. These applications were not so represented in the past.…”

Section: Research Questionsmentioning

confidence: 99%

Deep Learning and Medical Diagnosis: A Review of Literature

Bakator

Radosav

2018

MTI

398

159

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Abstract:In this review the application of deep learning for medical diagnosis is addressed. A thorough analysis of various scientific articles in the domain of deep neural networks application in the medical field has been conducted. More than 300 research articles were obtained, and after several selection steps, 46 articles were presented in more detail. The results indicate that convolutional neural networks (CNN) are the most widely represented when it comes to deep learning and medical image analysis. Furthermore, based on the findings of this article, it can be noted that the application of deep learning technology is widespread, but the majority of applications are focused on bioinformatics, medical diagnosis and other similar fields.

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“…There have been numerous works about applications of ar-tificial intelligence and machine learning in wireless networks [17]. In [18], the resource allocation schemes for vehicleto-vehicle (V2V) communications are investigated.…”

Section: A Related Workmentioning

confidence: 99%

Mode Selection and Resource Allocation in Sliced Fog Radio Access Networks: A Reinforcement Learning Approach

Xiang

Peng

Sun

et al. 2020

IEEE Trans. Veh. Technol.

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The mode selection and resource allocation in fog radio access networks (F-RANs) have been advocated as key techniques to improve spectral and energy efficiency. In this paper, we investigate the joint optimization of mode selection and resource allocation in uplink F-RANs, where both of the traditional user equipments (UEs) and fog UEs are served by constructed network slice instances. The concerned optimization is formulated as a mixed-integer programming problem, and both the orthogonal and multiplexed subchannel allocation strategies are proposed to guarantee the slice isolation. Motivated by the development of machine learning, two reinforcement learning based algorithms are developed to solve the original high complexity problem under traditional and fog UEs' specific performance requirements. The basic idea of the proposals is to generate a good mode selection policy according to the immediate reward fed back by an environment. Simulation results validate the benefits of our proposed algorithms and show that a tradeoff between system power consumption and queue delay can be achieved.Index Terms-fog radio access network, network slicing, reinforcement learning.

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State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems

Cited by 717 publications

References 172 publications

Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues

Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues

Deep Learning and Medical Diagnosis: A Review of Literature

Mode Selection and Resource Allocation in Sliced Fog Radio Access Networks: A Reinforcement Learning Approach

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