Towards Energy-Efficient Wireless Networking in the Big Data Era: A Survey

Cao, Xianghui; Liu, Lu; Cheng, Yu; Shen, Xuemin

doi:10.1109/comst.2017.2771534

Cited by 84 publications

(55 citation statements)

References 211 publications

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“…Moreover, authors in [19] and [20] focus on the potentials of machine learning in enabling the self-organization of cellular networks with the perspectives of self-configuration, self-healing and self-optimization. To achieve high energy efficiency in wireless networks, related promising approaches based on big data are summarized in [21]. In [22], a comprehensive tutorial on the applications of neural networks (NNs) is provided, which presents the basic architectures and training procedures of different types of NNs, and several typical application scenarios are identified.…”

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

518

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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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

518

290

View full text Add to dashboard Cite

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“…• The wireless data torrance may be conveniently managed by the big data processing capability of M-L [17]. For example, the tele-traffic volume generated by on-demand information and entertainment is predicted to substantially increase over the next decade, and an average smart phone may generate as much as 4.4 GB data per month by the year 2020 [18]- [20]. The massive amount of data constitutes a large training set, which can be statistically exploited for data-mining as well as for classification and for prediction with the aid of ML algorithms.…”

Section: A Motivationmentioning

confidence: 99%

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

Wang

Jiang

Zhang

et al. 2020

IEEE Commun. Surv. Tutorials

469

200

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Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (Het-Nets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.

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“…Energy saved could be immense for high-demand content of significant volume. In [7], a comprehensive survey of data management techniques, including local caching, is given. This technique is only applicable to networks with uniquely identifiable content in high demand.…”

Section: Efficient Storagementioning

confidence: 99%

“…Several EE surveys for wireless networks which exist in recent literature focus on either one or more aspects of wireless networks, on specific applications, or specific energyefficient techniques. Cao et al [7], for example, give a survey focused on big-data-based energy-efficient technologies in both high-and low-rate networks, and Anastasi et al [8] focus on wireless sensor networks (WSNs) EE. Recently Khan et al [9] and Buzzi et al [10] present surveys on EE in WSNs and 5G, respectively.…”

Section: Introductionmentioning

confidence: 99%

Energy Efficient Design Techniques in Next-Generation Wireless Communication Networks: Emerging Trends and Future Directions

Ogbebor

Imoize

Atayero

2020

Wireless Communications and Mobile Computing

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The projected rise in wireless communication traffic has necessitated the advancement of energy-efficient (EE) techniques for the design of wireless communication systems, given the high operating costs of conventional wireless cellular networks, and the scarcity of energy resources in low-power applications. The objective of this paper is to examine the paradigm shifts in EE approaches in recent times by reviewing traditional approaches to EE, analyzing recent trends, and identifying future challenges and opportunities. Considering the current energy concerns, nodes in emerging wireless networks range from limited-energy nodes (LENs) to high-energy nodes (HENs) with entirely different constraints in either case. In view of these extremes, this paper examines the principles behind energy-efficient wireless communication network design. We then present a broad taxonomy that tracks the areas of impact of these techniques in the network. We specifically discuss the preponderance of prediction-based energy-efficient techniques and their limits, and then discuss the trends in renewable energy supply systems for future networks. Finally, we recommend more context-specific energy-efficient research efforts and cross-vendor collaborations to push the frontiers of energy efficiency in the design of wireless communication networks.

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Towards Energy-Efficient Wireless Networking in the Big Data Era: A Survey

Cited by 84 publications

References 211 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

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

Energy Efficient Design Techniques in Next-Generation Wireless Communication Networks: Emerging Trends and Future Directions

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