Learning-Based Spectrum Sharing and Spatial Reuse in mm-Wave Ultradense Networks

Fan, Chongyi; Li, Bin; Zhao, Chenglin; Guo, Weisi; Liang, Ying-Chang

doi:10.1109/tvt.2017.2750801

Cited by 53 publications

(25 citation statements)

References 47 publications

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“…For example, authors in [90] use distributed Q learning that leads to a low-complexity sleep mode control algorithm for small cells. In summary, this motivation applies to literatures [9], [42], [46], [49], [50], [52], [55], [60], [80], [82], [90], [105].…”

Section: • Developing Low-complexity Algorithms For Wirelessmentioning

confidence: 99%

“…On the contrary, traditional approaches may require many information exchanges and hence lead to huge signalling cost. For example, as pointed out in [99], ad hoc ondemand distance vector routing will cause the constant flooding of routing messages in a CRN, and the centralized approach taken as the baseline in [52] allocates spectrum resource based on the complete information about SUs. This motivation has been highlighted in [33], [46], [52], [54], [60]- [62], [64], [69], [96], [99], [103], [104].…”

Section: • Developing Low-complexity Algorithms For Wirelessmentioning

confidence: 99%

See 1 more Smart Citation

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: • Developing Low-complexity Algorithms For Wirelessmentioning

confidence: 99%

Section: • Developing Low-complexity Algorithms For Wirelessmentioning

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

“…In this regards, D2D has the potential to accommodate for the expected explosion in the number of wireless devices (e.g. 10× increasing in 2019) [5], and forms part of the envisaged 5G ultra-dense networks [6], [7].…”

Section: Introductionmentioning

confidence: 99%

Asynchronous Device Detection for Cognitive Device-to-Device Communications

Guo

Liang

et al. 2018

IEEE Trans. Wireless Commun.

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“…millimeter-wave (mm-Wave) to uphold ultra broadband systems in fifth generation networks. Applying cognitive radio schemes represents a promising alternative and parallel solution at the same time [1]. The design of such networks is expected to support newly introduced technologies such as machine to machine (M2M) communication promoting the Internet of Things (IoT).…”

mentioning

confidence: 99%

“…On the other hand, cognitive radios allow a primary user (PU) to share its licensed channel resources namely spectrum to unlicensed cognitive secondary user (SU). Previous works considered collision scenarios where the SU is allowed to transmit with constraints on the interference temperature affecting the PU [1], [8]- [11]. For example, [8] proposes a three-dimensional Markov chain model to analyze the SU performance in dynamic spectrum access with interference temperature constraints.…”

mentioning

confidence: 99%

Ultra reliable communication via opportunistic ARQ transmission in cognitive networks

Shehab

Alves

2018

2018 IEEE Wireless Communications and Networking Conference (WCNC)

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This paper presents a novel opportunistic spectrum sharing scheme that applies ARQ protocol to achieve ultra reliability in the finite blocklength regime. A primary user shares its licensed spectrum to a secondary user, where both communicate to the same base station. The base station applies ARQ with the secondary user, which possess a limited number of trials to transmit each packet. We resort to the interweave model in which the secondary user senses the primary user activity and accesses the channel with access probabilities which depend on the primary user arrival rate and the number of available trials. We characterize the secondary user access probabilities and transmit power in order to achieve target error constraints for both users. Furthermore, we analyze the primary user performance in terms of outage probability and delay. The results show that our proposed scheme outperforms the open loop and non-opportunistic scenarios in terms of secondary user transmit power saving and primary user reliability.

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Learning-Based Spectrum Sharing and Spatial Reuse in mm-Wave Ultradense Networks

Abstract: (2017) Learning-based spectrum sharing and spatial reuse in mm-wave ultra dense networks. IEEE Transactions on Vehicular Technology . Permanent WRAP URL:

Cited by 53 publications

References 47 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

Asynchronous Device Detection for Cognitive Device-to-Device Communications

Ultra reliable communication via opportunistic ARQ transmission in cognitive networks

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