Memory-Based User-Centric Backhaul-Aware User Cell Association Scheme

Pervez, Farrukh; Jaber, Mona; Qadir, Junaid; Younis, Shahzad; Imran, Muhammad Ali

doi:10.1109/access.2018.2850752

Cited by 13 publications

(10 citation statements)

References 18 publications

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“…Besides the information related to SINR, backhaul capacity constraints and diverse attributes related to the QoE of users should also be taken into account for user association. In [82], authors propose a distributed, user-centric, backhaulaware user association scheme based on fuzzy Q learning to enable each cell to autonomously maximize its throughput under backhaul capacity constraints and user QoE constraints. More concretely, each cell broadcasts a set of bias values to guide users to associate with preferred cells, and each bias value reflects the capability to satisfy a kind of performance metrics like throughput and resilience.…”

Section: A Machine Learning Based Bs Association 1) Reinforcement Lementioning

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

502

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: A Machine Learning Based Bs Association 1) Reinforcement Lementioning

confidence: 99%

“…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%

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

Sun

Peng

Zhou

et al. 2019

IEEE Commun. Surv. Tutorials

502

290

View full text Add to dashboard Cite

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“…The general average ergodic throughput of a user u at a variable location associated with a SC with B s last mile can be expressed as in (21) shown at the top of the next page. This can be further expanded by substituting (19) and (15), as shown in (22).…”

Section: A Basic Ucb Association Policymentioning

confidence: 99%

“…Authors in [14] use Fuzzy-Q-learning in their implementation of the UCB and show superior gains compared to the basic Q-learning approach at the cost of additional complexity. A memorybased implementation of the UCB that takes advantage of historical knowledge of previously identified optimum policies is presented in [15] and is shown to deliver better performance in terms of both convergence and gains. In both [14] and [15], the authors select two attributes: throughput and resilience.…”

Section: B Q-learning Implementation Of Ucbmentioning

confidence: 99%

“…A memorybased implementation of the UCB that takes advantage of historical knowledge of previously identified optimum policies is presented in [15] and is shown to deliver better performance in terms of both convergence and gains. In both [14] and [15], the authors select two attributes: throughput and resilience. The memory-based scheme is the best implementation scheme as it outperforms the Fuzzy-Q-learning by 51% and 82%, with respect to throughput and resilience and it outperforms the basic Q-learning by 63% and 87%, respectively.…”

Section: B Q-learning Implementation Of Ucbmentioning

confidence: 99%

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Backhaul-Aware and Context-Aware User-Cell Association Approach

Jaber

Onireti

Imran

2019

ICC 2019 - 2019 IEEE International Conference on Communications (ICC)

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The cell range extension (CRE) has been successfully implemented to bias the user to base station (BS) association policy in a way that achieves load balancing and increases the capacity of heterogeneous networks. The user-centric backhaul (UCB) scheme is a CRE evolution that is both backhaul-aware and user-context-aware-two constraints that are shaping the 5G network development. In this work, we formulate and solve the multi-objective optimisation problem of the UCB user-BS association. We derive analytical expressions of the ergodic throughput resulting from the UCB and, accordingly, identify the optimum association policy. The study demonstrates the gain margins that can be realised with pertinent user-cell association which is aware of the end-to-end network limitations and users requirements.

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