Load Aware Self-Organising User-Centric Dynamic CoMP Clustering for 5G Networks

Bassoy, Selcuk; Jaber, Mona; Imran, Muhammad Ali; Xiao, Pei

doi:10.1109/access.2016.2569824

Cited by 98 publications

(78 citation statements)

References 26 publications

(27 reference statements)

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“…An interesting research area is to develop CoMP clustering algorithms to support load balancing while spectral efficiency is maximised. A loadaware, user-centric CoMP clustering approach is presented in our previous work [128], however further research is required to analyse fully dynamic CoMP clustering techniques and the trade-off between load balancing gains and potential losses on spectral efficiency. b) Backhaul Optimisation: A number of research are conducted for CoMP clustering which takes backhaul bandwidth limitation into account.…”

Section: Backhaul Bandwidth and Spectral Efficiency Jointly Optimisedmentioning

confidence: 99%

Coordinated Multi-Point Clustering Schemes: A Survey

Bassoy

Farooq

Imran

et al. 2017

IEEE Commun. Surv. Tutorials

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158

109

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Abstract-Mobile data traffic grew by 74% in 2015 and it's expected to grow 8-fold by 2020. Future wireless networks will need to deploy massive number of small cells to cope with this increasing demand. Dense deployment of small cells will require advanced interference mitigation techniques to improve spectral efficiency and enhance much needed capacity. Coordinated multi-point (CoMP) is a key feature for mitigating intercell interference, improve throughput and cell edge performance. However, cooperation will need to be limited to few cells only due to additional overhead required by CoMP due to channel state information (CSI) exchange, scheduling complexity and additional backhaul limitation. Hence small CoMP clusters will need to be formed in the network. This article surveys the stateof-the-art on one of the key challenges of CoMP implementation: CoMP clustering. As a starting point, we present the need for CoMP, the clustering challenge for 5G wireless networks and provide a brief essential background about CoMP and the enabling network architectures. We then provide the key framework for CoMP clustering and introduce self organisation as an important concept for effective CoMP clustering to maximise CoMP gains. Next, we present two novel taxonomies on existing CoMP clustering solutions, based on self organisation and aimed objective function. Strengths and weaknesses of the available clustering solutions in the literature are critically discussed. We then discuss future research areas and potential approaches for CoMP clustering. We present a future outlook on the utilisation of Big Data in cellular context to support proactive CoMP clustering based on prediction modelling. Finally we conclude this paper with a summary of lessons learnt in this field. This article aims to be a key guide for anyone who wants to research on CoMP clustering for future wireless networks.

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Section: Backhaul Bandwidth and Spectral Efficiency Jointly Optimisedmentioning

confidence: 99%

Coordinated Multi-Point Clustering Schemes: A Survey

Bassoy

Farooq

Imran

et al. 2017

IEEE Commun. Surv. Tutorials

Self Cite

158

109

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“…In SON, unsupervised learning is applied in several domains, ranging from configuration of operational parameters [83], [84], caching [82], [85], [86], resource optimization [56], [87], [88], HO management [89], [90], mobility [91], load balancing [92], fault detection [93]- [102], cell outage management [49], [103]- [106], to sleeping cell management [50], [107].…”

Section: B Unsupervised Learningmentioning

confidence: 99%

“…A recent work proposed by Bassoy et al, in [92], present an unsupervised clustering algorithm in a control/data separation plane. Results show that the proposed solution is able to offload traffic from highly loaded cells to neighbor cells and that the algorithm can work in a high dense deployment scenario, making it suitable for future cellular networks.…”

Section: F Load Balancingmentioning

confidence: 99%

A Survey of Machine Learning Techniques Applied to Self-Organizing Cellular Networks

Klaine

Imran

Onireti

et al. 2017

IEEE Commun. Surv. Tutorials

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412

200

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Abstract-In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future.

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“…The closest approach in spirit to our scheme is the user‐centric clustering approach . In this scheme, clustering and resource allocation are performed jointly.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…The closest approach in spirit to our scheme is the user-centric clustering approach. 12 In this scheme, clustering and resource allocation are performed jointly. In addition, aiming to improve UE's service performance, this scheme considers the user-centric clustering due to UE's optimal performance, and intercell resource allocation with CoMP constraints is also considered.…”

Section: • Proposed Jdapging-ra Scheme Vs User-centric Clustering Schemementioning

confidence: 99%

Joint dynamic access points grouping and resource allocation for coordinated transmission in user‐centric UDN

Wang

Chen

et al. 2017

Trans Emerging Tel Tech

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We consider the resource allocation problem in user‐centric ultradense network, where each user equipment (UE) is served by its own access points group (APG), and the APG will be dynamically adjusted to support UE's movement. In this context, our scheme is to jointly design dynamic APs grouping and the resource allocation for coordinated transmission in user‐centric ultradense network. First, a user‐centric dynamic APs grouping approach is considered, where the APGs are formed based on cooperation benefits that UEs experience to different access points (APs). Accordingly, due to resource allocation constraints unbounded among the overlapped APGs, which result in the resource allocation optimization problem being nonconvex. To address this issue, the resource allocation problem is divided into the subchannel (SC) allocation subproblem and the power allocation subproblem. With the SC allocation results, an iterative power allocation algorithm is carried out over all SCs at each AP to get an optimal power allocation. The performance of the proposed scheme is also demonstrated through simulations.

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Load Aware Self-Organising User-Centric Dynamic CoMP Clustering for 5G Networks

Cited by 98 publications

References 26 publications

Coordinated Multi-Point Clustering Schemes: A Survey

Coordinated Multi-Point Clustering Schemes: A Survey

A Survey of Machine Learning Techniques Applied to Self-Organizing Cellular Networks

Joint dynamic access points grouping and resource allocation for coordinated transmission in user‐centric UDN

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