Practical considerations in cluster design for Co-ordinated Multipoint (CoMP) systems

Pichapati, Venkatadheeraj; Gupta, Parul

doi:10.1109/icc.2013.6655533

Cited by 9 publications

(9 citation statements)

References 8 publications

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“…A joint, centralised scheduling is developed for this clustering type. In [42], static cluster shift idea from [41] is further enhanced with "full shift" and different frequency bands are allocated on shifted clusters. Static clusters are formed to maximise neighbouring cells in the same cluster for a given hexagonal network layout.…”

Section: B Semi-dynamic Clusteringmentioning

confidence: 99%

“…Non-realistic hexagonal grid approach, Increased scheduling/beamforming complexity, increased backhaul bandwidth requirement [40], [41], [42], [43] Network-centric dynamic clustering Greedy algorithm Sub-optimal clusters which are formed in later stages of the algorithm. Fixed cluster size [44] Network-centric dynamic clustering Greedy algorithm Sub-optimal clusters which are formed in later stages of the algorithm.…”

Section: Multiple Static Clustering Patternsmentioning

confidence: 99%

“…Inter-site static clustering solutions are employed in [36], [37] which is not able to respond to the dynamic changes in the network and user/service profiles, hence limiting the CoMP gains. Semi-dynamic clusters are proposed in [40], [41], [42], [43] where multiple static clustering patterns are designed to mitigate inter-cluster interference. This type of approach is more responsive to the dynamic changes of the network and user profile, however it still lacks on providing full spectral efficiency gain.…”

Section: Network-centric Dynamic Clusteringmentioning

confidence: 99%

See 2 more Smart Citations

Coordinated Multi-Point Clustering Schemes: A Survey

Bassoy

Farooq

Imran

et al. 2017

IEEE Commun. Surv. Tutorials

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: B Semi-dynamic Clusteringmentioning

confidence: 99%

Section: Multiple Static Clustering Patternsmentioning

confidence: 99%

Section: Network-centric Dynamic Clusteringmentioning

confidence: 99%

See 1 more Smart Citation

Coordinated Multi-Point Clustering Schemes: A Survey

Bassoy

Farooq

Imran

et al. 2017

IEEE Commun. Surv. Tutorials

158

109

View full text Add to dashboard Cite

show abstract

“…[17] proposes BSs clustering and precoding matrices per-user to maximize the weighted sum-rate of the CoMP system. In [18], the authors investigate the effects of the size and shape of the co-operating cluster on the performance of the CoMP system. [19] jointly optimizes the beamformer design and the BS assignment to minimize the user data transfer in the back-haul.…”

Section: Related Workmentioning

confidence: 99%

“…After obtaining all MISs of G D .V, E/, we calculate the energy efficiency of each MIS according to Equation (18). The Equation (18) and its detailed calculation procedures are presented in the following section.…”

Section: B3 Selection Of Final Clusters With Maximum Energy Efficiencymentioning

confidence: 99%

Energy-efficient cluster division for multi-cell joint transmission technology

Cao

Wang

et al. 2016

Wirel. Commun. Mob. Comput.

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Coordinated Multi-Point (CoMP) is an effective way to improve user performance in next-generation wireless cellular networks, such as 3GPP LTE-Advanced(LTE-A). The base station cooperation can reduce interference, and increase the signal to interference and noise ratio (SINR) of cell-edge users and improve the system capacity. However, the base station cooperation also adds additional power consumption for signal processing and sharing information through back-haul links between cooperative base stations. As such, CoMP may potentially consume more energy. This paper studies such energy consumption issue in CoMP, presents a semi-dynamic CoMP cluster division algorithm based on energy efficiency (SCCD-EE) that can effectively adapt to users' real-time interference, and employs the idea of Maximal Independent Set (MIS) to solve the problem of cluster overlapping. To verify the feasibility of the proposed algorithm, this paper performs comprehensive evaluations in terms of energy efficiency and system capacity. The simulation results show that the proposed semi-dynamic cluster division algorithm can not only improve the system capacity and the quality of service (QoS) of cell-edge users, but also achieve higher network energy efficiency compared with static cluster methods and Non-CoMP approaches.

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Load-Aware Greedy Dynamic CoMP Clustering Mechanism for DPS CoMP in 5G Networks

Chantaraskul

Tarbut

Nuanyai

2023

Int J Netw Distrib Comput

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Cooperative communications offered by the Coordinated Multipoint (CoMP) technique have been introduced and become an important technique in the 5G mobile networks to support very high system capacity and improve the end-user service quality, especially for the cell-edge users via the dynamic coordination of transceivers at different geographical sites. One major issue driving the performance of CoMP operation is the decision on the coopering clusters or the cluster formation strategies. In this work, an approach of CoMP clustering has been studied and proposed, the so-called load-aware greedy dynamic CoMP clustering mechanism. Our proposed CoMP clustering technique is deployed on top of the actual traffic-based load-aware Dynamic Point Selection CoMP (DPS CoMP) mechanism, which is our previously proposed mechanism. The approach is aimed at maximizing spectral efficiency with appropriate cell coordination based on well-balancing cell loads within the coordinating area of the 5G networks. System performance is numerically studied and observed for the 5G Non-Orthogonal Multiple Access (NOMA) systems embedded with our proposed mechanisms for the realistic scenarios, i.e., homogeneous network and Heterogeneous Network (HetNet) cases. Numerical results illustrate the benefit brought by our approaches in comparison with others CoMP clustering techniques as well as the potential for further improvement.

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Practical considerations in cluster design for Co-ordinated Multipoint (CoMP) systems

Cited by 9 publications

References 8 publications

Coordinated Multi-Point Clustering Schemes: A Survey

Coordinated Multi-Point Clustering Schemes: A Survey

Energy-efficient cluster division for multi-cell joint transmission technology

Load-Aware Greedy Dynamic CoMP Clustering Mechanism for DPS CoMP in 5G Networks

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