Base Station Sleeping with Dynamical Clustering Strategy of CoMP in LTE-advanced

Li, Yun; Ma, Yafei; Wang, Yong; Zhao, Weiliang

doi:10.1109/greencom-ithings-cpscom.2013.48

Cited by 17 publications

(11 citation statements)

References 10 publications

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“…They have identified transmit power, inter-site distance and SINR service demands as the main inputs for this trade-off. Li et al proposed a dynamic CoMP clustering algorithm with BS sleeping to maximise energy efficiency while maintaining high achievable rate for all users [82]. Candidate clusters are formed by all possible combinations of groups of cells with predefined cluster size and each BS selects a suitable cluster from the candidate clusters by maximising achievable rate for its users.…”

Section: E Multi-objective 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

150

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: E Multi-objective Clusteringmentioning

confidence: 99%

“…Energy efficiency with BS switch-off and Spectral Efficiency jointly optimised Energy efficiency by deploying BS Switch-off with CoMP while maximising spectral efficiency [82] Multi-Objective Clustering…”

Section: Multi-objective Clusteringmentioning

confidence: 99%

Coordinated Multi-Point Clustering Schemes: A Survey

Bassoy

Farooq

Imran

et al. 2017

IEEE Commun. Surv. Tutorials

150

109

View full text Add to dashboard Cite

show abstract

“…A number of multi-objective dynamic clustering algorithms are studied in literature where energy efficiency and spectral efficiency are jointly optimized [11]- [13] and also backhaul availability is taken into account for clustering design [14]- [16]. However, to our knowledge, load balancing has not been considered in network-centric CoMP clustering in literature which jointly optimize LB and SE.…”

Section: Related Work and Problem Statementmentioning

confidence: 99%

“…Once the cell is congested (i.e.l im ≥ 1), any load increase is penalized more than the case whenl im < 1. This is achieved by increasing the impact of load with the term (l m ) 3 in the utility function in (11) whenl im ≥ 1. In other terms, additional payoff incentive is introduced for reducing the load in high load range, when compared to low load, i.e.…”

Section: B Utility Functionmentioning

confidence: 99%

A Load-Aware Clustering Model for Coordinated Transmission in Future Wireless Networks

et al. 2019

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Coordinated multi-point (CoMP) transmission is one of the key features for long term evolution advanced (LTE-A) and a promising concept for interference mitigation in 5th generation (5G) and beyond future densely deployed wireless networks. Due to the cost of coordination among many transmission points (TP), radio access network (RAN) needs to be clustered into smaller groups of TPs for coordination. In this paper, we develop a novel, load-aware clustering model by employing a merge/split concept from coalitional game theory. A load-aware utility function is introduced to maximize both spectral efficiency (SE) and load balancing (LB) objectives. We show that proposed load-aware clustering model dynamically adapts into the network load conditions providing high SE in low-load conditions and results in better load distribution with significantly less unsatisfied users in overload conditions while keeping SE at comparable levels when compared to a greedy clustering model. Simulation results show that the proposed solution can reduce the number of unsatisfied users due to overload conditions by 68.5% when compared to the greedy clustering algorithm. Furthermore, we analyze the stability of the proposed solution and prove that it converges to a stable partition in both homogeneous network (HN) and random network (RN) with and without hotspot scenarios. In addition, we show the convergence of our algorithm into the unique clustering solution with the best payoff possible when such a solution exists. INDEX TERMS 5G, network MIMO, coordinated multi-point, SON, load balancing.

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“…To improve the energy efficiency and system throughput of the system in the HetNets, a cell throughput based on sleep control scheme for the small base stations was proposed in [21]. To improve the energy efficiency of the whole system, the authors in [22] proposed an algorithm combining BS sleeping with dynamic clustering (SMDC) to maximize EE. A separation architecture called a hyper-cellular network (HCN) was proposed in [23], which decoupled the control signals and data transmission functions.…”

Section: Introductionmentioning

confidence: 99%

Optimized sleep strategy based on clustering in dense heterogeneous networks

Wang

et al. 2018

J Wireless Com Network

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As a large number of small base stations have been deployed in dense heterogeneous networks, the increasing cost of energy has become a challenging issue. In this paper, we propose an optimized clustering-based sleep strategy to release the power consumption and interference in the system. First, we group the small base stations with large interference as a cluster in which the binary particle swarm optimization (BPSO) algorithm is used to formulate a sleep strategy for small base stations in the clusters. Since the small base stations between clusters have less interference, the whole spectrum resources can be shared in all clusters and the orthogonal spectrum resources are allocated for the small base stations in the same cluster. The strategy effectively restrains the co-tier interference in the system. Furthermore, a separate sleep strategy is applied for each cluster to improve the sleep and activation efficiency of the small base stations. The simulation results show that the proposed sleep strategy can improve the satisfaction of user equipment and reduce the power consumption. As the increasing number of user equipment, the outage probability of the system can be reduced effectively.

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Base Station Sleeping with Dynamical Clustering Strategy of CoMP in LTE-advanced

Cited by 17 publications

References 10 publications

Coordinated Multi-Point Clustering Schemes: A Survey

Coordinated Multi-Point Clustering Schemes: A Survey

A Load-Aware Clustering Model for Coordinated Transmission in Future Wireless Networks

Optimized sleep strategy based on clustering in dense heterogeneous networks

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