Optimized sleep strategy based on clustering in dense heterogeneous networks

Li, Jun; Wang, Hao; Wang, Xiu Min; Li, Zhengquan

doi:10.1186/s13638-018-1311-2

Cited by 10 publications

(6 citation statements)

References 30 publications

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“…Li et al proposed an optimized sleep strategy based on cluster to relieve power consumption and interference in dense heterogeneous networks (Li et al. 2018 ). Small base stations with large interference are divided into clusters and binary PSO determines their sleep strategy.…”

Section: Binary Metaheuristic Algorithms In Applicationsmentioning

confidence: 99%

A survey on binary metaheuristic algorithms and their engineering applications

Pan

Snåšel

et al. 2022

Artif Intell Rev

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This article presents a comprehensively state-of-the-art investigation of the engineering applications utilized by binary metaheuristic algorithms. Surveyed work is categorized based on application scenarios and solution encoding, and describes these algorithms in detail to help researchers choose appropriate methods to solve related applications. It is seen that transfer function is the main binary coding of metaheuristic algorithms, which usually adopts Sigmoid function. Among the contributions presented, there were different implementations and applications of metaheuristic algorithms, or the study of engineering applications by different objective functions such as the single- and multi-objective problems of feature selection, scheduling, layout and engineering structure optimization. The article identifies current troubles and challenges by the conducted review, and discusses that novel binary algorithm, transfer function, benchmark function, time-consuming problem and application integration are need to be resolved in future.

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Section: Binary Metaheuristic Algorithms In Applicationsmentioning

confidence: 99%

A survey on binary metaheuristic algorithms and their engineering applications

Pan

Snåšel

et al. 2022

Artif Intell Rev

View full text Add to dashboard Cite

show abstract

“…Then based on the iteration value, dynamic sleep algorithm for the FBSs were proposed to get the optimum BS sleep strategy. To release the interference and power‐consumption in the system an optimized sleep‐strategy based on the clustering is proposed by the authors in Reference 36. Here BPSO algorithm is used to formulate a sleep‐strategy of SBSs in the clusters.…”

Section: Related Workmentioning

confidence: 99%

A new method of hybrid optimization of small cell range development and density for energy efficient ultra‐dense networks

Sidiq

Sheikh

Mustafa

et al. 2022

Trans Emerging Tel Tech

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Ultra-dense networks (UDNs) which comprise of small and macro cells, are considered the principal solution for increasing demands of data. However, making energy efficient networks will be a challenge for researchers. In this article, a hybrid optimization technique is proposed to enhance the energy efficiency (EE) of the UDNs, considering the user correlation and hybrid optimization of the density and transmitting power of the small-cell-base-station (SBS). More precisely, the problem framed is a problem of convex-linear programming and hence is divided into two sub-problems: cell correlation and hybrid optimization. The expression for the EE of the system is derived in the closed-form as a factor of the density of SBSs and small cell-range-development (CRD) bias dependent on the Poisson point process (PPP) followed by the sequential-search-algorithm to improve the small CRD bias and SBS density correspondingly. Additionally, to realize hybrid optimization of the small CRD bias and density of SBSs, a heuristic algorithm is put forward to accomplish the EE of the system. It is revealed from the simulation outcomes that the proposed small CRD bias and SBS density hybrid optimization significantly enhances the EE of the system with low computational intricacy.

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“…In addition, a sorting algorithm based heuristic, was developed to determine the number of SBSs to switch off in each cluster. The authors in [40] proposed clustering based sleeping strategy to minimize the power consumption and interference in dense HetNets using clustering algorithm. In their proposed method, the SBSs are clustered based on their interference level, after which the clusters with large interference values are selected.…”

Section: Related Workmentioning

confidence: 99%

“…Different from previous works in [14], [39], [40] where a single type of SBS is deployed, in this work, we consider a UDHN deployment where different types of SBSs (remote radio head (RRH), micro, pico, and femto BSs) are deployed under the coverage of the macro BSs (MBSs). Moreover, unlike the previous works [14], [39], [40] where a SBS is selected as the cluster head and is responsible for controlling the switching off/on of other SBSs, here the MBSs are responsible for controlling the switching off/on of the different SBSs because it has more global information about all the SBSs under its coverage. Also, we consider a scenario where horizontal traffic offloading is not always possible, as a result, vertical traffic offloading is employed in this work.…”

Section: Related Workmentioning

confidence: 99%

A Lightweight Cell Switching and Traffic Offloading Scheme for Energy Optimization in Ultra-Dense Heterogeneous Networks

Abubakar¹,

Mollel²,

Öztürk³

et al. 2021

Preprint

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One of the major capacity boosters for 5G networks is the deployment of ultra-dense heterogeneous networks (UDHNs). However, this deployment results in tremendous increase in the energy consumption of the network due to the large number of base stations (BSs) involved. In addition to enhanced capacity, 5G networks must also be energy efficient for it to be economically viable and environmentally friendly. Dynamic cell switching is a very common way of reducing the total energy consumption of the network but most of the proposed methods are computationally demanding which makes them unsuitable for application in ultra-dense network deployment with massive number of BSs. To tackle this problem, we propose a lightweight cell switching scheme also known as Threshold-based Hybrid cEll swItching Scheme (THESIS) for energy optimization in UDHNs. The developed approach combines the benefits of clustering and exhaustive search (ES) algorithm to produce a solution whose optimality is close to that of the ES (which is guaranteed to be optimal), but is computationally more efficient than ES and as such can be applied for cell switching in real networks even when their dimension is large. The performance evaluation shows that the THESIS produces a significant reduction in the energy consumption of the UDHN and is able to reduce the complexity of finding a near-optimal solution from exponential to polynomial complexity.

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Optimized sleep strategy based on clustering in dense heterogeneous networks

Cited by 10 publications

References 30 publications

A survey on binary metaheuristic algorithms and their engineering applications

A survey on binary metaheuristic algorithms and their engineering applications

A new method of hybrid optimization of small cell range development and density for energy efficient ultra‐dense networks

A Lightweight Cell Switching and Traffic Offloading Scheme for Energy Optimization in Ultra-Dense Heterogeneous Networks

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