Base Station Sleep and Spectrum Allocation in Heterogeneous Ultra-dense Networks

Liu, Qiaoshou; Shi, Jiangpan

doi:10.1007/s11277-017-5031-4

Cited by 11 publications

(6 citation statements)

References 22 publications

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“…It is assessed from the total power consumed by the network ∼58% is consumed by the BSs. 13 Nearly 2% of world-wide carbon-dioxide emissions are because of the data traffic of mobile phones. 14 With the rising energy bills and the requirement to condense emissions researchers are trying to improve the EE of the system 15 by employing green networks.…”

Section: Small Cell Base Stations (Sbs)mentioning

confidence: 99%

“…The density of MBS slightly alters, consequently, the density of MBS is set to steady in Equation (25). Moreover, the proportion of NES is calculated by Equation (13). Because of the above evaluation, the EE given in ( 25) is augmented by 𝜌 s,m (ie, the density 𝜆 s because the density 𝜆 m slightly varies) also small-cell CRD bias B s optimization with a density of various users that is, the load of a network.…”

Section: Hybrid Optimizationmentioning

confidence: 99%

“…Figure 2 represents the small cell network. It is assessed from the total power consumed by the network ˜58% is consumed by the BSs 13 . Nearly 2% of world‐wide carbon‐dioxide emissions are because of the data traffic of mobile phones 14 .…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Small Cell Base Stations (Sbs)mentioning

confidence: 99%

Section: Hybrid Optimizationmentioning

confidence: 99%

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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

View full text Add to dashboard Cite

show abstract

“…Author in [93] used stochastic geometry approach to solve the cell switching issue in UDN. A two-tier UDN network is considered, considering MBS to be on all the time, even when small cells are sleeping.…”

Section: ) Bs Switchingmentioning

confidence: 99%

Energy-Efficient Ultra-Dense 5G Networks: Recent Advances, Taxonomy and Future Research Directions

Mughees¹,

Tahir

Sheikh

et al. 2021

IEEE Access

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The global surge of connected devices and multimedia services necessitates increased capacity and coverage of communication networks. One approach to address the unprecedented rise in capacity and coverage requirement is deploying several small cells to create ultra-dense networks. This, however, exacerbates problems with energy consumption and network management due to the density and unplanned nature of the deployment. This review discusses various approaches to solving energy efficiency problems in ultra-dense networks, ranging from deployment to optimisation. Based on the review, we propose a taxonomy, summarise key findings, and discuss operational and implementation details of past research contributions. In particular, we focus on popular approaches such as machine learning, game theory, stochastic and heuristic techniques in the ultra-dense network from an energy perspective due to their promise in addressing the issue in future networks. Furthermore, we identify several challenges for improving energy efficiency in an ultra-dense network. Finally, future research directions are outlined for improving energy efficiency in ultra-dense networks in 5G and beyond 5G networks.

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“…Second, ultradense deployment of SBSs leads to extremely high network power consumption. One study [8] estimated that BSs account for ∼57% of the total power consumption of wireless networks. With the rapid growth of mobile data traffic, information and communication systems are estimated to account for ∼2% of global carbon dioxide emissions [9].…”

Section: Introductionmentioning

confidence: 99%

User Association and Small-Cell Base Station On/Off Strategies for Energy Efficiency of Ultradense Networks

Gao

Ren

et al. 2019

Mobile Information Systems

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The widespread application of wireless mobile services and requirements of ubiquitous access have resulted in drastic growth of the mobile traffic and huge energy consumption in ultradense networks (UDNs). Therefore, energy-efficient design is very important and is becoming an inevitable trend. To improve the energy efficiency (EE) of UDNs, we present a joint optimization method considering user association and small-cell base station (SBS) on/off strategies in UDNs. The problem is formulated as a nonconvex nonlinear programming problem and is then decomposed into two subproblems: user association and SBS on/off strategies. In the user association strategy, users associate with base stations (BSs) according to their movement speeds and utility function values, under the constraints of the signal-to-interference ratio (SINR) and load balancing. In particular, taking care of user mobility, users are associated if their speed exceeds a certain threshold. The macrocell base station (MBS) considers user mobility, which prevents frequent switching between users and SBSs. In the SBS on/off strategy, SBSs are turned off according to their loads and the amount of time required for mobile users to arrive at a given SBS to further improve network energy efficiency. By turning off SBSs, negative impacts on user associations can be reduced. The simulation results show that relative to conventional algorithms, the proposed scheme achieves energy efficiency performance enhancements.

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Base Station Sleep and Spectrum Allocation in Heterogeneous Ultra-dense Networks

Cited by 11 publications

References 22 publications

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

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

Energy-Efficient Ultra-Dense 5G Networks: Recent Advances, Taxonomy and Future Research Directions

User Association and Small-Cell Base Station On/Off Strategies for Energy Efficiency of Ultradense Networks

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