Energy efficient heterogeneous network deployment with cell DTX

Andersson, Göran; Västberg, A.; Devlić, Alisa; Çavdar, Çiçek

doi:10.1109/icc.2016.7511218

Cited by 13 publications

(12 citation statements)

References 5 publications

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“…The load-dependent part consists of the baseband and PA PC. The PA, baseband, and total PC vary with the configuration of the BS and are given as [16],…”

Section: A Base Station Power Modelmentioning

confidence: 99%

Q-learning based Radio Resource Adaptation for Improved Energy Performance of 5G Base Stations

Peesapati

Olsson

Masoudi

et al. 2021

2021 IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

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Radio resource adaptation (RRA) is an effective strategy to reduce the energy consumption (EC) of a base station (BS) under variable input traffic demand. By combining RRA with advanced sleep modes (ASMs), one could achieve relatively higher energy savings (ES) during the low traffic hours of the day while managing to meet the quality of service (QoS) requirements of the user equipments (UEs). However, identifying appropriate resources for a certain period is challenging as different resources (i.e., the bandwidth and the antenna array size) have a varying impact on the instantaneous power consumption (PC) and activity of the BS. Various works have looked into the potential of RRA and ASMs in reducing the EC of a BS when implemented independently. In this work, we combine RRA with ASMs and propose a dynamic Q-learning algorithm that adapts a BS's resources according to the traffic demand. The algorithm also takes into account the sleep modes (SMs) that the BS can switch to during the idle periods. Through simulations, we show the convergence of our algorithm and the impact of combining RRA with ASMs on the overall ES as we observe up to 16% additional savings in a super-dense urban (SDU) deployment scenario by combining these techniques as compared to the baseline scenario using only ASMs.

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“…The load-dependent part consists of the baseband and PA PC. The PA, baseband, and total PC vary with the configuration of the BS and are given as [16],…”

Section: A Base Station Power Modelmentioning

confidence: 99%

Q-learning based Radio Resource Adaptation for Improved Energy Performance of 5G Base Stations

Peesapati

Olsson

Masoudi

et al. 2021

2021 IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

Self Cite

View full text Add to dashboard Cite

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“…To reduce the energy consumption in BSs by turning off BSs' transceivers in idle times, discontinuous transmission (DTX) models can be employed in the network architecture [29], [30]. In DTX based models Multicast Broadcasting Single Frequency Network (MBSFN) sub-frames are allocated by considering traffic load in the network [31].…”

Section: Related Workmentioning

confidence: 99%

Density-Aware, Energy- and Spectrum-Efficient Small Cell Scheduling

Mollahasani

Onur

2019

IEEE Access

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Future mobile networks have to be densified by employing small cells to handle the upsurge in traffic load. Although the amount of energy each small cell consumes is low, the total energy consumption of a large-scale network may be enormous. To enhance energy efficiency, we have to adapt the number of active base stations to the offered traffic load. Deactivating base stations may cause coverage holes, degrade the quality of service and throughput while redundant base stations waste energy. That is why we have to adapt the network to an effective density. In this paper, we show that achieving an optimal solution for adapting the density of base stations to the demand is NP-hard. We propose a solution that consists of two heuristic algorithms: a base station density adaptation algorithm and a cell-zooming algorithm that determines which base stations must be kept active and adapts transmit power of base stations to enhance throughput, energy, and spectral efficiency. We employ a multi-access edge cloud for taking a snapshot of the network state in nearly real time with a wider perspective and for collecting network state over a large area. We show that the proposed algorithm conserves energy up to 12% while the spectral efficiency and network throughput can be enhanced up to 30% and 26% in comparison with recent works, respectively. INDEX TERMS 5G mobile networks, densification, density-aware networking, energy-efficiency, green networks, multi-access edge cloud (MEC), self-organizing networks.

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“…In order to make the power control policy independent from the resource allocation strategy, as in [26], [27], we suppose that each user experiences the interference generated by the time average transmit power of the neighboring base stations:…”

Section: System Modelmentioning

confidence: 99%

“…In [24], [25], the authors proposed efficient resource allocation strategies for Cell DTx in femtocell networks, but the authors did not consider the issue of power control. In [26], [27], the authors studied the issue of the base station's density pertaining to the Cell DTx case. In these two papers, a simple distance-based power control policy is used.…”

Section: Introductionmentioning

confidence: 99%

“…In these two papers, a simple distance-based power control policy is used. Unlike [21]- [27] in which the analysis of Cell DTx is conducted considering a given power control policy, the aim of the present article is to find an efficient power control policy for Cell DTx. In this article, we propose a scheme, in which each base station applies the power control policy that minimizes its own energy consumption.…”

Section: Introductionmentioning

confidence: 99%

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Power Control and Cell Discontinuous Transmission Used As a Means of Decreasing Small-Cell Networks’ Energy Consumption

Bonnefoi

Moy

Palicot

2018

IEEE Trans. on Green Commun. Netw.

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Abstract-In this article, we analyze the issues of power control and Cell Discontinuous Transmission (Cell DTx) in a small-cell network. More precisely, we evaluate the performance of a policy, thanks to which each base station minimizes its own energy consumption. We have recently proposed this policy in a monocell scenario and we extend the scope of our previous work in this article, as we propose to use this policy in a multi-cell network. For that purpose, we first consider a network made of two base stations, in which we can compute the minimum network's energy consumption, which enables to provide users with the requested quality of service. If we compare the proposed policy with this minimum, we can demonstrate that the proposed policy has a good performance, which, however, can be improved by reducing the maximum transmit power of the base station. Then, we apply our algorithm in the case of a dense network, in which the proposed policy outperforms other policies and provides a 6% gain in energy, where compared to a policy featuring no power control. We show that the reduction of the maximum transmit power can increase this gain up to 11% of the network's energy consumption.

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Energy efficient heterogeneous network deployment with cell DTX

Cited by 13 publications

References 5 publications

Q-learning based Radio Resource Adaptation for Improved Energy Performance of 5G Base Stations

Q-learning based Radio Resource Adaptation for Improved Energy Performance of 5G Base Stations

Density-Aware, Energy- and Spectrum-Efficient Small Cell Scheduling

Power Control and Cell Discontinuous Transmission Used As a Means of Decreasing Small-Cell Networks’ Energy Consumption

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