Energy efficiency gains through traffic offloading and traffic expansion in joint macro pico deployment

Arshad, Malik Wahaj; Västberg, A.; Edler, Tomas

doi:10.1109/wcnc.2012.6214158

Cited by 35 publications

(12 citation statements)

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“…The fraction of maximum capacity of a BS is defined as the load of a BS [8]. When all resources are available for one or more users, the portion of RB allocated for users is described as the load factor (L), which defines the traffic load in [13,14].…”

Section: Sleep Mode Techniquementioning

confidence: 99%

Using Sleep Mode Mechanism to Improve Energy Efficiency in Heterogenous Network

Shakor¹,

Jasim²,

Cheng³

2017

IJCA

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The number of users for developed devices (e.g., mobile, tablet, laptop, iPad, computer) increased rapidly. This trend is expected to continue in the next upcoming years. Thus, the number of users will rapidly increase, which will cause a high demand for capacity in wireless access networks. In addition, they will cause high energy and power consumption. Heterogeneous network (HetNet) is one of the most promising network approaches to achieve high capacity and improve energy efficiency (EE). In This piper, will investigate the sleep mode mechanism to improve EE and reduce power consumption. When companies design communication network systems, they are considered the network service at peak load hour. Peak traffic load happens a few hours a day, but the rest of the time most of the power is wasted. The sleep mode mechanism is used by switching Micro and Pico base stations (BSs) when the traffic load is low or medium. Simulation results show that EE gain can be achieved 46.68% and 79.77%, when the traffic load is medium or low. General Terms Energy Efficiency improving and Power Consumption reducing KeywordsHeterogeneous network, energy efficiency, power consumption, sleep mode.

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Section: Sleep Mode Techniquementioning

confidence: 99%

Using Sleep Mode Mechanism to Improve Energy Efficiency in Heterogenous Network

Shakor¹,

Jasim²,

Cheng³

2017

IJCA

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“…For the power consumption of a single base station in current wireless access networks, several models are available in literature with all similar results [1,8,9,10]. To reduce power consumption and/or improve energy efficiency in those networks, the introduction of sleep modes [11,12,13,14,15] and small cells [16,17] are topics that are well investigated. However, deployment tools that focus on power consumption are rather limited.…”

Section: Related Workmentioning

confidence: 99%

Optimizing LTE wireless access networks towards power consumption and electromagnetic exposure of human beings

et al. 2016

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When developing future wireless access networks, it is important to focus not only on optimizing the power consumption, but also the electromagnetic field exposure for human beings. However, optimizing towards these two parameters results in conflicting requirements on the network. In this paper, a deployment tool for future green wireless access networks is proposed, allowing four different levels of optimizations: towards power consumption, towards human exposure, towards power consumption while satisfying a certain exposure limit, and towards both power consumption and human exposure. The proposed deployment tool is capacity-based, meaning that it responds to the instantaneous bit rate required by the users in the considered area by switching base stations dynamically on and off. The tool is applied on a realistic suburban case in Ghent, Belgium to compare the four different levels of optimizations. A first compromise between optimizing towards power consumption and human exposure is obtained by optimizing towards power consumption while satisfying an exposure limit (at 2.6 GHz). However, there is still room for improvement especially when considering the human exposure. A better trade-off is achieved by the optimization towards power consumption and human exposure at the same time. Furthermore, the influence of the most important parameters such as the weighting factors of the fitness function, the distance between base station and general public, and between the grid points for evaluating the exposure is investigated.

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“…However, it does not reflect actual power consumption. A linear power model that takes into account traffic load is used in [8],…”

Section: Energy Consumption Modelsmentioning

confidence: 99%

Energy-efficient femtocell networks: challenges and opportunities

Celebi

Daneshmand

et al. 2013

IEEE Wireless Commun.

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As a promising technique for next-generation wireless networks, femtocells expand the coverage of cellular networks, provide high data rate for users, decrease the transmission power of user equipments, and increase the spectrum efficiency. In a few years, the number of deployed femtocell base stations (FBSs) will reach hundreds of millions. This huge deployment will bring a lot of challenges in terms of interference management, resource scheduling, and energy consumption. In recent years, more and more attention has been paid to energy-efficient communications. The huge number of deployed FBSs will aggravate energy consumption. In this article, we comprehensively survey the related work on energy efficiency issues in femtocell networks, including energy efficiency metrics, energy consumption models, deployments of femtocells, and energy-efficient schemes. Then a simple sleeping scheme, fixed time sleeping, is presented as a case study for saving the energy of FBSs. Some interesting results are also presented to show that fixed time sleeping makes a good trade-off among energy efficiency, actual waiting time, and call loss.

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Energy efficiency gains through traffic offloading and traffic expansion in joint macro pico deployment

Cited by 35 publications

References 1 publication

Using Sleep Mode Mechanism to Improve Energy Efficiency in Heterogenous Network

Using Sleep Mode Mechanism to Improve Energy Efficiency in Heterogenous Network

Optimizing LTE wireless access networks towards power consumption and electromagnetic exposure of human beings

Energy-efficient femtocell networks: challenges and opportunities

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