Energy efficiency and cell coverage area analysis for macrocell networks

Abdulkafi, Ayad Atiyah; Tiong, Sieh Kiong; Koh, Johnny Siaw Paw; Chieng, David; Ting, Alvin

doi:10.1109/icfcn.2012.6206866

Cited by 10 publications

(6 citation statements)

References 9 publications

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“…A green planning for energy-efficient wireless network design is presented in [43] [44] [45]. Optimizations of the BS size, location and density have been investigated in [13], [40], [41], [46]- [52], where the tradeoff between EE and deployment cost is also discussed. It is shown that an optimal number of BSs or cell size with minimal power consumption exists.…”

Section: Network Planningmentioning

confidence: 99%

“…BSs cell size optimization [13], [46], [47], [89], [90] BSs density optimization [19], [52], [70], [91] BSs location optimization [48], [57], [92], [93] Network operation and management…”

Section: Reviews On Energy-awareness In Heterogeneous Cellular Networkmentioning

confidence: 99%

“…There are many parameters affecting the power consumption and coverage area of the network such as frequency band, shadowing, path loss and the receiver sensitivity. An investigation on the impact of these factors on EE has been presented in [46]. Authors of [116] presented the tradeoffs between gains in cell throughput and reception technologies and the increased energy consumption that they induced in cellular BSs.…”

Section: Bs's Energy-awarenessmentioning

confidence: 99%

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CloudSwitch: A State-aware Monitoring Strategy Towards Energy-efficient and Performance-aware Cloud Data Centers

2016

KSII TIIS

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The research on optimization of cellular network's energy efficiency (EE) towards environmental and economic sustainability has attracted increasing attention recently. In this survey, we discuss the opportunities, trends and challenges of this challenging topic. Two major contributions are presented namely 1) survey of proposed energy efficiency metrics; 2) survey of proposed energy efficient solutions. We provide a broad overview of the state of-the-art energy efficient methods covering base station (BS) hardware design, network planning and deployment, and network management and operation stages. In order to further understand how EE is assessed and improved through the heterogeneous network (HetNet), BS's energy-awareness and several typical HetNet deployment scenarios such as macrocell-microcell and macrocell-picocell are presented. The analysis of different HetNet deployment scenarios gives insights towards a successful deployment of energy efficient cellular networks.

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Section: Network Planningmentioning

confidence: 99%

“…BSs cell size optimization [13], [46], [47], [89], [90] BSs density optimization [19], [52], [70], [91] BSs location optimization [48], [57], [92], [93] Network operation and management…”

Section: Reviews On Energy-awareness In Heterogeneous Cellular Networkmentioning

confidence: 99%

Section: Bs's Energy-awarenessmentioning

confidence: 99%

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CloudSwitch: A State-aware Monitoring Strategy Towards Energy-efficient and Performance-aware Cloud Data Centers

2016

KSII TIIS

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“…The cell coverage area C , which is the fraction of cell area where the received power of user terminal is above P min , can be written as

C MathClass-rel= Q (a) MathClass-bin+ normalexp ((), \frac{2 MathClass-bin- 2 ab}{b^{2}}) Q ((), \frac{2 MathClass-bin- ab}{b})

where the Q‐function is defined as the probability that a Gaussian random variable X with mean 0 and variance 1 is greater than z

Q (z) MathClass-rel= prob (X ⩾ z) MathClass-rel= \frac{1}{\sqrt{2 π}} {falsefalse}_{MathClass-op\int}^{z} normalexp ((), MathClass-bin- \frac{x^{2}}{2}) dx

and

a MathClass-rel= \frac{P_{normalmin} MathClass-bin- P_{italicrx} (R_{g})}{σ_{ΨdB}} MathClass-punc, b MathClass-rel= \frac{10 α {normallog}_{10} (e)}{σ_{ΨdB}}

where α is the PL exponent and σ ψdB is the standard deviation of shadow fading, whereas P rx ( R g ) represents the received power at cell edge ( R g ). Hence, the coverage area of a cell is a function of receiver sensitivity P min , carrier frequency f , transmitted power P tx , PL exponent α and shadowing standard deviation σ ψdB . By limiting the coverage area to a certain size, the network performance in terms of achievable data rates and efficiencies within each BS can be estimated.…”

Section: Network Modelsmentioning

confidence: 99%

“…where˛is the PL exponent and dB is the standard deviation of shadow fading, whereas P rx .R g / represents the received power at cell edge .R g /. Hence, the coverage area of a cell is a function of receiver sensitivity P min , carrier frequency f , transmitted power P tx , PL exponent and shadowing standard deviation dB [19,20]. By limiting the coverage area to a certain size, the network performance in terms of achievable data rates and efficiencies within each BS can be estimated.…”

Section: Coverage Modelmentioning

confidence: 99%

Energy‐aware load adaptive framework for LTE heterogeneous network

Abdulkafi

Chieng

Kiong

et al. 2014

Trans. Emerging. Tel. Tech.

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One of the main approaches for improving the network energy efficiency (EE) is through the introduction of load adaptive techniques, where the network's components/subsystems are switched off when the network is lightly loaded. Optimising such a dynamic operation in a heterogeneous network (HetNet) remains an active topic of research. In this paper, a traffic load‐adaptive model that aims to evaluate the EE of base stations in Long Term Evolution (LTE) HetNet is presented. First, a model that simulates the load‐adaptive power consumption behaviour of LTE HetNet is developed. In this regard, a load adaptation factor is introduced to assess the network's EE performance. The model also adapts and predicts the achievable data rate of each base station with respect to the traffic load. Our study shows that the fully load‐adaptive LTE HetNet can significantly improve network's EE up to 10%, 40%, and 80% for high, medium, and low loads, respectively, as compared to the conventional non load‐adaptive HetNet. In addition, we show that the full adaptive network operation can achieve significant EE gains under a realistic daily traffic profile up to 86%. The proposed evaluation model is essential to assess the network EE and can be used in future studies that focus on improving the adaptation level of the already installed network equipments. Copyright © 2014 John Wiley & Sons, Ltd.

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