Traffic Demand and Energy Efficiency in Heterogeneous Cellular Mobile Radio Networks

Richter, Fred; Fehske, Albrecht; Marsch, Patrick; Fettweis, Gerhard

doi:10.1109/vetecs.2010.5493683

Cited by 84 publications

(56 citation statements)

References 5 publications

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“…From (17) we see that the power consumption model agrees with the general model assumed in other works ( [27]- [29]) which considers a component (a i ) dependent on the transmitted power and a component (b i ) independent of it. The power consumption can then be expressed as…”

Section: Energy Efficiencysupporting

confidence: 82%

“…From (29) it is straightforward to obtain this probability as p um = 1 + τ −2 −1 . So the coverage probability in the macrocell tier is expressed as…”

Section: Discussionmentioning

confidence: 99%

“…Substituting the values found in (29), (26), (27), (28) and (1) into (30) and integrating with respect to R s , the final expression in (12) is obtained.…”

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Stochastic Geometric Modeling and Analysis of Non-Uniform Two-Tier Networks: A Stienen’s Model-Based Approach

Hernandez-Aquino

Zaidi

Ghogho

et al. 2017

IEEE Trans. Wireless Commun.

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Section: Energy Efficiencysupporting

confidence: 82%

“…From (29) it is straightforward to obtain this probability as p um = 1 + τ −2 −1 . So the coverage probability in the macrocell tier is expressed as…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Stochastic Geometric Modeling and Analysis of Non-Uniform Two-Tier Networks: A Stienen’s Model-Based Approach

Hernandez-Aquino

Zaidi

Ghogho

et al. 2017

IEEE Trans. Wireless Commun.

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“…The relationship between area power consumption and area spectral efficiency was also examined. This work was extended by Richter et all in [12] where the relationship between area power consumption and the traffic dependent spectral efficiency is investigated. While spectral efficiency is used as a means of examining capacity in these works, this metric is not particularly useful if the performance of a set of users with particular traffic demands is to be analysed.…”

Section: Introductionmentioning

confidence: 97%

“…This has become a popular metric and is useful for evaluating different base station designs and identifying energy efficient design trends which can be applied in real world network design. Although the literature using this metric typically considers area coverage planning it has not considered (user and traffic) capacity planning in detail [8,[10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Optimizing Area Power Consumption in LTE type Cells

Keenan

Villing

2013

24th IET Irish Signals and Systems Conference (ISSC 2013)

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As wireless networks evolve to deal with increasing traffic demand and an increasing number of users, energy efficiency is becoming increasingly important. Area power consumption, defined as the ratio of total radio access network power consumption to the area covered, is a key metric for comparing the energy efficiency of different network designs. This paper examines the optimization of an LTE type cell for energy efficiency by minimizing the area power consumption (APC) subject to user coverage and capacity constraints. First we show that operating the base station at the maximum transmit power (and cell size) which services all users optimizes APC. Next we show that APC is optimum when the base station operates with the minimum bandwidth necessary to service all users. Finally we show that a modified admission scheme (which increases the minimum SINR at which a user will be serviced) improves APC by increasing the maximum capacity and size of cells that can be supported. In addition to improving energy efficiency, the modified scheme requires fewer cells in new or overlay deployments and may potentially permit removal of cells from an existing network.

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Decision‐making scenarios for reducing the computational complexity in a green cognitive radio receiver

et al. 2014

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(2014), Decision-making scenarios for reducing the computational complexity in a green cognitive radio receiver Radio Science, 49, 861-889, doi:10.1002 Abstract The idea that we highlight in this paper is how to reduce the computational complexity by limiting the processing in the receiver chain. For this we seek for limiting the use of the beamforming process and the equalization process according to the conditions. Indeed, with the cognitive features the receiver observes its environment and decides to either keep or turn off these processes without degrading its performances. The decision method that we developed is based on the statistical modeling of the radio environment, and the purpose of this approach is to minimize the percentage of bad decisions by considering the errors of observation. Furthermore, we first address the two decision scenarios separately and we seek to prove that the decisions to turn off the equalizer and the beamforming, when they are not necessary, lead to reduce the computational complexity of the receiver. Then we focus on the behavior of the receiver facing both decision scenarios. Thus, we formulate the decision problem in two different ways: in the first case we consider that the receiver performs joint decisions about the two operations of beamforming and equalization, in the second case we suppose that it handles the two decision scenarios sequentially. We compare then the performance of the receiver in the two cases.

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Traffic Demand and Energy Efficiency in Heterogeneous Cellular Mobile Radio Networks

Cited by 84 publications

References 5 publications

Stochastic Geometric Modeling and Analysis of Non-Uniform Two-Tier Networks: A Stienen’s Model-Based Approach

Stochastic Geometric Modeling and Analysis of Non-Uniform Two-Tier Networks: A Stienen’s Model-Based Approach

Optimizing Area Power Consumption in LTE type Cells

Decision‐making scenarios for reducing the computational complexity in a green cognitive radio receiver

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