Finding green spots and turning the spectrum dial: Novel techniques for green mobile wireless networks

Fusco, Giordano; Buddhikot, Milind M.; Gupta, Himanshu; Venkatesan, Sivarama

doi:10.1109/dyspan.2011.5936255

Cited by 14 publications

(13 citation statements)

References 7 publications

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“…Finally, we analyze how much accuracy is important. To this end, we study two different resource management applications -(i) energy savings [18] and (ii) opportunistic [29]. scheduling.…”

Section: B Estimating Base Station Loads and Applicationsmentioning

confidence: 99%

“…The first application we study uses load information to save energy expenditure at the base stations [18]. Energy is saved by turning off a subset of base stations and adjusting the transmission power of the other base stations so that there is no coverage hole.…”

Section: A Application 1: Energy Savingsmentioning

confidence: 99%

“…The constraint here is that no base station gets overloaded. We use the algorithm presented in [18] where power is allocated adaptively, depending on the users' demand. Given the load information and the number of subscribers under each base station, the algorithm decides which base stations to turn off and adjusts the transmission power of other active base stations so that all subscribers' demands in terms of throughput requirements are met.…”

Section: A Application 1: Energy Savingsmentioning

confidence: 99%

“…It is not always clear what type of accuracy is appropriate unless it is tied to an application that makes direct use of the measurement data. Thus, we also study the applicability of our proposed model for two applications in cellular networks -(i) energy savings [18] and (ii) opportunistic scheduling [29].…”

Section: Introductionmentioning

confidence: 99%

“…Comparison of the outcomes of the energy saving algorithm[18] when using (i) original data set and (ii) original data only for a set of base stations and estimations for the rest.base station for each two hour period, and (ii) again assuming that only a set of S base stations uses original data and the rest use estimates based on the described approach. Different values of |S| is used for the evaluation.…”

mentioning

confidence: 99%

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Learning probabilistic models of cellular network traffic with applications to resource management

Paul

Ortiz

Das

et al. 2014

2014 IEEE International Symposium on Dynamic Spectrum Access Networks (DYSPAN)

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Given the exponential increase in broadband cellular traffic it is imperative that scalable traffic measurement and monitoring techniques be developed to aid various resource management methods. In this paper, we use a machine learning technique to learn the underlying conditional dependence and independence structure in the base station traffic loads to show how such probabilistic models can be used to reduce the traffic monitoring efforts. The broad goal is to exploit the model to develop a spatial sampling technique that estimates the loads on all the base stations based on actual measurements only on a small subset of base stations. We take special care to develop a sparse model that focuses on capturing only key dependences. Using trace data collected in a network of 400 base stations we show the effectiveness of this approach in reducing the monitoring effort. To understand the tradeoff between the accuracy and monitoring complexity better, we also study the use of this modeling approach on real applications. Two applications are studied -energy saving and opportunistic scheduling. They show that load estimation via such modeling is quite effective in reducing the monitoring burden.

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“…Finally, we analyze how much accuracy is important. To this end, we study two different resource management applications -(i) energy savings [18] and (ii) opportunistic [29]. scheduling.…”

Section: B Estimating Base Station Loads and Applicationsmentioning

confidence: 99%

Section: A Application 1: Energy Savingsmentioning

confidence: 99%

Section: A Application 1: Energy Savingsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Learning probabilistic models of cellular network traffic with applications to resource management

Paul

Ortiz

Das

et al. 2014

2014 IEEE International Symposium on Dynamic Spectrum Access Networks (DYSPAN)

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Energy efficient base station maximization switch off scheme for LTE-advanced

Bousia

Kartsakli

Alonso

et al. 2012

2012 IEEE 17th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD)

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In this paper, we introduce a solution to a maximization problem for switching off Base Stations in an LTE-Advanced wireless network. Our scheme exploits the existence of idle periods in a cellular network during “night zone”, when the traffic load is low. This work focuses on finding the optimal combination of switched on and off Bases Stations leading to the maximum energy saving. In addition, the major goal lies on increasing the energy efficiency by optimizing the power utilization of the Base Stations without sacrificing the offered Quality of Service. The proposed solution achieves significant energy saving and its performance is evaluated by means of analytical model, as well as simulations results.Peer ReviewedPostprint (published version

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