Dynamic Base Station or Relay Station deployment and small cell On/Off strategy for green ultra dense networks

Ameer, Reshma; Rolly, Rinju Mariam; Jacob, Jaison

doi:10.1016/j.procs.2018.10.382

Cited by 3 publications

(2 citation statements)

References 3 publications

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“…With a neural network method, the LSTM neural network and the GRU neural network unit are respectively used, and the same feature vector is constructed. In the machine learning method, the event trigger identification classifier is constructed according to the method by Ameera et al [1], and the part-of-speech feature, called the entity feature, and the dependency relationship feature are used. The contextual characteristics of the word (including three words on the left side of the candidate event trigger and all its features, and two words on the right side of the and all its features).…”

Section: E Comparison Experiments With Other Machine Learning Methodsmentioning

confidence: 99%

“…With the development of communication technologies from 2G to 5G, the capacity of single base station access has increased. However, to avoid a dynamic crush from a flash crowd, automatically providing the appropriate response time to meet the user expectations and supporting an uninterrupted use of communication services remain challenging problems [1], [2].…”

Section: Introductionmentioning

confidence: 99%

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An Automatic Event Detection Method for Massive Wireless Access Prediction

et al. 2019

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The scale of mobile users for parallel access is constrained by the capacity of the base stations. When extremely dense terminal access exceeds the capacity of the base stations, access failure and a performance degradation will occur. The early detection and prediction of important events and the timely detection of possible large-scale terminal access are significant aspects in ensuring the quality of the communication achieved. For the automatic detection of events, methods based on a neural network can learn features automatically without feature engineering and have been proven to be efficient for event detection. As is well known, constructing an adequate input vector that can represent sufficient information is a challenge to a neural network-based approach, particularly for problems caused by Chinese word segmentation and too many unknown communication words. To cope with this problem, a novel representation method that combines the different features with word vectors is proposed to deal with the problem of Chinese event trigger identification. We then use a gated recurrent unit network to train and predict the event trigger and carry out comparative experiments on different methods and feature combinations. The experiment results of the proposed model show that the F1 value can reach 84% for the experimental dataset. Furthermore, the combination of lexical and syntactic features with a neural network was proven to be helpful for this task, although the contributions vary in magnitude for different features.Our study provides directions for further research on the use of lexical and syntactic features with a neural network for an event detection task.

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Section: E Comparison Experiments With Other Machine Learning Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Automatic Event Detection Method for Massive Wireless Access Prediction

et al. 2019

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A Fuzzy On/Off Switching Strategy for Green Cellular Networks

Dahmani

Gabli

Serghini

et al. 2021

Digital Technologies and Applications

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An Insight into Deployments of Green Base Stations (GBSs) for an Environmentally Sustainable World

Ehiagwina

Kehinde

Adewale

et al. 2021

IOP Conf. Ser.: Mater. Sci. Eng.

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Data traffic and the number of mobile subscribers have increased significantly prompting cellular network operators to install additional mobile cellular base stations (BSs) to meet the increasing demand. This proliferation of BSs has resulted in consequential increase in energy consumption and Green House Gases (GHGs) emission. Several techniques have been deployed to reduce the energy consumption of the base station in what is called a green base station. This paper presents an insight into these approaches and highlights key challenges and potential research directions. It also highlights what environmental sustainability activist, regulators and government can do to incentivize green BSs concept in Nigeria.

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Dynamic Base Station or Relay Station deployment and small cell On/Off strategy for green ultra dense networks

Cited by 3 publications

References 3 publications

An Automatic Event Detection Method for Massive Wireless Access Prediction

An Automatic Event Detection Method for Massive Wireless Access Prediction

A Fuzzy On/Off Switching Strategy for Green Cellular Networks

An Insight into Deployments of Green Base Stations (GBSs) for an Environmentally Sustainable World

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