A Deep-Learning Model for Estimating the Impact of Social Events on Traffic Demand on a Cell Basis

Bejarano-Luque, Juan L.; Toril, M.; Fernández-Navarro, Mariano; Gijón, Carolina; Luna-Ramírez, Salvador

doi:10.1109/access.2021.3078113

Cited by 13 publications

(5 citation statements)

References 58 publications

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“…Such as the modal particle "ba" and meaningless punctuation marks. This article adopts the stop word list published by the Chinese Academy of Sciences, and deletes the stop words in the text according to the matching of the stop word list [15]. If the number of remaining words in the text after removing the stop word is 0, the text will be judged as invalid and filtered out.…”

Section: Analysis Of Teaching Textmentioning

confidence: 99%

Extraction and Analysis of Influencing Factors of Scientific and Technological Ability Improvement of University Teachers Based on Deep Learning Model

Wang

Yan

2022

Advances in Multimedia

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In order to improve the development and construction level of colleges and universities and help teachers improve their scientific and technological abilities, a model based on deep learning model for the extraction and analysis of factors affecting the improvement of college teachers’ scientific and technological abilities was proposed. This article analyzes the data of teaching evaluation and finds that the text contains students’ subjective understanding of teachers’ teaching quality defects. By extracting key words from students’ teaching evaluation texts and combining with the teaching evaluation indexes, a teaching evaluation label system integrating teaching evaluation texts is designed. In order to find the defects of teachers’ teaching ability, this article, based on the principle of data portrait, combines the characteristics of teachers’ personal basic information, curriculum information, teaching evaluation information, and social relations to portrait teachers. The experimental results show that the F1 value extracted from the evaluation labels fluctuates in the evaluation text data of different colleges, with the lowest value of 91.7% in the School of Statistics and the highest value of 95.8% in the School of Foreign Languages. The algorithm in this article has a higher F1 value of the evaluation label vector extracted from the evaluation text of different grades. F1 values showed a trend of gradient decline with the increase of grade, and the decreasing range became bigger and bigger. Conclusion. The constructed teacher portraits are more accurate and effective, and provide a comprehensive and effective data model for teaching ability improvement method recommendation strategy.

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Section: Analysis Of Teaching Textmentioning

confidence: 99%

Extraction and Analysis of Influencing Factors of Scientific and Technological Ability Improvement of University Teachers Based on Deep Learning Model

Wang

Yan

2022

Advances in Multimedia

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“…Traffic forecasting is the most sophisticated part of network dimensioning [3]. Because to make cellular network business more profitable, investors are always looking for the proper Capital Expenditure (CAPEX) in the right cell/site/location and reducing Operational Expenditure (OPEX).…”

Section: A Motivationmentioning

confidence: 99%

“…Traffic prediction or forecasting is vital for anticipating cellular network status, identifying user usage patterns, and estimating quality-of-service or major resource allocation parameters [3]. Fang et al [10] revealed city-scale level traffic forecasting based on a cell handover-aware graph neural network.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Based Fusion Model for Multivariate LTE Traffic Forecasting and Optimized Radio Parameter Estimation

et al. 2023

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With the evaluation of cellular network internet data traffic, forecasting and understanding traffic patterns become the critical objectives for managing the network-designed Quality of Service (QoS) benchmark. For this purpose, cellular network planners often use different methodologies for predicting data traffic. However, traditional traffic forecasting approaches are erroneous. As well as most of the time, traditional traffic forecasts are high-level or a generously large regional cluster level. Also, eNodeB-level utilization with concerning traffic forecasting is not readily available. As a result, user experience degradation or unnecessary network expansion is triggered based on the traditional method. This research deals with extensive 6.2 million real network time series Long-Term Evolution (LTE) data traffic and other associate parameters, including eNodeB-wise Physical Resource Block (PRB) utilization, which focuses on building a traffic forecasting model with the help of multivariate feature inputs and deep learning algorithms. A stateof-the-art Deep Learning algorithm-based fusion model is proposed. The combination of different deep learning algorithms, namely Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM) and Gated Recurrent Unit (GRU), enables traffic forecasting at a granular eNodeB-level and also provides eNodeB-wise forecasted PRB utilization. In this research R 2 score value for the proposed fusion model is 0.8034, which outperforms traditional models. Apart from the PRB utilization, QoS threshold was devised as 70% from a real network experience to trigger soft parameter tuning decisions. Based on the forecasted PRB utilization, this research proposed a unique algorithm that estimates eNodeB-level soft capacity parameter optimization for a short-term step-up solution or long-term network expansion to ensure a guaranteed QoS benchmark.

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“…Similarly, areas of poor signal coverage or service performance (i.e., black spots) can be detected by processing geotagged text messages in social networks [32]. At the same time, social event information obtained from browser results or open data repositories can be used to explain abnormal network behavior during troubleshooting procedures [33], detect anomalies [34,35] or predict the network performance [36].…”

Section: Related Workmentioning

confidence: 99%

Statistical Model for Mobile User Positioning Based on Social Information

et al. 2021

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In spite of the vast set of measurements provided by current mobile networks, cellular operators have problems pinpointing problematic locations because the origin of such measurements (i.e., user location) is usually not registered. At the same time, social networks generate a huge amount of data that can be used to infer population density. In this work, a data-driven model is proposed to deduce the statistical distribution of connections, exploiting the knowledge of network layout and population density in the scenario. Due to the absence of GPS measurements, the proposed method combines data from radio connection traces stored in the network management system and geolocated posts from social networks. This information is enriched with user context information inferred from data traffic attributes. The method is tested with a large trace dataset from a live Long Term Evolution (LTE) network and a database of geotagged posts from social networks collected in real-time.

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A Deep-Learning Model for Estimating the Impact of Social Events on Traffic Demand on a Cell Basis

Cited by 13 publications

References 58 publications

Extraction and Analysis of Influencing Factors of Scientific and Technological Ability Improvement of University Teachers Based on Deep Learning Model

Extraction and Analysis of Influencing Factors of Scientific and Technological Ability Improvement of University Teachers Based on Deep Learning Model

Deep Learning Based Fusion Model for Multivariate LTE Traffic Forecasting and Optimized Radio Parameter Estimation

Statistical Model for Mobile User Positioning Based on Social Information

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