A Meta-Learning Based Framework for Cell-Level Mobile Network Traffic Prediction

dmTP: A Deep Meta-Learning Based Framework for Mobile Traffic Prediction

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Deep Transfer Learning for Intelligent Cellular Traffic Prediction Based on Cross-Domain Big Data

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Wireless Traffic Prediction With Scalable Gaussian Process: Framework, Algorithms, and Verification

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A survey on LSTM memristive neural network architectures and applications

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Citywide Cellular Traffic Prediction Based on Densely Connected Convolutional Neural Networks

(2018) Chuanting Zhang et al.   IEEE COMMUNICATIONS LETTERS

Spatio-Temporal Wireless Traffic Prediction with Recurrent Neural Network

(2018) Chen Qiu et al.   IEEE Wireless Communications Letters

Intelligent 5G: When Cellular Networks Meet Artificial Intelligence

(2017) Rongpeng Li et al.   IEEE WIRELESS COMMUNICATIONS

LSTM network: a deep learning approach for short-term traffic forecast

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Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction

(2017) Xiaolei Ma et al.   SENSORS

Next Generation 5G Wireless Networks: A Comprehensive Survey

(2016) Mamta Agiwal et al.   IEEE Communications Surveys and Tutorials

Big Data Driven Mobile Traffic Understanding and Forecasting: A Time Series Approach

(2016) Fengli Xu et al.   IEEE Transactions on Services Computing

A multi-source dataset of urban life in the city of Milan and the Province of Trentino

(2015) Gianni Barlacchi et al.   Scientific Data

The prediction analysis of cellular radio access network traffic: From entropy theory to networking practice

(2014) Rongpeng Li et al.   IEEE COMMUNICATIONS MAGAZINE

Design considerations for a 5G network architecture

(2014) Patrick Agyapong et al.   IEEE COMMUNICATIONS MAGAZINE

Metalearning: a survey of trends and technologies

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Energy savings scheme in radio access networks via compressive sensing-based traffic load prediction

(2012) Rongpeng Li et al.   Transactions on Emerging Telecommunications Technologies

Time Series Prediction Using Support Vector Machines: A Survey

(2009) Nicholas Sapankevych et al.   IEEE Computational Intelligence Magazine