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Deep Transfer Learning for City-scale Cellular Traffic Generation through Urban Knowledge Graph

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Yong LiAuthors Info & Claims

KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

Pages 4842 - 4851

https://doi.org/10.1145/3580305.3599801

Published: 04 August 2023 Publication History

Abstract

The problem of cellular traffic generation in cities without historical traffic data is critical and urgently needs to be solved to assist 5G base station deployments in mobile networks. In this paper, we propose ADAPTIVE, a deep transfer learning framework for city-scale cellular traffic generation through the urban knowledge graph. ADAPTIVE leverages historical data from other cities that have deployed 5G networks to assist cities that are newly deploying 5G networks through deep transfer learning. Specifically, ADAPTIVE can align the representations of base stations in the target city and source city while considering the environmental factors of cities, spatial and environmental contextual relations between base stations, and traffic temporal patterns at base stations. We next design a feature-enhanced generative adversarial network, which is trained based on the historical traffic data and representations of base stations in the source city. By feeding the aligned target city's base station representations into the trained model, we can then obtain the generated traffic data for the target city. Extensive experiments on real-world cellular traffic datasets show that ADAPTIVE generally outperforms state-of-the-art baselines by more than 40% in terms of Jensen-Shannon divergence and root-mean-square error. Also, ADAPTIVE has strong robustness based on the results of various cross-city experiments. ADAPTIVE has been successfully deployed on the 'Jiutian' Artificial Intelligence Platform of China Mobile to support cellular traffic generation and assist in the construction and operation of mobile networks.

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Deep Transfer Learning for City-scale Cellular Traffic Generation through Urban Knowledge Graph

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cover image ACM Conferences

KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 2023

5996 pages

ISBN:9798400701030

DOI:10.1145/3580305

General Chairs:
Ambuj Singh
UC Santa Barbara, USA
,
Yizhou Sun
UC Los Angeles, USA
,
Program Chairs:
Leman Akoglu
Carnegie Mellon University, USA
,
Dimitrios Gunopulos
University of Athens, Greece
,
Xifeng Yan
UC Santa Barbara, USA
,
Ravi Kumar
Google, USA
,
Fatma Ozcan
Google, USA
,
Jieping Ye
Alibaba DAMO Academy

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Published: 04 August 2023

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August 6 - 10, 2023

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https://dl.acm.org/doi/10.1145/3711682
Jia Z(2025)Research on Image Recognition and Classification Algorithms in Cloud Computing Environment Based on Deep Neural NetworksIEEE Access10.1109/ACCESS.2025.353024213(19728-19754)Online publication date: 2025
https://doi.org/10.1109/ACCESS.2025.3530242
Aazami RMoradi MShirkhani MHarrison AAl-Gahtani SElbarbary Z(2025)Technical Analysis of Comfort and Energy Consumption in Smart Buildings With Three Levels of Automation: Scheduling, Smart Sensors, and IoTIEEE Access10.1109/ACCESS.2025.352685813(8310-8326)Online publication date: 2025
https://doi.org/10.1109/ACCESS.2025.3526858
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