Regional Features Conditioned Diffusion Models for 5G Network Traffic Generation
Authors: 
Xiaoqian Qi, Haoye Chai, Li Yu, Yong Li, Zhaocheng WangAuthors Info & Claims
Pages 396 - 409
Abstract
The fifth-generation (5G) mobile network has significantly enhanced people's lives with faster internet speed and more reliable connections. However, there is still insufficient coverage of 5G networks worldwide, requiring telecom operators to deploy more base stations to meet the increasing demand for 5G's further commercialization. In this regard, a major challenge is understanding user network behaviors and traffic demands in target areas where 5G has not yet been deployed, which is crucial for developing a more efficient base station deployment strategy. Mobile traffic generation is a potential approach that enables operators to preemptively estimate user network demands in target areas, thereby specifying corresponding deployment strategies to enhance network performance. However, existing methods have limitations in capturing spatio-temporal features of 5G mobile traffic, particularly in areas with insufficient 5G coverage and limited historical 5G traffic data. To fill this gap, we introduce a regional feature conditioned diffusion framework for 5G network traffic generation. Our models explore the relationship between 5G traffic and existing 4G traffic, utilizing a customized cross attention mechanism and graph convolutional networks (GCN) to capture the correlation between network traffic and regional features. Based on this relationship, the framework can characterize mobile network traffic demands, thereby achieving high-fidelity 5G traffic generation in target regions with insufficient 5G coverage. Extensive experiments on real-world datasets have shown that the proposed scheme outperforms state-of-the-art baselines by more than 10%, demonstrating its high-fidelity generation capability, controllability, and generalizability. Moreover, we have deployed our scheme on China Mobile's Jiutian Platform as a network traffic simulator to improve 5G base station deployment strategies.
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October 2024
743 pages
ISBN:9798400711077
DOI:10.1145/3678717
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Published: 22 November 2024
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SIGSPATIAL '24: The 32nd ACM International Conference on Advances in Geographic Information Systems
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