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

By Chuanting Zhang, Haixia Zhang, Jingping Qiao, D. Yuan, Minggao Zhang. IEEE Journal on Selected Areas in Communications, 2019

316 citations recorded by the source corpus.

Machine (deep) learning-enabled accurate traffic modeling and prediction is an indispensable part for future big data-driven intelligent cellular networks, since it can help autonomic network control and management as well as service provisioning. Along this line, this paper proposes a novel deep learning architecture, namely Spatial–Temporal Cross-domain neural Network (STCNet), to effectively capture the complex patterns hidden in cellular data. By adopting a convolutional long short-term memory network as its subcomponent, STCNet has a strong ability in modeling spatial–temporal dependencies. Besides, three kinds of cross-domain datasets are actively collected and modeled by STCNet to capture the external factors that affect traffic generation. As diversity and similarity coexist among cellular traffic from different city functional zones, a clustering algorithm is put forward to segment city areas into different groups, and consequently, a successive inter-cluster transfer learning strategy is designed to enhance knowledge reuse. In addition, the knowledge transferring among different kinds of cellular traffic is also explored with the proposed STCNet model. The effectiveness of STCNet is validated through real-world cellular traffic datasets using three kinds of evaluation metrics. The experimental results demonstrate that STCNet outperforms the state-of-the-art algorithms. In particular, the transfer learning based on STCNet brings about 4%~13% extra performance improvements.

Subfields: Building and Construction, Electrical and Electronic Engineering, Transportation

View Paper: https://www.semanticscholar.org/paper/0b3e27f66fbbd268dfdb9be29173e8c6347bbfd3

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Electrical and Electronic Engineering

1. Lajos Hanzo
2. H. Vincent Poor
3. Chenyang Yang
4. Yonghui Li
5. Shiwen Mao
6. JingJing Wang
7. Branka Vucetic
8. Chunxiao Jiang

In Transportation

1. Shiwen Mao
2. H. Vincent Poor

Citations in the corpus

• A Tutorial on Ultrareliable and Low-Latency Communications in 6G: Integrating Domain Knowledge Into Deep Learning (2021, methodology): “2) Traffic Prediction: As shown in [212], [213], and [215], predicting the traffic load in a wireless network is helpful for network deployment.”
• Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks (2019, methodology): “[338] traffic prediction CNN traffic modeling and prediction based on a convolutional long short-term memory network [339] resource allocation DNN universal DNN framework for a number of common wireless resource allocation problems”
• Contour Stella Image and Deep Learning for Signal Recognition in the Physical Layer (2020, methodology): “Confronted with rapid changing characteristics of wireless signals, transfer learning can help DNN to quickly adapt to new data before it expires [9].”