Deep-broad Learning System for Traffic Flow Prediction toward 5G Cellular Wireless Network

• Published in: 2020 International Wireless Communications and Mobile Computing (IWCMC) pp. 940 - 945
• Main Authors: Chen, Mingzi, Wei, Xin, Gao, Yun, Huang, Liqi, Chen, Mingkai, Kang, Bin
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.06.2020
• Subjects: 5G cellular wireless network; 5G mobile communication; Artificial intelligence; Deep learning; Deep-broad learning system; Feature extraction; Learning systems; Machine learning; Neural networks; Predictive models; Time series analysis; Traffic flow prediction
• ISSN: 2376-6506
• DOI: 10.1109/IWCMC48107.2020.9148092

Nowadays, accurate traffic flow prediction toward 5G cellular wireless network has become an indispensable part for future artificial intelligence (AI)-assisted network. Meanwhile, low delay communication is also the essential part in the upcoming 5G era. However, traditional deep learning models applied in traffic flow prediction have many drawbacks, such as too much running time and computational resources. To tackle these issues, especially jointly considering effectiveness and efficiency, we design a deep-broad learning system (DBLS) for traffic flow prediction. Specifically, based on broad learning system (BLS), we firstly adopt deep representative learning to extract meaningful information from raw data in mapped feature nodes. Then, to further improve the performance of prediction, we add some other nodes i.e., enhancement nodes generated from mapped features as extra inputs to enhance the representative capability. Finally, taking mapped features nodes and enhancement nodes as inputs of the last-layer neural network, we apply ridge regression to compute the final weights quickly. Experimental results demonstrate that our proposed DBLS can make full use of advantages of both deep neural network and traditional BLS to increase the accuracy of traffic flow prediction, meanwhile, maintaining low complexity and running time.