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Model-driven Machine Learning Approaches for Mobility Classification in Intelligent 5G Network


Channel information is essential to unleash the benefits of 5G New Radio (NR) by enabling network intelligence that adapts transmissions to users’ channels. In this paper, we propose model-driven feature design and use support vector machine to classify users’ speed range. Our model-driven features are designed based on stochastic channel modeling. Multiple features are derived from time-domain cross-correlation and time-domain auto-correlation function of the sounding reference signals. The classifier is trained and verified with extensive standard compliant simulation channels at different SNR levels and speeds, and attains greater than 90% accuracy.

Author: Tiexing Wang, Yeqing Hu, Yang Li, Junmo Sung, Rui Wang, Charlie Zhang

Published: IEEE Wireless Communications and Networking Conference (WCNC)

Date: Dec 31, 2021