Deep Learning-Based Frequency Offset Estimation
Tao Chen Shilian Zheng Jiawei Zhu Qi Xuan Xiaoniu Yang
In wireless communication systems, the asynchronization of the oscillators in
the transmitter and the receiver along with the Doppler shift due to relative
movement may lead to the presence of carrier frequency offset (CFO) in the
received signals. Estimation of CFO is crucial for subsequent processing such
as coherent demodulation. In this brief, we demonstrate the utilization of deep
learning for CFO estimation by employing a residual network (ResNet) to learn
and extract signal features from the raw in-phase (I) and quadrature (Q)
components of the signals. We use multiple modulation schemes in the training
set to make the trained model adaptable to multiple modulations or even new
signals. In comparison to the commonly used traditional CFO estimation methods,
our proposed IQ-ResNet method exhibits superior performance across various
scenarios including different oversampling ratios, various signal lengths, and
different channels