Abstract
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.