Deep Learning-Based Frequency Offset Estimation
This addresses the problem of signal synchronization for wireless communication systems, but it is incremental as it applies an existing deep learning method to a known bottleneck.
The paper tackled carrier frequency offset (CFO) estimation in wireless communication by using a residual network (ResNet) on raw I/Q signal components, resulting in superior performance compared to traditional methods across various scenarios.
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