OFDM sensing based on deep learning
Shilian Zheng, Fei Wei, Xiaoyu Zhou, Fengyang Gu, Keqiang Yue, Caiyi Lou, Zhijin Zhao, Xiaoniu Yang · Physical Communication · 2023
Detection of OFDM signals has received paramount interest especially for spectrum sensing in cognitive radios. We present a deep learning-based OFDM signal detection method, namely OFDM-DetNet, which combines feature learning and classifier in a single densely connected convolutional network (DenseNet) structure. OFDM-DetNet completes the detection in an end-to-end manner and utilizes the raw in-phase (I) and quadrature (Q) components of the received signal as the input and the detection result as the output. In addition, a confidence-based fusion method, namely OFDM-DetNet-F, is proposed to further improve the detection performance under low signal-to-noise ratio (SNR) scenarios without retraining the network. We construct three datasets with simulations and three datasets with real-world signals to evaluate the performance of the proposed methods. Both simulation results and experimental results indicate the superior performance of the proposed OFDM-DetNet compared with two deep learning-based OFDM detection methods, i.e., stacked autoencoder (SAE)-based method and covariance matrix (CM)-based detection method, in terms of probability of detection. Furthermore, the performance can be further improved with our proposed fusion scheme in low SNR region.