Accurate Spectrum Sensing with Improved DeepLabV3+ for 5G-LTE Signals Identification
Gia-Vuong Nguyen, Ca Van PHAN, Thien Huynh‐The · 2023
This paper presents a deep learning approach for fifth-generation (5G) and Long-Term Evolution (LTE) signal discrimination, explicitly focusing on identifying modulated signals in next-generation wireless networks. The mixture of modulated signals, which is essentially difficult to discern in the form of a complex envelope, should be converted into a visually informative spectrogram image by applying the Fast Fourier transform (FFT). To segment spectral regions of 5G new radio (NR) and LTE in a spectrogram, we aptly improve DeepLabV3+, a deep encoder-decoder network for semantic segmentation, by incorporating an adaptive Atrous Spatial Pyramid Pooling (ASPP) block and an attention mechanism to accommodate intrinsic signal characteristics and amplify relevant features, respectively. Besides increasing the learning efficiency in the encoder, the improvement enriches the recovery capability of crucial 5G and LTE details, thus resulting in more accurate signal identification in the spectrogram image. Relying on the simulation results benchmarked on a dataset consisting of spectral images containing both LTE and 5G signals, the new network demonstrated effectiveness when compared to the original version by increasing global accuracy, mean intersection-over-union (IoU), and mean boundary-F1-score (BFScore) up to , and in that order. For medium SNR level, it can achieve global accuracy and mean IoU, while also showing robustness under various practical channel impairments.