Abstract:

The L-band Digital Aeronautical Communication System （LDACS） is a new generation of ground to air broadband data link for civil aviation. To dynamically occupy the idle spectrum of adjacent Distance Measuring Equipment （DME） and achieve broadband communication under limited spectrum resources， a frequency spectrum sensing method combining wavelet transform and deep convolutional neural network is proposed. Firstly， the received signal is transformed into the wavelet domain through wavelet transform. Then， the wavelet coefficients of the DME signal are extracted using signal differences， and the coefficients are subjected to low-frequency zeroing and hard thresholding to form a dataset. Meanwhile， multiple sets of datasets under signal-to-noise ratios are fused for processing. Finally， the preprocessed data is used to train the convolutional neural network Vgg19， resulting in a spectrum sensing model for DME signals. This method combines the multi-scale feature extraction ability of wavelet transform with the deep feature learning ability of Vgg19 to extract effective features of DME signals in low signal-to-noise ratio environments and generate a dataset. The trained perception model significantly improves the accuracy and stability of perception， and performs well in low signal-to-noise ratio environments. When the signal-to-noise ratio is greater than -13 dB， the detection probability can reach 100%.

Key words: LDACS, DME system, spectrum sensing, wavelet transform, convolutional neural network