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This paper investigates deep neural networks for radio signal classification.Instead of performing modulation recognition and combining it with furtheranalysis methods, the classifier operates directly on the IQ data of thesignals and outputs the transmission mode. A data set of radio signals of 18different modes, that commonly occur in the HF radio band, is presented andused as a showcase example. The data set considers HF channel properties and isused to train four different deep neural network architectures. The results ofthe best networks show an excellent accuracy of up to 98%.

Classification of Radio Signals and HF Transmission Modes with Deep Learning