# A Deep Learning Framework for Signal Detection and Modulation Classification  
[PERSON]  
1 PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, Henan, China; [PERSON]IL_ADDRESS] (H.P. ); [EMAIL_ADDRESS] (X.Q. ); [EMAIL_ADDRESS] (G.L. ); [EMAIL_ADDRESS] (S.Y)  
[PERSON]  
1 PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, Henan, China; [PERSON]IL_ADDRESS] (H.P. ); [EMAIL_ADDRESS] (X.Q. ); [EMAIL_ADDRESS] (G.L. ); [EMAIL_ADDRESS] (S.Y)  
[PERSON]  
1 PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, Henan, China; [PERSON]IL_ADDRESS] (H.P. ); [EMAIL_ADDRESS] (X.Q. ); [EMAIL_ADDRESS] (G.L. ); [EMAIL_ADDRESS] (S.Y)  
[PERSON]  
1 PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, Henan, China; [PERSON]IL_ADDRESS] (H.P. ); [EMAIL_ADDRESS] (X.Q. ); [EMAIL_ADDRESS] (G.L. ); [EMAIL_ADDRESS] (S.Y)  
[PERSON]  
1 PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, Henan, China; [PERSON]IL_ADDRESS] (H.P. ); [EMAIL_ADDRESS] (X.Q. ); [EMAIL_ADDRESS] (G.L. ); [EMAIL_ADDRESS] (S.Y)  
[PERSON]  
1 PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, Henan, China; [PERSON]IL_ADDRESS] (H.P. ); [EMAIL_ADDRESS] (X.Q. ); [EMAIL_ADDRESS] (G.L. ); [EMAIL_ADDRESS] (S.Y)  
[PERSON]  
1 PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, Henan, China; [PERSON]IL_ADDRESS] (H.P. ); qinxin_0920@163.  
algorithms [19; 20; 21; 22], such as signal high-order cumulants and cyclic spectrum, have excellent anti-noise performance. The computational complexity of these methods is relatively low, but the selection of features relies too much on expert experience. It is difficult to obtain features that can adapt to non-ideal conditions. In particular, it is challenging to set the decision threshold when there are plenty of modulation formats to be classified. Deep Learning (DL) techniques [23; 24] have made outstanding achievements in Computer Vision [25; 26] (CV) and Natural Language Processing [27; 28] (NLP) for their strong self-learning ability. Recently, more and more researchers use DL techniques to solve signal processing problems. For signal detection, many DL-based methods, such as [29; 30; 31], detect signals in narrowband environment. These methods only detect the existing of signal, but can not estimate the relevant parameters. Therefore, developing a technique leverages deep learning to detect signal efficiently and effectively is still a challenging problem. For DL-based modulation classification, there has been some reported work, including [32; 33; 34; 35; 36]. For example, some researchers used the signal IQ waveform as data representation and learned the sample using CNNs [32; 33; 34]. Other researchers focused on developing methods to represent modulated signals in data formats for CNNs. Among these methods, constellation-based algorithms [35; 36] have been widely utilized, where signal prior knowledge is fully considered. In this study, DL techniques are fully utilized in multi-signals detection and modulation recognition. For multi-signals detection, we use the deep learning target detection network to detect the location of each signal. In our initial research, the used model is SSD networks, which is a relatively advanced target detection network. Furthermore, we use the time-frequency spectrum as the signal characteristic expression. Due to the time-frequency characteristic of the M-ary Frequency Shift Keying (MFSK) format signals, we can identify the modulation format while the signal is detected. Meanwhile, for M-ary Phase Shift Keying (MPSK), M-ary Amplitude Phase Shift Keying (MAPSK), and M-ary Quadrature Amplitude Modulation (MQAM) signal, the difference in the time-frequency spectrum is not sufficient to identify the signal modulation. Therefore, during the signal detection process, we identify them in the same format, and only detect the signal presence or absence.