A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Burst Signal Recognition. <s> Signal identification is a crucial subject in cognitive radio (CR) systems. In GSM spectrum refarming or spectrum monitoring scenarios, CR is required to identify on-the-air signals like long term evaluation (LTE), global system mobile (GSM). Second-order cyclostationary detection is an identification method robust to noise uncertainty and used widely in spectrum sensing. However, it requires high sampling rate and long processing time. In this paper, we first propose a compressed sensing (CS) based sampling structure to reduce the sampling rate using the second-order cyclostationary features of Time Division-LTE (TD-LTE) and GSM signals. Furthermore, an identification method for TD-LTE and GSM signals based on CS is employed to reduce sensing time. The performance of the method is evaluated by the practical on-the-air-signals measured with a spectrum analyzer. Numerical results show that our method can achieve a high detection probability with a low sampling complexity. <s> BIB001 </s> A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Burst Signal Recognition. <s> Recently, radar frequency bands have been drawing much attention as possible candidates for cognitive radio, since their utilization was revealed to be relatively low. One important functionality of a cognitive radio system is the spectrum sensing, which detects the presence of a primary user (PU) signal. The pulse radar signal, which is the PU signal in radar bands, is characterized by its sparsity in the time domain. Exploiting this sparsity, we present a spectrum sensing technique that combines the intermediate detections from multiple small sliding sensing windows of the received radar signal into a final decision. We also show by computer simulation that it can achieve improvement of the sensing performance in comparison with the conventional single-window-based spectrum sensing approach. <s> BIB002
Burst signal has been widely used in military field and there will be broad prospects for dynamic spectrum access in the future. The burst signal has the characteristics of short duration and uncertainty of starting and ending time, which poses great challenges to Wireless Communications and Mobile Computing 9 signal recognition  BIB001 BIB002 . Generally, the processing flow of signal recognition includes data acquisition, preprocessing, classification, and decision. The high quality labels have a crucial role for a satisfactory recognition accuracy but are relatively time-consuming in burst communication systems. Therefore, there are still research prospects on the identifying burst signals.