Abstract
Deep learning (DL) is a powerful technique which has achieved great success in many applications. However, its usage in communication systems has not been well explored. This paper investigates algorithms for multi-signals detection and modulation classification, which are significant in many communication systems. In this work, a DL framework for multi-signals detection and modulation recognition is proposed. Compared to some existing methods, the signal modulation format, center frequency, and start-stop time can be obtained from the proposed scheme. Furthermore, two types of networks are built: (1) Single shot multibox detector (SSD) networks for signal detection and (2) multi-inputs convolutional neural networks (CNNs) for modulation recognition. Additionally, the importance of signal representation to different tasks is investigated. Experimental results demonstrate that the DL framework is capable of detecting and recognizing signals. And compared to the traditional methods and other deep network techniques, the current built DL framework can achieve better performance.
Highlights
Cognitive radio (CR) [1,2,3] has been used to refer to radio devices that are capable of learning and adapting to their environment
This paper addresses the topic of Deep learning (DL) based multi-signals detection and modulation classification
For DL target detection techniques, the existing algorithms are mainly divided into two kinds: algorithms based on region recommendation the existing algorithms are mainly divided into two kinds: algorithms based on region recommendation and algorithms based on regression
Summary
Cognitive radio (CR) [1,2,3] has been used to refer to radio devices that are capable of learning and adapting to their environment. Many wideband multi-signals detection algorithms are based on energy detector (ED). Many non-threshold-based detection algorithms have been proposed [14,15,16,17]. Algorithms based on signal phase, frequency, and amplitude have been widely used [18]. These algorithms are significantly affected by noise, and the performance can be substantially degraded in low SNR condition. Quadrature Amplitude Modulation (MQAM) signal, the difference in the time-frequency spectrum is not sufficient to identify the signal modulation. This paper addresses the topic of DL based multi-signals detection and modulation classification.
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