Deep learning‐based digital signal modulation identification under different multipath channels

Abstract

Deep learning (DL) has been applied to digital signal modulation identification (DSMI) due to its powerful feature learning ability. However, most of the existing DL-based DSMI methods are limited to specific experimental scene relating to the additive white Gaussian noise (AWGN) channel or static multipath channel. The result is that the trained network has deteriorative identification accuracy when the channel conditions change unless retrained. To solve the problem, this paper proposes a DSMI method suitable for orthogonal frequency division multiplexing (OFDM) under different multipath channels, including the variation of delay, path number and channel coefficient. This method can accurately detect the modulation feature rather than the channel's to identify the modulation type, thus reducing the network training amount. The method is divided into two parts. Firstly, traditional signal processing methods are combined, including various channel estimators and equalisers to compensate for the channel. Then a robust DL network, RSN-MI, is designed as a classifier. Unlike other DL-based DSMI methods, the influence of signal processing algorithms on DSMI performance are focused on rather than model parameters. Besides, the proposed classifier is compared with the DSMI classifier in other contributions. The results show that the classifier works better in different multipath channels. INTRODUCTIONDigital signal modulation identification (DSMI) is of great significance in wireless communication systems. As a necessary step between signal detection and demodulation, it has attracted more and more attention. The main task of DSMI is to identify the modulation type of the received signal without using any prior knowledge or using a small amount of prior knowledge, which lays the foundation for subsequent signal demodulation and information acquisition. DSMI plays an important role in both military and civil fields [1], especially in military fields, modulation identification is the prerequisite of jamming and monitoring enemy communications. It can also be used for spectrum detection and signal confirmation etc. in civil fields. Two traditional digital signal modulation identification approaches have been extensively studied: one is the maximum likelihood (ML) approach, and the other is the pattern recognition (PR) approach [2]. The maximum likelihood approach is based on the likelihood function of received signal, and the likelihood ratio is compared with the appropriate threshold value to achieve the best classification effect under the minimum errorThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…To improve the accuracy of the CNN network, there have been several methods using external algorithms (such as dynamic threshold updating) proposed and demonstrated [27]. They are based on signal preprocessing with a sacrifice of system complexity, which leads to additional computation.…”

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“…Zhang J, Hu S proposed a digital signal modulation identification (DSMI) method suitable for orthogonal frequency division multiplexing (OFDM) under different multipath channels. This method can accurately detect the modulation charac-teristics rather than the channel characteristics to identify the modulation type, thereby reducing the amount of network training [20]. In the maritime VHF communication scenarios, the signal differences of the same modulation mode are mainly formed by the equipment environment and channel environment, so this kind of method lacks common attention to the signal and channel.…”