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

IET CommunicationsVolume 15, Issue 15 p. 1950-1962 ORIGINAL RESEARCH PAPEROpen Access Deep learning-based digital signal modulation identification under different multipath channels Jiawen Zhang, Jiawen Zhang National Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China (UESTC), ChengDu, People's Republic of ChinaSearch for more papers by this authorSu Hu, Corresponding Author Su Hu husu@uestc.edu.cn orcid.org/0000-0002-7736-3753 National Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China (UESTC), ChengDu, People's Republic of China Correspondence Su Hu, National Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China (UESTC), ChengDu, People's Republic of China. Email: husu@uestc.edu.cnSearch for more papers by this authorZhaonan Du, Zhaonan Du School of Information and Software Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu, People's Republic of ChinaSearch for more papers by this authorWeiwei Wu, Weiwei Wu National Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China (UESTC), ChengDu, People's Republic of ChinaSearch for more papers by this authorYuan Gao, Yuan Gao Academy of Military Science of the PLA, Beijing, People's Republic of ChinaSearch for more papers by this authorJiang Cao, Jiang Cao Academy of Military Science of the PLA, Beijing, People's Republic of ChinaSearch for more papers by this author Jiawen Zhang, Jiawen Zhang National Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China (UESTC), ChengDu, People's Republic of ChinaSearch for more papers by this authorSu Hu, Corresponding Author Su Hu husu@uestc.edu.cn orcid.org/0000-0002-7736-3753 National Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China (UESTC), ChengDu, People's Republic of China Correspondence Su Hu, National Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China (UESTC), ChengDu, People's Republic of China. Email: husu@uestc.edu.cnSearch for more papers by this authorZhaonan Du, Zhaonan Du School of Information and Software Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu, People's Republic of ChinaSearch for more papers by this authorWeiwei Wu, Weiwei Wu National Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China (UESTC), ChengDu, People's Republic of ChinaSearch for more papers by this authorYuan Gao, Yuan Gao Academy of Military Science of the PLA, Beijing, People's Republic of ChinaSearch for more papers by this authorJiang Cao, Jiang Cao Academy of Military Science of the PLA, Beijing, People's Republic of ChinaSearch for more papers by this author First published: 24 May 2021 https://doi.org/10.1049/cmu2.12207AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat 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. 1 INTRODUCTION Digital 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 error criterion of Bayes [3-6]. Due to the need for more prior knowledge and high algorithm complexity, this approach's application has great limitations. For the pattern recognition approach, it is necessary to transform the received signal to extract features in different dimensions. Higher-order cumulants (HOC), wavelet transform, and signal power ratio are often selected as feature parameters. Then a suitable machine learning classifier, such as K-nearest neighbour (KNN), Decision Tree (DT) etc., is used to identify the modulation type [7-10]. The identification performance of this approach is affected by feature selection, and it is difficult to extract the same feature from multiple modulation types. Besides, when the task data volume is large, this approach is not efficient. With the development of computer science and hardware, Deep learning (DL) technology has been developed rapidly. Deep neural network has been widely applied in classification and object detection tasks which has shown excellent performance. In recent years, considering the massive data generated by the wireless communication system, deep learning has been gradually employed to DSMI tasks and has demonstrated its strong feature learning ability. In [11, 12], digital signals are converted into constellation diagrams as data sources, and then two well-known convolutional neural network (CNN) models, AlexNet and GoogleNet, are used for model training to identify ASK, PSK, and QAM signals successfully. An IC-AMCNET network model is proposed in [13], which can be applied to communication scenarios with deficient delay requirements. This model can greatly reduce the computation complexity. In [14], the cyclic spectrum is first adopted to preprocess the signal, and then the deep autoencoder network is used for modulation identification. The authors from [15] propose a novel deep hierarchical network that can combine high-level and low-level feature maps to predict the modulation type. In [16], a multi-modality fusion model is adopted to incorporate the handcrafted features and image features extracted from received signals. The identification accuracy gets a further improvement with the joint features. However, these methods focus on the impact of neural network structure and parameters on the classification effect. All of the methods mentioned above have common limitations that trained model's performance will deteriorate when the channel environment changes. Most of the work related DSMI is obtained in the additive white Gaussian noise (AWGN) channel, or although the data is received in a multipath environment, there is no communication-related processing on the signal. The result is that a trained network cannot effectively identify the modulation type of the signal which is generated from other channel environments, so it cannot be used in actual wireless communication systems. Besides, almost all of the current communication systems are built based on OFDM technology, but there are few OFDM related DSMI studies [17-19]. Therefore, it is of great research value to find an effective DSMI method suitable for multicarrier OFDM communication system. Considering the above problems, this paper proposes a DSMI method suitable for multicarrier OFDM systems under different multipath channels, including the variation of delay, path number, and channel coefficient. The method proposed in this paper is carried out in two steps. In the preprocessing stage, we use a second-order statistical blind (SOSB) channel estimator and a subspace blind (SB) channel estimator to obtain the fading coefficient of multipath channels. The purpose of using the blind estimation algorithms are to apply our method to non-cooperative communication. They are also compared with the least square (LS) estimator and minimum mean square error (MMSE) estimator based on the known sequence. Then the zero-forcing (ZF) equaliser and MMSE equaliser are used to recover the received data from different channel environments. In the identification stage, we propose a deep neural network model RSN-MI suitable for DSMI tasks, which is an improvement on the deep residual shrinkage network (DRSN) model. RSN-MI adopts the modified soft threshold update block as the nonlinear layer to reduce the influence of high noise on the feature extraction of the digital signal. In order to build up the optimal classifier for DSMI, we consider the features of different modulation types to design our network. Compared with other DSMI classifiers, the advantage of RSN-MI is that it can detect the digital signal waveform feature at different modulation types and avoid the influence of the channel feature on network training. Finally, we evaluate the performance of all different signal preprocessing methods adopted in this paper on DSMI, which are critical to realise the correct identification of received signals in different channel environments. Besides, we compare the channel-varying adaptability of our network model with other DSMI methods from [6], [7], and [18] include ML-based average likelihood ratio test (ALRT), two PR methods with HOC, and a CNN DL model. Compared with both DL-based DSMI methods or traditional's, our method does not need to train the model for each fixed multipath transmission scene, but it can be applied to the different multipath channel environment after training once or in a small amount. In other words, it is a robust digital signal modulation identification method. We also analyse the computation complexity of these methods mentioned in this paper, which further illustrate our work's feasibility. In this paper, the following notations are used: x, x, X represent a scalar, a vector, and a matrix, respectively. The superscript [ · ] T , [ · ] H and [ · ] ∗ denote the operation matrix transpose, Hermitian and conjugation. E { · } calculates the mathematical expectation and | | · | | defines the Frobenius norm. The rest of the paper is arranged as follows. Section 2 introduces the OFDM system model; The signal preprocessing methods are introduced in Section 3, including various channel estimation algorithms and equalisation algorithms; Section 4 introduces the proposed network model RSN-MI; Section 5 is the data sampling platform built for acquiring the datasets in the real channel environment; Section 6 is performance evaluation and comparison with other methods; Computation complexity of these methods mentioned in this paper are analysed in Section 7; Section 8 concludes our work. 2 OFDM SYSTEM MODEL Figure 1 shows the OFDM transmission model used in this paper. s ∼ N ( k ) = [ s ∼ 1 ( k ) s ∼ 2 ( k ) … s ∼ N ( k ) ] T is the k th digital signal vector after bit mapping, where N represents the number of digital symbols carried on each OFDM symbol, that is, the number of sub-carriers. The OFDM symbol s N ( k ) = [ s 1 ( k ) s 2 ( k ) … s N ( k ) ] T in the time domain can be expressed as the inverse discrete Fourier Transform (IDFT) of s ∼ N ( k ) as s N ( k ) = F H s ∼ N ( k ) where F is the DFT matrix and F H the DFT matrix. Let s c p ( k ) = [ s 1 c p ( k ) s 2 c p ( k ) … s P c p ( k ) ] T represents the k th OFDM symbol after adding the cyclic prefix (CP). This operation can be achieved by a matrix T = [ I c p I N ] , where I c p is the matrix formed by the last D rows of I N . The usually selected number of CP points D is equal to 1 / 4 N , and P = N + D = 5 D . FIGURE 1Open in figure viewerPowerPoint Matrix representation of an OFDM transmission model The multipath channel can be simulated by a tap delay line (TDL) model [20], and its discrete impulse response is expressed as follows: c ( n ) = ∑ l = 0 L − 1 h l δ n − τ l (1)where L represents the number of paths, and it satisfies L ≤ D . h l represents the channel fading coefficient of the lth channel, and its amplitude obeys the Rayleigh distribution. τ l is the path delay normalised by the sampling time T s of the lth path, written in vector form as h = { h 0 h 1 … h L − 1 } T . The signal at the receiving end can be convolved to simulate the linear superposition of signals from different paths. The inter-symbol interference caused by the multipath effect causes the received k th OFDM symbol r c p ( k ) to be generated jointly by s c p ( k ) and s c p ( k − 1 ) , which can be represented as followes: r c p ( k ) = H 1 H 0 s c p ( k − 1 ) s c p ( k ) + w P ( k ) (2)where H 0 is the P × P topelise matrix composed of 0 elements and the channel coefficient with first row [ h 0 0 … 0 ] T and first column [ h 0 … h L − 1 0 … 0 ] T . H 1 is the P × P topelise matrix with first row [ 0 … 0 h L − 1 … h 1 ] T and first column [ 0 0 … 0 ] T . The white Gaussian noise added to each symbol can be expressed as w P ( k ) = [ w 1 ( k ) w 2 ( k ) … w P ( k ) ] T . r N ( k ) = [ r 1 ( k ) r 2 ( k ) … r N ( k ) ] T can be obtained after using R = [ 0 c p I N ] to remove CP of r c p ( k ) . r ∼ N ( k ) = [ r ∼ 1 ( k ) r ∼ 2 ( k ) … r ∼ N ( k ) ] T is the frequency modulation symbol block obtained through DFT. Assuming that all sub-carriers of the OFDM symbol use the same modulation type, the purpose of this paper is to correctly identify the modulation type of the OFDM symbols within C = { BPSK, QPSK, 8PSK, 16QAM, 32QAM, 64QAM }. However, due to the influence of multipath channels, the received symbols will be distorted and stretched. The distribution of r ∼ N ( k ) in the 2D space is shown in Figure 2. FIGURE 2Open in figure viewerPowerPoint 2D distribution points of r ∼ N ( k ) with QPSK modulation at SNR = 10 dB. (a) Multipath channel 1, (b) Multipath channel 2, (c) Multipath channel 3 At present, most of the literature about the modulation identification of digital signals based on deep learning ignores this problem, which is obviously unreasonable. It is difficult for deep neural networks to extract modulation features from distorted digital symbols interfered with by multipath wireless channels. 3 SIGNAL PREPROCESSING This section mainly introduces the digital signal preprocessing algorithms used in this paper, including various channel estimators and channel equalisers, whose purpose is to eliminate the interference of multipath effect to the data sequence, which is the key to realise the robustness of our method. We focus on evaluating the influence of different signal preprocessing algorithms on DSMI, and this result is shown in Section 6. 3.1 SOSB channel estimator The second-order statistics blind channel estimation algorithm is to estimate the autocorrelation matrix R r r of the received signal and recover the channel coefficient vector h. The algorithm does not use or uses little prior knowledge to obtain a more accurate estimate, so it is very suitable for DSMI tasks [21]. Let H ∼ 0 and H ∼ 1 be the Topelise matrix with the size of D × D in the upper left corner and upper right corner of H 0 and H 1 , respectively. Then Equation (2) can be rewritten in Equation (3) which is shown at the below. In (3), s N i ( k ) is a vector composed of ( i − 1 ) D + 1 to i D elements of s N ( k ) , 1 ≤ i ≤ 4 . r c p ( k ) = H ∼ 1 0 0 0 H ∼ 0 0 H ∼ 0 0 0 H ∼ 1 0 H ∼ 1 H ∼ 0 0 0 0 0 H ∼ 1 H ∼ 0 0 0 0 0 H ∼ 0 H ∼ 0 s N 4 ( k − 1 ) s N 1 ( k ) s N 2 ( k ) s N 3 ( k ) s N 4 ( k ) + w P ( k ) (3) When the transmission symbols are independent of each other and the average value is 0, the autocorrelation matrix R rr = E { r c p ( k ) r c p H ( k ) } of the received signal can be obtained, which has the following structure: R rr = × × H ∼ 0 , 0 × × × H ∼ 0 , 0 × × + σ v 2 I (4)where H ∼ 0 , 0 = H ∼ 0 H ∼ 0 H , the element in the first column is h 0 ∗ [ h 0 h 1 … h D − 1 ] T . σ v 2 is the variance of the noise. R r r can be replaced by R ̂ r r = 1 K ∑ k = 1 K r c p ( k ) r c p H ( k ) . Take the last D elements in the first column of the R ̂ r r to get the channel coefficient which contains the fuzzy factor h 0 ∗ . h 0 ∗ can be estimated by little prior knowledge, which is called the semi-SOSB algorithm. 3.2 SB channel estimator Based on the orthogonality of the received signal's correlation matrix and its noise subspace E N , the channel coefficient vector h can be estimated by the mathematical relationship between transmission blocks. The blind subspace estimation algorithm is generally used in the case of D = 1 / 4 N without changing the transmitter's structure [22]. Define the vector: r ¯ c p ( k ) = r c p 1 ( k − 1 ) T … r c p 4 ( k − 1 ) T r c p 0 ( k ) T … r c p 4 ( k ) T T s ¯ c p ( k ) = s c p 1 ( k − 1 ) T … s c p 4 ( k − 1 ) T s c p 1 ( k ) T … s c p 4 ( k ) T T w ¯ c p ( k ) = w P 1 ( k − 1 ) T … w P 4 ( k − 1 ) T w P 0 ( k ) T … w P 4 ( k ) T T (5)From Equation (2), we can get r ¯ c p ( k ) = H s ¯ c p ( k ) + w ¯ c p ( k ) , where H is the matrix of size ( 2 N + D ) × 2 N defined in [23]. Define R r ¯ r ¯ and R s ¯ s ¯ to be the autocorrelation matrix of r c p ( k ) and s c p ( k ) respectively. Then r c p ( k ) can be represented as follows: R r ¯ r ¯ = H ( h ) R s ¯ s ¯ H ( h ) H + σ v 2 I 2 N + D (6)when R r ¯ r ¯ is full rank, the following cost function can be constructed: J ( h ) = min h H Q h s.t. | | h | | = 1 (7)where Q is a matrix of size ( D + 1 ) × ( D + 1 ) , which can be constructed by the normalised eigenvector corresponding to the D smallest eigenvalues of R r ¯ r ¯ . The SB estimation algorithm also has a fuzzy factor, which can be eliminated by the semi-SB algorithm through a few pilots. 3.3 Pilot-based estimators Two pilot-based estimators, LS estimator and MMSE estimator are adopted to compare with the blind estimators. LS estimation algorithm [24] constructs the cost function according to the least square criterion and obtains its minimum value through gradient operation. H ̂ L S = X − 1 Y (8)where X and Y are the frequency domain pilot sent and received respectively. H ̂ L S is the frequency domain response of the estimated channel. The MMSE estimation algorithm obtains the channel matrix H ̂ MMSE in a statistical sense by minimising the mean square error of the actual transmitted pilot X and the estimated X ̂ . In actual application, The LMMSE algorithm is usually used instead, as shown below: H ̂ M M S E = R H H R H H + β S N R ¯ I − 1 H ̂ L S (9)where R H H is the autocorrelation matrix of the channel, β is a constant related to the modulation type, and S N R ¯ is the defined average SNR in [25]. 3.4 Channel equaliser ZF equaliser and MMSE equaliser are used to compensate for channels. ZF equaliser is often used for signal preprocessing in various wireless communication systems due to its simple implementation. The realisation algorithm is expressed as: L ̂ = Y 1 H 1 Y 2 H 2 … Y N H N T (10)where Y i and H i are the received signal and channel response on the i t h subcarrier in the frequency domain after DFT respectively. L ̂ represents the estimated signal sequence. MMSE equaliser is also based on the minimum mean square error criterion, and the received signal can be estimated as follows: L ̂ = H ̂ H H ̂ H ̂ H + γ − 1 I N − 1 Y (11)where H ̂ is the channel matrix obtained by the estimator, represents the SNR of the communication chain, and I N is the identity matrix of size N × N . 4 RSN-MI CNN is the most popular deep learning framework at present. Its most important component unit is a filter using convolution operation, which is usually called convolution kernel. Because the convolution operation can describe image features well, it has been widely used in image recognition tasks. At present, much literature in the field of communication adopts the traditional CNN framework to deal with DSMI tasks, and IQ sampling is directly used for model training, which lacks reasonable interpretation. The typical structure of CNN is difficult to extract the features of different modulation types from the data sequence, and the extracted features are seriously disturbed by the multipath environment. 4.1 Dynamic soft threshold update block In this section, we fully consider the features of wireless communication transmission signals and propose an RSN-MI network structure. This network structure is based on the deep residual shrink network (DRSN), modified to be suitable for DSMI tasks. DRSN is a recently proposed deep learning network for mechanical fault diagnosis [26]. This structure consists of a series of residual shrink building units (RSBUs). In addition to the traditional CNN elements in RSBU, such as the convolutional layer, activation function, and batch normalisation (BN), the most important is introducing a dynamic threshold update block as shown in Figure 3, which makes our network have excellent identification accuracy in a high-level noise environment. FIGURE 3Open in figure viewerPowerPoint Dynamtic soft threshold update block in RSN-MI The dynamic soft threshold update block comprises a soft threshold function (i.e. a popular shrinkage function) [27] and a dynamic threshold generator. The threshold function can effectively remove the redundant noise features in the feature map, meanwhile retaining more digital signal modulation features. Essentially, the soft threshold function is equivalent to a new activation function, expressed as follows: y = x − τ x > τ 0 − τ ≤ x ≤ τ x + τ x < − τ (12)where x is the feature value at the corresponding position on the feature map, τ is the estimated soft threshold, y is the modified feature value. The soft threshold function is inserted into RSN-MI as a nonlinear layer to update the feature map, setting the smaller feature value (close to 0) to 0. These inputs have a high probability of being noise without useful information. Besides, for the signal data set, when the bitstream is mapped to the modulation constellation, the value of the IQ sequence can be positive or negative, which is not like picture pixels, only positive values. For the DSMI task, it is necessary to preserve the signal's negative features as much as possible. The threshold function structure can guarantee this very well, unlike ReLU who loses useful information for data training by transforming negative inputs into zero. But its gradient has the same form as ReLU, which ensures that the parameters can be effectively trained. The dynamic threshold generator is achieved through a DL-based attention mechanism. Different from the traditional fixed threshold update block, manual experience is required. Threshold dynamic update can ensure that the threshold of the threshold function is automatically updated, reducing the complexity of the threshold setting and improving the identification accuracy. More Specially, the attention mechanism is a brand-new feature recalibration strategy. By modelling the correlation between feature channels, the weight coefficient α i , 1 ≤ i ≤ C corresponding to each channel can be obtained by independent learning, which can reflect the importance of each channel for feature learning. In training, the model can increase the proportion of essential features while suppressing the features that have a weak impact on the current task. In RSN-MI, the dynamic threshold generator draws on the weight coefficient's learning mechanism in squeeze-and-excitation networks and uses the coefficient to dynamically generate soft thresholds by weighting the feature map. As shown in Figure 3, The weight coefficient learning is realised by combining the FFD block (flatten operation, fully connected (FC) layer and dropout) and two FC layers. First, the FFD block performs a "squeeze" operation on the feature map with input size (H,W,C) and outputs feature data with size (1,1,C). Then two FC layers with the same number of neurons as channel's are used to obtain each channel's corresponding weight coefficient. The sigmoid function ensures that the coefficient range is reduced to (0,1). This process is called "excitation". After obtaining the weight coefficient α i , 1 ≤ i ≤ C , the soft threshold corresponding to each channel can be generated according to the following equation: τ i = α i · | FFD ( X ) | i 1 ≤ i ≤ C (13)where τ i represents the i th channel's soft threshold, FFD ( X ) represents the result of processing feature map X by using the FFD block, | · | is the absolute value operator, whose purpose is to ensure that the resulting threshold is positive. The dynamic soft threshold obtained in this way can be adjusted adaptively according to the feature input, and it can be compressed to a small value by weighting α i to meet the needs. 4.2 Network structure With the help of dynamic soft threshold updating structure, RSN-MI is proposed and applied to DSMI task. The proposed RSN-MI is constructed according to the features of the IQ data sequence and the size of the selected modulation types, which has strong interpretability, as shown in Figure 4. ∀ χ ⊂ C , χ ∈ C 2 and | χ | = M where M is the size of the selected modulation types. For ∀ χ it can be regarded as a point distribution in a 2D space. Therefore, we can integrate the constellation features of each modulation type into the network construction. FIGURE 4Open in figure viewerPowerPoint Overall architecture of RSN-MI Our network's input is an IQ sampling sequence χ with the size of (2,2400) after OFDM demodulation. The length of 2400 is chosen because the distribution of more signal sampling points will not be changed by noise, where the network can analyse more dimensional information of the data. The convolutional layer is used as the first two layers of the network with kernel sizes (1,2) and (1,4) whose primary purpose is to extract the single-dimensional features of the IQ sequence. In detail, a small kernel of size (1,2) is used to accurately extract the feature range to BPSK signals with only two mapping points. The maximum feature extraction range can be expanded to 64QAM by combining the kernel with size (1,4) because there are at most eight values in each dimension of I and Q when χ adopts 64QAM. All these two kernels can extract all the high and low-order modulation features. Addition