CN114584441A - Digital signal modulation identification method based on deep learning - Google Patents
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Abstract
The invention discloses a digital signal modulation identification method based on deep learning and suitable for different multipath channel environments. The method comprises the following steps: an OFDM transmitter generates a multi-carrier digital signal and inputs the signal into a multi-path fading channel; an OFDM receiver receives transmission signals under different multipath channels; carrying out signal preprocessing on transmission signals under different multipath channels; constructing an RSN-MI neural network, and training the RSN-MI neural network; and inputting the preprocessed data into the RSN-MI neural network to obtain a digital signal modulation identification result. The invention has better identification precision, has robustness for the received data in different signal environments and greatly reduces the training number of the network.
Description
Technical Field
The invention belongs to the field of signal analysis, and particularly relates to a digital signal modulation identification method based on deep learning.
Background
Digital signal modulation and identification have important research significance in wireless mobile communication systems, and have attracted more and more attention as a necessary step before signal demodulation. The task of digital signal modulation identification is mainly to confirm the modulation mode of a received signal without any prior information or with a small amount of prior information, thereby laying a foundation for subsequent signal demodulation and information acquisition. In military and civil fields, digital signal modulation and identification play an important role, and particularly in the military field, modulation signal identification is a premise for interference and monitoring on enemy communication and can be used for spectrum detection, signal confirmation and the like in the civil field.
The conventional digital signal modulation type identification is roughly divided into two methods: one is a maximum likelihood hypothesis test method based on decision theory, and the other is a pattern recognition method based on feature extraction. For the decision theory method, the likelihood ratio is compared with a proper threshold value based on the likelihood function of the received signal, and the optimal classification effect is achieved under the Bayes minimum error criterion. Such methods typically require a large amount of a priori knowledge and are computationally complex. Meanwhile, the algorithm can generate a mismatch problem due to the change of a signal model, and the method has no applicability. For the pattern recognition method, the received signal needs to be transformed to extract features of different dimensions, and then a suitable machine learning classifier, such as an SVM, a decision tree, etc., is selected for classification. The classification performance of the method is influenced by the selected characteristics, the same characteristics are difficult to extract from a plurality of modulation modes, and in addition, the method is not efficient when the task data volume is large.
With the progress of computer science and the improvement of hardware level, the deep learning technology is rapidly developed, is widely applied to a classification and identification task, and obtains excellent performance. In recent years, due to the full use of deep learning for large data and low algorithm complexity, deep learning is gradually applied to a digital signal modulation task in the field of wireless communication, and remarkable performances are achieved. However, the existing deep learning method focuses on studying the influence of the neural network structure and parameters on the classification effect, and does not consider the accuracy loss brought by the change of the channel to the identification. Most of the work is developed in Additive White Gaussian Noise (AWGN) channels or static multipath experimental scenarios, and no processing related to signal communication is performed when training a model, so that the result is that a trained network cannot effectively identify signal data sets generated in other channel environments, and cannot be used in an actual wireless communication system. In addition, most current communication systems are based on OFDM technology, but the digital signal modulation identification related to OFDM is less studied. Therefore, the method for effectively identifying the digital signal modulation suitable for the multi-carrier OFDM communication system is extremely valuable to research.
Disclosure of Invention
Aiming at the defects in the prior art, the digital signal modulation identification method based on deep learning provided by the invention solves the problems in the prior art.
In order to achieve the purpose of the invention, the invention adopts the technical scheme that: a digital signal modulation identification method based on deep learning comprises the following steps:
s1, generating a multi-carrier digital signal through an OFDM transmitter, and inputting the digital signal into a multi-path fading channel;
s2, receiving transmission signals under different multipath channels through the OFDM receiver;
s3, preprocessing the transmission signal;
s4, constructing an RSN-MI neural network, and training the RSN-MI neural network;
and S5, inputting the preprocessed data into the RSN-MI neural network to obtain a digital signal modulation recognition result.
Further, the modulation mode of the digital signal in step S1 is BPSK, QPSK, 8PSK, 16QAM, 32QAM, or 64 QAM;
the digital signal is specifically:
wherein s iscp(k) Denotes the kth transmitted OFDM symbol, K1, 2, K denotes the total number of OFDM symbols to be transmitted,
all represent the digital discrete sequence of the kth OFDM symbol, the cp point is selected from D, D equals 1/4N, N represents the subcarrier number, P equals N + D equals 5D, and P represents the OFDM symbol length.
Further, the step S2 is specifically:
s2.1, constructing a discrete unit impulse response model c (n) of the multipath fading channel, wherein the discrete unit impulse response model c (n) is as follows:
wherein h islDenotes the L-th channel fading coefficient, L-1 denotes the total number of channels, L<D, n represents the discrete time after sampling, taulRepresents the sampled time T of the ith channelsNormalized path delay;
s2.2, a transmission signal r (n) after passing through a multipath fading channel by an OFDM receiver is as follows:
r(n)＝s(n)*c(n)+w(n)
wherein, s (n) represents the digital signal sent at the time of n, w (n) represents the white gaussian noise at the time of n, and x represents the convolution operation;
s2.3, obtaining the k OFDM symbol rcp(k) Comprises the following steps:
wherein H0Represents a first behavior h0 0...0]TThe first column is [ h ]0...hL-1 0...0]TA Topelize matrix of size P × P; h1To representFirst action [0.. 0 hL-1...h1]TFirst column [ 00.. 0 ]]TIs a Topelize matrix of size P × P; w is aP(k)＝[w1(k) w1(k)...wP(k)]，wP(k) Representing a superimposed gaussian white noise vector, w, on the signal1(k) w1(k)...wP(k) All represent the number of points in the Gaussian white noise vector;
further, the step S3 is specifically:
s3.1, according to OFDM symbol rcp(k) Obtaining an autocorrelation matrix of a received OFDM symbol
Comprises the following steps:
wherein, E represents the mathematical expectation,
denotes the reconstructed received sequence, H denotes the conjugate transpose,
is shown bycp(k) Equally dividing the sequence into 5 equal-length sequences with the length of D, wherein i is more than or equal to 0 and less than or equal to 5, and i represents the sequence number of the sequence;
s3.2, to autocorrelation matrix
Decomposing the eigenvalues to obtain the eigenvectors g corresponding to the minimum D eigenvalues0,g1,...,gi,...,gD-1And constructing a fading coefficient relation as follows:
wherein H (H) represents H0And H1A constructed intermediate matrix;
s3.3, using feature vector giForming a feature matrix G of size (D +1) × 8Di(ii) a And according to the feature matrix GiAnd (3) obtaining the following relation by sorting the fading coefficient relation:
hHGi＝0,0≤i≤D-1
wherein h isHRepresenting a channel coefficient vector;
s3.4, using feature matrix GiConstructing an intermediate relation Q as follows:
s3.5, carrying out characteristic value decomposition on the intermediate relation Q to obtain a normalization vector corresponding to the minimum characteristic value to obtain a channel fading coefficient h;
and S3.6, acquiring a fuzzy factor of the channel fading coefficient h by adopting a subspace blind channel estimation algorithm, and compensating the channel by adopting an MMSE equalizer according to the fuzzy factor to finish signal preprocessing.
Further, the RSN-MI neural network in step S4 includes an input layer, a first convolution layer, a second convolution layer, a residual contraction unit, a sum operation layer, a BPG layer, a first full-connection layer, and an output layer, which are connected in sequence; the output end of the second convolution layer is also connected with the input end of the sum operation layer;
the residual shrinkage unit comprises a third convolution layer, a second full-connection layer, a third full-connection layer, a fourth full-connection layer, a multiplication layer and a characteristic modification layer;
the input end of the third convolution layer is the input end of the residual shrinkage unit, and the output end of the third convolution layer is respectively connected with the input end of the second full-connection layer and the input end of the characteristic modification layer; the output end of the second full-connection layer is connected with the input end of the multiplication operation layer and the input end of the third full-connection layer respectively, the output end of the third full-connection layer is connected with the input end of the multiplication operation layer through the fourth full-connection layer, the output end of the multiplication operation layer is connected with the input end of the characteristic modification layer, and the output end of the characteristic modification layer is the output end of the residual shrinkage unit.
Further, the sizes of convolution kernels of the first convolution layer, the second convolution layer and the third convolution layer are (1,2), (1,4) and (1,2), respectively, and the number of convolution kernels is set to be 50; the activation functions of the first convolution layer, the second convolution layer and the third convolution layer are PRelu functions; the input data of the third convolution layer are subjected to Batch Normalization processing through a Batch Normalization operation, the output data of the second full-connection layer are processed through a Flatten operation, a Dropout operation and a Batch Normalization operation respectively, the output data of the third full-connection layer are processed through the Batch Normalization operation, and the activation function of the fourth full-connection layer is a sigmoid function; the multiplication layer is used for multiplying the characteristic diagram output by the second full connection layer with the compression coefficient a of each channel in the fourth full connection layer to obtain a soft threshold tau of each channel in the fourth full connection layer; the characteristic modifying layer is used for modifying and updating the characteristic diagram output by the third convolution layer according to the soft threshold tau, and the updating model is as follows:
wherein y represents the updated feature map, and x represents the feature map before updating;
the BPG layer represents a layer formed by packaging a Batch Normalization operation, a Prelu function and a Global Average Power operation, and the activation function of the first fully-connected layer is a Softmax activation function.
Further, the specific step of training the RSN-MI neural network in step S4 is:
s4.1, collecting a plurality of received signals in an AWGN channel environment as a training data set;
and S4.2, performing power normalization on the data in the training data set, and training the RSN-MI neural network by using the normalized training data.
Further, the specific step of training the RSN-MI neural network in step S4.2 is:
s4.2.1, collecting a plurality of signals of each modulation mode, and dividing the signals corresponding to each modulation mode into training samples and verification samples respectively;
s4.2.2, setting the iteration times as S;
s4.2.3, inputting the training samples into the RSN-MI neural network, setting the learning rate to be 0.002, and training the RSN-MI neural network by adopting an Adam optimizer;
s4.2.4, repeating the step S4.2.3, detecting the loss degree of the verification sample by adopting an early-stopping callback function, and obtaining the RSN-MI neural network after training if the loss degree of the verification sample is converged or the iteration times reach S.
The invention has the beneficial effects that:
(1) the signal preprocessing eliminates the interference of multipath effect on the modulation characteristics of the acquired signals, recovers the originating data as much as possible, adopts a subspace blind signal estimation algorithm to obtain a channel fading coefficient h, and utilizes a small amount of pilot frequency information to eliminate a fuzzy factor; after estimating the fading coefficient of the channel, compensating the channel by adopting an MMSE equalizer; because the MMSE equalizer considers the influence of channel noise, the signal transmitted by the transmitter can be recovered to the maximum extent.
(2) The RSN-MI neural network built by the invention can omit channel characteristics including interference of noise on model learning, and focuses on characteristic learning of modulation types.
(3) The invention has better identification precision, has robustness for the received data in different signal environments and greatly reduces the training number of the network.
(4) The method can be applied to the intelligent identification scene of large-scale data volume, and greatly reduces the calculation pressure of the network server.
Drawings
Fig. 1 is a flowchart of a digital signal modulation identification method based on deep learning according to the present invention.
FIG. 2 is a diagram of the RSN-MI neural network of the present invention.
Fig. 3 is a block diagram of the digital signal modulation recognition system of the present invention.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined in the appended claims, and all matters produced by the invention using the inventive concept are protected.
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
As shown in fig. 1, a digital signal modulation identification method based on deep learning includes the following steps:
s1, generating a multi-carrier digital signal through an OFDM transmitter, and inputting the digital signal into a multi-path fading channel;
s2, receiving transmission signals under different multipath channels through the OFDM receiver;
s3, preprocessing the transmission signal;
s4, constructing an RSN-MI neural network, and training the RSN-MI neural network;
and S5, inputting the preprocessed data into the RSN-MI neural network to obtain a digital signal modulation recognition result.
The modulation mode of the digital signal in step S1 is BPSK, QPSK, 8PSK, 16QAM, 32QAM, or 64 QAM;
the digital signal is specifically:
wherein s iscp(k) Denotes the kth transmitted OFDM symbol, K1, 2, K denotes the total number of OFDM symbols to be transmitted,
all represent the number of the k-th OFDM symbolIn the discrete sequence, the cp point number is D, D equals 1/4N, N represents the subcarrier number, P equals N + D equals 5D, and P represents the OFDM symbol length.
The step S2 specifically includes:
s2.1, constructing a discrete unit impulse response model c (n) of the multipath fading channel, wherein the discrete unit impulse response model c (n) is as follows:
wherein h islDenotes the channel fading coefficient of the L-th channel, L1, 2<D, n denotes the discrete time after sampling, taulRepresents the sampled time T of the ith channelsNormalized path delay;
s2.2, the transmission signal r (n) after passing through the multipath fading channel by the OFDM receiver is:
r(n)＝s(n)*c(n)+w(n)
wherein s (n) represents the digital signal transmitted at the time n, w (n) represents the white gaussian noise at the time n, and x represents the convolution operation;
s2.3, obtaining the k OFDM symbol rcp(k) Comprises the following steps:
wherein H0Represents a first behavior h0 0...0]TThe first column is [ h ]0...hL-1 0...0]TA Topelize matrix of size P × P; h1Represents a first behavior [0.. 0 hL-1...h1]TFirst column [ 00.. 0 ]]TIs a Topelize matrix of size P × P; w is aP(k)＝[w1(k)w1(k)...wP(k)]，wP(k) Representing a superimposed gaussian white noise vector, w, on the signal1(k)w1(k)...wP(k) All represent the number of points in the Gaussian white noise vector;
the step S3 specifically includes:
s3.1, according to OFDM symbol rcp(k) Obtaining an autocorrelation matrix of received OFDM symbols
Comprises the following steps:
wherein, E represents the mathematical expectation,
denotes the reconstructed received sequence, H denotes the conjugate transpose,
is shown ascp(k) Equally dividing the sequence into 5 equal-length sequences with the length of D, wherein i is more than or equal to 0 and less than or equal to 5, and i represents the sequence number of the sequence;
s3.2, to autocorrelation matrix
Decomposing the eigenvalues to obtain the eigenvectors g corresponding to the minimum D eigenvalues0,g1,...,gi,...,gD-1And constructing a fading coefficient relation as follows:
wherein H (H) represents H0And H1A constructed intermediate matrix;
s3.3, using feature vector giForming a feature matrix G of size (D +1) × 8Di(ii) a And according to the feature matrix GiAnd (3) obtaining the following relation by sorting the fading coefficient relation:
hHGi＝0,0≤i≤D-1
wherein h isHRepresenting a channel coefficient vector；
In this embodiment, the feature vector g is used in accordance with the method described in the inventioniForming a feature matrix G of size (D +1) × 8Di(ii) a Then, after each symbol in the equation in S3.2 is expanded according to the definition description, the constructed G is utilizediA new equation relationship can be obtained:
and because the equation in S3.2 is the same as 0, i.e.:
hHGi＝0,0≤i≤D-1
s3.4, using feature matrix GiConstructing an intermediate relation Q as follows:
s3.5, carrying out characteristic value decomposition on the intermediate relation Q to obtain a normalization vector corresponding to the minimum characteristic value to obtain a channel fading coefficient h;
and S3.6, acquiring a fuzzy factor of the channel fading coefficient h by adopting a subspace blind channel estimation algorithm, and compensating the channel by adopting an MMSE equalizer according to the fuzzy factor to finish signal preprocessing.
In the present embodiment, H (h) represents
And
the formed intermediate matrix is composed of a plurality of matrixes,
and
are each H0And H1The size of the upper left corner and the upper right corner is a square matrix of DxD. In a specific form such asThe following:
g of size (2N + D). times.1iAre divided into 9 equal parts with the size of Dx 1, which can be expressed as
Wherein
The following matrix is then constructed:
order to
Estimation for obtaining channel fading coefficient h by using subspace blind channel estimation algorithm
The ambiguity factor α can be obtained by a small amount of pilot information. And then, compensating the received signal by using an MMSE equalizer according to the estimation result to finish preprocessing.
As shown in fig. 2, the RSN-MI neural network in step S4 includes an input layer, a first convolution layer, a second convolution layer, a residual contraction unit, a sum operation layer, a BPG layer, a first fully-connected layer, and an output layer, which are connected in sequence; the output end of the second convolution layer is also connected with the input end of the sum operation layer;
the residual shrinkage unit comprises a third convolution layer, a second full-connection layer, a third full-connection layer, a fourth full-connection layer, a multiplication layer and a characteristic modification layer;
the input end of the third convolution layer is the input end of the residual shrinkage unit, and the output end of the third convolution layer is respectively connected with the input end of the second full-connection layer and the input end of the characteristic modification layer; the output end of the second full-connection layer is connected with the input end of the multiplication operation layer and the input end of the third full-connection layer respectively, the output end of the third full-connection layer is connected with the input end of the multiplication operation layer through the fourth full-connection layer, the output end of the multiplication operation layer is connected with the input end of the characteristic modification layer, and the output end of the characteristic modification layer is the output end of the residual shrinkage unit.
The sizes of convolution kernels of the first convolution layer, the second convolution layer and the third convolution layer are (1,2), (1,4) and (1,2) respectively, and the number of the convolution kernels is set to be 50; the activation functions of the first convolution layer, the second convolution layer and the third convolution layer are PRelu functions; the input data of the third convolution layer are subjected to Batch Normalization processing through a Batch Normalization operation, the output data of the second full-connection layer are processed through a Flatten operation, a Dropout operation and a Batch Normalization operation respectively, the output data of the third full-connection layer are processed through the Batch Normalization operation, and the activation function of the fourth full-connection layer is a sigmoid function; the multiplication layer is used for multiplying the characteristic diagram output by the second full connection layer with the compression coefficient a of each channel in the fourth full connection layer to obtain a soft threshold tau of each channel in the fourth full connection layer; the characteristic modifying layer is used for modifying and updating the characteristic diagram output by the third convolution layer according to the soft threshold tau, and the updating model is as follows:
wherein y represents the updated feature map, and x represents the feature map before updating;
the BPG layer represents a layer formed by packaging a Batch Normalization operation, a Prelu function and a Global Average Power operation, and the activation function of the first fully-connected layer is a Softmax activation function.
As shown in FIG. 3, the RSN-MI neural network constructed by the application is used for carrying out the signal debugging and identification process.
The specific steps of training the RSN-MI neural network in step S4 are as follows:
s4.1, collecting a plurality of received signals in an AWGN channel environment as a training data set;
and S4.2, performing power normalization on the data in the training data set, and training the RSN-MI neural network by using the normalized training data.
The specific steps of training the RSN-MI neural network in step S4.2 are:
s4.2.1, collecting a plurality of signals of each modulation mode, and dividing the signals corresponding to each modulation mode into training samples and verification samples respectively;
s4.2.2, setting the iteration times as S;
s4.2.3, inputting the training samples into the RSN-MI neural network, setting the learning rate to be 0.002, and training the RSN-MI neural network by adopting an Adam optimizer;
s4.2.4, repeating the step S4.2.3, detecting the loss degree of the verification sample by adopting an early-stop callback function, and obtaining the RSN-MI neural network after training if the loss degree of the verification sample is converged or the iteration number reaches S.
In this embodiment, the model training set samples are IQ two-way sequences constituting a tensor of size (2,2400). A number of samples of 5W were taken for each modulation scheme. The total number of samples of the six modulation modes is 30W. The samples are also divided into training and validation sets in an 8:2 ratio. The network training optimizer adopts an Adam optimizer, the learning rate is set to be 0.002, and in order to prevent the model from detecting the loss degree of the verification set by adopting an early-stop callback function in the training process, the trained model is obtained when 5 iterations do not decrease any more or the iteration times are finished. When the network model is tested, the test set data is composed of modulation signals collected under different multipath channels, 1W samples are collected by each modulation mode, and the total number of the samples is 6W. And sending the preprocessed sample into a trained model for testing to obtain a classification result.
The invention specially designs a deep neural network model for digital signal modulation and identification, which is named as RSN-MI. The network structure introduces a soft threshold processing mechanism which can reduce the influence of noise on the learning of network characteristics. And the soft threshold tau can be automatically updated without manual setting, thereby reducing the debugging difficulty of model training. The characteristic updating mechanism is expressed as follows:
let τ be the soft threshold of the corresponding channel, x represent the original eigenvalue, and y represent the updated eigenvalue. The structure is applied to a deep residual shrinkage neural network for mechanical fault diagnosis at present. We use this mechanism to apply it to the task of digital signal modulation identification in order to improve the robustness of the model for the received data in different channels. The designed network structure is as follows:
the network structure is designed in consideration of the dimensional characteristics of the IQ data stream and the size M of the selected constellation diagram. The input of RSN-MI is a two-dimensional data sequence of the in-phase branch I and the quadrature branch Q of the received data, which has a size (2,2400). The reason for choosing this size is that signal samples of this size are less affected by noise, allowing the network to detect modulation signatures deeper. The first and second layers of the network are convolutional layers, the convolutional kernel sizes are (1,2) and (1,4), respectively, and the number of convolutional kernels is set to 50. The main purpose of these two convolutional layers is to extract the characteristic information of the I and Q single dimensions, and the reason for the selected convolutional kernel size is that a maximum of 8 values occur in a single dimension in the specified modulation type C ═ BPSK, QPSK, 8PSK, 16QAM, 32QAM, 64QAM, just enough to be extracted efficiently by two kernels. In addition, (1,2) and (1,4) may also be taken into account for the low order modulation type.
After the convolution layer, a modified depth residual shrinking unit is introduced, and the main purpose of the module is to automatically learn and update the soft threshold value and modify the feature map. HeadThe received feature map is first normalized by the Batch Normalization (BN) operation to speed up the network training and convergence. The first layer in the module is a convolutional layer, the convolutional kernel size is (1,2), and the number of convolutional kernels is 50. The purpose of this convolutional layer is to extract the combined characteristics of both I and Q dimensions so that a richer modulation information can be obtained. The second layer of the module is a layer structure combined by a scatter layer, a Full Connection (FC) layer, and a Dropout operation, and the number of neurons of the FC layer is set to 50. This is denoted as FFD layer. The main purpose of the FFD layer is to compress the feature map into a one-dimensional space, when the feature map size is (50,1, 1). Meanwhile, the FC layer can further extract data features. This step is the first step of threshold learning. Then, the three and four layers of the module are all formed by FC layers, and the number of the neurons is 50. A BN operation is added after each FC layer to modify the distribution of data. The fourth FC layer compresses data to a range of (0,1) by adopting a sigmoid function to obtain a soft threshold compression coefficient alpha corresponding to each channeliI is more than or equal to 1 and less than or equal to 50. The soft threshold value of each channel can be expressed by multiplying the characteristic diagram obtained by the first FC layer and the compression coefficient of the corresponding channel. And then modifying and updating the characteristic diagram obtained by the convolution layer in the module according to a characteristic updating mechanism. The result of the input of the depth residual shrinking unit is to modify the updated feature map.
After the depth residual contraction unit, in order to reduce the feature loss, a new feature map is obtained by adding and modifying the updated values of the feature map through the sum operation of the feature map obtained by the second convolution layer of the whole module, and the operation is named as 'cross-layer identity path'. Then, a layer, called BPG layer, which is a combination of BN operation, PRelu (parametric reconstructed Linear Unit) function and GAP (Global Average Pooling) layer is added. The main purpose of this layer is to de-complicate the resulting feature map, preserving as much useful feature information as possible. In addition, the activation functions required by all convolutional layers in the invention are PRelu functions, and the aim is to increase the nonlinearity degree of the model and keep the negative value of the characteristic. Since the values of the IQ sequence have positive and negative values, this is different from the pixel values. The last layer of the model is an FC layer which adopts a Softmax activation function and has 6 neurons, each neuron corresponds to a label of a modulation mode, and the label is predicted by converting the characteristics output by each neuron into corresponding recognition probability. The neuron label corresponding to the maximum probability value is the modulation mode of the input sample.
Note VjRepresenting the corresponding output characteristic value, S, of each neuroniThe output probability corresponding to the ith neuron is shown, and e is a natural constant. The whole network is characterized in that the characteristics of the modulation type can be extracted by avoiding the characteristics of the channel as much as possible. Thus, correct identification can be achieved for multi-channel received data without performance landslide.
The training set and the test set of the model are correctly created. The training set data is a received signal collected in an AWGN channel environment or a channel environment with a strong direct radiation. And normalizing the sampled data to train the network model. The training is not carried out according to the sampling data of a specific multipath channel scene, and the training is only carried out once under each signal to noise ratio. The test set data is collected in different multipath channel environments and subjected to signal processing in the early stage, and the data is normalized and then sent to a network for prediction
For the acquisition of the annotation data set, the type of data sample employed in the present invention is a conventional OFDM signal. The labeled data set is the sampled signal after passing through the multipath fading channel. The OFDM signal transmission model is as described in step one. In a real laboratory scene, a labeling data sampling platform may be built by using Software Radio equipment, and as shown in fig. 3, data transceiving of a real air interface is simulated by using usrp (universal Software Radio peripheral). The data collected in the built data platform completely conforms to the OFDM transmission model.
The signal preprocessing is to eliminate the interference of multipath effect on the modulation characteristics of the collected signals, recover the data of the transmitting end as much as possible, obtain the channel fading coefficient h by adopting a subspace blind signal estimation algorithm, and eliminate the fuzzy factor by using a small amount of pilot frequency information. After estimating the fading coefficient of the channel, the MMSE equalizer is used to compensate the channel. Because the MMSE equalizer considers the influence of channel noise, the signal transmitted by the transmitter can be recovered to the maximum extent.
For the classification part, a classifier model RSN-MI is built by combining modulation identification data type characteristics. A residual shrinkage unit is introduced on the basis of a traditional convolutional neural network model to reduce the influence of noise characteristics on model learning. And combining the IQ sequence, identifying I and Q single-dimensional characteristics by the first two convolutional layers, synthesizing the two dimensional characteristics by the third convolutional layer, acquiring a soft threshold value through the full-connection layer, and correcting the characteristic diagram. All ReLu activation functions in the network are replaced with the PReLu activation function to preserve negative characteristic values.
The training set and the test set of the model are correctly created. The training set data is a received signal collected in an Additive White Gaussian Noise (AWGN) channel environment or a channel environment having a strong direct path. And normalizing the sampled data to train the network model. The training is not carried out according to the sampling data of a specific multipath channel scene, and the training is only carried out once under each signal to noise ratio. The test set data is a data set which is collected in different multipath channel environments and subjected to signal processing in the early stage, and the data is normalized and then can be sent to a network for prediction.
Claims (8)
1. A digital signal modulation identification method based on deep learning is characterized by comprising the following steps:
s1, generating multi-carrier digital signals through an OFDM transmitter, and inputting the digital signals into a multi-path fading channel;
s2, receiving transmission signals under different multipath channels through the OFDM receiver;
s3, preprocessing the transmission signal;
s4, constructing an RSN-MI neural network, and training the RSN-MI neural network;
and S5, inputting the preprocessed data into the RSN-MI neural network to obtain a digital signal modulation recognition result.
2. The deep learning-based digital signal modulation identification method according to claim 1, wherein the modulation scheme of the digital signal in step S1 is BPSK, QPSK, 8PSK, 16QAM, 32QAM, or 64 QAM;
the digital signal is specifically:
wherein s iscp(k) Denotes the kth transmitted OFDM symbol, K1, 2, K denotes the total number of OFDM symbols to be transmitted,
all represent the digital discrete sequence of the kth OFDM symbol, the cp point is selected from D, D equals 1/4N, N represents the subcarrier number, P equals N + D equals 5D, and P represents the OFDM symbol length.
3. The deep learning-based digital signal modulation recognition method according to claim 2, wherein the step S2 specifically comprises:
s2.1, constructing a discrete unit impulse response model c (n) of the multipath fading channel, wherein the discrete unit impulse response model c (n) is as follows:
wherein h islDenotes the L-th channel fading coefficient, L-1 denotes the total number of channels, L<D, n denotes the discrete time after sampling, taulRepresents the sampled time T of the ith channelsNormalized path delay;
s2.2, the transmission signal r (n) after passing through the multipath fading channel by the OFDM receiver is:
r(n)＝s(n)*c(n)+w(n)
wherein, s (n) represents the digital signal sent at the time of n, w (n) represents the white gaussian noise at the time of n, and x represents the convolution operation;
s2.3, obtaining the k OFDM symbol rcp(k) Comprises the following steps:
wherein H0Represents a first behavior h0 0...0]TThe first column is [ h ]0...hL-1 0...0]TA Topelize matrix of size P × P; h1Represents a first behavior [0.. 0 hL-1...h1]TFirst column [ 00.. 0 ]]TIs a Topelize matrix of size P × P; w is aP(k)＝[w1(k) w1(k)...wP(k)]，wP(k) Representing a superimposed gaussian white noise vector, w, on the signal1(k) w1(k)...wP(k) All represent the number of points in the gaussian white noise vector.
4. The deep learning-based digital signal modulation recognition method according to claim 3, wherein the step S3 specifically comprises:
s3.1, according to OFDM symbol rcp(k) Obtaining an autocorrelation matrix of a received OFDM symbol
Comprises the following steps:
wherein, E represents the mathematical expectation,
denotes the reconstructed received sequence, H denotes the conjugate transpose,
is shown ascp(k) Equally dividing the sequence into 5 equal-length sequences with the length D, wherein i is more than or equal to 0 and less than or equal to 5, and i represents the sequence number of the sequences;
s3.2, to autocorrelation matrix
Decomposing the eigenvalues to obtain the eigenvectors g corresponding to the minimum D eigenvalues0,g1,...,gi,...,gD-1And constructing a fading coefficient relation as follows:
wherein H (H) represents H0And H1Forming an intermediate matrix;
s3.3, using the feature vector giForming a feature matrix G of size (D +1) × 8Di(ii) a And according to the feature matrix GiAnd (3) obtaining the following relation by sorting the fading coefficient relation:
hHGi＝0,0≤i≤D-1
wherein h isHRepresenting a channel coefficient vector;
s3.4, using feature matrix GiConstructing an intermediate relation Q as follows:
s3.5, carrying out characteristic value decomposition on the intermediate relation Q to obtain a normalization vector corresponding to the minimum characteristic value to obtain a channel fading coefficient h;
and S3.6, acquiring a fuzzy factor of the channel fading coefficient h by adopting a subspace blind channel estimation algorithm, and compensating the channel by adopting an MMSE equalizer according to the fuzzy factor to finish signal preprocessing.
5. The deep learning-based digital signal modulation recognition method of claim 4, wherein the RSN-MI neural network in the step S4 comprises an input layer, a first convolution layer, a second convolution layer, a residual contraction unit, a sum operation layer, a BPG layer, a first fully-connected layer and an output layer which are connected in sequence; the output end of the second convolution layer is also connected with the input end of the sum operation layer;
the residual shrinkage unit comprises a third convolution layer, a second full-connection layer, a third full-connection layer, a fourth full-connection layer, a multiplication layer and a characteristic modification layer;
the input end of the third convolution layer is the input end of the residual shrinkage unit, and the output end of the third convolution layer is respectively connected with the input end of the second full-connection layer and the input end of the characteristic modification layer; the output end of the second full connecting layer is connected with the input end of the multiplication operation layer and the input end of the third full connecting layer respectively, the output end of the third full connecting layer is connected with the input end of the multiplication operation layer through the fourth full connecting layer, the output end of the multiplication operation layer is connected with the input end of the characteristic modification layer, and the output end of the characteristic modification layer is the output end of the residual error shrinkage unit.
6. The deep learning-based digital signal modulation identification method according to claim 5, wherein the convolution kernel sizes of the first convolution layer, the second convolution layer and the third convolution layer are (1,2), (1,4) and (1,2), respectively, and the number of convolution kernels is set to 50; the activation functions of the first convolution layer, the second convolution layer and the third convolution layer are PRelu functions; the input data of the third convolution layer are subjected to Batch Normalization processing through a Batch Normalization operation, the output data of the second full-connection layer are processed through a Flatten operation, a Dropout operation and a Batch Normalization operation respectively, the output data of the third full-connection layer are processed through the Batch Normalization operation, and the activation function of the fourth full-connection layer is a sigmoid function; the multiplication layer is used for multiplying the characteristic diagram output by the second full connection layer with the compression coefficient a of each channel in the fourth full connection layer to obtain a soft threshold tau of each channel in the fourth full connection layer; the characteristic modifying layer is used for modifying and updating the characteristic diagram output by the third convolution layer according to the soft threshold tau, and the updating model is as follows:
wherein y represents the updated feature map, and x represents the feature map before updating;
the BPG layer represents a layer formed by packaging a Batch Normalization operation, a Prelu function and a Global Average Power operation, and the activation function of the first fully-connected layer is a Softmax activation function.
7. The deep learning-based digital signal modulation recognition method of claim 6, wherein the step S4 of training the RSN-MI neural network comprises the following specific steps:
s4.1, collecting a plurality of received signals in an AWGN channel environment as a training data set;
and S4.2, performing power normalization on the data in the training data set, and training the RSN-MI neural network by using the normalized training data.
8. The deep learning-based digital signal modulation recognition method according to claim 7, wherein the step S4.2 of training the RSN-MI neural network comprises the following specific steps:
s4.2.1, collecting a plurality of signals of each modulation mode, and dividing the signals corresponding to each modulation mode into training samples and verification samples respectively;
s4.2.2, setting the iteration times as S;
s4.2.3, inputting the training samples into the RSN-MI neural network, setting the learning rate to be 0.002, and training the RSN-MI neural network by adopting an Adam optimizer;
s4.2.4, repeating the step S4.2.3, detecting the loss degree of the verification sample by adopting an early-stop callback function, and obtaining the RSN-MI neural network after training if the loss degree of the verification sample is converged or the iteration number reaches S.
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