Anti-interference method for FBMC underwater acoustic communication receiving end based on W-Net network

Abstract

The invention discloses an FBMC underwater acoustic communication receiving end anti-interference method based on a W-Net network, a receiving end network of an FBMC underwater acoustic communication model comprises the W-Net network, and the W-Net network is composed of a multipath elimination model UNet and an interference suppression model MS-UNet connected in series. Constructing a multipath elimination data set and an interference suppression data set; training a multipath elimination network UNet by using the multipath elimination data set, and training an interference suppression network MS-UNet by using the interference suppression data set; the interference suppression network MS-UNet comprises a UNet part, three SE modules and a multi-head self-attention module. The anti-interference capability of the underwater acoustic communication system is improved, the bit error rate is reduced, and the underwater acoustic signal transmission quality is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of underwater acoustic communication, and specifically relates to an anti-interference method for an FBMC underwater acoustic communication receiving end based on a W-Net network. Background Art

[0002] Underwater acoustic communication is currently the only means to achieve long-distance and high-speed data transmission underwater. However, if the underwater acoustic channel cannot accurately estimate and balance the multipath effect, the reliability of communication will be affected. In addition, underwater acoustic communication is easily interfered by the non-Gaussian ocean environment, so timely suppression of interference is also very important for the reliability of the underwater acoustic system. Traditional interference suppression algorithms have the limitations of requiring prior information and relying on fixed parameters of simulation. Summary of the invention

[0003] Purpose of the invention: In order to reduce the high system complexity problem that the traditional underwater acoustic channel receiver needs interference suppression, channel estimation and equalization to obtain the transmitted signal, the present invention proposes an anti-interference method for the FBMC underwater acoustic communication receiver based on the attention mechanism combined with the WNet network to improve the anti-interference ability, reduce the bit error rate, and improve the transmission quality of the underwater acoustic signal.

[0004] Technical solution: A W-Net network-based FBMC underwater acoustic communication receiving end anti-interference method, comprising the following steps:

[0005] Step 1: Establish a FBMC underwater acoustic communication model. The FBMC underwater acoustic communication model uses the FBMC / OQAM modulation method, inserts a pilot signal, modulates the symbol onto a subcarrier and sends it into the underwater acoustic channel.

[0006] In the FBMC underwater acoustic communication model, the interference signal is superimposed to form the received signal r(n) expressed as:

[0007] r(n)=s(n)*h(n)+w(n)+j(n)

[0008] Where s(n) represents the transmitted signal, the transmission mode is FBMC, * represents the convolution operator, h(n) is the impulse response of the multipath channel, w(n) is Gaussian white noise, and j(n) is interference;

[0009] Step 2: Construct a multipath elimination data set and an interference suppression data set; down-convert the received signal r(n) to obtain a baseband signal, and then filter it through a low-pass filter. The signal at this time is used as the input of the multipath elimination data set, and the received signal s(n)+j(n) that is only affected by interference without passing through the multipath channel is used as the output of the multipath elimination data set; the received signal s(n)+j(n) that is only affected by interference without passing through the multipath channel is used as the input of the interference suppression data set, and the transmitted signal s(n) is used as the output of the interference suppression data set;

[0010] Step 3: Use the multipath elimination data set to train the multipath elimination network UNet, save the trained network parameters, and obtain the trained underwater acoustic communication multipath elimination network model. The multipath elimination model UNet includes a ConvBlock encoder, an UpBlock decoder, a jump connection connecting the encoder and the decoder, and a bottleneck; the ConvBlock encoder is composed of convolution, DropBlock, batch normalization and activation function; after batch normalization, downsampling is achieved through average pooling, and the UpBlock decoder increases the data size through upsampling to finally match the dimension of the output data;

[0011] Step 4: Connect the interference suppression model MS-UNet in series after the multipath elimination model UNet, use the interference suppression data set to train the interference suppression network MS-UNet, save the trained network parameters, and obtain the trained underwater acoustic communication interference suppression network model. The interference suppression network MS-UNet includes a UNet part, three SE (Squeeze-and-Excitation module, SE) modules and a multi-head self-attention module (Multi-Head Self-Attention module, MHSA); with the assistance of the attention mechanism, the interference is extracted from the received signal, and then the estimated interference is eliminated from the input signal to achieve the purpose of interference suppression. The processed data is then received by the subsequent modules at the receiving end. The W-Net network consists of a multipath elimination model UNet and a serially connected interference suppression model MS-UNet;

[0012] Furthermore, the multipath elimination data set and the interference suppression data set are arranged in such a way that the real and imaginary parts are divided into two layers, and the real and imaginary parts are organized in a serpentine arrangement, wherein the odd rows are arranged in sequence and the even rows are arranged in reverse order, and the data are rearranged into a 2-layer 2-dimensional matrix.

[0013] Furthermore, the multipath elimination data set needs to be normalized before entering the multipath elimination network UNet and the interference suppression data set needs to enter the interference suppression network MS-UNet:

[0014]

[0015] x' is the received data after normalization, x is the received data, x max is the maximum value of the received data, x min It is the minimum value of received data.

[0016] Furthermore, the UNet part of the interference suppression network MS-UNet includes an encoder, a decoder, a jump connection connecting the encoder and the decoder, and a bottleneck.

[0017] Furthermore, the encoder of the UNet part includes an encoder block, and the decoder includes a decoder block. An encoder block consists of 2 convolutional layers, 1 DropBlock layer, 1 batch normalization layer, 1 RELU layer and 1 downsampling layer; a decoder block consists of 1 connection layer, 2 transposed convolutional layers, and 1 upsampling layer.

[0018] Furthermore, adding an SE module at the jump connection connecting the encoder and decoder of the interference suppression network MS-UNet can increase network parameters, prevent falling into local optimality, and accelerate network convergence. The SE module analyzes the input features of the jump connection from the encoder, compresses the features along the spatial dimension through the Squeeze operation, obtains the global receptive field, and then generates weights related to the feature channel correlation through the Excitation operation. The weight of the output of Excitation is regarded as the importance of each channel pair to the interference estimation after feature selection, and the weight is normalized channel by channel by multiplication to the input feature to obtain the output of the SE module, which is connected to the receiving end decoder of the jump connection. The SE attention mechanism can enhance the acquisition of interference features and filter out features that are not related to interference, so as to more accurately estimate interference and enhance the anti-interference performance of the network.

[0019] Furthermore, a multi-head attention mechanism MHSA module is applied at the bottleneck of the interference suppression network MS-UNet. The input features transmitted to the bottleneck at the end of the encoder path are first processed by the convolution layer. Before entering the multi-head attention mechanism MHSA module, the data is adjusted to a one-dimensional vector sequence. Then, the attention map obtained by the multi-head attention mechanism MHSA module and the input features at the bottleneck are adjusted to the corresponding one-dimensional vector sequence for residual connection and addition. After the connection is obtained, the result is layer normalized and then converted into a two-dimensional format. After upsampling, it is connected to the decoder module.

[0020] Furthermore, the loss function LOSS is constructed to train the interference suppression network MS-UNet as follows:

[0021]

[0022] The loss function LOSS uses the mean square error as the main loss function and the mean absolute error as the secondary loss function, where n represents the length of the signal sequence, α is a hyperparameter that adjusts the weights of the mean square error and the mean absolute error, and y i is the true value, is the predicted value.

[0023] Beneficial effects: On the one hand, the technical solution of the present invention changes the order of real and imaginary data into a snake-like arrangement, which is more conducive to capturing the structural characteristics of interference. On the other hand, the network of the technical solution of the present invention fully combines the multi-scale feature extraction capability of the UNET network, the advantage of multi-head self-attention in capturing global dependencies, and the channel attention mechanism focusing on the feature correlation between different channels, so as to better understand the pattern and structural characteristics of interference, thereby improving the effect of interference suppression. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A flow chart of the generation of a multipath elimination and interference suppression module at the receiving end of FBMC underwater acoustic communication provided by the invention;

[0025] Figure 2 Schematic diagram of the snake arrangement of the data set;

[0026] Figure 3 This is the structure diagram of the SE (Squeeze-and-Excitation) module;

[0027] Figure 4 This is the structural diagram of the multipath elimination network UNet;

[0028] Figure 5 This is the structure diagram of the interference suppression network MS-UNet;

[0029] Figure 6 This is the module structure diagram of the multi-head attention mechanism MHSA. DETAILED DESCRIPTION

[0030] The technical solution of the present invention is further described below in conjunction with the accompanying drawings.

[0031] like Figure 1 As shown, the present invention provides a technical solution:

[0032] A W-Net network-based FBMC underwater acoustic communication receiving end anti-interference method comprises the following steps:

[0033] Step 1: Establish an FBMC underwater acoustic communication model, which uses the FBMC / OQAM modulation method. After inserting the pilot, the symbol is modulated onto the subcarrier and sent into the underwater acoustic channel. Add additive interference signals through the FBMC underwater acoustic channel model to form a target receiving signal. In the communication system, interference signals are divided into artificial interference and non-artificial interference. Artificial interference mainly comes from ship signals in marine activities, which are simulated by SαS pulses; non-artificial interference mainly comes from natural biological activities such as killer whale echolocation clicks, which are simulated by the actual collected killer whale echolocation clicks. They are all sources of pulse interference in the marine environment.

[0034] In the FBMC underwater acoustic communication model, the interference signal is superimposed to form the received signal r(n) expressed as:

[0035] r(n)=s(n)*h(n)+w(n)+j(n)

[0036] Where s(n) represents the transmitted signal, the transmission mode is FBMC, * represents the convolution operator, h(n) is the impulse response of the multipath channel, w(n) is Gaussian white noise, and j(n) is interference;

[0037] Step 2: Construct a multipath elimination data set and an interference suppression data set; down-convert the received signal r(n) to obtain a baseband signal, and then filter it through a low-pass filter. The signal at this time is used as the input of the multipath elimination data set, and the received signal s(n)+j(n) that is only affected by interference without passing through the multipath channel is used as the output of the multipath elimination data set; the received signal s(n)+j(n) that is only affected by interference without passing through the multipath channel is used as the input of the interference suppression data set, and the transmitted signal s(n) is used as the output of the interference suppression data set;

[0038] Step 3: Use the multipath elimination data set to train the multipath elimination network UNet, save the trained network parameters, and obtain the trained underwater acoustic communication multipath elimination network model. The multipath elimination model UNet includes a ConvBlock encoder, an UpBlock decoder, a jump connection connecting the encoder and the decoder, and a bottleneck; a lightweight network is used as the basic architecture based on the amount of data. The ConvBlock encoder consists of convolution, DropBlock, batch normalization and activation function; after batch normalization, downsampling is achieved through average pooling, and the UpBlock decoder increases the data size through upsampling, and finally matches the dimension of the output data;

[0039] In order to reduce overfitting, the DropBlock layer is added after the conv block of the ConvBlock encoder part. Batch normalization of the data can speed up the network convergence, and then nonlinearity is introduced through the activation function. Compared with the traditional Dropout technology, DropBlock has a different shielding strategy. Dropout shields by randomly selecting a single feature, while DropBlock shields by randomly selecting multiple continuous areas. This method effectively reduces the redundant connections in the neural network and helps to improve the generalization ability of the model. Multipath elimination network UNet can be seen Figure 4 .

[0040] Step 4: Connect the interference suppression model MS-UNet in series after the multipath elimination model UNet, use the interference suppression data set to train the interference suppression network MS-UNet, save the trained network parameters, and obtain the trained underwater acoustic communication interference suppression network model. The interference suppression network MS-UNet includes a UNet part, three SE modules, and a multi-head self-attention module; with the assistance of the attention mechanism, the interference is extracted from the received signal, and then the estimated interference is eliminated from the input signal to achieve the purpose of interference suppression, and the processed data is then received by the subsequent modules at the receiving end. The W-Net network consists of a multipath elimination model UNet and a serially connected interference suppression model MS-UNet; the overall interference suppression network MS-UNet can be seen Figure 5 .

[0041] In the FBMC system, the transmitter first performs symbol mapping on the input bit stream to generate a complex symbol sequence. Subsequently, the complex symbols are grouped according to the real and imaginary parts, and OQAM (Offset Quadrature Amplitude Modulation) modulation is used to stagger the real and imaginary parts for half a symbol period. After the pilot symbol is inserted at the specified position of the modulated signal, the signal is transformed by IFFT and then processed by a multi-phase filter bank composed of prototype filters to achieve signal filtering and shaping. Finally, each symbol is modulated onto the corresponding subcarrier and transmitted to the underwater acoustic channel. Interference is added to the underwater acoustic channel to form a multipath elimination data set and an interference suppression data set.

[0042] Preferably, the multipath elimination data set and the interference suppression data set are arranged in such a way that the real and imaginary parts are divided into two layers, and the real and imaginary parts are organized in a serpentine arrangement, wherein the odd rows are arranged sequentially and the even rows are arranged in reverse order, and the data are rearranged into a 2-layer 2-dimensional matrix.

[0043] If the conventional reshape method is used, the direct distance d between sequences may fluctuate greatly as the signal scale increases, and the structural signal characteristics may not be well captured and maintained. The data is organized in a serpentine arrangement, where odd rows are arranged in order and even rows are arranged in reverse order. The specific arrangement can be seen in Figure 2 The signal points are rearranged into a 2-layer 2D matrix to facilitate the attention mechanism to capture the correlation between the previous and next data sequences. The performance is improved by converting the sequence signal anti-interference task into an image anti-interference task to meet the needs of local sampling and perception range of the convolution kernel. Figure 2The arrangement is intuitive. Each channel contains all the real or imaginary parts of the received data. Rei(n) represents the real part of the i-th received data of the n-th FBMC block. Imi(n) represents the imaginary part of the i-th received data of the n-th FBMC block. Compared with the simple reshape arrangement method, the snake arrangement method is more suitable for the training of anti-multipath neural network models and interference suppression neural network models.

[0044] Preferably, the multipath elimination data set needs to be normalized before entering the multipath elimination network UNet and the interference suppression data set needs to be normalized before entering the interference suppression network MS-UNet:

[0045]

[0046] x' is the received data after normalization, x is the received data, x max is the maximum value of the received data, x min It is the minimum value of received data.

[0047] Preferably, the UNet part of the interference suppression network MS-UNet includes an encoder, a decoder, a jump connection connecting the encoder and the decoder, and a bottleneck.

[0048] Preferably, the encoder of the UNet part includes an encoder block, and the decoder includes a decoder block, one encoder block consists of 2 convolutional layers, 1 DropBlock layer, 1 batch normalization layer, 1 RELU layer and 1 downsampling layer; one decoder block consists of 1 connection layer, 2 transposed convolutional layers, and 1 upsampling layer.

[0049] Preferably, adding a SE module at the jump connection connecting the encoder and decoder of the interference suppression network MS-UNet can increase network parameters, prevent falling into local optimality, and accelerate network convergence. The SE module analyzes the input features of the jump connection from the encoder, compresses the features along the spatial dimension through the Squeeze operation, obtains the global receptive field, and then generates weights related to the feature channel correlation through the Excitation operation. The weight of the output of Excitation is regarded as the importance of each channel pair to the interference estimation after feature selection, and the weight is normalized channel by channel by multiplication to the input feature to obtain the output of the SE module, which is connected to the receiving end decoder of the jump connection. The SE attention mechanism can enhance the acquisition of interference features and filter out features that are not related to interference, so as to more accurately estimate the interference and enhance the network's anti-interference performance. The structure of the SE attention mentioned in this step is as follows: Figure 3 shown.

[0050] Preferably, a multi-head attention mechanism MHSA module is applied at the bottleneck of the interference suppression network MS-UNet, and the input features passed to the bottleneck at the end of the encoder path are first processed by the convolution layer. Before entering the multi-head attention mechanism MHSA module, the data is adjusted to a one-dimensional vector sequence, and then the attention map obtained by the multi-head attention mechanism MHSA module and the input features at the bottleneck are adjusted to the corresponding one-dimensional vector sequence for residual connection and addition. After the connection is obtained, the result is layer normalized and then converted into a two-dimensional format. After upsampling, it is connected to the decoder module.

[0051] MHSA Modules:

[0052] MultiHead(Q,K,V)=Concat(head1,head2,...,head h )W O

[0053] h is the number of heads, that is, the number of parallel heads in the multi-head attention mechanism. O is a weight matrix of the output linear transformation, which is used to merge the outputs of different heads together. i The calculation formula is as follows:

[0054] head i =Attention(QW i Q ,KW i K ,VW i V )

[0055] Q represents the query vector, K represents the key vector, and V represents the value vector. i Q ,W i K ,W i V are the projection matrices of the query, key, and value. These matrices are trainable parameters used to map the input to the new representation space. Q, K, V are first transformed by different linear transformations W. i Q ,W i K ,W i V Map to a new subspace.

[0056] Attention calculation for each head:

[0057]

[0058] T represents transpose, d kRepresents the dimension of K, which is used to scale the dot product to prevent the gradient vanishing problem. Attention is calculated within the sequence, focusing on the relationship between different parts of the sequence.

[0059] The MHSA module is applied in the bottleneck passed to the decoder to obtain the spatial attention distribution of the input sequence, thereby more comprehensively capturing the potential multiple associations in the sequence. The MHSA attention module mentioned in this step is as follows Figure 6 shown.

[0060] Preferably, the loss function MSE is constructed to train the multipath elimination network.

[0061]

[0062] It is used to input the received signal affected by multipath into the multipath elimination network, train the multipath elimination network based on the constructed loss function MSE, and save the trained network parameters to obtain a network model that eliminates the influence of multipath.

[0063] Preferably, the loss function LOSS is constructed to train the interference suppression network MS-UNet as follows:

[0064]

[0065] The loss function LOSS uses the mean square error as the main loss function and the mean absolute error as the secondary loss function, where n represents the length of the signal sequence, α is a hyperparameter that adjusts the weights of the mean square error and the mean absolute error, and y i is the true value, is the predicted value.

[0066] The W-Net network is an end-to-end processing module that can perform multipath distortion correction and interference suppression in FBMC underwater acoustic communication systems without the need for traditional channel estimation and equalization algorithms. Without prior knowledge, the integrated model is designed to reduce the impact of multipath effects when processing received signals in underwater acoustic communication environments, have stronger anti-interference capabilities, and obtain better communication effects.

[0067] According to the above description, the present invention provides a method of anti-interference through deep learning. Although the present invention has been described in detail through specific embodiments, it should be understood that the embodiments are only used to illustrate and not to limit the present invention. Those skilled in the art may modify and change the embodiments without departing from the spirit and scope of the present invention. Therefore, the scope of protection of the present invention shall be based on the scope defined by the claims.

Claims

1. A W-Net network-based FBMC underwater acoustic communication receiving end anti-interference method, characterized in that: The following steps are involved: Step 1: Establish an FBMC underwater acoustic communication model. The FBMC underwater acoustic communication model uses the FBMC / OQAM modulation method. After inserting the pilot, the symbol is modulated onto the subcarrier and sent into the underwater acoustic channel. The interference signal is superimposed in the FBMC underwater acoustic communication model to form a received signal r(n) expressed as: r(n)=s(n)*h(n)+w(n)+j(n) Where s(n) represents the transmitted signal, the transmission mode is FBMC, * represents the convolution operator, h(n) is the impulse response of the multipath channel, w(n) is Gaussian white noise, and j(n) is interference; Step 2: Construct a multipath elimination data set and an interference suppression data set; down-convert the received signal r(n) to obtain a baseband signal, and then filter it through a low-pass filter. The signal at this time is used as the input of the multipath elimination data set, and the received signal s(n)+j(n) that is only affected by interference without passing through the multipath channel is used as the output of the multipath elimination data set; the received signal s(n)+j(n) that is only affected by interference without passing through the multipath channel is used as the input of the interference suppression data set, and the transmitted signal s(n) is used as the output of the interference suppression data set; Step 3: Use the multipath elimination dataset to train the multipath elimination network UNet, the multipath elimination model UNet includes a ConvBlock encoder, an UpBlock decoder, a jump connection connecting the encoder and the decoder, and a bottleneck; the ConvBlock encoder is composed of convolution, DropBlock, batch normalization and activation function; after batch normalization, downsampling is achieved by average pooling, and the UpBlock decoder increases the data size by upsampling, and finally matches the dimension of the output data; Step 4: Use the interference suppression data set to train the interference suppression network MS-UNet; the interference suppression network MS-UNet includes a UNet part, three SE modules and a multi-head self-attention module; With the assistance of the attention mechanism, the interference is extracted from the received signal, and then the estimated interference is eliminated from the input signal. The processed data is then received by the subsequent modules at the receiving end. The interference suppression model MS-UNet is connected in series after the multipath elimination model UNet to form a W-Net network as the underwater acoustic communication receiving end.

2. According to the W-Net network-based FBMC underwater acoustic communication receiving end anti-interference method of claim 1, it is characterized in that: The multipath elimination data set and the interference suppression data set are arranged in such a way that the real and imaginary parts are divided into two layers, and the real and imaginary parts are organized in a serpentine arrangement, wherein the odd rows are arranged sequentially and the even rows are arranged in reverse order, and the data are rearranged into a 2-layer 2-dimensional matrix.

3. According to claim 2, a W-Net network-based FBMC underwater acoustic communication receiving end anti-interference method is characterized in that: The multipath elimination data set needs to be normalized before entering the multipath elimination network UNet and the interference suppression data set needs to be normalized before entering the interference suppression network MS-UNet: x' is the received data after normalization, x is the received data, x max is the maximum value of the received data, x min It is the minimum value of received data.

4. According to the W-Net network-based FBMC underwater acoustic communication receiving end anti-interference method of claim 1, it is characterized in that: The UNet part of the interference suppression network MS-UNet includes an encoder, a decoder, a jump connection connecting the encoder and the decoder, and a bottleneck.

5. According to the W-Net network-based FBMC underwater acoustic communication receiving end anti-interference method of claim 4, it is characterized in that: The encoder of the UNet part includes an encoder block, and the decoder includes a decoder block. An encoder block consists of 2 convolutional layers, 1 DropBlock layer, 1 batch normalization layer, 1 RELU layer and 1 downsampling layer; a decoder block consists of 1 connection layer, 2 transposed convolutional layers, and 1 upsampling layer.

6. According to the W-Net network-based FBMC underwater acoustic communication receiving end anti-interference method of claim 4, it is characterized in that: An SE module is added to the jump connection connecting the encoder and decoder of the interference suppression network MS-UNet. The SE module analyzes the input features of the jump connection from the encoder, compresses the features along the spatial dimension through the Squeeze operation, obtains the global receptive field, and then generates weights related to the feature channel correlation through the Excitation operation. The weight of the output of Excitation is regarded as the importance of each channel pair to the interference estimation after feature selection, and the weight is weighted to the input feature through multiplication of the normalized weight channel by channel to obtain the output of the SE module, which is connected to the receiving end decoder of the jump connection.

7. According to the W-Net network-based FBMC underwater acoustic communication receiving end anti-interference method of claim 4, it is characterized in that: A multi-head attention mechanism MHSA module is applied at the bottleneck of the interference suppression network MS-UNet. The input features passed to the bottleneck at the end of the encoder path are first processed by the convolution layer. Before entering the multi-head attention mechanism MHSA module, the data is adjusted to a one-dimensional vector sequence. Then, the attention map obtained by the multi-head attention mechanism MHSA module and the input features at the bottleneck are adjusted to the corresponding one-dimensional vector sequence for residual connection and addition. After the connection is obtained, the result is layer normalized and then converted into a two-dimensional format. After upsampling, it is connected to the decoder module.

8. The anti-interference method for the FBMC underwater acoustic communication receiving end based on the W-Net network according to claim 1 is characterized in that: Construct the loss function LOSS to train the interference suppression network MS-UNet as follows: The loss function LOSS uses the mean square error as the main loss function and the mean absolute error as the secondary loss function, where n represents the length of the signal sequence, α is a hyperparameter that adjusts the weights of the mean square error and the mean absolute error, and y i is a true value, is the predicted value.

9. A computer storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, an anti-interference method for an FBMC underwater acoustic communication receiving end based on a W-Net network as described in any one of claims 1 to 8 is implemented.

10. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, an anti-interference method for an FBMC underwater acoustic communication receiving end based on a W-Net network is implemented as described in any one of claims 1-8.

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