Intelligent metasurface-assisted high-speed rail wireless communication channel estimation method

Abstract

The invention provides a channel estimation method for intelligent metasurface-assisted high-speed rail wireless communication, which comprises the following steps: firstly, establishing a link channel model based on a communication system, then, carrying out preliminary estimation on a channel by adopting a least square algorithm to obtain noisy channel data, then, constructing a channel estimation network model based on deep learning, and finally, carrying out channel estimation on the noisy channel data. Noise-containing channel data is used as input of a channel estimation network model, in the channel estimation network model, pilot signals and a channel matrix are used as dual-channel images with different sizes, then a loss function of the channel estimation network model is optimized, estimation errors are calculated according to an estimated channel, model parameters are subjected to back propagation training, and the channel estimation network model is obtained. And finally, carrying out channel estimation by using the trained channel estimation network model. According to the invention, an accurate channel estimation result can be realized by using a small amount of pilot frequency.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless communications, and in particular to a channel estimation method for intelligent metasurface-assisted high-speed rail wireless communications. Background Art

[0002] As an emerging technology that improves the coverage and resource utilization efficiency of wireless systems, Reconfigurable Intelligent Surface (RIS) is attracting extensive attention from researchers. Introducing RIS into wireless communication systems can make the transmission environment controllable and effectively improve the quality of wireless communication services.

[0003] The good performance gain brought by RIS depends on accurate channel state information. In the communication process, in order to decode the received data as accurately as possible, it is necessary to estimate the characteristics of the channel from the state of the received signal. Therefore, channel estimation is an essential part of the RIS-assisted communication system.

[0004] However, unlike public wireless networks, high-speed rail networks have more stringent reliability requirements. In high-speed rail scenarios, not only does the rapid movement of trains lead to frequent handoffs, severe Doppler shifts, and smaller channel coherence, but communication scenarios are also diverse and complex. In the RIS-assisted high-speed rail communication scenario, how to accurately estimate the channel is a technical problem that technicians in this field need to solve. Summary of the invention

[0005] In view of this, the present invention provides a channel estimation method for intelligent metasurface-assisted high-speed rail wireless communication, so as to accurately estimate the channel in the RIS-assisted high-speed rail communication scenario.

[0006] A channel estimation method for intelligent metasurface-assisted high-speed rail wireless communication, comprising: Step S1, establishing a communication system for intelligent metasurface-assisted high-speed rail wireless communication, the communication system comprising a mobile relay, an intelligent metasurface and a base station, the base station being deployed linearly along the rails of the high-speed rail, the mobile relay being deployed on the roof of the high-speed rail, and the intelligent metasurface being deployed on lamp posts around the base station; Step S2, establishing a link channel model based on the communication system, wherein the link channel model includes a direct channel from the base station to the mobile relay, and a cascade channel from the base station to the smart metasurface and then to the mobile relay for reflection; Step S3, using the established link channel model and a least squares algorithm to perform channel estimation to obtain noisy channel data; Step S4, constructing a channel estimation network model based on deep learning, introducing a Laplace pyramid network structure into the channel estimation network model, wherein the network structure includes a plurality of generative adversarial networks at different levels; Step S5, using the noisy channel data as the input of the channel estimation network model, in the channel estimation network model, using the pilot signal and the channel matrix as dual-channel images of different sizes, optimizing the loss function of the channel estimation network model, calculating the estimation error according to the estimated channel and back-propagating the training model parameters until the model parameters converge, thereby obtaining a trained channel estimation network model; Step S6: performing channel estimation using the trained channel estimation network model.

[0007] According to the channel estimation method for intelligent metasurface-assisted high-speed rail wireless communication provided by the present invention, a link channel model is first established based on the communication system, and the link channel model includes a direct channel from a base station to a mobile relay, and a cascade channel from a base station to an intelligent metasurface and then to a mobile relay reflection. Then, a least squares algorithm is used to perform a preliminary estimation of the channel to obtain noisy channel data, and then a channel estimation network model based on deep learning is constructed, and the noisy channel data is used as the input of the channel estimation network model. In the channel estimation network model, the pilot signal and the channel matrix are used as dual-channel images of different sizes, and the real part and the imaginary part of the complex matrix can correspond to the two channels of the image respectively. In this way, the channel estimation problem can be converted into a channel image generation problem, and the channel image is generated by using a generative adversarial network (GAN). Through adversarial training of the generator and the discriminator, the residual between each layer of the Laplace pyramid and the adjacent layers is learned, and the channel image from rough to precise can be reconstructed step by step. By optimizing the loss function, the accuracy of the channel estimation can be further improved, and the correct direction of the generator optimization is also ensured. The present invention can achieve accurate channel estimation results using a small amount of pilot signals, can significantly improve communication efficiency, and is more suitable for the scenario of RIS-assisted high-speed rail communication. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 A flow chart of a channel estimation method for intelligent metasurface-assisted high-speed rail wireless communication provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0009] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the embodiments of the present invention, and should not be construed as limiting the present invention.

[0010] See also Figure 1, an embodiment of the present invention provides a channel estimation method for intelligent metasurface-assisted high-speed rail wireless communication, comprising steps S1 to S6: Step S1, establish a communication system for intelligent metasurface-assisted high-speed rail wireless communication, the communication system includes a mobile relay, an intelligent metasurface and a base station, the base station is deployed along the high-speed rail track, the mobile relay is deployed on the roof of the high-speed rail, and the intelligent metasurface is deployed on the lamp posts around the base station.

[0011] Each base station (BS) is equipped with M To avoid the penetration loss of train carriages and reduce frequent group switching, mobile relays (MR) are deployed on the roof of the train and equipped with P Antennas are used to communicate with the base station. To improve spectrum efficiency, smart metasurfaces are symmetrically deployed on the lamp posts around the base station. Each RIS consists of N The RIS is composed of passive reflective elements, which adjust the propagation of the signal toward the target direction by changing the amplitude and phase shift of the incident signal. In this embodiment, both the BS antenna and the MR antenna are modeled as a uniform linear array (ULA) architecture. Since the RIS elements are arranged in a plane, the RIS is modeled as a uniform platform array (UPA) architecture.

[0012] Step S2, establishing a link channel model based on the communication system, wherein the link channel model includes a direct channel from the base station to the mobile relay, and a cascade channel from the base station to the smart metasurface and then to the mobile relay for reflection.

[0013] The cascade channel includes a link channel between the base station and the smart metasurface, and a link channel between the smart metasurface and the mobile relay.

[0014] Since both the BS and RIS are fixed at specific locations in advance, the link between the base station and the smart metasurface can be line of sight (LOS) wireless transmission.

[0015] For the link between the smart metasurface and the mobile relay, since the line-of-sight wireless transmission channel between RIS and MR may be blocked by trees or buildings in actual situations, the channel between the smart metasurface and the mobile relay link is modeled as a Rice channel.

[0016] Specifically, the established link channel model satisfies the following conditional formula: ; in, Indicates The joint channel of the downlink channel model of time slots, represents the link channel between the base station and the smart metasurface, represents the reflection coefficient matrix of the smart metasurface, Indicates The channel between the smart metasurface and the mobile relay link in time slots, Indicates Direct channel under time slots; ; = ; ; ; in, represents the path complex gain of the link channel between the base station and the smart metasurface, represents the receiving array response of the smart metasurface, and The intelligent hypersurface is the guiding vector along the x-axis and y-axis respectively, is the Kronecker product, and They represent the elevation angle and azimuth angle of the signal reaching the smart metasurface, and are the number of reflection units placed along the x-axis and y-axis on the three-dimensional Cartesian coordinate plane, and represent the first receiving array response parameter and the second receiving array response parameter respectively, represents the distance between two adjacent smart metasurfaces, Indicates the wavelength of the received signal; ; ; ; ; in, is the Rice factor, For the The line-of-sight wireless propagation component in the link between the smart metasurface and mobile relay in time slots, For the The non-line-of-sight wireless propagation component in the link between the smart metasurface and the mobile relay in time slots. represents the path complex gain of the channel between the smart metasurface and the mobile relay link, is an imaginary number, The Doppler shift of the channel between the smart metasurface and the mobile relay link, is the duration of a time slot, represents the receiving array response of the smart metasurface and the mobile relay link, and represent the steering vectors of the reflection link along the x-axis and y-axis respectively, and Respectively represent the elevation angle and azimuth angle of the signal leaving the smart metasurface, and Respectively represent the guiding vectors of the smart hypersurface along the x-axis and y-axis, Indicates the speed of the high-speed rail; ; in, is a diagonal matrix, represents the amplitude reflection coefficient vector of the smart metasurface, and Respectively represent the first passive reflective element and the first passive reflective element in the smart metasurface. The reflection phase shift of a passive reflection element; ; ; in, represents the path complex gain of the direct channel, is the Doppler shift of the direct channel, and They respectively represent the elevation angle and azimuth angle when the signal reaches the mobile relay.

[0017] Step S3, using the established link channel model, a least squares algorithm is used to perform channel estimation to obtain noisy channel data.

[0018] Among them, based on the established link channel model, the joint channel of the link channel model under all time slots is obtained, thereby obtaining the joint channel matrix , and then the least squares algorithm (LS) is used for channel estimation. The cost function of the least squares algorithm is The expression is: ; in, represents the matrix of the received signal, represents the matrix of the transmitted signal, Indicates transpose.

[0019] The core idea of ​​LS algorithm for channel estimation is to use pilot symbol sequence, construct training matrix and solve by least square method to obtain the estimated channel impulse response value. According to the principle of channel estimation, in order to obtain high-precision channel state information (CSI), the cost function must be minimized. Therefore, the minimum value of the above cost function is obtained, the partial derivative of the cost function is obtained, and the result is set to 0, and then the estimated value of LS algorithm is obtained by solving this.

[0020] Specifically, for the cost function Solve and get the estimated value of the least squares algorithm ,Will As noisy channel data, The expression is: .

[0021] Step S4, constructing a channel estimation network model based on deep learning, introducing a Laplace pyramid network structure into the channel estimation network model, and the network structure includes multiple generative adversarial networks at different levels.

[0022] Among them, in this embodiment, the pilot signal and the channel matrix are regarded as dual-channel images of different sizes, and the time domain and the frequency domain correspond to the two channels of the image respectively, so that the channel estimation problem is converted into a channel image generation problem. Then, the image pyramid that realizes multi-scale expression of the image in the image scaling technology is introduced to help channel reconstruction. Through the constructed deep learning-based channel estimation network model, the signal received by the BS is preprocessed by the LS algorithm to achieve a preliminary estimation of the channel; then the preprocessed signal is regarded as a dual-channel image as a conditional input, and the channel estimation problem is converted into a problem of restoring a high-resolution image with a given low-resolution image. By learning these data, the channel matrix is ​​reconstructed through each layer of the pyramid.

[0023] Step S5, using the noisy channel data as the input of the channel estimation network model, in the channel estimation network model, using the pilot signal and the channel matrix as dual-channel images of different sizes, optimizing the loss function of the channel estimation network model, calculating the estimation error according to the estimated channel and back-propagating the training model parameters until the model parameters converge, thereby obtaining a trained channel estimation network model.

[0024] Among them, in the channel estimation network model, As the input of the generator, a convolution layer and a ReLU layer are used to extract the low-level features of the received signal; then, an upsampling module is used to It is enlarged to the size of the real channel, and multiple denoising blocks are connected in series to achieve denoising. Each denoising block has a Conv2d+BN+ReLU structure and is filtered with a filter size of 64×3×3. Finally, a combination of Conv2d+BN+ReLU and Conv2d is used. The residual information generated by the generator is input into the discriminator for judgment. The discriminator is designed as a fully connected neural network with strong nonlinear modeling capabilities. Specifically, the discriminator includes 3 fully connected layers, a BN layer, an activation function layer, a Dropout layer, and a Flatten layer. First, three fully connected layers are used to make nonlinear changes to the features and extract the associations between the features. A BN layer is added to normalize the input of each unit to stabilize learning and help gradient propagation. LeakyReLU is added as the activation function layer, and a Dropout layer is added to prevent overfitting, improve the training speed of the model, and reduce network parameters. Then, a Flatten layer is used to convert the channel matrix into a one-dimensional vector. Finally, a fully connected layer and a Sigmoid function are used to output a scalar, indicating that the input residual matrix is ​​real rather than a fake residual channel matrix generated by the generator. The learning goal of the generator is to generate residuals as close as possible. The subsequent pyramid repeats this process. After multiple iterations of training the generator and the discriminator, the network finally reaches the Nash equilibrium state and the network model is in convergence.

[0025] Finally, the channel estimation network model reconstructs the channel Satisfy the following formula: ; in, represents the total number of layers of the Laplacian pyramid, Indicates i The residuals generated by the generator on the layer Laplacian pyramid.

[0026] In this embodiment, the loss function of the channel estimation network model is for: ; ; ; in, represents the loss function of the generative adversarial network, represents the complementary loss function, represents a generator, represents the discriminator, It means taking the expected value. Indicates i The channels generated by the layer Laplacian pyramid, Indicates i-1 channel generated by Laplacian pyramid.

[0027] The above loss function , by introducing a supplementary loss function , which can ensure the correct direction of generator optimization. By designing the above loss function , which can further improve the accuracy.

[0028] Step S6: performing channel estimation using the trained channel estimation network model.

[0029] Among them, the optimal channel estimation scheme can be determined by using the trained channel estimation network model, and finally channel estimation can be realized.

[0030] According to the channel estimation method for intelligent metasurface-assisted high-speed rail wireless communication in the above embodiment, a link channel model is first established based on the communication system. The link channel model includes a direct channel from a base station to a mobile relay, and a cascade channel from a base station to an intelligent metasurface and then reflected from a mobile relay. Then, a least squares algorithm is used to perform a preliminary estimation of the channel to obtain noisy channel data. Then, a channel estimation network model based on deep learning is constructed, and the noisy channel data is used as the input of the channel estimation network model. In the channel estimation network model, the pilot signal and the channel matrix are used as dual-channel images of different sizes, and the real part and the imaginary part of the complex matrix can correspond to the two channels of the image respectively. In this way, the channel estimation problem can be converted into a channel image generation problem. The channel image is generated by using a generative adversarial network (GAN). Through adversarial training of the generator and the discriminator, the residual between each layer of the Laplace pyramid and the adjacent layers is learned, and the channel image from rough to precise can be reconstructed step by step. By optimizing the loss function, the accuracy of the channel estimation can be further improved, and the correct direction of the generator optimization is also ensured. The present invention can achieve accurate channel estimation results using a small amount of pilot signals, can significantly improve communication efficiency, and is more suitable for the scenario of RIS-assisted high-speed rail communication.

[0031] The above-mentioned embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A channel estimation method for high-speed rail wireless communication assisted by intelligent metasurface, characterized in that: include: Step S1, establishing a communication system for intelligent metasurface-assisted high-speed rail wireless communication, the communication system comprising a mobile relay, an intelligent metasurface and a base station, the base station being deployed linearly along the rails of the high-speed rail, the mobile relay being deployed on the roof of the high-speed rail, and the intelligent metasurface being deployed on lamp posts around the base station; Step S2, establishing a link channel model based on the communication system, wherein the link channel model includes a direct channel from the base station to the mobile relay, and a cascade channel from the base station to the smart metasurface and then to the mobile relay for reflection; Step S3, using the established link channel model and a least squares algorithm to perform channel estimation to obtain noisy channel data; Step S4, constructing a channel estimation network model based on deep learning, introducing a Laplace pyramid network structure into the channel estimation network model, wherein the network structure includes a plurality of generative adversarial networks at different levels; Step S5, using the noisy channel data as the input of the channel estimation network model, in the channel estimation network model, using the pilot signal and the channel matrix as dual-channel images of different sizes, optimizing the loss function of the channel estimation network model, calculating the estimation error according to the estimated channel and back-propagating the training model parameters until the model parameters converge, thereby obtaining a trained channel estimation network model; Step S6: performing channel estimation using the trained channel estimation network model.

2. The channel estimation method for intelligent metasurface-assisted high-speed rail wireless communication according to claim 1 is characterized in that: The cascade channel includes a link channel between the base station and the smart metasurface, and a channel between the smart metasurface and the mobile relay link. The established link channel model satisfies the following conditional formula: ； in, Indicates The joint channel of the downlink channel model of time slots, represents the link channel between the base station and the smart metasurface, represents the reflection coefficient matrix of the smart metasurface, Indicates The channel between the smart metasurface and the mobile relay link in time slots, Indicates Direct channel under time slots; ； = ； ； ； in, represents the path complex gain of the link channel between the base station and the smart metasurface, represents the receiving array response of the smart metasurface, and The intelligent hypersurface is the guiding vector along the x-axis and y-axis respectively, is the Kronecker product, and They represent the elevation angle and azimuth angle of the signal reaching the smart metasurface, and are the number of reflection units placed along the x-axis and y-axis on the three-dimensional Cartesian coordinate plane, and represent the first receiving array response parameter and the second receiving array response parameter respectively, represents the distance between two adjacent smart metasurfaces, Indicates the wavelength of the received signal; ； ； ； ； in, is the Rice factor, For the The line-of-sight wireless propagation component in the link between the smart metasurface and mobile relay in time slots, For the The non-line-of-sight wireless propagation component in the link between the smart metasurface and the mobile relay in time slots. represents the path complex gain of the channel between the smart metasurface and the mobile relay link, is an imaginary number, The Doppler shift of the channel between the smart metasurface and the mobile relay link, is the duration of a time slot, represents the receiving array response of the smart metasurface and the mobile relay link, and represent the steering vectors of the reflection link along the x-axis and y-axis respectively, and Respectively represent the elevation angle and azimuth angle of the signal leaving the smart metasurface, and Respectively represent the guiding vectors of the smart hypersurface along the x-axis and y-axis, Indicates the speed of the high-speed rail; ； in, is a diagonal matrix, represents the amplitude reflection coefficient vector of the smart metasurface, and Respectively represent the first passive reflective element and the first passive reflective element in the smart metasurface. The reflection phase shift of a passive reflection element; ； ； in, represents the path complex gain of the direct channel, is the Doppler shift of the direct channel, and They respectively represent the elevation angle and azimuth angle when the signal reaches the mobile relay.

3. The channel estimation method for intelligent metasurface-assisted high-speed rail wireless communication according to claim 2 is characterized in that: Based on the established link channel model, the joint channel of the link channel model in all time slots is obtained, thereby obtaining the joint channel matrix , and then the least squares algorithm is used for channel estimation. The cost function of the least squares algorithm is The expression is: ； in, represents the matrix of the received signal, represents the matrix of the transmitted signal, represents transpose; For the cost function Solve and get the estimated value of the least squares algorithm ,Will As noisy channel data, The expression is: 。 4. The channel estimation method for intelligent metasurface-assisted high-speed rail wireless communication according to claim 3 is characterized in that: In the channel estimation network model, As the input of the generator, a convolution layer and a ReLU layer are used to extract the low-level features of the received signal; then, an upsampling module is used to It is enlarged to the size of the real channel, and multiple denoising blocks are connected in series to achieve denoising. Each denoising block has a Conv2d+BN+ReLU structure and is filtered with a filter of size 64×3×3. Finally, a combination of Conv2d+BN+ReLU and Conv2d is used. The residual information generated by the generator is input into the discriminator for judgment. The discriminator includes 3 fully connected layers, a BN layer, an activation function layer, a Dropout layer, and a Flatten layer.

5. The channel estimation method for intelligent metasurface-assisted high-speed rail wireless communication according to claim 4 is characterized in that: Channel estimation network model reconstructs the channel Satisfy the following formula: ； in, represents the total number of layers of the Laplacian pyramid, Indicates i The residuals generated by the generator on the layer Laplacian pyramid.

6. The channel estimation method for intelligent metasurface-assisted high-speed rail wireless communication according to claim 5 is characterized in that: Loss function of the channel estimation network model for: ； ； ； in, represents the loss function of the generative adversarial network, represents the complementary loss function, represents a generator, represents the discriminator, It means taking the expected value. Indicates i The channels generated by the layer Laplacian pyramid, Indicates i -1 channel generated by Laplacian pyramid.

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