CN113726711B - OFDM receiving method and device, and channel estimation model training method and device - Google Patents
OFDM receiving method and device, and channel estimation model training method and device Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L27/00—Modulated-carrier systems
- H04L27/26—Systems using multi-frequency codes
- H04L27/2601—Multicarrier modulation systems
- H04L27/2647—Arrangements specific to the receiver only
- H04L27/2655—Synchronisation arrangements
- H04L27/2689—Link with other circuits, i.e. special connections between synchronisation arrangements and other circuits for achieving synchronisation
- H04L27/2695—Link with other circuits, i.e. special connections between synchronisation arrangements and other circuits for achieving synchronisation with channel estimation, e.g. determination of delay spread, derivative or peak tracking
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L25/00—Baseband systems
- H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
- H04L25/0202—Channel estimation
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L27/00—Modulated-carrier systems
- H04L27/26—Systems using multi-frequency codes
- H04L27/2601—Multicarrier modulation systems
- H04L27/2647—Arrangements specific to the receiver only
- H04L27/2655—Synchronisation arrangements
- H04L27/2668—Details of algorithms
- H04L27/2669—Details of algorithms characterised by the domain of operation
- H04L27/2672—Frequency domain
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L27/00—Modulated-carrier systems
- H04L27/26—Systems using multi-frequency codes
- H04L27/2601—Multicarrier modulation systems
- H04L27/2647—Arrangements specific to the receiver only
- H04L27/2655—Synchronisation arrangements
- H04L27/2668—Details of algorithms
- H04L27/2681—Details of algorithms characterised by constraints
- H04L27/2688—Resistance to perturbation, e.g. noise, interference or fading
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L27/00—Modulated-carrier systems
- H04L27/26—Systems using multi-frequency codes
- H04L27/2601—Multicarrier modulation systems
- H04L27/2647—Arrangements specific to the receiver only
- H04L27/2655—Synchronisation arrangements
- H04L27/2689—Link with other circuits, i.e. special connections between synchronisation arrangements and other circuits for achieving synchronisation
- H04L27/2691—Link with other circuits, i.e. special connections between synchronisation arrangements and other circuits for achieving synchronisation involving interference determination or cancellation
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Abstract
The application provides an OFDM receiving method and device, a channel estimation model training method and device, an electronic device and a storage medium, wherein the OFDM receiving method comprises the following steps: acquiring a frequency domain signal; preprocessing the frequency domain signal to obtain channel estimation information; determining a plurality of low-resolution two-dimensional images according to the channel estimation information; inputting a plurality of low-resolution two-dimensional images into a pre-trained channel estimation model to obtain channel state information; bit information transmitted by the transmitting end is determined based on the frequency domain signal and the channel state information. The channel estimation information is decomposed into a plurality of low-resolution two-dimensional images, the plurality of low-resolution two-dimensional images are input into a channel estimation model, characteristic information in the plurality of low-resolution two-dimensional images is extracted by utilizing the pre-trained channel estimation model, and channel state information with higher precision is determined, so that bit information sent by a sending end is determined based on the channel state information, and the receiving performance of the OFDM receiver is improved.
Description
Technical Field
The present application relates to the field of communications technologies, and in particular, to an OFDM receiving method and apparatus, a channel estimation model training method and apparatus, an electronic device, and a computer storage medium.
Background
Orthogonal frequency division multiplexing (Orthogonal Frequency Division Multiplexing, OFDM) is widely used in wireless communications and is a key modulation scheme for wireless communication systems. However, the peak-to-average power of the OFDM system is high, and the linear range of the amplifier is limited, which causes nonlinear distortion of the signal, thereby seriously affecting the estimation accuracy and the channel capacity. In order to ensure high rate and reliable transmission of data, the receiver of an OFDM system often employs coherent demodulation, which requires estimation of channel state information.
In the prior art, a least square method is generally adopted for channel state information estimation. However, the least square method is adopted to estimate the channel state information, the accuracy of the obtained estimation result is low, and the accurate estimation of the channel state information cannot be realized, so that the receiving performance of the receiver is affected.
Disclosure of Invention
An object of an embodiment of the present application is to provide an OFDM receiving method and apparatus, a channel estimation model training method and apparatus, an electronic device, and a computer storage medium, which are used to improve the accuracy of estimating channel state information, thereby improving the receiving performance of a receiver.
In a first aspect, the present application provides an OFDM receiving method, including: acquiring a frequency domain signal; preprocessing the frequency domain signal to obtain channel estimation information; determining a plurality of low-resolution two-dimensional images according to the channel estimation information; inputting the plurality of low-resolution two-dimensional images into a pre-trained channel estimation model to obtain channel state information; and determining bit information sent by a sending end based on the frequency domain signal and the channel state information.
In the embodiment of the application, the channel estimation information is determined according to the frequency domain signal, the channel estimation information (channel state information with lower precision) is decomposed into a plurality of low-resolution two-dimensional images, the plurality of low-resolution two-dimensional images are input into the channel estimation model, and the characteristic information in the plurality of low-resolution two-dimensional images is extracted by utilizing the pre-trained channel estimation model, so that the channel state information with higher precision is determined, and the bit information sent by the sending end is determined based on the channel state information, thereby improving the receiving performance of the OFDM receiver.
In an optional implementation manner, the channel estimation model includes an image super-resolution network and an up-sampling network, and the inputting the plurality of low-resolution two-dimensional images into the pre-trained channel estimation model to obtain channel state information includes: extracting features of the plurality of low-resolution two-dimensional images by using the super-resolution network to obtain features corresponding to the two-dimensional images; and up-sampling the features corresponding to the two-dimensional image by utilizing the up-sampling network to obtain the channel state information.
In the embodiment of the application, the image super-resolution technology is used for extracting the characteristics of a plurality of low-resolution two-dimensional images to obtain the corresponding characteristics, and then up-sampling is carried out based on the characteristics to determine the channel state information with higher precision, so as to realize more accurate channel estimation.
In an optional implementation manner, the determining, based on the frequency domain signal and the channel state information, bit information sent by a sending end includes: zero-forcing equalization is carried out on the frequency domain signal and the channel state information, and the estimated information of the transmitting symbol is determined; and inputting the estimated information of the transmitting symbol into a pre-trained signal detection model to obtain bit information sent by a sending end.
In an optional implementation manner, the signal detection model includes a denoising network and a parallel detection network, the inputting the estimated information of the transmitting symbol into the pre-trained signal detection model, to obtain bit information sent by a sending end, includes: inputting the transmitted symbol estimation information into the denoising network to denoise, so as to obtain denoised transmitted symbol estimation information; and inputting the denoised transmitted symbol estimation information into the parallel detection network to obtain the bit information.
In the embodiment of the application, the noise removing network is utilized to remove the noise in the transmitted symbol estimation information, and the denoised transmitted symbol estimation information is input into the parallel detection network, so that the error rate of the determined bit information is reduced, and the receiving performance of the OFDM receiver is improved.
In an optional embodiment, the preprocessing the frequency domain signal to obtain channel estimation information includes: determining a pilot signal according to the frequency domain signal and the pilot position; and carrying out least square channel estimation on the pilot signal and the local pilot signal, and determining the channel estimation information.
In a second aspect, the present application provides a channel estimation model training method, including: acquiring a plurality of frequency domain signals and complete channel state information corresponding to the plurality of frequency domain signals; preprocessing each frequency domain signal to obtain corresponding channel estimation information; determining a plurality of low-resolution two-dimensional images according to the corresponding channel estimation information; and taking the plurality of low-resolution two-dimensional images as training samples, and inputting the complete channel state information corresponding to each frequency domain signal as a training label to a preset channel estimation model for training until the model is trained to be converged, so as to obtain a trained channel estimation model.
In the embodiment of the application, the channel estimation information corresponding to the plurality of frequency domain signals is used as a training sample, the complete channel state information corresponding to each frequency domain signal is used as a training label to train the channel estimation model, and the characteristic information in the plurality of low-resolution two-dimensional images is extracted by utilizing the pre-trained channel estimation model, so that the channel state information similar to the real channel state information is determined, and the accuracy of channel state information estimation is improved.
In a third aspect, the present application provides an OFDM receiving apparatus, the apparatus comprising: the acquisition module is used for acquiring the frequency domain signal; the preprocessing module is used for preprocessing the frequency domain signals to obtain channel estimation information; a determining module, configured to determine a plurality of low-resolution two-dimensional images according to the channel estimation information; the channel prediction module is used for inputting the plurality of low-resolution two-dimensional images into a pre-trained channel estimation model to obtain channel state information; and the bit signal determining module is used for determining bit information sent by a sending end based on the frequency domain signal and the channel state information.
In an optional implementation manner, the channel estimation model includes an image super-resolution network and an up-sampling network, and the channel prediction module is specifically configured to perform feature extraction on the plurality of low-resolution two-dimensional images by using the super-resolution network to obtain features corresponding to the two-dimensional images; and up-sampling the features corresponding to the two-dimensional image by utilizing the up-sampling network to obtain the channel state information.
In an optional implementation manner, the bit signal determining module is specifically configured to perform zero forcing equalization on the frequency domain signal and the channel state information, and determine transmit symbol estimation information; and inputting the estimated information of the transmitting symbol into a pre-trained signal detection model to obtain bit information sent by a sending end.
In an optional implementation manner, the signal detection model includes a denoising network and a parallel detection network, and the bit signal determining module is specifically configured to input the estimated information of the transmitted symbol into the denoising network to denoise, so as to obtain the estimated information of the denoised transmitted symbol; and inputting the denoised transmitted symbol estimation information into the parallel detection network to obtain the bit information.
In an alternative embodiment, the preprocessing module is specifically configured to determine a pilot signal according to the frequency domain signal and the pilot position; and carrying out least square channel estimation on the pilot signal and the local pilot signal, and determining the channel estimation information.
In a fourth aspect, the present application provides a channel estimation model training apparatus, the apparatus comprising: the acquisition module is used for acquiring a plurality of frequency domain signals and complete channel state information corresponding to the plurality of frequency domain signals; the preprocessing module is used for preprocessing each frequency domain signal to obtain corresponding channel estimation information; a determining module, configured to determine a plurality of low-resolution two-dimensional images according to the corresponding channel estimation information; the training module is used for taking the plurality of low-resolution two-dimensional images as training samples, and inputting the complete channel state information corresponding to each frequency domain signal as a training label to a preset channel estimation model for training until the model is trained to be converged, so that a trained channel estimation model is obtained.
In a fifth aspect, the present application provides an electronic device, comprising: a processor, a memory, and a bus; the processor and the memory complete communication with each other through the bus; the memory stores program instructions executable by the processor, the processor invoking the program instructions to perform the method of any of the preceding embodiments.
In a sixth aspect, the present application provides a computer storage medium having stored thereon computer program instructions which, when read and executed by a computer, perform a method according to any of the preceding embodiments.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and should not be considered as limiting the scope, and other related drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of an OFDM receiving method according to an embodiment of the present application;
fig. 2 is a flowchart of a channel estimation model training method according to an embodiment of the present application;
fig. 3 is a block diagram of a channel estimation model according to an embodiment of the present application;
FIG. 4 is a block diagram of a signal detection model according to an embodiment of the present application;
fig. 5 is a block diagram of an OFDM receiving apparatus according to an embodiment of the present application;
fig. 6 is a block diagram of a channel estimation model training device according to an embodiment of the present application;
fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Icon: 500-OFDM receiving device; 501-an acquisition module; 502-a preprocessing module; 503-a determination module; 504-a channel prediction module; 505-bit signal determination module; 600-channel estimation model training means; 601-an acquisition module; 602-a preprocessing module; 603-a determination module; 604-a training module; 700-an electronic device; 701-a processor; 702-a communication interface; 703-a memory; 704-bus.
Detailed Description
The technical solutions in the embodiments of the present application will be described below with reference to the accompanying drawings in the embodiments of the present application.
Referring to fig. 1, fig. 1 is a flowchart of an OFDM receiving method according to an embodiment of the present application, where the OFDM receiving method may include the following steps:
step 101: a frequency domain signal is acquired.
Step 102: and preprocessing the frequency domain signal to obtain channel estimation information.
Step 103: a plurality of low resolution two-dimensional images are determined from the channel estimation information.
Step 104: and inputting a plurality of low-resolution two-dimensional images into a pre-trained channel estimation model to obtain channel state information.
Step 105: bit information transmitted by the transmitting end is determined based on the frequency domain signal and the channel state information.
The above-described flow will be described in detail with reference to examples.
According to the steps, the channel estimation is performed by adopting a pre-trained channel estimation model, so that channel state information is obtained, and further bit information sent by a sending end is determined according to the channel state information and the frequency domain signal. To facilitate understanding of the present embodiment, before the step S101, a training process of the channel estimation model is described.
Referring to fig. 2, fig. 2 is a flowchart of a channel estimation model training method according to an embodiment of the present application, where the channel estimation model training method may include the following steps:
step 201: and acquiring the plurality of frequency domain signals and complete channel state information corresponding to the plurality of frequency domain signals.
Step 202: and preprocessing each frequency domain signal to obtain corresponding channel estimation information.
Step 203: a plurality of low resolution two-dimensional images are determined from the corresponding channel estimation information.
Step 204: and taking a plurality of low-resolution two-dimensional images as training samples, and inputting complete channel state information corresponding to each frequency domain signal as training labels to a preset channel estimation model for training to obtain a trained channel estimation model.
The above steps 201-203 are described in detail below.
Step 201: and acquiring the plurality of frequency domain signals and complete channel state information corresponding to the plurality of frequency domain signals.
In the embodiment of the application, a plurality of groups of transmission signals are generated locally and randomly, and after passing through an OFDM transmitter and a nonlinear amplifier, the plurality of groups of transmission signals are transmitted in channels with different signal to noise ratios, and are received by an OFDM receiver. In an OFDM transmission signal, N is contained c Sub-carriers and N s A time slot in which the pilot frequency adopts a comb mode with a size of N cp ×N sp 。
The signal received by the OFDM receiver is a time domain signal, and the received time domain signal is subjected to fast Fourier transform to obtain a frequency domain signal Y.
In performing channel estimation model training, the condition of the channel is determined by manual simulation. Therefore, the complete channel state information corresponding to the frequency domain signal is a known quantity, and can be directly determined according to the condition of the manually simulated channel.
Step 202: and preprocessing each frequency domain signal to obtain corresponding channel estimation information.
In the embodiment of the application, after the frequency domain signal Y is obtained, the pilot frequency position is referred to, and the pilot frequency signal Y is extracted p . To transmit local pilot signal X p And pilot signal Y p Performing least square channel estimation to determine channel estimation information corresponding to the frequency domain signal Y
Wherein X is p,i ，Y p,i ，H p,i ，Respectively representing a local pilot signal, a pilot signal, frequency domain channel information and channel estimation information in the ith training. Since the channel is a manually simulated channel, the frequency domain channel information is a known quantity. diag (X) p,i ) Representing the square diagonal matrix of pilots, vector X p,i Is located on the main diagonal of the square diagonal matrix.
Step 203: a plurality of low resolution two-dimensional images are determined from the corresponding channel estimation information.
In the embodiment of the application, the real part of the channel estimation information in the ith training is usedAnd imaginary partSplit and stitch into a two-dimensional tensor. The two-dimensional tensor can be considered as a two-dimensional low resolution image. Since the pilot size is N cp ×N sp Thus, the two-dimensional tensor can be regarded as an N cp ×N sp Is a matrix of (a) in the matrix. Decomposing the two-dimensional tensor into N according to the time slot dimension of the pilot frequency sp N number cp Column vector x 1. Then according to pilot frequency N sp Is equal to the size of N sp N number cp X 1 column vector reshape to N sp A low resolution two-dimensional image.
In the shaping, the method can be based on N cp Whether the square can be opened determines the form of the low resolution two-dimensional image. For example, if N cp 9,N of a shape of 9,N cp Can open square to get N cp The x 1 column vector is shaped into a 3 x 3 low resolution two dimensional image; if N cp 10, N cp Cannot square, N cp The x 1 column vector is shaped into a 2 x 5 low resolution two dimensional image. It should be understood that the shaping manner is only one specific implementation manner provided by the embodiment of the present application, and those skilled in the art may make corresponding adjustments according to actual needs, which is not limited to the present application.
Step 204: and taking a plurality of low-resolution two-dimensional images as training samples, and inputting complete channel state information corresponding to each frequency domain signal as a training label to a preset channel estimation model for training until the model is trained to be converged, so as to obtain a trained channel estimation model.
In an embodiment of the present application, as an optional implementation manner, as shown in fig. 3, the preset channel estimation model may include an image super-resolution network and an upsampling network.
The input layer of the image super-resolution network is a convolution layer. Wherein the convolution layer comprises a convolution kernel and a filter. Specifically, a convolution kernel of 3*3 is adopted, and in order to ensure that the number of pictures is unchanged, N is adopted sp The filter filtersAnd (3) a device. The hidden layer is composed of a plurality of residual feature aggregation (Residual Feature Aggregation, RFA) modules and a convolutional layer. By adding jump connection between residual feature aggregation modules, features are directly propagated on each local residual branch, and residual features in pilot frequency information are extracted more effectively. Each RFA module consists of four residual modules, each consisting of two convolutional layers (step size 1) and one leak ReLU activation function. The jumps of the first three residual modules are connected to the end of the RFA module and combined with the last residual module. All features are eventually integrated by a convolution layer.
Image super-resolution network pair N sp And extracting features of the low-resolution two-dimensional images, and extracting pilot frequency information and correlation.
The up-sampling network replaces the traditional interpolation process, is composed of two layers of neural networks, has the size twice of the complete channel state information, and does not adopt an activation function. The first layer network is used for mapping the low-resolution image into a high-resolution image, and the second layer network finely adjusts the image through the weights and the offsets of the neurons to finally output a high-resolution estimation resultWill->Real part of->And imaginary part->Combining to obtain channel state information->In practical use, the number of network layers and the network parameters of each layer can be modified according to practical situations, so as to improve the generalization capability and robustness of the network.
The loss function for training the preset channel estimation model is:
wherein θ s ,θ m Parameters of super-resolution network and up-sampling network, f s ,f m Functions are activated for both networks.
As an alternative implementation manner, the channel estimation model training adopts 50 batches, the training times are 4000, and each training comprises 300 time slot samples; the learning rate is initialized to 0.01 and decreases with increasing training times; the optimizer employs an Adam optimizer.
It can be understood that the values of the training batch, the training times, the time slot samples and the learning rate are all specific implementations provided by the embodiments of the present application, and those skilled in the art can perform corresponding adjustment according to actual needs, which is not limited to the present application.
As an optional implementation manner, after determining the channel state information, zero-forcing equalization is performed on the frequency domain signal and the channel state information, so as to determine the estimated information of the transmitting symbol, and then the estimated information of the transmitting symbol is input into a pre-trained signal detection model to obtain bit information sent by the transmitting end. For easy understanding, the training process of the signal detection model is described first.
The training step of the signal detection model may be performed after the training step of the channel estimation model, and may be performed using the frequency domain signal determined by the channel condition simulated during the training of the channel estimation model and the channel state information corresponding to the frequency domain signal.
In the embodiment of the application, a plurality of groups of frequency domain signals and channel state information corresponding to the plurality of groups of frequency domain signals are firstly obtained. And carrying out zero forcing equalization on the channel state information and the frequency domain signal corresponding to the channel state information, determining an estimated value of a transmitting symbol, and taking the estimated value of the transmitting symbol as a training sample of a signal detection model.
Specifically, zero-forcing equalization of the frequency domain signal and the channel state information can be expressed as:
wherein Y is i Andtime-frequency domain signal and channel state information for the ith training, respectively, < >>For the transmitted symbol estimate determined during the ith training.
Bit information B to be transmitted by transmitting end i Training tags as a signal detection model. The frequency domain signal and the channel state information corresponding to the frequency domain signal are determined by using the channel condition simulated during the training of the channel estimation model. Therefore, the bit information transmitted by the transmitting end is a known amount.
Estimated value of transmitted symbolReal part of->And imaginary part->Splitting and splicing the two-dimensional tensor into a training sample of a signal detection model, and transmitting bit information B sent by a transmitting end i And inputting the training label serving as the signal detection model into a preset signal detection model for training until the model is trained to be converged, and obtaining a trained signal detection model.
As shown in fig. 4, the preset signal detection model may include a denoising network and a parallel detection network.
As an alternative embodiment, the denoising network comprises an input layer, an intermediate hidden layer, and an output layer. Specifically, the input layer of the denoising network uses 16 filters of size 3×3×1 for generating 16 feature maps, while adding a batch normalization and nonlinear activation function ReLU. The intermediate hidden layer consists of multiple convolutional layers, each with 16 size filters, and a batch normalization and nonlinear activation function ReLU is introduced at each layer. The output layer uses 1 filter of size 3 x 16 to reconstruct the signal and output the denoised transmit symbol estimates.
The parallel detection network can be composed of three identical parallel networks, each detection network is composed of a plurality of layers of neural networks, and each layer of neural network adopts a full-connection mode to map the denoised transmission symbol estimated value into bit information of a transmitting end. The number of neurons of the output layer of each parallel detection network is the mapping bit number of the current network, and the bit numbers of a plurality of parallel networks are connected in series to obtain bit information sent by a sending end.
In the training process, the average mean square error is adopted as a loss function of a training signal detection model:
wherein θ D Is a network parameter, f D Is a nonlinear mapping function.
As an alternative implementation manner, the training of the signal detection model adopts 50 batches, the training times are 2000 times, and each training comprises 300 time slot samples; the learning rate is initialized to 0.01 and decreases with increasing training times; the optimizer employs an Adam optimizer.
It can be understood that the values of the training batch, the training times, the time slot samples and the learning rate are all specific implementations provided by the embodiments of the present application, and those skilled in the art can perform corresponding adjustment according to actual needs, which is not limited to the present application.
The embodiment of the application considers that the channel is a time-varying channel and Gaussian white noise exists in OFDM transmission. And adding a denoising network into the preset signal detection model for removing Gaussian white noise in the estimated value of the transmitted symbol, so that the trained signal detection model can remove Gaussian white noise introduced by a time-varying channel in use, and the accuracy of signal detection is improved.
The embodiment of the application introduces deep learning into the traditional OFDM receiver for OFDM channel estimation and signal detection, improves the error rate performance of the receiver, and can overcome nonlinear distortion generated by a nonlinear amplifier on signals. The receiver adopts an off-line training mode to train the channel estimation model and the signal detection model, and can be subjected to migration training in actual use so as to obtain better performance. The two models adopt a multi-signal-to-noise ratio training mode, and can finish the estimation and signal detection of a nonlinear channel under the condition of no prior information such as channel, noise and the like.
After the training process of the channel estimation model and the signal detection model is introduced, the above steps S101 to S105 will be described in detail with reference to examples.
Step 101: a frequency domain signal is acquired.
In the embodiment of the application, the OFDM system can adopt a single-input single-output mode, and one OFDM transmission signal comprises N c Sub-carriers and N s A time slot in which the pilot frequency adopts a comb mode with a size of N cp ×N sp . An OFDM transmit signal is transmitted via an OFDM transmitter and a nonlinear amplifier, and then a time domain signal is received by an OFDM receiver via transmission of a channel. And performing fast Fourier transform on the received time domain signal to obtain a frequency domain signal.
Step 102: and preprocessing the frequency domain signal to obtain channel estimation information.
Step 103: a plurality of low resolution two-dimensional images are determined from the channel estimation information.
In the embodiment of the present application, step 102 and step 103 correspond to step 202 and step 203, and the same or similar parts can be referred to each other for brevity of description, and are not repeated here.
Step 104: and inputting a plurality of low-resolution two-dimensional images into a pre-trained channel estimation model to obtain channel state information.
In the embodiment of the present application, as can be seen from the foregoing description of the training process of the channel estimation model, the channel estimation model includes an image super-resolution network and an up-sampling network. The super-resolution network performs feature extraction on a plurality of low-resolution two-dimensional images to obtain features corresponding to the two-dimensional images; and the up-sampling network up-samples the features corresponding to the two-dimensional image to obtain channel state information. The specific implementation process of step 104 corresponds to that of step 204, and for brevity, the same or similar parts are referred to each other, and will not be described again here.
Step 105: bit information transmitted by the transmitting end is determined based on the frequency domain signal and the channel state information.
As an alternative embodiment, step 105 may comprise the steps of:
the method comprises the steps of firstly, carrying out zero-forcing equalization on a frequency domain signal and channel state information, and determining transmitted symbol estimation information;
and secondly, inputting the estimated information of the transmitted symbol into a pre-trained signal detection model to obtain bit information transmitted by a transmitting end.
Specifically, zero-forcing equalization of the frequency domain signal and the channel state information can be expressed as:
wherein, the liquid crystal display device comprises a liquid crystal display device,for the channel state information of the kth subcarrier output by the channel estimation module, y (k) is the kth subcarrier of the frequency domain signal,/for the k subcarrier>The transmission symbol estimation information of the kth subcarrier.
Will beReal part of->And imaginary part->Splitting and splicing the two-dimensional tensor into a pre-trained signal detection model, and obtaining bit information sent by a sending end.
From the foregoing description of the training process of the signal detection model, the signal detection model includes a denoising network and a parallel detection network. The working principles of the denoising network and the parallel detection network correspond to the description of the training process of the signal detection model, and the same or similar parts can be referred to each other for simplifying the description, so that the description is not repeated here.
In summary, the embodiment of the application determines the channel estimation information according to the frequency domain signal, decomposes the channel estimation information (the channel state information with lower precision) into a plurality of low-resolution two-dimensional images, inputs the plurality of low-resolution two-dimensional images into the channel estimation model, extracts the characteristic information in the plurality of low-resolution two-dimensional images by using the pre-trained channel estimation model, thereby determining the channel state information with higher precision, and further determines the bit information sent by the sending end based on the channel state information, thereby improving the receiving performance of the OFDM receiver.
Based on the same inventive concept, the embodiment of the application also provides an OFDM receiving device. Referring to fig. 5, fig. 5 is a block diagram illustrating an OFDM receiving apparatus according to an embodiment of the present application, the OFDM receiving apparatus 500 may include:
an acquisition module 501, configured to acquire a frequency domain signal;
a preprocessing module 502, configured to preprocess the frequency domain signal to obtain channel estimation information;
a determining module 503, configured to determine a plurality of low-resolution two-dimensional images according to the channel estimation information;
the channel prediction module 504 is configured to input the plurality of low-resolution two-dimensional images into a pre-trained channel estimation model, so as to obtain channel state information;
a bit signal determining module 505, configured to determine bit information sent by a transmitting end based on the frequency domain signal and the channel state information.
In an optional implementation manner, the channel estimation model includes an image super-resolution network and an up-sampling network, and the channel prediction module 504 is specifically configured to perform feature extraction on the plurality of low-resolution two-dimensional images by using the super-resolution network to obtain features corresponding to the two-dimensional images; and up-sampling the features corresponding to the two-dimensional image by utilizing the up-sampling network to obtain the channel state information.
In an optional embodiment, the bit signal determining module 505 is specifically configured to perform zero forcing equalization on the frequency domain signal and the channel state information, and determine transmit symbol estimation information; and inputting the estimated information of the transmitting symbol into a pre-trained signal detection model to obtain bit information sent by a sending end.
In an optional implementation manner, the signal detection model includes a denoising network and a parallel detection network, and the bit signal determining module 505 is specifically configured to input the estimated information of the transmitted symbol into the denoising network to denoise, so as to obtain the estimated information of the denoised transmitted symbol; and inputting the denoised transmitted symbol estimation information into the parallel detection network to obtain the bit information.
In an alternative embodiment, the preprocessing module 502 is specifically configured to determine a pilot signal according to the frequency domain signal and a pilot position; and carrying out least square channel estimation on the pilot signal and the local pilot signal, and determining the channel estimation information.
In addition, the embodiment of the application also provides a channel estimation model training device. Referring to fig. 6, fig. 6 is a block diagram of a channel estimation model training apparatus according to an embodiment of the present application, and the channel estimation model training apparatus 600 may include:
an obtaining module 601, configured to obtain a plurality of frequency domain signals and complete channel state information corresponding to the plurality of frequency domain signals;
a preprocessing module 602, configured to preprocess each of the frequency domain signals to obtain corresponding channel estimation information;
a determining module 603, configured to determine a plurality of low-resolution two-dimensional images according to the corresponding channel estimation information;
the training module 604 is configured to take the plurality of low-resolution two-dimensional images as training samples, and input complete channel state information corresponding to each frequency domain signal as training labels to a preset channel estimation model for training until the model is trained to converge, so as to obtain a trained channel estimation model.
Referring to fig. 7, fig. 7 is a schematic structural diagram of an electronic device 700 according to an embodiment of the application, where the electronic device 700 includes: at least one processor 701, at least one communication interface 702, at least one memory 703 and at least one bus 704. Where bus 704 is used to enable direct connection communication of these components, communication interface 702 is used to communicate signaling or data with other node devices, and memory 703 stores machine-readable instructions executable by processor 701. When the electronic device 700 is in operation, the processor 701 communicates with the memory 703 via the bus 704, and machine readable instructions when invoked by the processor 701 perform an OFDM reception method as described above.
The processor 701 may be an integrated circuit chip having signal processing capabilities. The processor 701 may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), and the like; but also digital signal processors (Digital Signal Processing, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. Which may implement or perform the various methods, steps, and logical blocks disclosed in embodiments of the application. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The Memory 703 may include, but is not limited to, random access Memory (Random Access Memory, RAM), read Only Memory (ROM), programmable Read Only Memory (Programmable Read-Only Memory, PROM), erasable Read Only Memory (Erasable Programmable Read-Only Memory, EPROM), electrically erasable Read Only Memory (Electric Erasable Programmable Read-Only Memory, EEPROM), and the like.
It is to be understood that the configuration shown in fig. 7 is illustrative only, and that electronic device 700 may also include more or fewer components than those shown in fig. 7, or have a different configuration than that shown in fig. 7. The components shown in fig. 7 may be implemented in hardware, software, or a combination thereof. In the embodiment of the present application, the electronic device 700 may be, but is not limited to, a physical device such as a desktop, a notebook, a smart phone, an intelligent wearable device, a vehicle-mounted device, or a virtual device such as a virtual machine. In addition, the electronic device 700 is not necessarily a single device, but may be a combination of a plurality of devices, such as a server cluster, or the like.
In addition, the embodiment of the present application further provides a computer storage medium, on which a computer program is stored, which when executed by a computer, performs the steps of the OFDM receiving method in the above embodiment.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. The above-described apparatus embodiments are merely illustrative, for example, the division of the units is merely a logical function division, and there may be other manners of division in actual implementation, and for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some communication interface, device or unit indirect coupling or communication connection, which may be in electrical, mechanical or other form.
Further, the units described as separate units may or may not be physically separate, and units displayed as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
Furthermore, functional modules in various embodiments of the present application may be integrated together to form a single portion, or each module may exist alone, or two or more modules may be integrated to form a single portion.
It should be noted that the functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM) random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
In this document, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and variations will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.
Claims (10)
1. An OFDM receiving method, comprising:
acquiring a frequency domain signal;
preprocessing the frequency domain signal to obtain channel estimation information;
determining a plurality of low-resolution two-dimensional images according to the channel estimation information; wherein the plurality of low resolution two-dimensional images are determined by:
splitting and splicing the real part and the imaginary part of the channel estimation information into a two-dimensional tensor;
time slot N according to the frequency domain signal sp Dividing the two-dimensional tensor into N sp A plurality of column vectors;
for the N sp Shaping the column vectors to determine N sp A low resolution two-dimensional image, the N sp The low-resolution two-dimensional images are the plurality of low-resolution two-dimensional images;
inputting the plurality of low-resolution two-dimensional images into a pre-trained channel estimation model to obtain channel state information; the pre-trained channel estimation model comprises: an image super-resolution network and an up-sampling network; the image super-resolution network comprises an input layer and a hidden layer, wherein the hidden layer comprises a plurality of residual characteristic aggregation modules and a convolution layer; the input layer, the residual characteristic aggregation modules and the convolution layer are sequentially connected, and the convolution layer is connected with the up-sampling network;
and determining bit information sent by a sending end based on the frequency domain signal and the channel state information.
2. The method of claim 1, wherein the channel estimation model comprises an image super-resolution network and an upsampling network, wherein the inputting the plurality of low-resolution two-dimensional images into the pre-trained channel estimation model to obtain channel state information comprises:
extracting features of the plurality of low-resolution two-dimensional images by using the super-resolution network to obtain features corresponding to the two-dimensional images;
and up-sampling the features corresponding to the two-dimensional image by utilizing the up-sampling network to obtain the channel state information.
3. The method of claim 1, wherein the determining the bit information sent by the transmitting end based on the frequency domain signal and the channel state information comprises:
zero-forcing equalization is carried out on the frequency domain signal and the channel state information, and the estimated information of the transmitting symbol is determined;
and inputting the estimated information of the transmitting symbol into a pre-trained signal detection model to obtain bit information sent by a sending end.
4. The method of claim 3, wherein the signal detection model includes a denoising network and a parallel detection network, the inputting the transmitted symbol estimation information into a pre-trained signal detection model, and obtaining bit information sent by a transmitting end, includes:
inputting the transmitted symbol estimation information into the denoising network to denoise, so as to obtain denoised transmitted symbol estimation information;
and inputting the denoised transmitted symbol estimation information into the parallel detection network to obtain the bit information.
5. The method of claim 1, wherein the preprocessing the frequency domain signal to obtain channel estimation information comprises:
determining a pilot signal according to the frequency domain signal and the pilot position;
and carrying out least square channel estimation on the pilot signal and the local pilot signal, and determining the channel estimation information.
6. A method for training a channel estimation model, comprising:
acquiring a plurality of frequency domain signals and complete channel state information corresponding to the plurality of frequency domain signals;
preprocessing each frequency domain signal to obtain corresponding channel estimation information;
based on corresponding channel estimationThe meter information determines a plurality of low resolution two-dimensional images; wherein the plurality of low resolution two-dimensional images are determined by: splitting and splicing the real part and the imaginary part of the corresponding channel estimation information into a two-dimensional tensor; time slot N according to the frequency domain signal sp Dividing the two-dimensional tensor into N sp A plurality of column vectors; for the N sp Shaping the column vectors to determine N sp A low resolution two-dimensional image, the N sp The low-resolution two-dimensional images are the plurality of low-resolution two-dimensional images;
taking the plurality of low-resolution two-dimensional images as training samples, and inputting complete channel state information corresponding to each frequency domain signal as training labels to a preset channel estimation model for training until the model is trained to be converged, so as to obtain a trained channel estimation model, wherein the preset channel estimation model comprises: an image super-resolution network and an up-sampling network; the image super-resolution network comprises an input layer and a hidden layer, wherein the hidden layer comprises a plurality of residual characteristic aggregation modules and a convolution layer; the input layer, the residual characteristic aggregation modules and the convolution layer are sequentially connected, and the convolution layer is connected with the up-sampling network.
7. An OFDM receiving apparatus, the apparatus comprising:
the acquisition module is used for acquiring the frequency domain signal;
the preprocessing module is used for preprocessing the frequency domain signals to obtain channel estimation information;
a determining module, configured to determine a plurality of low-resolution two-dimensional images according to the channel estimation information; splitting and splicing the real part and the imaginary part of the channel estimation information into a two-dimensional tensor; time slot N according to the frequency domain signal sp Dividing the two-dimensional tensor into N sp A plurality of column vectors; for the N sp Shaping the column vectors to determine N sp A low resolution two-dimensional image, the N sp The low-resolution two-dimensional images are the plurality of low-resolution two-dimensional images;
the channel prediction module is used for inputting the plurality of low-resolution two-dimensional images into a pre-trained channel estimation model to obtain channel state information; the pre-trained channel estimation model comprises: an image super-resolution network and an up-sampling network; the image super-resolution network comprises an input layer and a hidden layer, wherein the hidden layer comprises a plurality of residual characteristic aggregation modules and a convolution layer; the input layer, the residual characteristic aggregation modules and the convolution layer are sequentially connected, and the convolution layer is connected with the up-sampling network;
and the bit signal determining module is used for determining bit information sent by a sending end based on the frequency domain signal and the channel state information.
8. A channel estimation model training apparatus, the apparatus comprising:
the acquisition module is used for acquiring a plurality of frequency domain signals and complete channel state information corresponding to the plurality of frequency domain signals;
the preprocessing module is used for preprocessing each frequency domain signal to obtain corresponding channel estimation information;
a determining module, configured to determine a plurality of low-resolution two-dimensional images according to the corresponding channel estimation information; splitting and splicing the real part and the imaginary part of the corresponding channel estimation information into a two-dimensional tensor; time slot N according to the frequency domain signal sp Dividing the two-dimensional tensor into N sp A plurality of column vectors; for the N sp Shaping the column vectors to determine N sp A low resolution two-dimensional image, the N sp The low-resolution two-dimensional images are the plurality of low-resolution two-dimensional images;
the training module is configured to take the plurality of low-resolution two-dimensional images as training samples, and input complete channel state information corresponding to each frequency domain signal as training labels to a preset channel estimation model for training until the model is trained to be converged, so as to obtain a trained channel estimation model, where the preset channel estimation model includes: an image super-resolution network and an up-sampling network; the image super-resolution network comprises an input layer and a hidden layer, wherein the hidden layer comprises a plurality of residual characteristic aggregation modules and a convolution layer; the input layer, the residual characteristic aggregation modules and the convolution layer are sequentially connected, and the convolution layer is connected with the up-sampling network.
9. An electronic device, comprising: a processor, a memory, and a bus; the processor and the memory complete communication with each other through the bus; the memory stores program instructions executable by the processor, the processor invoking the program instructions to perform the method of any of claims 1-6.
10. A computer storage medium having stored thereon computer program instructions which, when read and executed by a computer, perform the method of any of claims 1-6.
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| WO2021112360A1 (en) * | 2019-12-01 | 2021-06-10 | 엘지전자 주식회사 | Method and device for estimating channel in wireless communication system |
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