A pilot design and channel estimation method and system for OFDM system

Abstract

The present invention provides an OFDM system pilot design and channel estimation method to improve the performance of the system in dynamic channel scenarios. The method specifically includes two modules: a pilot design module and an upsampling module. First, in the pilot design module, the neural network responsible for pilot design selects corresponding elements from the LS estimate of the complete channel based on the maximum Doppler shift and delay spread that reflect the speed of channel changes in time and frequency as available information, designs the pilot format that best suits the current channel environment, and obtains the LS estimation result of the designed pilot position. Then, the LS estimation result of the pilot position is sent to the upsampling module composed of a full connection for interpolation to obtain an estimated value of the complete channel. Compared with existing channel estimation algorithms, the method proposed by the present invention can adapt to different channel scenarios, and the time required for pilot design is shorter. It has good robustness to dynamic scenarios and significantly improves channel estimation performance.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless communications, and in particular relates to an OFDM system pilot design and channel estimation method. Background Art

[0002] OFDM systems can effectively counteract frequency-selective fading, thereby ensuring accurate communication in complex and dynamically changing communication scenarios. Channel estimation is a key component of OFDM systems, and its accuracy profoundly impacts overall system performance. Traditional LS estimation algorithms are widely used in practical communication systems, but their performance is limited because they ignore the effects of noise.

[0003] In recent years, deep learning has achieved tremendous success in fields such as computer vision and natural language processing, and a large body of research has been conducted to integrate deep learning with wireless communications, such as resource allocation, channel state information feedback, and signal detection. At the same time, a large body of work has also used deep learning neural networks, such as DNNs and CNNs, for channel estimation, achieving excellent performance. These channel estimation methods often use a fixed pilot format. However, using a fixed pilot format in different channel environments cannot effectively track channel changes and has difficulty adapting to increasingly complex and dynamically changing channel scenarios. Therefore, how to use current deep learning techniques to design a channel estimation method for OFDM systems that is adaptable to dynamic channel environments, highly robust, and has excellent performance is an urgent problem that needs to be solved. Summary of the Invention

[0004] Purpose of the invention: The present invention provides an OFDM system pilot design and channel estimation method, aiming to utilize the powerful learning ability of neural networks to design appropriate pilot formats for different channel environments, and to solve the problems of poor robustness and limited channel estimation performance of existing channel estimation methods in dynamic scenarios.

[0005] Technical solution: The technical solution adopted by the present invention specifically includes the following steps:

[0006] Step 1. Consider having N f subcarriers, N t OFDM system with 1 symbol. Assume that the transmitted signal Known, and LS channel estimation is performed on the entire channel to obtain the estimation matrix of the complete channel The matrix Convert to vector form Where d = N f ×N t , represents the total number of elements in the entire OFDM system; a size of And each row has only one element value of 1 and the rest of the elements are 0. PD With vector Multiply them, and then select the location of the k pilots that can most effectively track the current channel environment from the positions of the d elements of the entire channel matrix, and obtain the LS estimate at that position

[0007] Step 2: The maximum Doppler shift D1 and delay spread D2, which reflect the speed of channel changes in time and frequency, are used as available information and input into the Concrete DNN network composed of fully connected layers, and the vector is output. This vector will be used to construct the pilot design matrix M PD ;

[0008] Step 3: A PD Perform shape change and get the matrix And perform discretization operation on the matrix to convert it into a matrix The number of rows and columns of this matrix are the number of pilots k and the total number of OFDM elements d, respectively, and each row is close to a one-hot vector, with only one element value being 1 and the rest being 0.

[0009] Step 4: Matrix M PD With vector Multiply by to obtain the LS channel estimate of the designed pilot position Then it is sent to the upsampling module composed of upsampling DNN for interpolation to obtain the complete channel estimation result Then the whole process of pilot design and channel estimation is completed;

[0010] Step 5: Use a dataset containing channels with different maximum Doppler shifts and delay spreads to jointly train the Concrete DNN and the upsampling DNN, so that they can ultimately design the most appropriate pilot format based on different channel environments and complete the corresponding channel estimation process.

[0011] As a further technical solution of the present invention, in step 1, during the network training process, assuming that the entire transmission channel matrix X is known, the LS channel estimation process of the complete channel can be expressed as:

[0012]

[0013] Among them, y, h, are the vector forms of Y, H, and X respectively, ||·||2 is the l2 distance, (·) ⊙-1 is the inverse Hadamard product.

[0014] As a further technical solution of the present invention, in step 2, the output vector A of Concrete DNN PD The expression is:

[0015] A PD =f PD (D1,D2,Θ PD ) (2)

[0016] Among them, f PD (·) is the function of Concrete DNN, Θ PD are all the trainable parameters contained in the structure. PD is the output of the neural network. Its internal values ​​are all floating point numbers, so it is difficult to transform it into the required pilot design matrix M by only changing its shape. PD .

[0017] As a further technical solution of the present invention, in step 3, the matrix α is calculated by the following formula: PD Perform discretization operation and then convert it into matrix M PD :

[0018]

[0019] in, is the matrix M PD The element in row l and column i, g i is a vector sampled from the Gumbel distribution The i-th element of is the matrix α PD The element in row l and column i, is the matrix α PD The element in the lth row and the jth column, T, represents temperature and is used to control the degree of discretization.

[0020] As a further technical solution of the present invention, in step 4, the pilot is designed. The process is expressed as:

[0021]

[0022] LS channel estimation for the designed pilot positions The interpolation process is expressed as:

[0023]

[0024] Among them, f UP (·) is the function of upsampling DNN, Θ UP are all the trainable parameters contained in the structure, and are the operations of taking the real part and imaginary part respectively.

[0025] As a further technical solution of the present invention, in step 5, the loss function for training the entire network structure is as follows:

[0026]

[0027] Among them, H i is the i-th channel sample in the training data set, is the final estimate of the i-th channel sample obtained by the network after pilot design and channel estimation, and N is the total number of samples in the training set.

[0028] Beneficial effects: Compared with the prior art, the present invention adopts the above technical solution and has the following beneficial effects:

[0029] The OFDM system pilot design and channel estimation method designed by the present invention uses the maximum Doppler shift and delay spread, which reflect the speed of channel changes in time and frequency, as available information input into a neural network. This assists the network in designing a pilot format that best suits the current channel environment, thereby improving the network's tracking ability for fading channels and enhancing the system's channel estimation performance. The pilot design method of the present invention can dynamically design the most suitable pilot structure based on the current channel environment. Compared to traditional, fixed pilot formats, it can adapt to different channel scenarios, requires less time to design the pilot, and has good robustness in dynamic scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 Schematic diagram of the principle of the pilot design and channel estimation method of the OFDM system designed by the present invention;

[0031] Figure 2 This is a schematic diagram of the specific structure of the pilot design network in the present invention. DETAILED DESCRIPTION

[0032] The embodiments of the present invention will be described below with reference to the accompanying drawings.

[0033] like Figure 1As shown, the present invention designs a pilot design and channel estimation method for an OFDM system. Specifically, it includes two modules: pilot design and upsampling. The input of the pilot design module includes the LS estimate of the complete channel, the maximum Doppler frequency shift and delay spread of the channel. It selects corresponding elements from the LS estimate of the complete channel based on the maximum Doppler frequency shift and delay spread, designs a pilot format that best suits the current channel environment, and outputs the LS estimation result of the designed pilot position. The input of the upsampling module is the LS estimation result of the designed pilot position. It interpolates the input and outputs the estimated value of the complete channel. This method specifically includes the following steps:

[0034] Step 1. Consider having N f subcarriers, N t OFDM system with 1 symbol. Assume that the transmitted signal Known, and LS channel estimation is performed on the entire channel to obtain the estimation matrix of the complete channel The matrix Convert to vector form Where d = N f ×N t , represents the total number of elements in the entire OFDM system; by And each row has only one element value of 1 and the rest of the elements are 0. PD With vector Multiply them, and then select the location of the k pilots that can most effectively track the current channel environment from the positions of the d elements of the entire channel matrix, and obtain the LS estimate at that position

[0035] Specifically, at the receiver, after removing the cyclic prefix and performing a fast Fourier transform, the frequency domain representation of the received signal is It can be expressed as:

[0036] Y=H⊙X+W (1)

[0037] Among them, ⊙ is the Hadamard product, H, They are the channel matrices in frequency domain and have a mean of 0 and a variance of Additive white Gaussian noise.

[0038] Because in the process of training the network, it is assumed that the entire transmission channel matrix X is known, the LS channel estimation process of the complete channel can be expressed as:

[0039]

[0040] Among them, y, h, are the vector forms of Y, H, and X respectively, ||·||2 is the l2 distance, (·)⊙-1 is the inverse Hadamard product.

[0041] Step 2: The maximum Doppler shift D1 and delay spread D2, which reflect the speed of channel changes in time and frequency, are used as available information and input into the Concrete DNN network composed of fully connected layers, and the vector is output. This vector will be used to construct the pilot design matrix M PD ;

[0042] Specifically, the maximum Doppler shift reflects how fast the channel changes in time, while the delay spread reflects how fast the channel changes in frequency. By inputting these parameters into the network, the network can learn to determine the number and location of pilots in time and frequency. The output vector A of the Concrete DNN is PD The expression is:

[0043] A PD =f PD (D1,D2,Θ PD ) (3)

[0044] Among them, f PD (·) is the function of Concrete DNN, Θ PD are all the trainable parameters contained in the structure. PD is the output of the neural network. Its internal values ​​are all floating point numbers, so it is difficult to transform it into the required pilot design matrix M by only changing its shape. PD .

[0045] Step 3: A PD Perform shape change and get the matrix And perform discretization operation on the matrix to convert it into a matrix The number of rows and columns of this matrix are the number of pilots k and the total number of OFDM elements d, respectively, and each row is close to a one-hot vector, with only one element value being 1 and the rest being 0.

[0046] Specifically, the matrix α is given by the following formula PD Perform discretization operation and then convert it into matrix M PD :

[0047]

[0048] in, is the matrix M PD The element in row l and column i, g i is a vector sampled from the Gumbel distribution The i-th element of is the matrix α PDThe element in row l and column i, is the matrix α PD The element in the lth row and the jth column; T represents the temperature, which is used to control the degree of discretization. It is set to a large value in the initial stage of Concrete DNN network training and gradually decreases and tends to 0 as the network trains. When T tends to 0, the matrix M calculated by formula (4) is PD Each row of will approach the form of a one-hot vector.

[0049] Step 4: Matrix M PD With vector Multiply by to obtain the LS channel estimate of the designed pilot position Then it is sent to the upsampling module composed of upsampling DNN for interpolation to obtain the complete channel estimation result Then the whole process of pilot design and channel estimation is completed;

[0050] Specifically, design the pilot The process is expressed as:

[0051]

[0052] Among them, the matrix M PD In the initial stage of training, the elements of each row are relatively random, so that the network can learn as many channel-related features as possible; as the training progresses, each row will approach a one-hot vector. The matrix M PD With size Vector When multiplied, it is equivalent to selecting k pilot positions that can best track channel changes from d channel elements, thereby completing the design of the pilot format and obtaining the LS channel estimate of the designed pilot position.

[0053] LS channel estimation for the designed pilot positions The interpolation process is expressed as:

[0054]

[0055] Among them, f UP (·) is the function of upsampling DNN, Θ UP are all the trainable parameters contained in the structure, and are the operations of taking the real part and imaginary part respectively.

[0056] Step 5: Use a dataset containing channels with different maximum Doppler shifts and delay spreads to jointly train the Concrete DNN and the upsampling DNN, so that they can ultimately design the most appropriate pilot format based on different channel environments and complete the corresponding channel estimation process.

[0057] Specifically, the Concrete DNN and upsampling DNN in the network structure can be one or more fully connected layers. The channel model adopts the TDL-A channel model in 3GPP. The channel SNR is randomly selected from the set [0, 5, ..., 25] dB, the maximum Doppler shift is randomly selected from the three sets [0, 20, ..., 100] Hz, [150, 200, ..., 550] Hz, and [600, 700, ..., 1000] Hz, and the delay spread is randomly selected from the three sets [0, 20, ..., 100] ns, [150, 200, ..., 550] ns, and [600, 800, ..., 1200] ns. The dataset contains 102,400 independent channels, 80% of which are used for training and 20% for validation. The entire network is trained using the Adam optimizer for 1000 epochs, with a batch size of 512, an initial learning rate of 0.001, and a 10-fold decay every 500 epochs.

[0058] The loss function for training the entire network structure is as follows:

[0059]

[0060] Among them, H i is the i-th channel sample in the training data set, is the final estimate of the i-th channel sample obtained by the network through pilot design and channel estimation, and N is the total number of samples in the training set. Since the network is trained using the above mixed data set, it can learn the channel characteristics of different channel environments and design the pilot format that can best track the changes in the current channel environment based on the maximum Doppler frequency shift and delay spread of the channel.

[0061] like Figure 2 As shown, first, the LS estimate of the complete channel matrix Converted into vector form At the same time, the maximum Doppler shift and delay spread that reflect the speed of the channel change in time and frequency are fed into ConcreteDNN to generate vector A PD , and after shape change and discretization operations, it is transformed into a matrix M in which each row is in one-hot form PD ; Finally, the matrix M PD With vector Multiply to obtain the designed pilot frequency The pilot designed under the current channel environment can accurately track the changes in the channel, thereby improving the channel estimation performance of the overall system.

[0062] The method of this invention applies a deep learning network to an OFDM system. By using the maximum Doppler shift and delay spread, which reflect the speed of channel changes in time and frequency, as available information, the network is assisted in designing a pilot format that best suits the current channel environment, thereby improving the channel estimation performance of the entire system. Compared to traditional pilot formats, the proposed method can adapt to different channel scenarios and design the optimal pilot format for each channel scenario, with the advantages of strong robustness and good performance.

[0063] What is described above in conjunction with the accompanying drawings is only one of the preferred examples of the present invention and cannot be used to limit the scope of rights included in the present invention. It should be pointed out that any equivalent modifications made in accordance with the principles of the present invention are covered by the protection scope of the claims of the present invention.

Claims

1. A pilot design and channel estimation method for an OFDM system, characterized in that: The OFDM system has N f subcarriers, N t symbols, the method comprising the following steps: Step 1: Assume that the signal is sent Known, and LS channel estimation is performed on the entire channel to obtain the estimation matrix of the complete channel The matrix Convert to vector form Where d = N f ×N t , represents the total number of elements in the entire OFDM system; a size of And each row has only one element value of 1 and the rest of the elements are 0. PD With vector Multiply them, and then select the location of the k pilots that can most effectively track the current channel environment from the positions of the d elements of the entire channel matrix, and obtain the LS estimate at that position Step 2: The maximum Doppler shift D1 and delay spread D2, which reflect the speed of channel changes in time and frequency, are used as available information and input into the Concrete DNN network composed of fully connected layers, and the vector is output. This vector will be used to construct the pilot design matrix M PD ; Step 3: A PD Perform shape change and get the matrix And perform discretization operation on the matrix to convert it into a matrix The number of rows and columns of this matrix are the number of pilots k and the total number of OFDM elements d, respectively, and each row is close to a one-hot vector, with only one element value being 1 and the rest being 0. Step 4: Matrix M PD With vector Multiply by to obtain the LS channel estimate of the designed pilot position Then it is sent to the upsampling module composed of upsampling DNN for interpolation to obtain the complete channel estimation result Then the whole process of pilot design and channel estimation is completed; Step 5: Use a dataset containing channels with different maximum Doppler shifts and delay spreads to jointly train ConcreteDNN and upsampling DNN, so that it can eventually design the most appropriate pilot format according to different channel environments and complete the corresponding channel estimation process.

2. The OFDM system pilot design and channel estimation method according to claim 1, characterized in that: In step 2, the output vector A of Concrete DNN PD The expression is: A PD =f PD (D1,D2,Θ PD )(1) Among them, f PD (·) is the function of Concrete DNN, Θ PD are all the trainable parameters contained in this structure.

3. The OFDM system pilot design and channel estimation method according to claim 1, wherein: In step 3, the matrix α is calculated by the following formula: PD Perform discretization operation and then convert it into matrix M PD : in, is the matrix M PD The element in row l and column i, g i is a vector sampled from the Gumbel distribution The i-th element of is the matrix α PD The element in row l and column i, is the matrix α PD The element in the lth row and the jth column, T, represents temperature and is used to control the degree of discretization.

4. The OFDM system pilot design and channel estimation method according to claim 3, characterized in that: In step 3, the temperature T is set to a large value in the initial stage of Concrete DNN network training, and gradually decreases and approaches 0 as the network trains; when T approaches 0, the matrix M calculated by formula (2) PD Each row of will approach the form of a one-hot vector.

5. The OFDM system pilot design and channel estimation method according to claim 1, characterized in that: In step 4, the pilot is designed The process is expressed as: Among them, the matrix M PD In the initial stage of training, the elements of each row are relatively random, so that the network can learn as many channel-related features as possible; as training progresses, each row will approach a one-hot vector.

6. The OFDM system pilot design and channel estimation method according to claim 1, characterized in that: In step 4, the LS channel of the designed pilot position is estimated The upsampling process is expressed as: Among them, f UP (·) is the function of upsampling DNN, Θ UP are all the trainable parameters contained in the structure, and are the operations of taking the real part and imaginary part respectively.

7. The OFDM system pilot design and channel estimation method according to claim 1, characterized in that: In step 5, the loss function for training the entire network structure is as follows: Among them, H i is the i-th channel sample in the training data set, is the final estimate of the i-th channel sample obtained by the network after pilot design and channel estimation, and N is the total number of samples in the training set.

8. An OFDM system pilot design and channel estimation system, applied to the method according to any one of claims 1 to 7, characterized in that: include: Pilot design module and upsampling module; The pilot design module selects the corresponding elements from the LS estimation of the complete channel based on the maximum Doppler shift and delay spread of the channel, designs the pilot format that best suits the current channel environment, and obtains the LS estimation result of the designed pilot position; The upsampling module is responsible for interpolating the LS estimates of the designed pilot positions to obtain an estimate of the complete channel.

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