CN116319195A - Millimeter wave and terahertz channel estimation method based on pruned convolutional neural network - Google Patents
Millimeter wave and terahertz channel estimation method based on pruned convolutional neural network Download PDFInfo
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Abstract
A millimeter wave and terahertz channel estimation method based on a pruned convolutional neural network utilizes an approximate message transfer compressed sensing Algorithm (AMP) to process channel data in an off-line stage to obtain initial channel prediction, and then inputs the initial channel prediction into a convolutional neural network (DCNN) for training; then, on the premise that the parameters of the network remain unchanged after pruning operation is carried out on the trained convolutional neural network, the initial channel prediction is input into the neural network, and meanwhile, the real channel is used as a label for retraining; and performing millimeter wave and terahertz channel estimation by adopting the trained convolutional neural network in an online stage. The invention deletes redundant connection in the convolutional neural network by using the pruning algorithm through the channel estimation algorithm based on the pruning convolutional neural network, greatly compresses the scale of the neural network, improves the efficiency of channel estimation, and ensures the high precision of channel estimation.
Description
Technical Field
The invention relates to a technology in the field of wireless communication, in particular to a millimeter wave and terahertz channel estimation method based on a pruned convolutional neural network.
Background
The existing improved channel estimation method is realized based on a deep learning technology and comprises model driving and data driving. Model-driven methods construct deep neural networks from models, typically using neural networks to simulate the iterative process of conventional algorithms. The deep learning network only needs to learn parameters required by the iterative algorithm to realize quick and efficient channel estimation. However, such methods are limited by the performance of the conventional algorithms that are simulated. In contrast, the data-driven method trains a deep neural network for channel estimation from a large amount of channel transmission data, is not limited by the conventional algorithm, and can realize high channel estimation accuracy. However, while approaching the best performance of channel estimation, the size of neural networks is also increasing at a very rapid rate, resulting in very high computational complexity, inefficiency and inapplicability to practical deployment.
Disclosure of Invention
Aiming at the problems that the existing terahertz ultra-large-scale channel estimation method based on the deep convolutional neural network is very large in scale, redundant in structure and low in efficiency and unsuitable for practical deployment, the invention provides the millimeter wave and terahertz channel estimation method based on the pruned convolutional neural network.
The invention is realized by the following technical scheme:
the invention relates to a millimeter wave and terahertz channel estimation method based on a pruned convolutional neural network, which utilizes an approximate message transfer compressed sensing Algorithm (AMP) to process channel data in an off-line stage to obtain initial channel prediction, and inputs the initial channel prediction into the convolutional neural network (DCNN) for training; then, on the premise that the parameters of the network remain unchanged after pruning operation is carried out on the trained convolutional neural network, the initial channel prediction is input into the neural network, and meanwhile, the real channel is used as a label for retraining; and performing millimeter wave and terahertz channel estimation by adopting the trained convolutional neural network in an online stage.
Technical effects
The invention combines the approximate message transmission compressed sensing algorithm and the convolutional neural network, and simultaneously uses the pruning algorithm to delete redundant convolutional kernels and characteristic diagrams in the deep convolutional network.
Drawings
FIG. 1 is a schematic diagram of a very large scale Multiple Input Multiple Output (MIMO) system;
FIG. 2 is a schematic diagram of a process for estimating a channel matrix from an approximate message passing compressed sensing algorithm and a convolutional neural network;
FIG. 3 is a schematic diagram of a process for pruning and retraining convolutional neural networks;
FIG. 4 is a diagram showing the accuracy of the designed AMP-DCNN channel estimation method in comparison with the conventional method;
FIG. 5 is a diagram showing the comparison of the accuracy of channel estimation after pruning at different scales;
fig. 6 is a schematic diagram of the number of parameters of the neural network after pruning at different scales.
Detailed Description
The embodiment relates to a millimeter wave and terahertz ultra-large-scale channel estimation method based on a pruned convolutional neural network, which comprises the following steps:
step 1, a channel estimation problem model is built according to a very large scale MIMO system as shown in figure 1, and a pilot (pilot) signal x [ k ] is transmitted at a transmitting end]＝F RF F BB s[k]Based on the pilot signal known at the receiving end, from the receiving end signal
Recovering the channel matrix H [ k ]]The method comprises the steps of carrying out a first treatment on the surface of the Further based on the pilot signal and antenna precoding and combining matrices are known,according to sparsity H [ k ] of channel]＝A R h[k]A T Thereby, when transmitting a plurality of pilot signals, a plurality of received signal results are collected in the channel coherence time to obtain a received signal Y [ k ]]＝φH[k]+N[k]Wherein: k is different sub-carriers, s [ k ]]F for transmitted data RF And F BB Analog and digital beamforming matrices for the precoding process, respectively; w (W) RF And W is BB Respectively analog and digital combining matrices, n [ k ]]Representing the received noise; h [ k ]]As a sparse matrix, A R And A T A coefficient matrix relating to the channel multipath reception angle and the transmission angle, respectively; the observation matrix phi includes: pilot signals and antenna precoding and combining matrix information.
The approximate message passing compressed sensing algorithm specifically comprises the following steps:
2.1 setting an initial value: t=0, h 0 ＝0，r -1 ＝0，b 0 ＝0，c 0 =0, m, n is the number of rows and columns of the matrix Φ, λ is a parameter set in advance.
2.2 calculating residual terms:
wherein: b t r t-1 And->
Is an Oncorhynchi correction term (Onsager Correction term) for accelerating the convergence rate.
2.3 calculating a predicted value: variance of
Wherein: η (eta) st Will be less than the threshold λσ as a function of setting the threshold t The term of (2) is set to 0.
2.4 update parameters:
2.5 repeating the steps 2.2-2.4 ten times to obtain a predicted result h T Recovering the original matrix
The convolutional neural network comprises 12 convolutional layers, the number of the convolutional kernels is 64 except for the last convolutional layer which is 2, and the convolutional layers are connected with each other through an activation function and a standardization layer. The convolutional neural network is to be input
As a 2-dimensional picture, 2 dimensions represent the real part and the imaginary part of the data, respectively,/output +.>
And->
The dimensions are the same.
The convolution layers all contain ReLU activation functions to enhance the nonlinear expression capacity of the neural network.
The convolutional neural network adopts a loss function of
Wherein: MSE is mean square error, lambda is the regularized coefficient, gamma is the scale coefficient of the normalized layer.
The scale factor of the standardized layer measures the importance of the convolutional neural feature map connected with the factor to a certain extent. Taking gamma as regularization term, the value is reduced as much as possible in the training process, so that the proportionality coefficient corresponding to the redundant characteristic diagram is close to 0.
The index used for measuring the channel recovery precision in the training of the convolutional neural network is normalized mean square error
Wherein: h is the real channel matrix, < >>
For the channel matrix obtained by AMP-DCNN prediction method, the adopted Norm is French Luo Beini Usneius Norm (Frobenius Norm), and +.>
The convolutional neural network adopts an Adam optimizer during training, the learning rate is 0.001, lambda is 0.0001, 6000 data are contained in a data set, 10% is a test set, and 90% is a training set.
Step 3, as shown in fig. 2, pruning the convolutional neural network trained in step 2 to obtain a lightweight optimized channel estimation network, so as to significantly reduce required memory and operation resources, including: and determining the proportion p to be pruned and the proportion coefficient threshold value of the standardized layer, wherein in the pruning process, the proportion coefficient of the characteristic diagram connection of the deconvolution neural network is smaller than the proportion coefficient threshold value of the standardized layer.
The proportion to be pruned refers to: the number of scaling factor thresholds less than the normalization layer is scaled by p.
And 4, retraining the pruned convolutional neural network on the data set obtained in the step 2 by taking a real channel as a label to finally obtain a lightweight neural network (PRINCE), and further carrying out millimeter wave and terahertz channel estimation by adopting the trained convolutional neural network in an online stage.
Through specific experiments, at the communication frequency of 0.3THz and the communication distance of 20m, 512 antennas are arranged at the receiving and transmitting ends respectively, as shown in fig. 4, and compared with the traditional method, the channel estimation accuracy is improved by 8dB. As shown in fig. 5 and 6, compared with the channel estimation algorithm based on the neural network, the channel estimation method based on pruning can reduce the complexity by 80%, ensure the high precision of channel estimation, and the error rate is as low as.10 dB under the condition that the signal-to-noise ratio is 10dB.
The foregoing embodiments may be partially modified in numerous ways by those skilled in the art without departing from the principles and spirit of the invention, the scope of which is defined in the claims and not by the foregoing embodiments, and all such implementations are within the scope of the invention.
Claims (7)
1. The millimeter wave and terahertz channel estimation method based on the pruned convolutional neural network is characterized in that channel data are processed by using an approximate message transfer compressed sensing Algorithm (AMP) in an off-line stage to obtain initial channel prediction, and the initial channel prediction is input into a convolutional neural network (DCNN) for training; then, on the premise that the parameters of the network remain unchanged after pruning operation is carried out on the trained convolutional neural network, the initial channel prediction is input into the neural network, and meanwhile, the real channel is used as a label for retraining; and performing millimeter wave and terahertz channel estimation by adopting the trained convolutional neural network in an online stage.
2. The method for estimating millimeter wave and terahertz channel based on a pruned convolutional neural network according to claim 1, wherein the initial channel prediction is obtained by:
step 1, establishing a channel estimation problem model according to a super-large-scale MIMO system, and transmitting pilot frequency (pilot) signals x [ k ] at a transmitting end]＝F RF F BB s[k]Based on the pilot signal known at the receiving end, from the receiving end signal
Recovering the channel matrix H [ k ]]The method comprises the steps of carrying out a first treatment on the surface of the Further based on pilot signal and antenna precoding and combining matrix, based on sparsity of channel H [ k ]]＝A R h[k]A T Thereby, when transmitting a plurality of pilot signals, a plurality of received signal results are collected in the channel coherence time to obtain a received signal Y [ k ]]＝φH[k]+N[k]Wherein: k is different sub-carriers, s [ k ]]F for transmitted data RF And F BB Analog and digital beamforming matrices for the precoding process, respectively; w (W) RF And W is BB Respectively analog and digital combining matrices, n [ k ]]Representing the received noise; h [ k ]]As a sparse matrix, A R And A T A coefficient matrix relating to the channel multipath reception angle and the transmission angle, respectively; the observation matrix phi includes: matrix information of pilot signal and antenna precoding and combining;
step 2, according to the received signal Y [ k ] obtained in step 1]And measuring the matrix phi, and obtaining a preliminary estimated channel by adopting an approximate message transfer compressed sensing algorithm
As a training set;
the approximate message passing compressed sensing algorithm specifically comprises the following steps:
2.1 setting an initial value: t=0, h 0 ＝0，r -1 ＝0，b 0 ＝0，c 0 =0, m, n is the number of rows and columns of the matrix Φ, λ is a parameter set in advance;
2.2 calculating residual terms:
wherein: b t r t-1 And->
The correction term is an Oncorhynchi correction term and is used for accelerating the convergence rate;
2.3 calculating a predicted value: variance of
Wherein: η (eta) st Will be less than the threshold λσ as a function of setting the threshold t The term of (2) is set to 0;
2.4 update parameters:
2.5 repeating the steps 2.2-2.4 ten times to obtain a predicted result h T Recovering the original matrix
3. The method for estimating millimeter wave and terahertz channel based on a pruned convolutional neural network as claimed in claim 1, wherein the convolutional neural network includes 12 convolutional layers, the number of convolutional kernels is 64 except for the last convolutional layer which is 2, and the convolutional layers are connected by an activation function and a normalization layer, and the convolutional neural network connects the input convolutional layers
As a 2-dimensional picture, 2 dimensions represent the real part and the imaginary part of the data, respectively,/output +.>
And->
The dimensions are the same.
4. The method for estimating millimeter wave and terahertz channel based on a pruned convolutional neural network according to claim 1 or 3, wherein the convolutional layers each contain a ReLU activation function to enhance the nonlinear expression capability of the neural network;
the convolutional neural network adopts a loss function of
Wherein: MSE is mean square error, lambda is the regularized coefficient, gamma is the scale coefficient of the normalized layer.
5. The method for estimating millimeter wave and terahertz channel based on a pruned convolutional neural network as set forth in claim 4, wherein the convolutional neural network is trained by using normalized mean square error as an index for measuring channel recovery accuracy
Wherein: h is the real channel matrix, < >>
For the channel matrix obtained by AMP-DCNN prediction method, the adopted Norm is French Luo Beini Usneius Norm (Frobenius Norm), and +.>
6. The method for estimating millimeter wave and terahertz channel based on a pruned convolutional neural network according to claim 1, wherein the pruning operation is: pruning operation is carried out on the trained convolutional neural network to obtain a lightweight optimized channel estimation network so as to remarkably reduce required memory and operation resources, and the method specifically comprises the following steps: and determining the proportion p to be pruned and the proportion coefficient threshold value of the standardized layer, wherein in the pruning process, the proportion coefficient of the characteristic diagram connection of the deconvolution neural network is smaller than the proportion coefficient threshold value of the standardized layer.
7. The method for estimating millimeter wave and terahertz channel based on a pruned convolutional neural network according to claim 1, wherein the retraining means: and retraining the pruned convolutional neural network by taking a real channel as a label on initial channel prediction, and finally obtaining the lightweight neural network.
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Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20190340497A1 (en) * | 2016-12-09 | 2019-11-07 | William Marsh Rice University | Signal Recovery Via Deep Convolutional Networks |
| CN111030952A (en) * | 2019-12-25 | 2020-04-17 | 内蒙古大学 | Beam space channel estimation method and system of millimeter wave system |
| CN111224906A (en) * | 2020-02-21 | 2020-06-02 | 重庆邮电大学 | Approximate message transfer large-scale MIMO signal detection algorithm based on deep neural network |
| WO2020220278A1 (en) * | 2019-04-30 | 2020-11-05 | 华为技术有限公司 | Channel estimation model training method and device |
| CN112910806A (en) * | 2021-01-19 | 2021-06-04 | 北京理工大学 | Joint channel estimation and user activation detection method based on deep neural network |
| CN112968853A (en) * | 2021-01-29 | 2021-06-15 | 北京交通大学 | Multi-user access detection and channel estimation method for MMTC (multimedia messaging and control) system |
| CN113271270A (en) * | 2021-06-28 | 2021-08-17 | 上海交通大学 | Terahertz ultra-large scale channel estimation method based on deep convolutional neural network |
| CN115022134A (en) * | 2022-06-28 | 2022-09-06 | 河南工业大学 | Millimeter wave large-scale MIMO system channel estimation method and system based on non-iterative reconstruction network |
| CN115664898A (en) * | 2022-10-24 | 2023-01-31 | 四川农业大学 | OFDM system channel estimation method and system based on complex convolution neural network |
| US20230073228A1 (en) * | 2020-05-15 | 2023-03-09 | Vivo Mobile Communication Co., Ltd. | Pilot information system sending method, channel estimation method, and communications device |
-
2023
- 2023-04-04 CN CN202310352756.1A patent/CN116319195B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20190340497A1 (en) * | 2016-12-09 | 2019-11-07 | William Marsh Rice University | Signal Recovery Via Deep Convolutional Networks |
| WO2020220278A1 (en) * | 2019-04-30 | 2020-11-05 | 华为技术有限公司 | Channel estimation model training method and device |
| CN111030952A (en) * | 2019-12-25 | 2020-04-17 | 内蒙古大学 | Beam space channel estimation method and system of millimeter wave system |
| CN111224906A (en) * | 2020-02-21 | 2020-06-02 | 重庆邮电大学 | Approximate message transfer large-scale MIMO signal detection algorithm based on deep neural network |
| US20230073228A1 (en) * | 2020-05-15 | 2023-03-09 | Vivo Mobile Communication Co., Ltd. | Pilot information system sending method, channel estimation method, and communications device |
| CN112910806A (en) * | 2021-01-19 | 2021-06-04 | 北京理工大学 | Joint channel estimation and user activation detection method based on deep neural network |
| CN112968853A (en) * | 2021-01-29 | 2021-06-15 | 北京交通大学 | Multi-user access detection and channel estimation method for MMTC (multimedia messaging and control) system |
| CN113271270A (en) * | 2021-06-28 | 2021-08-17 | 上海交通大学 | Terahertz ultra-large scale channel estimation method based on deep convolutional neural network |
| CN115022134A (en) * | 2022-06-28 | 2022-09-06 | 河南工业大学 | Millimeter wave large-scale MIMO system channel estimation method and system based on non-iterative reconstruction network |
| CN115664898A (en) * | 2022-10-24 | 2023-01-31 | 四川农业大学 | OFDM system channel estimation method and system based on complex convolution neural network |
Non-Patent Citations (3)
| Title |
|---|
| ZHENGDONG HU: "PRINCE: A Pruned AMP Integrated Deep CNN Method for Efficient Channel Estimation of Millimeter-wave and Terahertz Ultra-Massive MIMO Systems", 《IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS ( EARLY ACCESS )》, pages 1 - 6 * |
| 朱啸豪;孙超;姜述超;: "基于深度学习和AMP的MIMO检测算法", 微型电脑应用, no. 05 * |
| 邱佳锋;: "基于优化BM3D的毫米波大规模MIMO信道估计", 电信科学, no. 09 * |
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