CN114785643A - OFDM system channel estimation method based on deep learning - Google Patents

OFDM system channel estimation method based on deep learning Download PDF

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CN114785643A
CN114785643A CN202210409459.1A CN202210409459A CN114785643A CN 114785643 A CN114785643 A CN 114785643A CN 202210409459 A CN202210409459 A CN 202210409459A CN 114785643 A CN114785643 A CN 114785643A
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袁晓军
黄周洋
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University of Electronic Science and Technology of China
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Abstract

The invention belongs to the technical field of information and communication, and relates to an OFDM system channel estimation method based on deep learning. In order to further improve the performance of channel estimation of an OFDM system, the invention provides a deep learning-based channel estimation method of the OFDM system, which comprises the following steps: firstly, estimating channel state information of a pilot frequency position from a received signal by using a linear estimation method; secondly, an interpolation algorithm is used for obtaining a channel state information estimation value of the whole OFDM time-frequency resource block; and finally, the estimated channel state information is subjected to noise reduction by using the multi-scale parallel expansion convolutional neural network provided by the invention. The performance test is carried out on the method under the complex TDL mixed scene, and the result shows that the method of the invention exceeds the existing method.

Description

OFDM system channel estimation method based on deep learning
Technical Field
The invention belongs to the technical field of information and communication, and relates to an OFDM system channel estimation method based on deep learning.
Background
Accurate channel estimation and efficient channel information feedback are basic conditions for guaranteeing the performance of a wireless communication system. In recent years, Deep Learning (DL) or AI has been widely studied in wireless communication systems, both academic and industrial. OFDM is one of core technologies of 4G and 5G standards, can well support multiple access, and has stable performance in a frequency selective fading environment. In a wireless communication system, a transmission signal is subject to various fading and multipath propagation. To demodulate the transmitted signal, a pilot signal is designed to estimate channel information. The pilot signal is transmitted with the data signal, precoded using the same data signal transmission, and subject to similar channel fading. Since the location and sequence of the pilot signal are known to the receiver, the receiver can estimate the channel using the received signal.
Document 1 "m.soltani, v.pourhmadi, a.mirzaei and h.sheikhzadeh," Deep Learning-Based Channel Estimation, "in IEEE Communications Letters, vol.23, No.4, pp.652-655, April 2019, doi:10.1109/lcomm.2019.2898944," considers the time-frequency response of a fast fading communication Channel as a two-dimensional image, considers the pilot value as a low-resolution image, and estimates the Channel using a super-resolution network in cascade with a de-noising network, and names the proposed network as ChannelNet.
Document 2 "l.li, h.chen, h. -h.chang and l.liu," Deep Residual Learning means OFDM Channel Estimation, "in IEEE Wireless Communications Letters, vol.9, No.5, pp.615-618, May 2020, doi: 10.1109/lwc.2019.2962796" designs a Deep neural network based on Residual Learning for Channel Estimation, named reeset.
Disclosure of Invention
In order to further improve the performance of channel estimation of the OFDM system, the invention provides an OFDM system channel estimation method based on deep learning.
The technical scheme adopted by the invention comprises the following steps:
s1, suppose the system has NtOne OFDM symbol, NfSubcarrier, MTxA transmitting antenna and MRxA receiving antenna; assuming that the Cyclic Prefix (CP) length of the OFDM system is greater than the maximum channel delay, after removing the CP and performing Discrete Fourier Transform (DFT), the mth channel isRxThe signal on the k-th OFDM symbol and the i-th subcarrier received by the receiving antennas can be expressed as
Figure BDA0003603554710000021
wherein ,
Figure BDA0003603554710000022
and
Figure BDA0003603554710000023
respectively corresponding frequency domain channel and mthTxA precoding factor of the root antenna; s isk，iIn order to transmit a symbol, a symbol is transmitted,
Figure BDA0003603554710000024
is additive white gaussian noise; since both the pilot and data signals are transmitted using the same transmit precoder, the above equations can be rewritten as
Figure BDA0003603554710000025
wherein ,
Figure BDA0003603554710000026
is a precoded channel. For all OFDM symbols and subcarriers, there are
Figure BDA0003603554710000027
wherein ,
Figure BDA0003603554710000028
sk，iand
Figure BDA0003603554710000029
are respectively
Figure BDA00036035547100000210
S and
Figure BDA00036035547100000211
(k, i) -th elements of (a);
Figure BDA00036035547100000212
representing the product of the corresponding position elements of the matrix;
in general, in total NtIn one OFDM symbol, pilot frequency occupies Np，t＜NtIn total NfIn the number of carriers, pilot frequency occupies Np，f＜NfA plurality of; for the m-thRxA receiving antenna receiving a pilot signal of
Figure BDA00036035547100000213
At the receiving end, the least square method LS is used to obtain the known pilot SpFrom observation
Figure BDA00036035547100000214
Estimate the channel state information at the pilot position
Figure BDA00036035547100000215
Namely that
Figure BDA00036035547100000216
Wherein,/represents the division of the corresponding element position of the matrix;
s2, obtaining channel estimation value of pilot frequency position of OFDM time frequency resource block
Figure BDA00036035547100000217
Then, obtaining the channel state information of the whole OFDM time-frequency resource block by an interpolation algorithm
Figure BDA0003603554710000031
For the k-th position of the pilot frequency position of the OFDM time-frequency resource blockp1 and kp2One OFDM symbol, using DFT interpolation, i.e.
Figure BDA0003603554710000032
Figure BDA0003603554710000033
wherein ,
Figure BDA0003603554710000034
and
Figure BDA0003603554710000035
respectively represent the m-thRxAt the receiving antenna, kp1All pilot carriers (i) on one OFDM symbolp) A vector formed by the frequency domain channel and a corresponding delay domain impulse response vector are processed; the same method can obtain
Figure BDA0003603554710000036
And
Figure BDA0003603554710000037
for each line of the OFDM time-frequency resource block, a linear interpolation method is adopted, namely
Figure BDA0003603554710000038
Thus, the channel estimation value of the whole OFDM time frequency resource block can be obtained
Figure BDA0003603554710000039
S3, judging whether a multi-scale parallel expanded convolutional neural network (MPDCNN) has finished training and storage; if the MPDCNN has been trained and saved, perform step S5, otherwise perform step S4;
s4, performing noise reduction on the channel estimation value of the whole OFDM time-frequency resource block by offline training of the MPDCNN; the specific structure of the proposed MPDCNN is shown in fig. 2: input data is subjected to one-layer two-dimensional convolution inside the MPDCNN, then sequentially subjected to a plurality of multi-scale parallel expanded volume blocks (MPDCBs) and one-layer two-dimensional convolution, and finally, the output after the two-dimensional convolution and the data input into the MPDCNN are added to be used as the output of the MPDCNN; the input data sequentially passes through a layer of two-dimensional convolution and three parallel expanded convolution blocks (DCBs) with different expansion rates in the MPDCB; then, the outputs of the 3 DCBs are spliced according to the dimensionality of the tensor channel and then sent to a channel attention module (CA), and the output after CA is subjected to one-layer two-dimensional convolution; finally, adding the output after the two-dimensional convolution and the data input into the MPDCB to be used as the output of the MPDCB; input data passes through a plurality of residual blocks (ResBlock) in the DCB, and finally, the output of the last ResBlock is added with the input of the DCB to be used as the output of the DCB; the input data sequentially passes through a two-dimensional convolution layer, a parametric rectified linear unit (PRelu) and a two-dimensional convolution layer in ResBlock; finally, the output of the second two-dimensional convolution layer is added with the input of ResBlock to be used as the output of ResBlock; the input data sequentially passes through two-dimensional convolution, PRelu, two-dimensional convolution, global average pooling, 1X1 convolution, PRelu, 1X1 convolution and Sigmoid functions in the CA module; finally, multiplying the output of the Sigmoid function and the input of the CA module in a channel dimension according to broadcast multiplication to be used as the output of the CA module;
MPDCNN noise reducer used is defined as
Figure BDA0003603554710000041
Where θ is a network parameter. Handle
Figure BDA0003603554710000042
After being sent to the noise reducer, the output after noise reduction can be obtained:
Figure BDA0003603554710000043
the loss function used in the off-line training phase is
Figure BDA0003603554710000044
S5, loading the stored MPDCNN to reduce the noise of the whole OFDM time frequency resource block channel estimation value, namely
Figure BDA0003603554710000045
The invention has the beneficial effects that: the deep neural network is used for reducing noise of the time-frequency domain resource block channel with high noise by utilizing the structural characteristics of the OFDM system channel, and the method has outstanding performance on the OFDM system channel estimation problem.
Drawings
FIG. 1 is a schematic diagram of a pilot configuration in a 5G OFDM system;
fig. 2 is a flow chart of the proposed channel estimation method;
FIG. 3 is a diagram of the proposed de-noising network architecture;
fig. 4 is a simulation curve of different channel estimation methods in an OFDM system.
Detailed Description
The following detailed description of the present invention is provided in connection with the accompanying drawings and examples.
The following provides a specific implementation method of the present invention based on the above method, and the parameters of the specific method are set as follows:
fig. 1 shows a typical pilot resource allocation in the current 5G system. In the frequency domain, there are 12 subcarriers, 14 OFDM symbols, in one Resource Block (RB). The pilot occupies 6 subcarriers and 2 OFDM symbols in one RB and one slot. In the simulation, 8 RBs shown in FIG. 1 were used in the frequency domain and 14 OFDM symbols, N, were used in the time domaint＝14，Nf＝96，Np，t＝2，Np，f48. Number of transmitting antennas MTx32, receiving dayNumber of lines M Rx2. The modulation scheme is QPSK.
The link level simulator follows the 3GPP Tapped Delay Line (TDL) model, which has been calibrated. The carrier frequency is 3.5GHz and the subcarrier space is 15 KHz. A new hybrid training data construction method is proposed to solve the generalization problem in DL. For training data, the channel model is a mixed model of TDL-A, TDL-B, TDL-C, TDL-D and TDL-E, wherein one sample randomly selects one channel model from the 5 models; the delay spread is randomly selected from 0 seconds to 300 ns; the speed is randomly generated between 0km/h and 50 km/h; the signal-to-noise ratio of each sample was chosen randomly from 0dB to 20 dB.
In training the MPDCNN network, the training set and the test set contained 189000 samples (9000 samples per snr) and 21000 samples (1000 samples per snr), respectively. In mpdcn, 4 different MPDCB are used, each DCB containing 4 different resblocks. Note that the first layer convolution of MPDCNN is 48 different 3 × 3 × 2 filters, the last layer convolution of MPDCNN is two different 3 × 3 × 48 filters, and the 1 × 1 convolution in CA is 48 different 1 × 1 × 48 filters. All other convolutional layers of the MPDCNN are 48 different 3 × 3 × 48 filters. Adam optimizer and learning rate set to 1e-4。
According to the parameter setting, the simulation comprises the following specific steps:
s1, suppose the system has NtOne OFDM symbol, NfSubcarrier, MTxA transmitting antenna and MRxA receiving antenna; assuming that the Cyclic Prefix (CP) length of the OFDM system is greater than the maximum channel delay, after removing the CP and performing Discrete Fourier Transform (DFT), the mth unitRxThe signal on the k-th OFDM symbol and the i-th subcarrier received by the receiving antennas can be expressed as
Figure BDA0003603554710000051
wherein ,
Figure BDA0003603554710000052
and
Figure BDA0003603554710000053
respectively corresponding frequency domain channel and mthTxA precoding factor of the root antenna; s isk，iIn order to transmit a symbol, a symbol is transmitted,
Figure BDA0003603554710000054
is additive white gaussian noise; since both the pilot and data signals are transmitted using the same transmit precoder, the above equations can be rewritten as
Figure BDA0003603554710000061
wherein ,
Figure BDA0003603554710000062
is a precoded channel. For all OFDM symbols and subcarriers, there are
Figure BDA0003603554710000063
wherein ,
Figure BDA0003603554710000064
sk，iand
Figure BDA0003603554710000065
are respectively
Figure BDA0003603554710000066
S and
Figure BDA0003603554710000067
(k, i) -th elements of (a);
Figure BDA0003603554710000068
representing the product of the corresponding position elements of the matrix;
in general, in total NtIn one OFDM symbol, pilot frequency occupies Np，t＜NtIn total NfIn the number of carriers, pilot frequency occupies Np，f＜NfA plurality of; for the m-thRxA receiving antenna receiving a pilot signal of
Figure BDA0003603554710000069
At the receiving end, the least square method LS is used to obtain the known pilot SPFrom observation
Figure BDA00036035547100000610
Estimate the channel state information at the pilot position
Figure BDA00036035547100000611
Namely that
Figure BDA00036035547100000612
Wherein,/represents the division of the corresponding element position of the matrix;
s2, obtaining channel estimation value of pilot frequency position of OFDM time frequency resource block
Figure BDA00036035547100000613
Then, obtaining the channel state information of the whole OFDM time-frequency resource block by an interpolation algorithm
Figure BDA00036035547100000614
For the k-th position of the pilot frequency position of the OFDM time-frequency resource blockp1 and kp2One OFDM symbol, using DFT interpolation, i.e.
Figure BDA00036035547100000615
Figure BDA00036035547100000616
wherein ,
Figure BDA0003603554710000071
and
Figure BDA0003603554710000072
respectively represent the m-thRxAt the receiving antenna, kp1All pilot carriers (i) on one OFDM symbolp) A vector formed by the frequency domain channel and a corresponding delay domain impulse response vector are processed; the same method can obtain
Figure BDA0003603554710000073
And
Figure BDA0003603554710000074
for each line of the OFDM time-frequency resource block, a linear interpolation method is adopted, namely
Figure BDA0003603554710000075
Thus, the channel estimation value of the whole OFDM time frequency resource block can be obtained
Figure BDA0003603554710000076
S3, judging whether a multi-scale parallel expanded convolutional neural network (MPDCNN) has finished training and storing; if the MPDCNN has been trained and saved, perform step S5, otherwise perform step S4;
s4, performing noise reduction on the channel estimation value of the whole OFDM time-frequency resource block by offline training of the MPDCNN; the specific structure of the proposed MPDCNN is shown in fig. 2: input data is firstly subjected to one-layer two-dimensional convolution in the MPDCNN, then sequentially subjected to a plurality of multi-scale parallel expanded convolution blocks (MPDCBs) and one-layer two-dimensional convolution, and finally, the output after the two-dimensional convolution is added with the data input into the MPDCNN to be used as the output of the MPDCNN; the input data sequentially passes through a layer of two-dimensional convolution and three parallel expanded convolution blocks (DCBs) with different expansion rates in the MPDCB; then, the outputs of the 3 DCBs are spliced according to the dimensionality of the tensor channel and then sent to a channel attention module (CA), and the output after CA is subjected to one-layer two-dimensional convolution; finally, adding the output after the two-dimensional convolution and the data input into the MPDCB to be used as the output of the MPDCB; input data passes through a plurality of residual blocks (ResBlock) in the DCB, and finally, the output of the last ResBlock is added with the input of the DCB to be used as the output of the DCB; the input data sequentially passes through a two-dimensional convolution layer, a parametric rectification linear unit (PRelu) and a two-dimensional convolution layer in the ResBlock; finally, the output of the second two-dimensional convolution layer is added with the input of ResBlock to be used as the output of ResBlock; the input data sequentially passes through two-dimensional convolution, PRelu, two-dimensional convolution, global average pooling, 1X1 convolution, PRelu, 1X1 convolution and Sigmoid functions in the CA module; finally, multiplying the output of the Sigmoid function and the input of the CA module in a channel dimension according to broadcast multiplication to be used as the output of the CA module;
MPDCNN noise reducer used is defined as
Figure BDA0003603554710000077
Where θ is a network parameter. Handle
Figure BDA0003603554710000078
After being sent to the noise reducer, the output after noise reduction can be obtained:
Figure BDA0003603554710000081
the loss function used in the off-line training phase is
Figure BDA0003603554710000082
S5, loading the stored MPDCNN to reduce the noise of the whole OFDM time frequency resource block channel estimation value, namely
Figure BDA0003603554710000083
Fig. 4 is a simulation curve of different channel estimation methods. LS correspondingly executes the channel estimation result obtained after the first two steps of the method; reeset corresponds to a channel estimation result obtained by using the method of document 2; the ChannelNet corresponds to a channel estimation result obtained by using the method in the literature 1; MPDCNN corresponds to the performance results of the proposed method. As can be seen from the figure, the performance of the method provided by the invention is far superior to that of the other three methods, and the effectiveness of the method is proved.

Claims (2)

1. A deep learning-based OFDM system channel estimation method is characterized by comprising the following steps:
s1, defining the system to have NtOne OFDM symbol, NfSub-carriers, MTxA transmitting antenna and MRxA receiving antenna; when the length of the Cyclic Prefix (CP) of the OFDM system is larger than the maximum channel time delay, after the cyclic prefix is removed and Discrete Fourier Transform (DFT) is carried out, the mthRxThe signal on the ith subcarrier of the kth OFDM symbol received by each receiving antenna is represented as:
Figure FDA0003603554700000011
wherein ,
Figure FDA0003603554700000012
and
Figure FDA0003603554700000013
respectively corresponding frequency domain channel and mthTxA precoding factor of a root antenna; sk，iIn order to transmit a symbol, a symbol is transmitted,
Figure FDA0003603554700000014
is additive white gaussian noise; since both the pilot and data signals are transmitted using the same transmit precoder, the above equation is rewritten as:
Figure FDA0003603554700000015
wherein ,
Figure FDA0003603554700000016
is a precoded channel; for all OFDM symbols and subcarriers, there are:
Figure FDA0003603554700000017
wherein ,
Figure FDA0003603554700000018
sk，iand
Figure FDA0003603554700000019
are respectively
Figure FDA00036035547000000110
S and
Figure FDA00036035547000000111
(k, i) -th elements of (a);
Figure FDA00036035547000000116
representing the product of the corresponding position elements of the matrix;
in total NtIn one OFDM symbol, pilot frequency occupies Np，t＜NtIn total NfIn the number of carriers, pilot frequency occupies Np，f＜NfA plurality of; for the m-thRxThe antenna is received at the root of the antenna,the pilot signal it receives is:
Figure FDA00036035547000000112
at the receiving end, the least square method LS is used to derive the known pilot SpFrom observation
Figure FDA00036035547000000113
To estimate the channel state information at the pilot position
Figure FDA00036035547000000114
Namely, it is
Figure FDA00036035547000000115
Wherein,/represents the division of the corresponding element position of the matrix;
s2, obtaining channel estimation value of pilot frequency position of OFDM time frequency resource block
Figure FDA0003603554700000021
Then, obtaining the channel state information of the whole OFDM time frequency resource block by an interpolation algorithm
Figure FDA0003603554700000022
For the k-th position where the pilot frequency position of the OFDM time frequency resource block is positionedp1 and kp2The OFDM symbol adopts a DFT interpolation method, namely:
Figure FDA0003603554700000023
Figure FDA0003603554700000024
wherein ,
Figure FDA0003603554700000025
and
Figure FDA0003603554700000026
respectively represent the m-thRxAt the receiving antenna, kp1All pilot carriers (i) on one OFDM symbolp) A vector formed by the frequency domain channel and a corresponding delay domain impulse response vector; the same method can obtain
Figure FDA0003603554700000027
And
Figure FDA0003603554700000028
for each line of the OFDM time-frequency resource block, a linear interpolation method is adopted, namely:
Figure FDA0003603554700000029
thus, the channel estimation value of the whole OFDM time frequency resource block is obtained
Figure FDA00036035547000000210
S3, judging whether the multi-scale parallel expansion convolutional neural network (MPDCNN) has finished training and storage; if the MPDCNN has been trained and saved, perform step S5, otherwise perform step S4;
s4, performing noise reduction on the channel estimation value of the whole OFDM time-frequency resource block by offline training of the MPDCNN; MPDCNN noise reducer used is defined as
Figure FDA00036035547000000211
Where θ is a network parameter, and
Figure FDA00036035547000000212
after the output is sent into a noise reducer, the output after noise reduction is obtained:
Figure FDA00036035547000000213
the loss function used during the off-line training phase is:
Figure FDA00036035547000000214
s5, loading the stored MPDCNN to perform noise reduction on the channel estimation value of the whole OFDM time-frequency resource block, namely:
Figure FDA00036035547000000215
2. the method as claimed in claim 1, wherein in step S4, the MPDCNN training process comprises:
the method comprises the steps that input data are subjected to one-layer two-dimensional convolution inside the MPDCNN, then sequentially subjected to a plurality of multi-scale parallel expansion volume blocks (MPDCBs) and one-layer two-dimensional convolution, and finally, the output after the two-dimensional convolution and the data input into the MPDCNN are added to be used as the output of the MPDCNN;
the input data sequentially passes through a layer of two-dimensional convolution and three parallel expansion convolution blocks (DCBs) with different expansion rates in the MPDCB; then, the outputs of the 3 DCBs are spliced according to the dimensionality of the tensor channel and then sent to a channel attention module (CA), and the output after CA is subjected to one-layer two-dimensional convolution; finally, adding the output after the two-dimensional convolution and the data input into the MPDCB to be used as the output of the MPDCB;
the input data passes through a plurality of residual blocks (ResBlock) in the DCB, and finally, the output of the last ResBlock is added with the input of the DCB to be used as the output of the DCB;
the input data sequentially passes through a two-dimensional convolutional layer, a parametric rectification linear unit (PRelu) and a two-dimensional convolutional layer in ResBlock; finally, the output of the second two-dimensional convolution layer is added with the input of ResBlock to be used as the output of ResBlock;
the input data sequentially passes through two-dimensional convolution, PRelu, two-dimensional convolution, global average pooling, 1X1 convolution, PRelu, 1X1 convolution and Sigmoid functions in the CA module; and finally, multiplying the output of the Sigmoid function and the input of the CA module in a channel dimension by broadcast multiplication to be used as the output of the CA module.
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