CN111970078A - Frame synchronization method for nonlinear distortion scene - Google Patents

Frame synchronization method for nonlinear distortion scene Download PDF

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CN111970078A
CN111970078A CN202010821398.0A CN202010821398A CN111970078A CN 111970078 A CN111970078 A CN 111970078A CN 202010821398 A CN202010821398 A CN 202010821398A CN 111970078 A CN111970078 A CN 111970078A
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卿朝进
余旺
董磊
杜艳红
唐书海
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Abstract

The invention discloses a frame synchronization method of a nonlinear distortion scene, which comprises the following steps: collecting NtM frames of N long sample sequences yi (1),yi (2),…yi (M),i＝1,2,…,Nt(ii) a Weighted superposition to obtain a superposed sample sequence yi (S),i＝1,2,…,Nt(ii) a For the superimposed sample sequence yi (S)Preprocessing to obtain synchronization metrics
Figure DDA0002634511110000011
i＝1,2,…,Nt(ii) a Constructing an ELM-based network, and constructing a tag T according to a frame synchronization deviation value of a received signali,i＝1,2,…,NtLearning network parameters; learning to obtain frame synchronization offset estimation value by using learning-obtained ELM network model
Figure DDA0002634511110000012
The invention can improve the frame synchronization performance under the nonlinear distortion scene, and compared with the traditional correlation method, the frame synchronization performance of the invention is greatly improved.

Description

Frame synchronization method for nonlinear distortion scene
Technical Field
The invention relates to the technical field of wireless communication frame synchronization, in particular to a frame synchronization method for a nonlinear distortion scene.
Background
As one of the important components in a communication system, the performance of the frame synchronization method is good and bad, which directly affects the performance of the whole wireless communication system. However, the wireless communication system inevitably has nonlinear distortion, such as high-efficiency power amplifier distortion, analog-to-digital or digital-to-analog converter distortion, and distortion caused by two-way imbalance of I/Q, and so on. In addition, in the next generation wireless communication system (e.g. 6G system), in order to avoid the transceiver being too expensive, low cost and low resolution devices (e.g. power amplifier, AD sampler) are required, which causes the non-linear distortion to be particularly prominent. The traditional frame synchronization method (such as the correlation method) and the time-new frame synchronization method mostly do not consider the nonlinear distortion scene, so that the method is difficult to be applied under the nonlinear distortion condition. Machine learning has excellent learning ability for nonlinear distortion, however, frame synchronization techniques based on machine learning have little research and do not achieve good synchronization performance, and improvement is urgently needed.
Therefore, the invention utilizes a machine learning method and develops interframe correlation prior information to form a frame synchronization method for improving the error probability performance of frame synchronization. At a receiving end, firstly, carrying out weighted superposition preprocessing on frames, developing interframe correlation prior information, and preliminarily capturing frame synchronization measurement characteristics; then, an ELM frame synchronization network is constructed, and the estimation of frame synchronization deviation is trained off line; and finally, estimating the frame synchronization offset on line by combining the preprocessed ELM network parameters with the learned ELM network parameters. Aiming at wireless communication scenes with nonlinear distortion, such as 5G and 6G systems, the method can improve the error probability performance of the frame synchronization and promote the intelligent processing level of the frame synchronization, brings a plurality of implementable schemes for intelligent frame synchronization research, and has great significance.
Disclosure of Invention
Compared with the traditional related synchronization method, the method combines multi-frame weighted superposition and an ELM network, and effectively improves the frame synchronization performance under the nonlinear distortion system.
The specific invention scheme is as follows:
a frame synchronization method for a nonlinear distortion scene comprises the following steps:
(a) collecting NtM frames of N long sample sequences yi (1),yi (2),…yi (M),i＝1,2,…,Nt；
(b) For yi (1),yi (2),…yi (M)Carrying out weighted superposition to obtain a superposed sample sequence yi (S),i＝1,2,…,Nt
(c) For the superimposed sample sequence yi (S)Preprocessing to obtain standard measurement vector
Figure BDA00026345110900000210
(d) Constructing an ELM network, and constructing a tag T according to the frame synchronization deviation value of the received signali,i＝1,2,…,NtLearning network parameters;
(e) learning to obtain frame synchronization offset estimation value by using learning-obtained ELM network model
Figure BDA0002634511090000029
Further, the obtaining of the M frames of N-long sample sequence of step (a) may be represented as:
Figure BDA0002634511090000021
wherein M and N are set according to engineering experience.
Further, the method step (b) the weighted overlap-add is represented as:
yi (S)＝μ1yi (1)+μ2yi (2)+…+μMyi (M)；
the muiI is 1,2, …, and M is a weighting coefficient; and setting according to the received signal-to-noise ratio of each frame.
Further, the pretreatment step in step (c) of the method is:
(c1) one-time training superposition sample sequence y(S)Middle observation length of NsObservation sequence of
Figure BDA0002634511090000022
And length NsTraining sequence of
Figure BDA0002634511090000023
After the cross-correlation operation, the cross-correlation measurement is obtained "tNamely:
Figure BDA0002634511090000024
the observation length is NsSetting according to engineering experience;
the t represents the initial index position of the observed superposition sequence, and t belongs to [1, K ]]For example, t-1 denotes a sequence y of samples from the superposition(S)Begins to observe NsA long sample sequence;
Figure BDA0002634511090000025
denotes y(S)Middle t to t + Ns-1 element;
the K is N-Ns+1, representing the size of the search window;
(c2) measured by K correlations
Figure BDA0002634511090000026
Constructing a metric vector
Figure BDA0002634511090000027
To NtIndividual measurement vector gammaiNormalization processing is carried out to obtain a standard measurement vector
Figure BDA0002634511090000028
Namely:
Figure BDA0002634511090000031
said N istAccording to the engineering experience setting, the | | | gammai| represents the measurement vector γiFrobenius norm of (1).
Further, the network model and parameters in step (d) are:
the ELM network model comprises 1 input layer, 1 hidden layer and 1 output layer, wherein the number of nodes of the input layer is K, and the number of nodes of the hidden layer is K
Figure BDA0002634511090000032
The number of nodes of an output layer is K, a hidden layer adopts sigmoid as an activation function, and preprocessed standard measurement vectors are integrated
Figure BDA0002634511090000033
As an input;
and m is set according to engineering experience.
Further, the step (d) of constructing the tag comprises the steps of:
according to the synchronization deviation value taui,i＝1,2,…,NtForming a set of tags
Figure BDA00026345110900000315
The label Ti,i＝1,2,…,NtAccording to the synchronization deviation value tauiObtained by one-hot coding, i.e.
Figure BDA0002634511090000034
Said tauiFrom the received signal yiAnd determining, and collecting according to a statistical channel model or according to an actual scene by combining the existing method or equipment.
Further, the offline training process of step (d) specifically includes the following steps:
(d1) generating weights from random distributions
Figure BDA0002634511090000035
And bias
Figure BDA0002634511090000036
Sequentially combining the standard metric vectors
Figure BDA0002634511090000037
Input to ELM network, hidden layer output
Figure BDA0002634511090000038
Expressed as:
Figure BDA0002634511090000039
the σ (-) represents an activation function sigmoid;
(d2) from NtIndividual metric vector
Figure BDA00026345110900000310
Obtained NtA hidden layer output HiConstructing hidden layer output matrices
Figure BDA00026345110900000311
Obtaining output weight according to hidden layer output matrix H and label set T constructed in step (a3)
Figure BDA00026345110900000312
Figure BDA00026345110900000313
The above-mentioned
Figure BDA00026345110900000314
Moore-Penrose pseudoinverse representing H;
(d3) model parameters W, b and β are saved.
Further, the on-line operation process of the step (e) comprises the following steps:
(e1) receiving M frames of N long online sample sequences yonline (1),yonline (2),…,yonline (M)Performing superposition preprocessing according to the steps (b) and (c) to obtain an online standard measurement vector
Figure BDA0002634511090000041
Will be provided with
Figure BDA0002634511090000042
The vector is sent to an ELM network model to learn an output vector
Figure BDA0002634511090000043
Expressed as:
Figure BDA0002634511090000044
(e2) finding the index position of the maximum of the squared amplitudes in the output vector O, i.e. the frame synchronization estimate
Figure BDA0002634511090000047
Figure BDA0002634511090000045
The invention has the beneficial effects that: the frame synchronization performance under the nonlinear distortion system is improved.
Drawings
FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a flowchart of ELM network offline training;
fig. 3 is a diagram of an on-line operation process of the ELM network.
Detailed Description
The technical solutions of the present invention are further described in detail below with reference to the accompanying drawings, but the scope of the present invention is not limited to the following.
As shown in fig. 1, a method for frame synchronization of a non-linear distortion scene includes the following steps:
(a) collecting NtM frames of N long sample sequences yi (1),yi (2),…yi (M)，i＝1,2,…,Nt；
Specifically, the obtaining of the M frames of N-long sample sequence in step (a) of the method may be represented as:
Figure BDA0002634511090000046
wherein M and N are set according to engineering experience.
(b) For yi (1),yi (2),…yi (M)Carrying out weighted superposition to obtain a superposed sample sequence yi (S)，i＝1,2,…,Nt
Specifically, the weighted overlap-add of the method step (b) can be expressed as:
yi (S)＝μ1yi (1)+μ2yi (2)+…+μMyi (M)；
the muiI is 1,2, …, and M is a weighting coefficient; and setting according to the received signal-to-noise ratio of each frame.
Example 1: the weighting coefficients are set as follows:
suppose that M is 3 and the signal-to-noise ratio of the 3-frame signal is alpha respectively1,α2,α3，
Figure BDA0002634511090000051
(c) For the superimposed sample sequence yi (S)Preprocessing to obtain synchronization metrics
Figure BDA0002634511090000052
Specifically, the pretreatment step in step (c) of the method is:
(c1) one-time training superposition sample sequence y(S)Middle observation length of NsObservation sequence of
Figure BDA0002634511090000053
And length NsTraining sequence of
Figure BDA0002634511090000054
After the cross-correlation operation, the cross-correlation measurement is obtained "tNamely:
Figure BDA0002634511090000055
the observation length is NsSetting according to engineering experience;
the t represents the initial index position of the observed superposition sequence, and t belongs to [1, K ]]For example, t-1 denotes a sequence y of samples from the superposition(S)Begins to observe NsA long sample sequence;
Figure BDA0002634511090000056
denotes y(S)Middle t to t + Ns-1 element;
the K is N-Ns+1, representing the size of the search window;
(c2) measured by K correlations
Figure BDA0002634511090000057
Constructing a metric vector
Figure BDA0002634511090000058
To NtIndividual measurement vector gammaiNormalization processing is carried out to obtain a standard measurement vector
Figure BDA0002634511090000059
Namely:
Figure BDA00026345110900000510
said N istAccording to the engineering experience setting, the | | | gammai| represents the measurement vector γiFrobenius norm of (1).
(d) Constructing an ELM network, and constructing a tag T according to the frame synchronization deviation value of the received signali,i＝1,2,…,NtLearning network parameters;
in an embodiment of the present application, the network model and parameters in step (d) of the method are:
the ELM network model comprises 1 input layer, 1 hidden layer and 1 output layer, wherein the number of nodes of the input layer is K, and the number of nodes of the hidden layer is K
Figure BDA00026345110900000511
The number of nodes of an output layer is K, a hidden layer adopts sigmoid as an activation function, and preprocessed standard measurement vectors are integrated
Figure BDA00026345110900000512
As an input;
and m is set according to engineering experience.
Specifically, the step (d) of the method for constructing the tag comprises the following steps:
according to the synchronization deviation value taui,i＝1,2,…,NtForming a set of tags
Figure BDA0002634511090000061
The label Ti,i＝1,2,…,NtAccording to the synchronization deviation value tauiObtained by one-hot coding, i.e.
Figure BDA0002634511090000062
Said tauiFrom the received signal yiAnd determining, and collecting according to a statistical channel model or according to an actual scene by combining the existing method or equipment.
Example 2: the labels in step (d) are exemplified as follows:
let N be 64, τi＝5，Nt＝105，
Training labels:
Figure BDA0002634511090000063
as shown in fig. 2, in the embodiment of the present application, the offline training process of step (d) of the method specifically includes the following steps:
(d1) generating weights from random distributions
Figure BDA0002634511090000064
And bias
Figure BDA0002634511090000065
Sequentially combining the standard metric vectors
Figure BDA0002634511090000066
Input to ELM network, hidden layer output
Figure BDA0002634511090000067
Expressed as:
Figure BDA0002634511090000068
the σ (-) represents an activation function sigmoid;
(d2) from NtIndividual metric vector
Figure BDA0002634511090000069
Obtained NtA hidden layer output HiConstructing hidden layer output matrices
Figure BDA00026345110900000610
Output matrix H and step according to hidden layerObtaining an output weight from the tag set T constructed in step (d)
Figure BDA00026345110900000611
Figure BDA00026345110900000612
The above-mentioned
Figure BDA00026345110900000613
Moore-Penrose pseudoinverse representing H;
(d3) model parameters W, b and β are saved.
(e) Learning to obtain frame synchronization offset estimation value by using learning-obtained ELM network model
Figure BDA00026345110900000614
As shown in fig. 3, in the embodiment of the present application, specifically, the online operation process of step (e) includes the following steps:
(e1) receiving M frames of N long online sample sequences yonline (1),yonline (2),…,yonline (M)Performing superposition preprocessing according to the steps (b) and (c) to obtain an online standard measurement vector
Figure BDA0002634511090000071
Will be provided with
Figure BDA0002634511090000072
The vector is sent to an ELM network model to learn an output vector
Figure BDA0002634511090000073
Expressed as:
Figure BDA0002634511090000074
(e2) finding the index position of the maximum of the square of the amplitude in the output vector O, i.e. frame synchronizationEstimated value
Figure BDA0002634511090000075
Figure BDA0002634511090000076
It is to be understood that the embodiments described herein are for the purpose of assisting the reader in understanding the manner of practicing the invention and are not to be construed as limiting the scope of the invention to such particular statements and embodiments. Those skilled in the art can make various other specific changes and combinations based on the teachings of the present invention without departing from the spirit of the invention, and these changes and combinations are within the scope of the invention.

Claims (8)

1. A frame synchronization method for a nonlinear distortion scene is characterized by comprising the following steps:
(a) collecting NtM frames of N long sample sequences yi (1),yi (2),…yi (M),i＝1,2,…,Nt；
(b) For yi (1),yi (2),…yi (M)Carrying out weighted superposition to obtain a superposed sample sequence yi (S),i＝1,2,…,Nt
(c) For the superimposed sample sequence yi (S)Preprocessing to obtain standard measurement vector
Figure FDA0002634511080000011
(d) Constructing an ELM network, and constructing a tag T according to the frame synchronization deviation value of the received signali,i＝1,2,…,NtLearning network parameters;
(e) learning to obtain frame synchronization offset estimation value by using learning-obtained ELM network model
Figure FDA0002634511080000012
2. The method for frame synchronization of a non-linear distortion scene according to claim 1, wherein the sequence of samples of M frames N length in step (a) is represented as:
Figure FDA0002634511080000013
wherein M and N are set according to engineering experience.
3. The method for frame synchronization of a non-linearly distorted scene as claimed in claim 1, wherein the weighted overlap-add of step (b) is represented by:
yi (S)＝μ1yi (1)+μ2yi (2)+…+μMyi (M)；
the muiWhere i is 1,2, …, and M is a weighting coefficient, and is set according to the received snr of each frame.
4. The method for frame synchronization of a non-linear distortion scene as claimed in claim 1, wherein the preprocessing step of step (c) is:
(c1) one-time training superposition sample sequence y(S)Middle observation length of NsObservation sequence of
Figure FDA0002634511080000014
And length NsTraining sequence of
Figure FDA0002634511080000015
After the cross-correlation operation, the cross-correlation measurement is obtained "tNamely:
Figure FDA0002634511080000016
the observation length is NsSetting according to engineering experience;
the t represents the initial index position of the observed superposition sequence, and t belongs to [1, K ]]For example, t-1 denotes a sequence y of samples from the superposition(S)Begins to observe NsA long sample sequence;
Figure FDA0002634511080000017
denotes y(S)Middle t to t + Ns-1 element;
the K is N-Ns+1, representing the size of the search window;
(c2) measured by K correlations
Figure FDA0002634511080000021
Constructing a metric vector
Figure FDA0002634511080000022
To NtIndividual measurement vector gammaiNormalization processing is carried out to obtain a standard measurement vector
Figure FDA0002634511080000023
Namely:
Figure FDA0002634511080000024
said N istAccording to the engineering experience setting, the | | | gammai| represents the measurement vector γiFrobenius norm of (1).
5. The improvement method of claim 1, wherein said network model and parameters of step (d) are:
the ELM network model comprises 1 input layer, 1 hidden layer and 1 output layer, wherein the number of nodes of the input layer is K, and the number of nodes of the hidden layer is K
Figure FDA0002634511080000025
The number of nodes of an output layer is K, a hidden layer adopts sigmoid as an activation function, and preprocessed standard measurement vectors are integrated
Figure FDA0002634511080000026
As an input;
and m is set according to engineering experience.
6. The method for frame synchronization of a non-linear distortion scene as claimed in claim 1, wherein the step of constructing the label in step (d) is:
according to the synchronization deviation value taui,i＝1,2,…,NtForming a set of tags
Figure FDA0002634511080000027
The label Ti,i＝1,2,…,NtAccording to the synchronization deviation value tauiObtained by one-hot coding, i.e.
Figure FDA0002634511080000028
Said tauiFrom the received signal yiAnd determining, and collecting according to a statistical channel model or according to an actual scene by combining the existing method or equipment.
7. The improvement method of claim 1, wherein the offline training process (d) comprises the following steps:
(d1) generating weights from random distributions
Figure FDA0002634511080000029
And bias
Figure FDA00026345110800000210
Sequentially combining the standard metric vectors
Figure FDA00026345110800000211
Input to ELM network, hidden layer output
Figure FDA00026345110800000212
Expressed as:
Figure FDA00026345110800000213
the σ (-) represents an activation function sigmoid;
(d2) from NtIndividual metric vector
Figure FDA0002634511080000031
Obtained NtA hidden layer output HiConstructing hidden layer output matrices
Figure FDA0002634511080000032
Obtaining output weight according to hidden layer output matrix H and label set T constructed in step (a3)
Figure FDA0002634511080000033
Figure FDA0002634511080000034
The above-mentioned
Figure FDA0002634511080000035
Moore-Penrose pseudoinverse representing H;
(d3) model parameters W, b and β are saved.
8. The improvement method of claim 1, wherein the on-line operation process of step (e) comprises the steps of:
receiving M frames of N long online sample sequences yonline (1),yonline (2),…,yonline (M)Performing superposition preprocessing according to the steps (b) and (c) to obtain an online standard measurement vector
Figure FDA0002634511080000036
Will be provided with
Figure FDA0002634511080000037
The vector is sent to an ELM network model to learn an output vector
Figure FDA0002634511080000038
Expressed as:
Figure FDA0002634511080000039
(e1) finding the index position of the maximum of the squared amplitudes in the output vector O, i.e. the frame synchronization estimate
Figure FDA00026345110800000310
Figure FDA00026345110800000311
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