CN115250217B - OFDM system channel estimation method and device assisted by differential coding and neural network - Google Patents

OFDM system channel estimation method and device assisted by differential coding and neural network Download PDF

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CN115250217B
CN115250217B CN202210873315.1A CN202210873315A CN115250217B CN 115250217 B CN115250217 B CN 115250217B CN 202210873315 A CN202210873315 A CN 202210873315A CN 115250217 B CN115250217 B CN 115250217B
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CN115250217A (en
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卿朝进
凌国伟
王莉
胡文权
张岷涛
陈金良
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Xihua University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0202—Channel estimation
    • H04L25/024—Channel estimation channel estimation algorithms
    • H04L25/0254—Channel estimation channel estimation algorithms using neural network algorithms
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0202—Channel estimation
    • H04L25/024—Channel estimation channel estimation algorithms
    • H04L25/0256—Channel estimation using minimum mean square error criteria
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L27/00—Modulated-carrier systems
    • H04L27/26—Systems using multi-frequency codes
    • H04L27/2601—Multicarrier modulation systems
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

Abstract

The invention provides a differential coding and neural network assisted OFDM system channel estimation method and device, comprising the following steps of transmitting end processing: digitally modulating a signal
Figure DDA0003760063180000011
Differential encoding to obtain differential encoded signals
Figure DDA0003760063180000012
And transmitting the differential coded signal c after OFDM modulation; OFDM demodulation is carried out by the receiving end to obtain a receiving signal
Figure DDA0003760063180000013
And the differential decoding mode of character-by-character differential decoding is adopted for the received signal y to obtain a differential decoding signal
Figure DDA0003760063180000014
Data information to be differentially detected
Figure DDA0003760063180000015
Regarding as guidance, performing LS-based channel estimation to obtain initial channel estimation
Figure DDA0003760063180000016
Will be
Figure DDA0003760063180000017
Regarding as initial characteristics, with the assistance of the initial characteristics, constructing a channel estimation enhancement network to estimate the CSI:
Figure DDA0003760063180000018
the invention improves the NMSE performance of the system under the condition of not using second order statistics by means of differential coding and a neural network.

Description

OFDM system channel estimation method and device assisted by differential coding and neural network
Technical Field
The invention relates to the technical field of channel estimation in wireless communication, and discloses a method and a device for estimating an OFDM system channel assisted by differential coding and a neural network.
Background
Orthogonal frequency division multiplexing (orthogonal frequency division multiplexing, OFDM) technology is widely used in communication systems such as fifth-generation mobile communication (5th generation,5G) and the internet of things because of its excellent ability to combat multipath interference. In wireless communication systems, signal detection and data recovery typically require obtaining channel state information (channel state information, CSI). Channel estimation is therefore crucial for OFDM system receivers.
For channel estimation of OFDM systems, pilot-assisted based approaches are one of the most common approaches. Channel estimation using received steering has emerged a series of classical channel estimation methods, such as Least Square (LS), minimum root mean square error (minimum mean square error, MMSE) and linear MMSE channel estimation methods. In recent years, with the development of neural network technology, deep neural networks have been applied to aspects of a wireless communication physical layer. Neural networks exhibit excellent performance in terms of channel estimation.
Although the above method can better estimate the channel parameters, improvements in terms of spectral efficiency and reduced energy consumption are still needed. On the one hand, existing pilot-based channel estimation must allocate additional spectral resources to transmit the pilot, reducing the spectral efficiency for data transmission. In particular, the battery life of terminals (e.g., sensor nodes) of an Internet of things system needs to be as long as more than ten years, for which UE energy consumed by the pilot transmission is a significant waste.
Inspired by differential coding, the invention aims to provide an OFDM system channel estimation method assisted by differential coding and a neural network. Specifically, at the system UE end, the pilot for channel estimation is not transmitted, and only the data information to be transmitted is modulated by differential coding; thereby saving the occupation of spectrum resources and reducing the energy consumption of the UE transmitter during air interface transmission. At the BS end, the differential detection data information is regarded as guidance to carry out channel estimation by utilizing the decision feedback channel estimation idea, so that the CSI information is obtained. On the basis, we construct a neural network; and solving the advantage of the nonlinear problem by utilizing the neural network, capturing the channel characteristics and inhibiting nonlinear superposition interference, thereby improving the performance of channel estimation and signal detection. Numerical simulation experiments show that compared with the traditional method, the method improves the normalized mean square error (normalized root mean square error, NMSE) performance of channel estimation on the basis of improving the frequency spectrum utilization rate and reducing the energy consumption.
Disclosure of Invention
Compared with the traditional channel estimation algorithm, the method improves the NMSE performance of the system by means of differential coding and the neural network under the condition that second-order statistics are not used.
The differential coding assisted machine learning channel state information feedback method and device comprises the following steps:
1. the OFDM system channel estimation method assisted by differential coding and a neural network is characterized by comprising the following steps of:
and (3) transmitting end processing:
s1, modulating signal vector
Figure BDA0003760063160000011
Differential encoding to obtain differential encoded signals
Figure BDA0003760063160000012
And performs OFDM modulation on the differential coded signal c for transmission.
The digital modulation is to digitally modulate bit stream information to form a modulated signal
Figure BDA0003760063160000021
The differential coding is to the modulated signal
Figure BDA0003760063160000022
According to rule c n ＝c n-1 ·a n Differential encoding is carried out to obtain differential encoded signals +.>
Figure BDA0003760063160000023
The differential coded signal c is c 0 Differential encoded signal after=1;
and (3) processing at a receiving end:
s2, the receiving end carries out OFDM demodulation to obtain a receiving signal
Figure BDA0003760063160000024
And differential decoding is carried out on the received signal y in a character-by-character differential decoding mode to obtain a differential decoding signal ++>
Figure BDA0003760063160000025
The differential decoding is realized by solving
Figure BDA0003760063160000026
Obtaining;
wherein ,
Figure BDA0003760063160000027
representing possible attempted values in the constellation set when making decisions on the nth value of the differential detection (or differential decoding); superscript indicates the conjugation taking operation, re [. Cndot.]The representation takes the real part, max [. Cndot.]Representing a maximum value taking operation;
s3, decoding the differential signals
Figure BDA0003760063160000028
Regarding as guidance, performing channel estimation to obtain initial channel state information +.>
Figure BDA0003760063160000029
The guiding is to
Figure BDA00037600631600000210
Differential encoding to +.>
Figure BDA00037600631600000211
The channel estimation is based on the received signal y and
Figure BDA00037600631600000212
obtaining initial channel state information using LS channel estimation
Figure BDA00037600631600000213
S4, will
Figure BDA00037600631600000214
Regarding as initial characteristics, with the aid of the initial characteristics, constructing a channel estimation enhancement network (enhanced channel estimation network, en-CENet) to estimate CSI: />
Figure BDA00037600631600000215
The channel estimation enhancement network En-CENet further includes:
building training data sets
Figure BDA00037600631600000216
Training the channel estimation enhancement network En-CENet to obtain network parameters of the channel estimation enhancement network En-CENet;
the training set
Figure BDA00037600631600000217
Is +.>
Figure BDA00037600631600000218
Performing real value obtaining, namely: />
Figure BDA00037600631600000219
Wherein Re (-) and Im (-) represent the operations of taking the real part and the imaginary part respectively;
the label
Figure BDA00037600631600000220
Real value obtaining is carried out according to the actually measured complex value CSI data;
during online operation, initial channel estimation
Figure BDA00037600631600000221
Inputting the channel estimation enhancement network En-CENet to obtain the channel state information with enhanced accuracy +.>
Figure BDA00037600631600000222
2. The invention provides a differential coding and neural network assisted OFDM system channel estimation method and device, a transmitting end device comprises:
a modulation module for obtaining a modulated signal a according to the design of S1 in claim 1;
a differential encoding module for obtaining a differential encoded signal c according to the design described in S1 of claim 1;
the differential coding module is connected with the modulating signal acquisition module;
the receiving end device comprises:
a differential decoding module for obtaining differential decoded signals according to the design of S2 in claim 1
Figure BDA00037600631600000223
A precision enhancement module for obtaining channel state information with enhanced precision according to the design of S4 in claim 1
Figure BDA00037600631600000224
The modulation module includes:
a modulating unit for digitally modulating the bit stream information to form a modulated signal a;
the precision enhancing module comprises:
initial feature extraction unit for differentially decoding signals
Figure BDA00037600631600000225
Differential encoding is performed again as +.>
Figure BDA00037600631600000226
From the received signals y and->
Figure BDA00037600631600000227
Obtaining channel initial characteristics->
Figure BDA00037600631600000228
Network enhancement unit for inputting initial characteristics
Figure BDA0003760063160000031
Channel estimation enhancement network En-CENet, obtaining channel state information with enhanced accuracy +.>
Figure BDA0003760063160000032
Drawings
FIG. 1 is a schematic flow chart of the present invention;
FIG. 2 is a flow diagram of a sender-side of the present invention;
fig. 3 is a flow diagram of a receiver-side of the present invention.
Detailed Description
The present invention will be described in detail with reference to the following examples and drawings, but it should be understood that the examples and drawings are only for illustrative purposes and are not intended to limit the scope of the present invention in any way. All reasonable variations and combinations that are included within the scope of the inventive concept fall within the scope of the present invention.
Referring to fig. 1, a specific method for estimating an OFDM system channel assisted by differential coding and neural network includes:
a1 Referring to fig. 2, modulated signal vectors
Figure BDA0003760063160000033
Differential encoding is carried out to obtain differential encoded signals +.>
Figure BDA0003760063160000034
And transmitting the differential coded signal c after OFDM modulation;
among them, more specifically some embodiments are as follows:
the digital modulation is to digitally modulate bit stream information to form a modulated signal
Figure BDA0003760063160000035
/>
The differential coding is to the modulated signal
Figure BDA0003760063160000036
According to rule c n ＝c n-1 ·a n Differential encoding is carried out to obtain differential encoded signals +.>
Figure BDA0003760063160000037
c 0 ＝1。
a2 Referring to fig. 3, the receiving end performs OFDM demodulation to obtain a received signal
Figure BDA0003760063160000038
And differential decoding is carried out on the received signal y in a character-by-character differential decoding mode to obtain a differential decoding signal ++>
Figure BDA0003760063160000039
The differential decoding is realized by solving
Figure BDA00037600631600000310
Obtaining;
wherein ,
Figure BDA00037600631600000311
representing possible constellation sets when making decisions on the nth value of differential detection (or differential decoding)Is tried to take the value; superscript indicates the conjugation taking operation, re [. Cndot.]The representation takes the real part, max [. Cndot.]Indicating a maximum value taking operation.
a3 To differentially decode signals
Figure BDA00037600631600000312
Regarding as guidance, performing channel estimation to obtain initial channel state information +.>
Figure BDA00037600631600000313
The guiding is to
Figure BDA00037600631600000314
Differential encoding to +.>
Figure BDA00037600631600000315
The channel estimation is based on the received signal y and
Figure BDA00037600631600000316
obtaining initial channel state information using LS channel estimation
Figure BDA00037600631600000317
a4 Construction of a channel estimation enhancement network En-CENet pair
Figure BDA00037600631600000318
Enhancement processing is performed to obtain channel state information with enhanced accuracy +.>
Figure BDA00037600631600000319
The channel estimation enhancement network En-CENet further includes:
building training data sets
Figure BDA00037600631600000320
Training a channel estimation enhancement network En-CENet to obtain a network of the channel estimation enhancement network En-CENetComplexing parameters;
the training set
Figure BDA00037600631600000321
Is +.>
Figure BDA00037600631600000322
Performing real value obtaining, namely:
Figure BDA00037600631600000323
wherein Re (-) and Im (-) represent the operations of taking the real part and the imaginary part respectively;
the label
Figure BDA00037600631600000324
Real value obtaining is carried out according to the actually measured complex value CSI data;
during online operation, initial channel estimation
Figure BDA00037600631600000325
Inputting the channel estimation enhancement network En-CENet to obtain the channel state information with enhanced accuracy +.>
Figure BDA00037600631600000326
Example 1:
in step a 1), the modulated signal vector a is differentially encoded to obtain a differentially encoded signal c, as follows:
taking QPSK modulation as an example, consider an OFDM system with n=4 subcarriers, then a is:
Figure BDA0003760063160000041
according to the differential coding rule c n ＝c n-1 ·a n The differential encoded signal c can be calculated as:
Figure BDA0003760063160000042
/>
example 2:
in step a 2), the receiving end adopts a character-by-character differential decoding mode to obtain a differential decoding signal
Figure BDA0003760063160000043
One specific example of which is as follows:
assume that a signal is received
Figure BDA0003760063160000044
According to the word-by-word Fu Chafen coding rules
Figure BDA0003760063160000045
Differential decoding the received signal y to obtain differential decoded signal +.>
Figure BDA0003760063160000046
With the second element y in y 2 For example, the specific solving process is as follows:
Figure BDA0003760063160000047
includes->
Figure BDA0003760063160000048
Four constellation points, first constellation point +.>
Figure BDA0003760063160000049
Substituted into->
Figure BDA00037600631600000410
The result obtained is 1;
substituting the second constellation points to the fourth constellation points in sequence to obtain the results of 0,0 and-1 respectively;
the maximum result when substituting the first constellation point is obtained
Figure BDA00037600631600000411
Sequentially solving the rest elements in y according to the same method, and differentially decoding signals
Figure BDA00037600631600000412
The final representation can be:
Figure BDA00037600631600000413
example 3:
in step a 3), the differential decoded signal
Figure BDA00037600631600000414
Taking the pilot as a guide, and performing channel estimation to obtain initial channel state information
Figure BDA00037600631600000415
And constructing a channel estimation enhancement network En-CENet pair +.>
Figure BDA00037600631600000416
Enhancement processing is carried out to obtain channel state information with enhanced precision
Figure BDA00037600631600000417
One specific example is as follows:
let the received signal y be:
y＝[0.5516-0.5087i,0.3023-0.0436i,0.1760-0.3639i,0.3277-0.2575i,0.3883-0.4727i] T
differential decoding of the signal
Figure BDA00037600631600000418
The method comprises the following steps:
Figure BDA00037600631600000419
for a pair of
Figure BDA00037600631600000420
Differential encoding to obtain +.>
Figure BDA00037600631600000421
The method comprises the following steps:
Figure BDA00037600631600000422
based on the received signals y and
Figure BDA00037600631600000423
obtaining initial channel state information using LS channel estimation:
Figure BDA00037600631600000424
example 4:
in step a 4), a channel estimation enhancement network En-CENet pair is constructed
Figure BDA0003760063160000051
Enhancement processing is performed to obtain channel state information with enhanced accuracy +.>
Figure BDA0003760063160000052
One specific example is as follows:
in example 3
Figure BDA0003760063160000053
Inputting the channel estimation enhancement network En-CENet to obtain the channel state information with enhanced accuracy +.>
Figure BDA0003760063160000054
The method comprises the following steps:
Figure BDA0003760063160000055
the corresponding NMSE is:
NMSE＝[2.40×10 -5 ,2.49×10 -5 ,2.54×10 -5 ,1.18×10 -5 ,9.37×10 -6 ]
before network improvement, the corresponding NMSE is:
NMSE＝[2.19,1.69,1.16,7.5×10 -2 ,1.0×10 -2 ]
it should be noted that the embodiments described herein are for the purpose of aiding the reader in understanding the practice of the invention, and it should be understood that the scope of the invention is not limited to such specific statements and embodiments. Those of ordinary skill in the art can make various other specific modifications and combinations from the teachings of the present disclosure without departing from the spirit thereof, and such modifications and combinations remain within the scope of the present disclosure.

Claims (4)

1. The OFDM system channel estimation method assisted by differential coding and a neural network is characterized by comprising the following steps of:
and (3) transmitting end processing:
s1, modulating a digital modulation signal
Figure FDA0004155036350000011
Differential encoding is carried out to obtain differential encoded signals +.>
Figure FDA0004155036350000012
And transmitting the differential coded signal c after OFDM modulation;
the differential coding is to the modulated signal
Figure FDA0004155036350000013
According to rule c n ＝c n-1 ·a n Differential encoding is carried out to obtain differential encoded signals +.>
Figure FDA0004155036350000014
wherein ,c0 ＝1；
And (3) processing at a receiving end:
s2, the receiving end carries out OFDM demodulation to obtain a receiving signal
Figure FDA0004155036350000015
And differential decoding is carried out on the received signal y in a character-by-character differential decoding mode to obtain a differential decoding signal ++>
Figure FDA0004155036350000016
The differential decoding is realized by solving
Figure FDA0004155036350000017
Obtaining;
wherein ,
Figure FDA0004155036350000018
representing possible attempted values in the constellation set when making decisions on the nth value of differential detection or differential decoding; superscript indicates the conjugation taking operation, re [. Cndot.]The representation takes the real part, max [. Cndot.]Representing a maximum value taking operation;
s3, data information of differential detection is obtained
Figure FDA0004155036350000019
Regarding as a pilot, performing LS-based channel estimation to obtain an initial channel estimate +.>
Figure FDA00041550363500000110
The data information to be differentially detected
Figure FDA00041550363500000111
Regarded as guidance, is->
Figure FDA00041550363500000112
Differential encoding to +.>
Figure FDA00041550363500000113
The LS-based channel estimation is based on the received signal y and
Figure FDA00041550363500000114
with LS channel estimation, an estimate of the channel vector can be obtained as +.>
Figure FDA00041550363500000115
S4, will
Figure FDA00041550363500000116
Regarding as initial characteristics, with the assistance of the initial characteristics, constructing a channel estimation enhancement network En-CENet to estimate CSI: />
Figure FDA00041550363500000117
2. The method for channel estimation in an OFDM system with differential coding and neural network assistance as claimed in claim 1, wherein the channel estimation enhancement network En-CENet in step S4 comprises
Building training data sets
Figure FDA00041550363500000118
Training the channel estimation enhancement network En-CENet to obtain network parameters of the channel estimation enhancement network En-CENet;
training set
Figure FDA00041550363500000119
Is +.>
Figure FDA00041550363500000120
Performing real value obtaining, namely:
Figure FDA0004155036350000021
wherein Re (-) and Im (-) represent the operations of taking the real part and the imaginary part respectively;
label (Label)
Figure FDA0004155036350000022
Real value obtaining is carried out according to the actually measured complex value CSI data;
during online operation, initial channel estimation
Figure FDA0004155036350000023
Inputting the channel estimation enhancement network En-CENet to obtain the channel state information with enhanced accuracy +.>
Figure FDA0004155036350000024
/>
3. A differential coding and neural network assisted OFDM system channel estimation apparatus for implementing the method of claims 1 to 2;
the transmitting end device comprises:
a modulation module for obtaining a modulated signal a according to the design of S1 in claim 1;
a differential encoding module for obtaining a differential encoded signal c according to the design described in S1 of claim 1;
the differential coding module is connected after the modulation module;
the modulation module includes:
a modulating unit for digitally modulating the bit stream information to form a modulated signal a;
the receiving end device comprises:
a differential decoding module for obtaining differential decoded signals according to the design of S2 in claim 1
Figure FDA0004155036350000025
The precision enhancement module according to claim 1S4, obtaining channel state information with enhanced precision by the design
Figure FDA0004155036350000026
4. The differential coding and neural network aided OFDM system channel estimation apparatus of claim 3, wherein said accuracy enhancement module comprises:
initial feature extraction unit for differentially decoding signals
Figure FDA0004155036350000027
Differential encoding is performed again as +.>
Figure FDA0004155036350000028
From the received signals y and->
Figure FDA0004155036350000029
Obtaining channel initial characteristics->
Figure FDA00041550363500000210
Network enhancement unit for inputting initial characteristics
Figure FDA00041550363500000211
Channel estimation enhancement network En-CENet, obtaining channel state information with enhanced accuracy +.>
Figure FDA00041550363500000212
/>
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