CN115250217A - Differential coding and neural network assisted OFDM system channel estimation method and device - Google Patents

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

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CN115250217A
CN115250217A CN202210873315.1A CN202210873315A CN115250217A CN 115250217 A CN115250217 A CN 115250217A CN 202210873315 A CN202210873315 A CN 202210873315A CN 115250217 A CN115250217 A CN 115250217A
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CN115250217B (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
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    • 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
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Abstract

The invention provides a differential coding and neural network assisted OFDM system channel estimation method and device, which comprises the following steps: will digital modulation signal
Figure DDA0003760063180000011
Performing differential encoding to obtain differential encoded signal
Figure DDA0003760063180000012
Carrying out OFDM modulation on the differential coding signal c and then transmitting; the receiving end carries out OFDM demodulation to obtain a received signal
Figure DDA0003760063180000013
And obtaining a differential decoded signal by performing symbol-by-symbol differential decoding on the received signal y
Figure DDA0003760063180000014
Data information to be differentially detected
Figure DDA0003760063180000015
As pilot, LS-based channel estimation is performed to obtain an initial channel estimate
Figure DDA0003760063180000016
Will be provided with
Figure DDA0003760063180000017
And (2) as an initial feature, under the assistance of the initial feature, constructing a channel estimation enhancement network to estimate the CSI:
Figure DDA0003760063180000018
the invention improves the NMSE performance of the system by means of differential coding and a neural network under the condition of not using second-order statistics.

Description

Differential coding and neural network assisted OFDM system channel estimation method and device
Technical Field
The invention relates to the technical field of channel estimation in wireless communication, in particular to a differential coding and neural network assisted OFDM system channel estimation method and device.
Background
Orthogonal Frequency Division Multiplexing (OFDM) technology is widely used in communication systems such as fifth generation mobile communication (5 g) and the internet of things due to its excellent capability of resisting multipath interference. In wireless communication systems, signal detection and data recovery typically require obtaining Channel State Information (CSI). Therefore, channel estimation plays a crucial role for OFDM system receivers.
For channel estimation in OFDM systems, the pilot-aided based approach is one of the most common approaches. Using the received pilot for channel estimation, a series of classical channel estimation methods such as Least Square (LS), minimum Mean Square Error (MMSE), and linear MMSE channel estimation methods are emerging. In recent years, with the development of neural network technology, deep neural networks have been applied to aspects of the physical layer of wireless communication. The neural network exhibits excellent performance in terms of channel estimation.
Although the above method can better estimate the channel parameters, improvements in spectral efficiency and energy consumption reduction are still needed. On the one hand, existing pilot-based channel estimation must allocate additional spectral resources to transmit the pilot, reducing spectral efficiency for data transmission. In particular, terminals of the internet of things system (e.g., sensor nodes) require a battery life of up to ten years or more for which UE energy consumed by pilot transmission is particularly wasteful.
Inspired by differential coding, the invention aims to provide a channel estimation method of an OFDM system assisted by differential coding and a neural network. Specifically, at the system UE, no pilot for channel estimation is transmitted, and only the data information to be transmitted is modulated by differential coding; therefore, the occupied frequency spectrum resources are saved, and the energy consumption during the transmission of the UE transmitter air interface is reduced. And at the BS end, the data information detected in the difference mode is taken as guidance to carry out channel estimation by utilizing the thought of judging feedback channel estimation, so that the CSI information is obtained. On the basis, a neural network is constructed; the advantages of solving the nonlinear problem by using the neural network are utilized to capture the channel characteristics and inhibit nonlinear superposition interference, thereby improving the performance of channel estimation and signal detection. Numerical simulation experiments show that compared with the traditional method, the provided method improves the Normalized Mean Square Error (NMSE) performance of channel estimation on the basis of improving the 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 the differential coding and the neural network under the condition of not using second-order statistics.
The machine learning channel state information feedback method and device assisted by differential coding comprises the following steps:
1. the differential coding and neural network assisted OFDM system channel estimation method is characterized by comprising the following steps of:
processing at a transmitting end:
s1. For modulation signal vector
Figure BDA0003760063160000011
Carrying out differential coding to obtain a differential coded signal
Figure BDA0003760063160000012
And transmitting the differential coded signal c after OFDM modulation.
The digital modulation is to carry out digital modulation on 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 Performing differential encoding to obtain differential encoded signal
Figure BDA0003760063160000023
The differential coding signal c is c 0 A differentially encoded signal after = 1;
processing at a receiving end:
s2, the receiving end carries out OFDM demodulation to obtain a receiving signal
Figure BDA0003760063160000024
And obtaining a differential decoded signal by performing symbol-by-symbol differential decoding on the received signal y
Figure BDA0003760063160000025
The differential decoding is realized by solving
Figure BDA0003760063160000026
Obtaining;
wherein ,
Figure BDA0003760063160000027
representing possible attempted values in a constellation set when the nth value of differential detection (or differential decoding) is judged; the superscript denotes taking the conjugate operation, re [. Cndot.)]Representing the real part, max [ ·]Representing a maximum operation;
s3, decoding the differential signal
Figure BDA0003760063160000028
As pilot, performing channel estimation to obtain initial channel state information
Figure BDA0003760063160000029
The guide is to guide
Figure BDA00037600631600000210
Then differentially encoded into
Figure BDA00037600631600000211
The channel estimation is based on the received signals y and
Figure BDA00037600631600000212
obtaining initial channel state information using LS channel estimation
Figure BDA00037600631600000213
S4, mixing
Figure BDA00037600631600000214
As an initial feature, with the assistance of the initial feature, an enhanced channel estimation network (En-CENet) is constructed to estimate CSI:
Figure BDA00037600631600000215
the channel estimation enhancement network En-CENet further comprises:
constructing a training data set
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 to the initial channel estimation
Figure BDA00037600631600000218
Carrying out real-valued obtaining, namely:
Figure BDA00037600631600000219
wherein, re (·), im (·) respectively represents the operation of taking a real part and an imaginary part;
the label
Figure BDA00037600631600000220
Obtaining the actual value of the actually measured complex value CSI data;
in online operation, the initial channel estimation is performed
Figure BDA00037600631600000221
Inputting channel estimation enhancement network En-CENet to obtain channel state information with enhanced precision
Figure BDA00037600631600000222
2. The invention provides a differential coding and neural network assisted OFDM system channel estimation method and device, wherein a transmitting end device comprises:
a modulation module, which obtains 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 of S1 in claim 1;
the differential coding module is connected behind the modulation signal acquisition module;
the receiving end device includes:
differential decoding module for obtaining a differential decoded signal according to the design of S2 in claim 1
Figure BDA00037600631600000223
Precision enhancing module for obtaining precision enhanced channel state information according to the design of S4 in claim 1
Figure BDA00037600631600000224
The modulation module includes:
the modulation unit is used for digitally modulating the bit stream information to form a modulated signal a;
the accuracy enhancement module comprises:
an initial feature extraction unit for differentially decoding the signal
Figure BDA00037600631600000225
Differential encoding is performed again to
Figure BDA00037600631600000226
From the received signal y and
Figure BDA00037600631600000227
obtaining channel initial characteristics
Figure BDA00037600631600000228
A network enhancing unit for inputting the initial characteristics
Figure BDA0003760063160000031
The channel estimation enhances the network En-CENet to obtain the channel state information with enhanced precision
Figure BDA0003760063160000032
Drawings
FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a flow chart of a transmitting end according to the present invention;
fig. 3 is a flow chart of the receiving end according to the present invention.
Detailed Description
The present invention is described in detail below with reference to the following embodiments and the attached drawings, but it should be understood that the embodiments and the attached drawings are only used for the illustrative description of the present invention and do not limit the protection scope of the present invention in any way. All reasonable variations and combinations included within the spirit of the invention are within the scope of the invention.
Referring to fig. 1, the specific differential coding and neural network assisted OFDM system channel estimation method includes:
a1 Radix Ginseng IndiciFIG. 2, for modulated signal vectors
Figure BDA0003760063160000033
Carrying out differential coding to obtain a differential coded signal
Figure BDA0003760063160000034
Carrying out OFDM modulation on the differential coding signal c and then transmitting;
among these, some embodiments are more specifically as follows:
the digital modulation is to carry out digital modulation on bit stream information to form a modulated signal
Figure BDA0003760063160000035
The differential coding is to modulate signals
Figure BDA0003760063160000036
According to rule c n ＝c n-1 ·a n Performing differential encoding to obtain differential encoded signal
Figure BDA0003760063160000037
c 0 ＝1。
a2 Referring to fig. 3, the receiving end performs OFDM demodulation to obtain a received signal
Figure BDA0003760063160000038
And obtaining a differential decoded signal by performing symbol-by-symbol differential decoding on the received signal y
Figure BDA0003760063160000039
The differential decoding is realized by solving
Figure BDA00037600631600000310
Obtaining;
wherein ,
Figure BDA00037600631600000311
indicating differential detection (or)Differential decoding), possible trial values in the constellation set are taken when the nth value of the constellation set is judged; the superscript denotes the taking of the conjugation operation, re [. Cndot.)]Representing the real part, max [. Cndot]Indicating a max operation.
a3 Will differentially decode the signal
Figure BDA00037600631600000312
As pilot, performing channel estimation to obtain initial channel state information
Figure BDA00037600631600000313
The guide is to guide
Figure BDA00037600631600000314
Then differentially encoded into
Figure BDA00037600631600000315
The channel estimation is based on the received signals y and
Figure BDA00037600631600000316
obtaining initial channel state information using LS channel estimation
Figure BDA00037600631600000317
a4 Build a channel estimation enhancement network En-CENet pair
Figure BDA00037600631600000318
Enhancing to obtain channel state information with enhanced precision
Figure BDA00037600631600000319
The channel estimation enhancement network En-CENet further comprises:
constructing a training data set
Figure BDA00037600631600000320
Performing on a channel estimation enhancement network En-CENetTraining to obtain network parameters of a channel estimation enhancement network En-CENet;
the training set
Figure BDA00037600631600000321
Is to the initial channel estimation
Figure BDA00037600631600000322
Carrying out real-valued obtaining, namely:
Figure BDA00037600631600000323
wherein, re (·), im (·) respectively represents the operation of taking a real part and an imaginary part;
the label
Figure BDA00037600631600000324
Obtaining the actual value of the actually measured complex value CSI data;
in online operation, the initial channel estimation is performed
Figure BDA00037600631600000325
Inputting channel estimation enhancement network En-CENet to obtain channel state information with enhanced precision
Figure BDA00037600631600000326
Example 1:
in step a 1), a specific embodiment of obtaining a differential coded signal c by performing differential coding on the modulation signal vector a is as follows:
taking QPSK modulation as an example, considering an OFDM system with N =4 subcarriers, a is:
Figure BDA0003760063160000041
according to the differential coding rule c n ＝c n-1 ·a n The differential encoding signal c can be calculated as:
Figure BDA0003760063160000042
example 2:
in step a 2), the receiving end obtains a differential decoding signal by adopting a character-by-character differential decoding mode for the received signal y
Figure BDA0003760063160000043
One specific example of (a) is as follows:
assuming a received signal
Figure BDA0003760063160000044
According to character-by-character differential decoding rules
Figure BDA0003760063160000045
Differentially decoding the received signal y to obtain a differentially decoded signal
Figure BDA0003760063160000046
With the second element y in y 2 For example, the specific solving process is as follows:
Figure BDA0003760063160000047
comprises
Figure BDA0003760063160000048
Four constellation points, first the first one
Figure BDA0003760063160000049
Substitution into
Figure BDA00037600631600000410
In (1), the result is obtained;
then, substituting the second to the four constellation points in sequence to obtain results of 0,0 and-1 respectively;
the result obtained when substituting the first constellation point is maximum, then the result is obtained
Figure BDA00037600631600000411
Solving the residual elements in the y in turn according to the same method, and then differentially decoding the signal
Figure BDA00037600631600000412
The final can be expressed as:
Figure BDA00037600631600000413
example 3:
in step a 3), the differential decoding signal is transmitted
Figure BDA00037600631600000414
As guidance, performing channel estimation to obtain initial channel state information
Figure BDA00037600631600000415
And constructing a channel estimation enhancement network En-CENet pair
Figure BDA00037600631600000416
Enhancing to obtain channel state information with enhanced precision
Figure BDA00037600631600000417
One specific embodiment is as follows:
assume that the received signal y is:
y＝[0.5516-0.5087i,0.3023-0.0436i,0.1760-0.3639i,0.3277-0.2575i,0.3883-0.4727i] T
then differentially decode the signal
Figure BDA00037600631600000418
Comprises the following steps:
Figure BDA00037600631600000419
to pair
Figure BDA00037600631600000420
Then differential coding is carried out to obtain
Figure BDA00037600631600000421
Comprises the following steps:
Figure BDA00037600631600000422
from the received signals y and
Figure BDA00037600631600000423
using LS channel estimation to obtain initial channel state information:
Figure BDA00037600631600000424
example 4:
in the step a 4), a channel estimation enhancement network En-CENet pair is constructed
Figure BDA0003760063160000051
Enhancing to obtain channel state information with enhanced precision
Figure BDA0003760063160000052
One specific embodiment is as follows:
will be that of example 3
Figure BDA0003760063160000053
Inputting the channel estimation enhancement network En-CENet to obtain the channel state information with enhanced precision
Figure BDA0003760063160000054
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 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 (6)

1. The differential coding and neural network assisted OFDM system channel estimation method is characterized by comprising the following steps of:
processing at a transmitting end:
s1. Modulating a signal with a digital signal
Figure FDA0003760063150000011
Carrying out differential coding to obtain a differential coded signal
Figure FDA0003760063150000012
Carrying out OFDM modulation on the differential coded signal c and then transmitting;
the differential coding is to the modulated signal
Figure FDA0003760063150000013
According to rule c n ＝c n-1 ·a n Performing differential encoding to obtain differential encoded signal
Figure FDA0003760063150000014
wherein ,c0 ＝1；
Processing at a receiving end:
s2, the receiving end carries out OFDM demodulation to obtain a received signal
Figure FDA0003760063150000015
And obtaining a differential decoded signal by performing symbol-by-symbol differential decoding on the received signal y
Figure FDA0003760063150000016
The differential decoding is realized by solving
Figure FDA0003760063150000017
Obtaining;
wherein ,
Figure FDA0003760063150000018
representing possible attempted values in a constellation set when the nth value of differential detection (or differential decoding) is judged; the superscript denotes taking the conjugate operation, re [. Cndot.)]Representing the real part, max [. Cndot]Representing a maximum value operation;
s3, data information of differential detection
Figure FDA0003760063150000019
As pilot, LS-based channel estimation is performed to obtain an initial channel estimate
Figure FDA00037600631500000110
The data information to be detected differentially
Figure FDA00037600631500000111
Regarded as guidance, are
Figure FDA00037600631500000112
Then differentially encoded into
Figure FDA00037600631500000113
The LS-based channel estimation is based on the sum of the received signals y
Figure FDA00037600631500000114
Using LS channel estimation, an estimated value of the channel vector is obtained as
Figure FDA00037600631500000115
S4, mixing
Figure FDA00037600631500000116
Regarding as an initial feature, under the assistance of the initial feature, an enhanced channel estimation network (En-CENet) is constructed to estimate CSI:
Figure FDA00037600631500000117
2. the method as claimed in claim 1, wherein the channel estimation enhancement network En-CENet of step S4 comprises
Constructing a training data set
Figure FDA00037600631500000118
Training the channel estimation enhancement network En-CENet to obtain network parameters of the channel estimation enhancement network En-CENet;
the training set
Figure FDA00037600631500000119
Is to the initial channel estimation
Figure FDA00037600631500000120
And (3) carrying out real value obtaining, namely:
Figure FDA00037600631500000121
wherein, re (·), im (·) respectively represents the operation of taking a real part and an imaginary part;
the label
Figure FDA00037600631500000122
Obtaining the actual value of the actually measured complex value CSI data;
on-line operation, the initial channel is estimated
Figure FDA0003760063150000021
Inputting channel estimation enhancement network En-CENet to obtain channel state information with enhanced precision
Figure FDA0003760063150000022
3. The OFDM system channel estimation device assisted by differential coding and neural network is characterized in that the transmitting terminal device comprises:
a modulation module, which obtains 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 of S1 in claim 1;
the differential coding module is connected behind the modulation module.
4. The OFDM system channel estimation device with differential coding and neural network assistance as claimed in claim 3, wherein the modulation module comprises:
and a modulation unit for digitally modulating the bit stream information to form a modulated signal a.
5. The OFDM system channel estimation device with differential coding and neural network assistance as claimed in claim 3, wherein the receiving end device comprises:
differential decoding module for obtaining a differential decoded signal according to the design of S2 in claim 1
Figure FDA0003760063150000023
Precision enhancement module for obtaining precision-enhanced channel state information according to the design of S4 in claim 1
Figure FDA0003760063150000024
6. The differential coding and neural network assisted OFDM system channel estimation device of claim 5, wherein said precision enhancement module comprises:
an initial feature extraction unit for differentially decoding the signals
Figure FDA0003760063150000025
Differential encoding is performed again to
Figure FDA0003760063150000026
From the received signal y and
Figure FDA0003760063150000027
obtaining channel initial characteristics
Figure FDA0003760063150000028
A network enhancing unit for inputting the initial characteristics
Figure FDA0003760063150000029
The channel estimation enhances the network En-CENet to obtain the channel state information with enhanced precision
Figure FDA00037600631500000210
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