CN110691049A - Large-scale MIMO system channel prediction method under frequency division duplex mode - Google Patents

Large-scale MIMO system channel prediction method under frequency division duplex mode Download PDF

Info

Publication number
CN110691049A
CN110691049A CN201911052517.4A CN201911052517A CN110691049A CN 110691049 A CN110691049 A CN 110691049A CN 201911052517 A CN201911052517 A CN 201911052517A CN 110691049 A CN110691049 A CN 110691049A
Authority
CN
China
Prior art keywords
channel
fading coefficient
prediction
uplink
downlink
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN201911052517.4A
Other languages
Chinese (zh)
Inventor
彭薇
李文刚
谢一梅
江涛
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Huazhong University of Science and Technology
Original Assignee
Huazhong University of Science and Technology
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Huazhong University of Science and Technology filed Critical Huazhong University of Science and Technology
Priority to CN201911052517.4A priority Critical patent/CN110691049A/en
Publication of CN110691049A publication Critical patent/CN110691049A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • 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
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0413—MIMO systems
    • H04B7/0452—Multi-user MIMO systems

Abstract

The invention discloses a large-scale MIMO system channel prediction method under a frequency division duplex mode, which comprises the following steps: s1, in the uplink, based on the time-varying characteristic of the channel, predicting different channel parameters by adopting different prediction methods, thereby obtaining the multipath arrival angle, time delay and fading coefficient of the uplink channel at each moment; and S2, reconstructing the response of the downlink channel at the corresponding moment according to the multipath arrival angle, the time delay, the fading coefficient and the carrier frequency of the downlink channel at each moment of the obtained uplink channel in the downlink based on the reciprocity of the physical paths of the uplink channel and the downlink channel, thereby realizing the prediction of the downlink channel. The invention can realize the prediction of the downlink channel without sending a pilot frequency sequence in the downlink transmission process based on the reciprocity of physical paths of the uplink channel and the downlink channel according to the obtained parameters of the uplink channel, thereby greatly reducing the expenses of the pilot frequency and the frequency spectrum under the non-steady environment and having higher prediction accuracy.

Description

Large-scale MIMO system channel prediction method under frequency division duplex mode
Technical Field
The invention belongs to the technical field of large-scale MIMO signal processing, and particularly relates to a large-scale MIMO system channel prediction method in a frequency division duplex mode.
Background
The massive multiple-input multiple-output (MIMO) technology is one of the key technologies in the fifth generation mobile communication system due to its advantages of higher spectrum energy efficiency and link reliability. In order to take advantage of many potential advantages of massive MIMO technology, it is essential for a Base Station (BS) to acquire accurate Channel State Information (CSI).
The large-scale MIMO system generally assumes to work in a Time Division Duplex (TDD) mode, and avoids a huge pilot overhead required for downlink channel estimation by using reciprocity of uplink and downlink channel responses. While applying massive MIMO technology to a Frequency Division Duplex (FDD) system can obtain better transmission rate and link reliability. However, in FDD mode, uplink and downlink data transmission occupy different frequency bands, and since the steering matrix is related to frequency and arrival angle, and uplink and downlink channel responses are not directly reciprocal, the conventional downlink channel estimation scheme needs to acquire channel state information at the user side, and generates huge pilot overhead, which seriously affects the performance of the system. Therefore, research on downlink channel estimation focuses on solving the problem of huge pilot frequency overhead, some researches propose that pilot frequency signals are transmitted on only a part of antennas of a base station by using spatial correlation so as to save the pilot frequency overhead, and some researches design sparse pilot frequency, and channel estimation is performed by a compressed sensing method so as to reduce the pilot frequency overhead. Under the assumption that the spatial correlation or sparsity of the channel is known a priori, the method can actually reduce the pilot overhead to a certain extent, however, the assumptions are not reasonable in the actual environment, because the channel is usually fast and time-varying due to the fast movement of the user, thereby destroying the spatial correlation and sparsity, resulting in that when the channel parameters, especially fading coefficients, obtained by the method are fast and too long, the accuracy of channel prediction is low, and the pilot overhead cannot be reduced in the non-stationary environment. Therefore, in order to obtain more accurate channel state information, the pilot overhead in the actual system cannot be reduced according to the above method, so that the above method cannot really achieve the purpose of saving the pilot.
In summary, it is an urgent need to solve the problem of providing a channel prediction method with low pilot overhead and high accuracy in a non-stationary environment for a large-scale MIMO system channel operating in an FDD mode.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide a large-scale MIMO system channel prediction method in a frequency division duplex mode, and aims to solve the problem that the prior art cannot adapt to a non-stationary environment due to specific channel assumption, so that the pilot frequency overhead cannot be reduced.
In order to achieve the above object, the present invention provides a large-scale MIMO system channel prediction method in a frequency division duplex mode, comprising the following steps:
s1, in the uplink, based on the time-varying characteristic of the channel, predicting different channel parameters by adopting different prediction methods, thereby obtaining the multipath arrival angle, time delay and fading coefficient of the uplink channel at each moment;
and S2, reconstructing the response of the downlink channel at the corresponding moment according to the multipath arrival angle, the time delay, the fading coefficient and the carrier frequency of the downlink channel at each moment of the obtained uplink channel in the downlink based on the reciprocity of the physical paths of the uplink channel and the downlink channel, thereby realizing the prediction of the downlink channel.
Further preferably, each time the multipath angle of arrival, the time delay and the fading coefficient of an uplink channel are obtained, the downlink channel at the corresponding time is predicted in real time according to the method described in step S2.
Further preferably, the method of step S1 includes the steps of:
s11, the user sends the initial pilot frequency;
s12, the base station estimates the multipath arrival angle, the time delay and the fading coefficient of the initial pilot frequency at each moment according to the received initial pilot frequency, wherein the multipath arrival angle and the time delay are slowly changed in the channel and are kept unchanged in the subsequent steps and are equal to the multipath arrival angle and the time delay of the initial pilot frequency at the last moment;
s13, the user sends the tracking pilot frequency;
s14, the base station tracks the fast-changing fading coefficient according to the multipath fading coefficient of the previous moment to obtain the multipath fading coefficient vector of each moment of the tracking pilot frequency;
s15, expanding the fading coefficient of each path by adopting a first-order Taylor expansion, and solving the coefficient in the first-order Taylor expansion by adopting a linear regression method according to the obtained multipath fading coefficient vector of each moment of the tracking pilot frequency band to obtain a fading coefficient prediction model on each path;
s16, determining an effective prediction interval after pilot frequency tracking according to the obtained fading coefficient prediction model;
s17, the user sends uplink data;
s18, in the effective prediction interval of the fading coefficient, the base station calculates the multipath fading coefficient at each moment based on the obtained fading coefficient prediction model;
and S19, repeating the steps S13-S18 and continuously predicting the uplink channel.
Further preferably, the multipath fading coefficient vector is composed of fading coefficients of each path, and when a certain path disappears, the corresponding fading coefficient is 0.
Further preferably, the effective prediction interval is determined by the maximum doppler shift and the direction of the user movement, and the faster the user moves toward the scatterer, the smaller the effective prediction interval.
Further preferably, the effective prediction interval NIEPThe expression of (a) is:
Figure BDA0002255667330000031
Figure BDA0002255667330000032
wherein the coefficients
Figure BDA0002255667330000033
E is the preset threshold value, and the value is,
Figure BDA0002255667330000034
is n thpThe power of the strip path is such that,
Figure BDA0002255667330000035
for maximum Doppler shift, v is the user's velocity of movement, c is the speed of light, fupFor the frequency of the uplink carrier wave,
Figure BDA0002255667330000036
is n thpAngle between user moving direction and base station scatterer direction on a single path, NpThe number of multipath.
Further preferably, the reconstructed downlink channel response h (t) is:
Figure BDA0002255667330000041
wherein N ispThe number of the pieces of the multi-path,
Figure BDA0002255667330000042
and τpFor the n-th prediction in the uplink channelpThe fading coefficients and the time delays of the strip paths,for the nth channel in the downlink channelpA steering vector of the strip path.
Further preferably, the nth channel in the downlink channelpGuide vector of strip path
Figure BDA0002255667330000044
Wherein N isaThe number of base station antennas is the number of base station antennas,
Figure BDA0002255667330000045
for the nth channel in the downlink channelpAdjacent antenna of base station on strip path with transmitting angle
Figure BDA0002255667330000046
Phase difference, λ, of the emitted signalsdownIs the carrier frequency of the downlink, l is the distance between adjacent antennas on the base station,
Figure BDA0002255667330000047
for the nth channel in the uplink channelpAngle of arrival of the strip path.
Further preferably, the channel prediction method of the massive MIMO system in the frequency division duplex mode provided by the invention is applied to the technical field of massive MIMO signal processing in the frequency division duplex mode.
Through the technical scheme, compared with the prior art, the invention can obtain the following beneficial effects:
1. the invention has proposed the channel prediction method of MIMO system of a large scale under the frequency division duplex mode, in the up-link at first, on the basis of the time varying characteristic of the signal channel, adopt different prediction methods to predict different channel parameters, thus receive multipath arrival angle, time delay and fading coefficient on every moment of the up-link signal channel; then based on reciprocity of physical paths of the uplink channel and the downlink channel, reconstructing response of the downlink channel according to the obtained multipath arrival angle, time delay and channel fading coefficient of the uplink channel, thereby realizing prediction of the downlink channel. The channel under the non-stationary environment can be predicted in the effective prediction interval by establishing a fading coefficient prediction model in the uplink, pilot frequency transmission is not needed in the prediction process in the whole effective prediction interval, and compared with the existing method for retransmitting the pilot frequency in a short time when accurate channel parameters are obtained, the pilot frequency overhead is saved. Meanwhile, based on reciprocity of physical paths of an uplink channel and a downlink channel, according to a prediction result of the uplink channel, the downlink channel can be predicted without sending a pilot frequency sequence in a downlink transmission process, pilot frequency overhead in a non-stable environment is greatly reduced, and the frequency spectrum utilization rate of the system is high.
2. The large-scale MIMO system channel prediction method under the frequency division duplex mode provided by the invention can obtain a good fading coefficient prediction value in the effective prediction interval by determining the effective prediction interval of the fading coefficient prediction method, and the channel prediction accuracy is higher.
3. According to the channel prediction method of the large-scale MIMO system in the frequency division duplex mode, the channel parameters are separated and processed in an uplink according to the time-varying characteristics of the channel parameters, and the calculation complexity of the predicted channel is greatly reduced. The fast-changing channel fading coefficient is predicted through a tracking algorithm, and the channel change under the non-stable environment is effectively adapted.
Drawings
Fig. 1 is a flowchart of a channel prediction method of a massive MIMO system in a frequency division duplex mode according to the present invention;
fig. 2 is a schematic diagram of uplink data and downlink data transmission provided by the present invention;
fig. 3 is a comparison graph of error rate curves obtained by symbol detection using perfect channel information and the prediction results of the uplink and downlink channels obtained by the present invention, respectively.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
In order to achieve the above object, the present invention provides a method for predicting a channel of a massive MIMO system in a frequency division duplex mode, as shown in fig. 1, comprising the following steps:
s1, in the uplink, based on the time-varying characteristic of the channel, predicting different channel parameters by adopting different prediction methods, thereby obtaining the multipath arrival angle, time delay and fading coefficient of the uplink channel at each moment;
specifically, the method comprises the following steps:
s11, the user sends the initial pilot frequency;
s12, the base station estimates the multipath arrival angle, the time delay and the fading coefficient of the initial pilot frequency at each moment according to the received initial pilot frequency, wherein the multipath arrival angle and the time delay are slowly changed in the channel and are kept unchanged in the subsequent steps and are equal to the multipath arrival angle and the time delay of the initial pilot frequency at the last moment;
specifically, for a multi-user MIMO system, it is generally assumed that a user is far away from a base station, and as the user moves, an arrival angle of the base station changes slowly and a time delay also changes slowly, so in this process, the arrival angle and the time delay of each path can be considered to be quasi-static and remain unchanged.
Specifically, in this embodiment, the base station estimates, according to the received initial pilot frequency, a steering matrix, a delay matrix, and a fading coefficient matrix of each time of the initial pilot frequency in the uplink multipath channel by using a factor analysis method and an ALS algorithm, and extracts a multipath arrival angle, a delay, and a fading coefficient of each time of the initial pilot frequency from the three matrices, respectively. Taking the last moment of the initial pilot frequency as an example, respectively taking A as a guide matrix, a delay matrix and a fading coefficient matrix of the last moment of the initial pilot frequencyupC and F.
Specifically, the steering matrix A of the final time of the initial pilot frequencyupIs represented as follows:
Figure BDA0002255667330000061
wherein the content of the first and second substances,
Figure BDA0002255667330000062
indicating the nth on the uplinkpAdjacent antenna of base station on strip path with transmitting angle
Figure BDA0002255667330000063
The phase difference of the transmitted signals, l is the antenna spacing,
Figure BDA0002255667330000064
is the n-thpAngle of arrival, λ, of the strip pathupIs the wavelength, N, corresponding to the carrier frequency of the uplink channelpNumber of multipath, NaThe number of base station antennas.
For steering matrix AupN of (2)pColumn, n-thpThe signals of the strip path arrive at the adjacentPhase difference between antennas:
Figure BDA0002255667330000065
then n ispThe angle of arrival on the strip path is estimated as:
Figure BDA0002255667330000071
specifically, the steering matrix C of the initial pilot last time is represented as follows:
Figure BDA0002255667330000072
wherein the content of the first and second substances,
Figure BDA0002255667330000073
Figure BDA0002255667330000074
is n thpThe latency of the stripe path.
The nth can be known from the steering matrix CpTime delay of strip pathWherein the content of the first and second substances,
Figure BDA0002255667330000076
for the fading coefficient, the multipath fading coefficient directly corresponds to each element matrix in the fading coefficient matrix obtained by estimation and can be directly obtained.
S13, the user sends the tracking pilot frequency;
s14, the base station tracks the fast-changing fading coefficient according to the multipath fading coefficient of the previous moment to obtain the multipath fading coefficient vector of each moment of the tracking pilot frequency;
specifically, in this embodiment, an RLS algorithm is used to track a fast-changing multipath fading coefficient in an uplink channel, so as to obtain a multipath fading coefficient at each time of tracking pilot frequency.
S15, expanding the fading coefficient of each path by adopting a first-order Taylor expansion, and solving the coefficient in the first-order Taylor expansion by adopting a linear regression method according to the obtained multipath fading coefficient vector of each moment of the tracking pilot frequency band to obtain a fading coefficient prediction model on each path;
specifically, the first-order taylor expansion is adopted to expand the fading coefficient on a certain path to obtain the fading coefficient expression as follows:
f(t)＝f(t0)+f'(t0)(t-t0)+ωTIT(t)
wherein, t0For the first moment, f' (t), of the tracking pilot received by the base station0) Is t at the fading coefficient0The first derivative of the signal is a derivative of,
Figure BDA0002255667330000081
is an additive error.
The obtained fading coefficient of each moment of the tracking pilot frequency band
Figure BDA0002255667330000082
Substituting the expression of the fading coefficient into the expression of the fading coefficient, and solving by adopting a linear regression method to obtain a coefficient f' (t) in the expression of the fading coefficient0) And f (t)0) Respectively is as follows:
Figure BDA0002255667330000084
wherein N is the tracking pilot symbol length,
Figure BDA0002255667330000085
is tiEstimation of the fading coefficient at the sampling instant gi＝ti-t0，i＝0，…，N-1。
S16, determining an effective prediction interval after pilot frequency tracking according to the obtained fading coefficient prediction model;
specifically, as the user moves, the channel is time-varying, when the first derivative of the fading coefficient is smaller than a certain threshold e within a period of time, the multipath fading coefficient can be effectively predicted within the time interval, and the prediction accuracy of the multipath fading coefficient is ensured by calculating the effective prediction interval.
Specifically, the nthpThe fading coefficient of a strip path is defined as
Figure BDA0002255667330000086
Wherein
Figure BDA0002255667330000087
Is the n-thpPower of the strip path, phi0Is the initial phase of the signal and,
Figure BDA0002255667330000088
for maximum Doppler shift, v is the user's velocity of movement, c is the speed of light, fupFor the frequency of the uplink carrier wave,
Figure BDA0002255667330000089
is the n-thpThe angle between the direction of user movement and the scatterer direction of the base station on a single path.
In the effective prediction interval, when the first derivative of the fading coefficient in the fading coefficient prediction model is smaller than a certain threshold belonging to the same category, the accuracy of the fading coefficient prediction in the effective prediction interval is ensured. Specifically, will
Figure BDA00022556673300000810
Bringing in
Figure BDA0002255667330000091
The effective prediction interval N can be obtained by calculationIEPThe expression of (a) is:
Figure BDA0002255667330000092
Figure BDA0002255667330000093
wherein the coefficients
Figure BDA0002255667330000094
E is the preset threshold value, and the value is,
Figure BDA0002255667330000095
is n thpThe power of the strip path is such that,
Figure BDA0002255667330000096
for maximum Doppler shift, v is the user's velocity of movement, c is the speed of light, fupFor the frequency of the uplink carrier wave,
Figure BDA0002255667330000097
is n thpAngle between user moving direction and base station scatterer direction on a single path, NpThe number of multipath. Specifically, in this embodiment, the value of the preset threshold e is 0.15.
The effective prediction interval is determined by the maximum doppler shift and the direction of the user movement, and the faster the user moves towards the scatterer, the smaller the effective prediction interval.
S17, the user sends uplink data;
s18, in the effective prediction interval of the fading coefficient, the base station calculates the multipath fading coefficient at each moment based on the obtained fading coefficient prediction model;
and S19, repeating the steps S13-S18 and continuously predicting the uplink channel.
And S2, reconstructing the response of the downlink channel at the corresponding moment according to the multipath arrival angle, the time delay, the fading coefficient and the carrier frequency of the downlink channel at each moment of the obtained uplink channel in the downlink based on the reciprocity of the physical paths of the uplink channel and the downlink channel, thereby realizing the prediction of the downlink channel.
Specifically, in order to reconstruct the downlink channel response, it is necessary toA steering matrix corresponding to the downlink carrier frequency is calculated. The steering matrix is observed to be related to the angle of arrival and the carrier frequency. As the physical paths of the uplink and downlink channels have reciprocity in a short time, the arrival angle, the attenuation coefficient and the time delay of the uplink and downlink channels have reciprocity, and the guide matrix A of the downlink channel is reconstructeddownComprises the following steps:
Figure BDA0002255667330000101
wherein the content of the first and second substances,
Figure BDA0002255667330000102
for the nth channel in the downlink channelpAdjacent antenna of base station on strip path with transmitting angle
Figure BDA0002255667330000103
Phase difference, λ, of the emitted signalsdownIs the carrier frequency of the downlink channel, l is the distance between adjacent antennas on the base station,for the nth channel in the uplink channelpAngle of arrival of the strip path.
Thus reconstructing the resulting nthpThe downlink channel response h (t) of each path is:
Figure BDA0002255667330000105
wherein N ispThe number of the pieces of the multi-path,
Figure BDA0002255667330000106
for the nth channel in the downlink channelpSteering vectors for strip paths, i.e. steering matrix A for downlink channelsdownThe column vector of (a) is,
Figure BDA0002255667330000107
and τpFor the n-th prediction in the uplink channelpFading coefficients and delays of the strip paths.
Specifically, as shown in fig. 2, the uplink data and downlink data transmission diagram provided by the present invention is shown, as can be seen from the diagram, the transmission in the uplink includes three stages, which are an initialization pilot transmission stage, a tracking pilot transmission stage and an uplink data transmission stage in sequence, while the downlink includes only a downlink data transmission stage, the transmission times in the process of transmitting the uplink and downlink channel data are in one-to-one correspondence, and at each time, the arrival angle, the time delay and the channel fading coefficient of the uplink channel are predicted, that is, the downlink channel at the corresponding time can be reconstructed, thereby completing the prediction of the downlink channel at the same time. As shown in fig. 3, under different signal-to-noise ratios, the perfect channel information and the prediction results of the uplink and downlink channels obtained by the present invention are respectively adopted to perform symbol detection, and the error rate is calculated, and the error rate curve is obtained as a comparison graph. It can be seen from the figure that the error rate curves of the symbol detection performed on the uplink and downlink channel prediction results obtained by the method provided by the invention are both very close to the error rate curve of the symbol detection performed on the basis of perfect channel information, and the accuracy of channel prediction is higher.
In summary, the present invention provides a large-scale MIMO system channel prediction method in a frequency division duplex mode, which can predict a channel in a non-stationary environment in an effective prediction interval by establishing a fading coefficient prediction model in an uplink, wherein pilot frequency transmission is not required in the prediction process in the entire effective prediction interval, and pilot frequency overhead is saved compared with the existing method that pilot frequency needs to be retransmitted in a shorter time to obtain accurate channel parameters. Meanwhile, based on reciprocity of physical paths of an uplink channel and a downlink channel, according to a prediction result of the uplink channel, the downlink channel can be predicted without sending a pilot frequency sequence in a downlink transmission process, pilot frequency overhead in a non-stable environment is greatly reduced, and the frequency spectrum utilization rate of the system is high.
It will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.

Claims (9)

1. A large-scale MIMO system channel prediction method under a frequency division duplex mode is characterized by comprising the following steps:
s1, in the uplink, based on the time-varying characteristic of the channel, predicting different channel parameters by adopting different prediction methods, thereby obtaining the multipath arrival angle, time delay and fading coefficient of the uplink channel at each moment;
and S2, reconstructing the response of the downlink channel at the corresponding moment according to the multipath arrival angle, the time delay, the fading coefficient and the carrier frequency of the downlink channel at each moment of the obtained uplink channel in the downlink based on the reciprocity of the physical paths of the uplink channel and the downlink channel, thereby realizing the prediction of the downlink channel.
2. The method for predicting the channel of the massive MIMO system under the FDD mode as recited in claim 1, wherein each time the multipath angle of arrival, the time delay and the fading coefficient of an uplink channel are obtained, the downlink channel at the corresponding time is predicted in real time according to the method of step S2.
3. The method of claim 1, wherein the step S1 comprises the steps of:
s11, the user sends the initial pilot frequency;
s12, the base station estimates the multipath arrival angle, the time delay and the fading coefficient of the initial pilot frequency at each moment according to the received initial pilot frequency, wherein the multipath arrival angle and the time delay are slowly changed in the channel and are kept unchanged in the subsequent steps and are equal to the multipath arrival angle and the time delay of the initial pilot frequency at the last moment;
s13, the user sends the tracking pilot frequency;
s14, the base station tracks the fast-changing fading coefficient according to the multipath fading coefficient of the previous moment to obtain the multipath fading coefficient vector of each moment of the tracking pilot frequency;
s15, expanding the fading coefficient of each path by adopting a first-order Taylor expansion, and solving the coefficient in the first-order Taylor expansion by adopting a linear regression method according to the obtained multipath fading coefficient vector of each moment of the tracking pilot frequency band to obtain a fading coefficient prediction model on each path;
s16, determining an effective prediction interval after pilot frequency tracking according to the obtained fading coefficient prediction model;
s17, the user sends uplink data;
s18, in the effective prediction interval of the fading coefficient, the base station calculates the multipath fading coefficient at each moment based on the obtained fading coefficient prediction model;
and S19, repeating the steps S13-S18 and continuously predicting the uplink channel.
4. The method as claimed in claim 3, wherein the multi-path fading coefficient vector is composed of fading coefficients of each path, and when a path disappears, the corresponding fading coefficient is 0.
5. The method as claimed in claim 3, wherein the effective prediction interval is determined by the maximum doppler shift and the direction of the user's movement, and the faster the user moves towards the scatterer, the smaller the effective prediction interval.
6. The method as claimed in claim 5, wherein the effective prediction interval N is a prediction interval of the MIMO system channelIEPThe expression of (a) is:
Figure FDA0002255667320000021
wherein the coefficientsE is the preset threshold value, and the value is,is n thpThe power of the strip path is such that,
Figure FDA0002255667320000025
for maximum Doppler shift, v is the user's velocity of movement, c is the speed of light, fupFor the frequency of the uplink carrier wave,
Figure FDA0002255667320000026
is n thpAngle between user moving direction and base station scatterer direction on a single path, NpThe number of multipath.
7. The method of claim 1, wherein the downlink channel response h (t) obtained by reconstructing is:
Figure FDA0002255667320000027
wherein N ispThe number of the pieces of the multi-path,
Figure FDA0002255667320000028
and τpFor the n-th prediction in the uplink channelpThe fading coefficients and the time delays of the strip paths,
Figure FDA0002255667320000031
for the nth channel in the downlink channelpA steering vector of the strip path.
8. The method as claimed in claim 7, wherein the channel prediction method for massive MIMO system in FDD mode is characterized in that the nth channel in downlink channelpGuide vector of strip pathWherein N isaThe number of base station antennas is the number of base station antennas,
Figure FDA0002255667320000033
for the nth channel in the downlink channelpAdjacent antenna of base station on strip path with transmitting angle
Figure FDA0002255667320000034
Phase difference, λ, of the emitted signalsdownIs the carrier frequency of the downlink, l is the distance between adjacent antennas on the base station,for the nth channel in the uplink channelpAngle of arrival of the strip path.
9. The method of claim 1, wherein the method is applied to the technical field of massive MIMO signal processing in the frequency division duplex mode.
CN201911052517.4A 2019-10-31 2019-10-31 Large-scale MIMO system channel prediction method under frequency division duplex mode Pending CN110691049A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201911052517.4A CN110691049A (en) 2019-10-31 2019-10-31 Large-scale MIMO system channel prediction method under frequency division duplex mode

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201911052517.4A CN110691049A (en) 2019-10-31 2019-10-31 Large-scale MIMO system channel prediction method under frequency division duplex mode

Publications (1)

Publication Number Publication Date
CN110691049A true CN110691049A (en) 2020-01-14

Family

ID=69115127

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201911052517.4A Pending CN110691049A (en) 2019-10-31 2019-10-31 Large-scale MIMO system channel prediction method under frequency division duplex mode

Country Status (1)

Country Link
CN (1) CN110691049A (en)

Cited By (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111817757A (en) * 2020-06-08 2020-10-23 武汉大学 Channel prediction method and system for MIMO wireless communication system
WO2021008255A1 (en) * 2019-07-16 2021-01-21 中兴通讯股份有限公司 Method and apparatus for eliminating multipath interference, and computer-readable storage medium
CN112702092A (en) * 2020-12-22 2021-04-23 杭州电子科技大学 Channel estimation method in FDD downlink multi-user large-scale MIMO system
CN113472396A (en) * 2020-03-30 2021-10-01 北京大学 Uplink space division multiple access system based on Doppler effect and implementation method
CN113595666A (en) * 2021-07-13 2021-11-02 华中科技大学 Channel prediction method and system of large-scale MIMO system based on time-frequency joint correlation
WO2021238850A1 (en) * 2020-05-28 2021-12-02 维沃移动通信有限公司 Channel information processing method and apparatus
CN113890583A (en) * 2021-11-12 2022-01-04 北京理工大学 Millimeter wave and submillimeter wave frequency band asymmetric channel parameter prediction method
CN114448474A (en) * 2020-11-05 2022-05-06 中国移动通信集团设计院有限公司 Multi-antenna channel matrix prediction method and device and electronic equipment
CN114448535A (en) * 2022-01-21 2022-05-06 华中科技大学 Channel prediction method based on FDD partial reciprocity, network side equipment and terminal
CN116707687A (en) * 2023-06-27 2023-09-05 南京盛航海运股份有限公司 Channel prediction method for de-cellular large-scale MIMO system

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102215186A (en) * 2011-05-04 2011-10-12 西安电子科技大学 Time varying TDD-MIMO (Time Division Duplex-Multiple Input Multiple Output) channel reciprocity compensating method based on LS-SVM (Least Square Support Vector Machine)
CN108418645A (en) * 2018-01-26 2018-08-17 南京航空航天大学 A kind of modeling of non-stationary mobile telecommunication channel and parameter smoothing evolution method
US20180248592A1 (en) * 2016-07-21 2018-08-30 Nxgen Partners Ip, Llc System and method for reducing pilot signal contamination using orthogonal pilot signals

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102215186A (en) * 2011-05-04 2011-10-12 西安电子科技大学 Time varying TDD-MIMO (Time Division Duplex-Multiple Input Multiple Output) channel reciprocity compensating method based on LS-SVM (Least Square Support Vector Machine)
US20180248592A1 (en) * 2016-07-21 2018-08-30 Nxgen Partners Ip, Llc System and method for reducing pilot signal contamination using orthogonal pilot signals
CN108418645A (en) * 2018-01-26 2018-08-17 南京航空航天大学 A kind of modeling of non-stationary mobile telecommunication channel and parameter smoothing evolution method

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
WEI PENG 等: ""Downlink Channel Prediction for Time-Varying FDD Massive MIMO Systems"", 《 IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING 》 *

Cited By (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2021008255A1 (en) * 2019-07-16 2021-01-21 中兴通讯股份有限公司 Method and apparatus for eliminating multipath interference, and computer-readable storage medium
CN113472396A (en) * 2020-03-30 2021-10-01 北京大学 Uplink space division multiple access system based on Doppler effect and implementation method
WO2021238850A1 (en) * 2020-05-28 2021-12-02 维沃移动通信有限公司 Channel information processing method and apparatus
CN111817757A (en) * 2020-06-08 2020-10-23 武汉大学 Channel prediction method and system for MIMO wireless communication system
CN114448474B (en) * 2020-11-05 2022-11-22 中国移动通信集团设计院有限公司 Multi-antenna channel matrix prediction method and device and electronic equipment
CN114448474A (en) * 2020-11-05 2022-05-06 中国移动通信集团设计院有限公司 Multi-antenna channel matrix prediction method and device and electronic equipment
CN112702092B (en) * 2020-12-22 2021-12-14 杭州电子科技大学 Channel estimation method in FDD downlink multi-user large-scale MIMO system
CN112702092A (en) * 2020-12-22 2021-04-23 杭州电子科技大学 Channel estimation method in FDD downlink multi-user large-scale MIMO system
CN113595666A (en) * 2021-07-13 2021-11-02 华中科技大学 Channel prediction method and system of large-scale MIMO system based on time-frequency joint correlation
CN113890583A (en) * 2021-11-12 2022-01-04 北京理工大学 Millimeter wave and submillimeter wave frequency band asymmetric channel parameter prediction method
CN114448535A (en) * 2022-01-21 2022-05-06 华中科技大学 Channel prediction method based on FDD partial reciprocity, network side equipment and terminal
CN114448535B (en) * 2022-01-21 2022-10-14 华中科技大学 Channel prediction method based on FDD partial reciprocity, network side equipment and terminal
CN116707687A (en) * 2023-06-27 2023-09-05 南京盛航海运股份有限公司 Channel prediction method for de-cellular large-scale MIMO system

Similar Documents

Publication Publication Date Title
CN110691049A (en) Large-scale MIMO system channel prediction method under frequency division duplex mode
Bajwa et al. Compressed channel sensing: A new approach to estimating sparse multipath channels
US11121760B2 (en) Multiple-input multiple output communication system with virtual trajectory reception for doubly selective channels
US20040008803A1 (en) Method and device for channel estimation in digital radio communication systems
CN109005133B (en) Double-sparse multi-path channel model and channel estimation method based on model
CN108964725B (en) Sparse estimation method of channel parameters in time-varying large-scale MIMO network
Akbarpour-Kasgari et al. Massive MIMO-OFDM channel estimation via distributed compressed sensing
Jiang et al. Deep Learning-Aided Delay-Tolerant Zero-Forcing Precoding in Cell-Free Massive MIMO
CN106330251A (en) Doppler spread estimation method of underwater acoustic communication system based on zero correlation zone sequence
Balakumar et al. Joint MIMO channel tracking and symbol decoding using Kalman filtering
Wang et al. CAP-Net: A deep learning-based angle prediction approach for ISAC-enabled RIS-assisted V2I communications
EP3364620B1 (en) Method and apparatus for channel estimation in wireless communication system
Xiao et al. Exploiting double timescales for integrated sensing and communication with delay-Doppler alignment modulation
CN101296010B (en) Channel estimation method suitable for space-frequency encoding cascade cycle detention diversity mode
KR100901760B1 (en) Method of Cyclic delay diversity with the Optimal Cyclic delay value, and Transmitter performing the same
CN115021843B (en) Cooperative sensing method for millimeter wave communication multi-user system
CN116248210A (en) Method, system and medium for predicting large-scale multiple-input multiple-output system channel
CN115865575A (en) Reconfigurable intelligent surface-assisted MIMO system separation channel reconstruction method
Sun et al. Multi-pair two-way massive MIMO AF full-duplex relaying with ZFR/ZFT and imperfect CSI
CN101257324B (en) Linear combined channel estimation method in TD-SCDMA system
CN101262265A (en) A delay diversity transmission and receiving method for time division duplex wireless communication system
Ferrara et al. Soft iterative channel estimation with subspace and rank tracking
Haider et al. Reducing impact of doppler effect by employing distributed compressive sensing
de Almeida et al. Multipath parameter estimation of time-varying space-time communication channels using parallel factor analysis
CN113572506B (en) High-speed rail multi-TRP wireless communication method based on FDD precoding

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
RJ01 Rejection of invention patent application after publication

Application publication date: 20200114

RJ01 Rejection of invention patent application after publication