CN108696465A - Extensive mimo system uplink channel estimation method with both-end phase noise - Google Patents

Extensive mimo system uplink channel estimation method with both-end phase noise Download PDF

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CN108696465A
CN108696465A CN201811045597.6A CN201811045597A CN108696465A CN 108696465 A CN108696465 A CN 108696465A CN 201811045597 A CN201811045597 A CN 201811045597A CN 108696465 A CN108696465 A CN 108696465A
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phase noise
matrix
vector
receiving terminal
channel
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成先涛
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University of Electronic Science and Technology of China
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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
    • H04L25/0204—Channel estimation of multiple channels
    • 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
    • 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

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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Power Engineering (AREA)
  • Radio Transmission System (AREA)

Abstract

The invention belongs to wireless communication technology field, it is related to the extensive mimo system uplink channel estimation method with both-end phase noise.The invention mainly comprises:First in the starting stage, it is assumed that phase noise is not present, and carries out rough channel estimation, is then iterated by variation bayesian algorithm, and finally channel vector will converge on a stable value under conditions of known reception signal.Beneficial effects of the present invention are to realize to the channel estimation of extensive mimo system uplink under the conditions of can be existing for phase noise, obtain accurate channel information, significantly improve system performance.

Description

Extensive mimo system uplink channel estimation method with both-end phase noise
Technical field
The invention belongs to wireless communication technology field, it is related to the extensive mimo system uplink with both-end phase noise Path channels method of estimation.
Background technology
In modern wireless communication systems, extensive mimo system due to its higher spectrum efficiency and energy efficiency and by It is broadly recognized as the core technology of next generation mobile communication, it is generally the case that base station possesses hundreds of antennas, can be same at the same time It is dozens of user service under conditions of frequency, to significantly improve spectrum efficiency.With the increase of antenna for base station number, on a large scale The antenna gain of MIMO can be such that the power of the transmission signal of each user significantly reduces, to improve energy efficiency.
In order to give full play to the advantage of extensive mimo system, channel state information needs to obtain in advance, however accurately obtains It obtains channel state information to be often difficult, especially in the presence of phase noise.Extensive MIMO communication system Signal other than the decline of experience channel, will also be influenced, this two in transmission process by radio-frequency devices non-linear factor A factor makes the reduced performance in receiving terminal system.The non-ideal part of radio-frequency front-end is mainly made an uproar including phase in communication system Sound, IQ amplitude-phases are uneven, and non-linearity of power amplifier distortion etc., phase noise actually stablizes frequency source frequency A kind of characterization of degree.Under normal conditions, frequency stability is divided into long-term frequency stability and short-term frequency stability.It is so-called short-term Frequency stability refers to the phase fluctuation caused by random noise or frequency fluctuation.As for because of frequency caused by temperature, aging etc. Rate slow drift, then referred to as long-term frequency stability.Usually primary concern is that short-term stability problem, it is believed that phase is made an uproar Sound is exactly short-term frequency stability, only the different representations of the two of a physical phenomenon kind.For oscillator, frequency is steady Fixed degree is a kind of measurement of its generation identical frequency in entire defined time range.If there are instantaneous changes for signal frequency Change, cannot remain unchanged, then signal source there is unstability, cause is exactly phase noise.It is communicated in extensive MIMO In system, transmitting terminal is required for generating corresponding carrier wave to complete the conversion of the frequency spectrum between corresponding radio frequency and base band with receiving terminal. However the crystal oscillator and having a certain difference property of phaselocked loop of carrier wave are generated, it causes carrier frequency and exists with target frequency Random difference in short-term in turn results in generated sine wave signal and random phase saltus step occurs, shows as phase noise.And letter Road is compared, and faster, the channel information obtained during channel estimation can be utilized in data demodulation for the variation of phase noise In the process, the phase noise needs but when data demodulation re-start calculating.
Invention content
It is a kind of for big rule it is an object of the invention in the case where base station and user terminal all have phase noise, provide The channel estimation methods of mould MIMO-OFDM system up-links, accurately estimate channel under severe hardware condition, to fully Play the advantage of extensive mimo system.
Present invention employs variational Bayesian algorithm, variational Bayesian algorithm is a kind of unknown random change of solution The algorithm of the Posterior distrbutionp of amount passes through constantly iteration, the mean value and variance of the hidden variable under the conditions of obtaining known to sample.
Understanding for the ease of those skilled in that art to technical solution of the present invention, the system that the present invention is used first Model illustrates.
Consider that the model of the MIMO ofdm system uplinks with phase noise, transmitting terminal have K user, Mei Geyong Family has 1 antenna, receiving terminal base station to have M root antennas, the time domain letter between k-th of user of transmitting terminal and receiving terminal m root antennas Road vector is denoted asWherein L is the length of channel vector.For each OFDM symbol, receiving terminal The time-domain signal expression formula of m root antennas is
Wherein,It is the time-domain received signal on m root antennas, N is the number of OFDM subcarriers,It is the phase noise matrix of receiving terminal m root antennas,It is the phase noise matrix of k-th of user of transmitting terminal,It is k-th of user to the circulant channel matrixes between receiving terminal m root antennas, its 1st is classified asWherein 01×(N-L)Indicate that element is all the row vector that 0, length is N-L.F∈CN×NIt is to return The one FFT matrixes changed, its j-th of element of the i-th row aredk=[dk,1,dk,2,…,dk,N]TIt is The data or pilot frequency sequence that k-th of user sends.It is the white complex gaussian noise sequence of time domain,
Form below can be decomposed into:
Wherein Hm,k=diag {s [Hm,k,1,Hm,k,2,…,Hm,k,N]T,
And(2) are substituted into (1) to obtain
(3) are rewritten as
Indicate non-normalized FFT matrixes, its i-th row jth column element is Indicate byPreceding L row composition matrix.Note
(4) are rewritten as
Unify the vector of phase noise θ receiving terminal belowr,m=[θr,m,1,θr,m,2,…,θr,m,N]TWith the phase of transmitting terminal Position noise vector θt,k=[θt,k,1,θt,k,2,…,θt,k,N]TUniformly it is denoted as θ=s [θ1,θ2,…,θN]T,WithUniformly it is denoted asDue to θn Value very little, approximation relation can be utilizedThen there are p ≈ 1+j θ, wherein 1 expression element is all 1 N-dimensional column vector.
θ=s [θ1,θ2,…,θN]TFor the vector of phase noise of real Gaussian Profile, i.e. θ=N (0, Φ).Due to the covariance of θ Matrix Φ is real symmetric matrix, and characteristic value is real number, and can carry out similarity diagonalization with orthogonal matrix:
Φ=U Λ UT (6)
Wherein Λ=diag {s [λ1,λ2,…,λN]TIt is diagonal matrix, the characteristic value that the descending that diagonal element is Φ arranges, U is orthogonal matrix, its each row are the feature vectors of the characteristic value of Λ respective columns.By calculating pair it can be found that in Λ Angle member only has preceding several values larger, and other elements compare very little with preceding several items, therefore can only take first I to come closely Seemingly, i.e.,
Φ≈VΓVT (7)
Γ=diag {s [λ1,λ2,…,λI]TIt is the V ∈ C using preceding I characteristic value in Λ as the diagonal matrix of diagonal elementN×I The matrix formed is arranged by the preceding I of preceding U.Linear transformation is made to vector of phase noise θ
θ=Ux'≈Vx (8)
By the property of Gaussian Profile it is found that x=N (0, Γ), since Γ is diagonal matrix, so being between each component of x It is mutually independent.Transmitting terminal phase noise matrix can be approximated to be P as a result,t,k=diag { 1+jVxt,k, receiving terminal phase noise Matrix can be approximated to be Pr,m=diag { 1+jVxr,m, substituting into (5) can obtain
Wherein,Be through Cross approximate matrix.
Receiving terminal antenna is now divided into G groups, then has M/G=S root antennas, every group of S root antennas to use same oscillation for every group Device, then the value for organizing the phase noise on interior each antenna is identical, i.e., for the antenna in g (g=1,2 ..., G) group, has Priori probability density function be
hmObey the prior distribution of multiple Gauss
Wherein covariance matrix
Under the conditions of phase noise and channel are all known, the reception signal on m root antennasObey following multiple height This distribution
WhereinRounding in expression.
Thenh1,h2,…,hM,xt,1,xt,2,…,xt,KJoint probability density function be
Logarithm is taken to (13), is obtained
It, can be successively to receiving terminal end vector of phase noise according to the principle of variational BayesianTransmitting terminal phase Noise vector xt,kWith channel vector hmIt is updated, other variables can be obtained as constant successively when updating each variable To the posterior probability density function of each variable.
The present invention is achieved by the steps of:
S1, in the starting stage, it is assumed that phase noise is not present, and carries out rough channel estimation;
S2, the iteration that variation bayesian algorithm is realized by following step:
S21, channel vector h is calculatedmPosterior distrbutionp mean value and variance:
S22, receiving terminal phase noise expansion vector is calculatedPosterior distrbutionp mean value and variance:
S23, transmitting terminal phase noise expansion vector x is calculatedt,kPosterior distrbutionp mean value and variance:
S24, the priori covariance matrix D of channel vector is updated:
S25, circulation step S21-S24, under conditions of known reception signal, channel vector will converge on a stabilization Value.
Beneficial effects of the present invention are to realize to extensive mimo system uplink under the conditions of can be existing for phase noise The channel estimation of link obtains accurate channel information, significantly improves system performance.
Description of the drawings
Fig. 1 is the extensive mimo system uplink schematic diagram under the effect of phase noise that the present invention uses;
Fig. 2 is the channel model figure that the present invention uses;
Fig. 3 is the flow chart that the present invention realizes channel estimation method;
Fig. 4 is the MSE impact of performance curve graphs of channel estimation.
Specific implementation mode
Illustrate the actual effect of the present invention below in conjunction with the accompanying drawings.
The invention mainly comprises:
S1, under initial situation, it is assumed that phase noise is not present, i.e., phase noise under initial situation expansion vector it is equal Value vector sum covariance matrix is all zero.
S2, the iteration that variation bayesian algorithm is realized by following step:
S21, channel vector h is calculatedmPosterior distrbutionp mean value and variance:
Wherein, Mm=diag { Mm,1,Mm,2,…,Mm,K, i.e., with Mm,1,Mm,2,…,Mm,KFor block diagonal element block to angular moment Battle array,
Nm=diag { Nm,1,Nm,2,…,Nm,K},
Diag { } indicates that a diagonal matrix, the diagonal element of diagonal matrix are the diagonal element of the symbol internal matrix.
S22, receiving terminal phase noise expansion vector is calculatedPosterior distrbutionp mean value and variance:
Wherein the corresponding element of ⊙ representing matrixes is multiplied,It is circulant matrixes, its 1st is classified asWhereinIt isPosterior probability mean value, Indicate handleAfter being divided into the matrix in block form of K × K, jth row The corresponding submatrix of kth row block.
S23, transmitting terminal phase noise expansion vector x is calculatedt,kPosterior distrbutionp mean value and variance:
S24, the priori covariance matrix to channel vectorIt is updated:
WhereinIndicate vectorFirst of element,Representing matrixFirst of diagonal element.
S25, circulation step S21-S24, under conditions of known reception signal, the estimated value of channel vector will converge on One stable value.
Fig. 4 is the MSE performance curves of channel estimation under the conditions of different phase noise levels, and channel length 64 is used It is 64 to use the complex exponential symbol of uniform phase distribution, antenna for base station number in the pilot tone of estimation channel, number of users 5, OFDM Variable number is 512, and phase noise level takes -100dBc/Hz@1MHz, -95dBc/Hz@1MHz and -90dBc/Hz@respectively Under conditions of 1MHz, algorithm iteration number is 2.
From simulation curve as can be seen that precision of channel estimation is affected by phase noise, utilization is proposed by the invention Variational Bayesian algorithm, accurate channel estimation may be implemented, MSE levels are below -14dB.

Claims (1)

1. the extensive mimo system uplink channel estimation method with both-end phase noise, initialization system transmitting terminal have K A user, each user have 1 antenna, receiving terminal base station to have M root antennas, k-th of user of transmitting terminal and receiving terminal m root antennas Between time domain channel vector be denoted asWherein L is the length of channel vector, for each The time-domain signal expression formula of OFDM symbol, receiving terminal m root antennas is
Wherein,It is the time-domain received signal on m root antennas, N is the number of OFDM subcarriers,It is the phase noise matrix of receiving terminal m root antennas,It is the phase noise matrix of k-th of user of transmitting terminal, It is k-th of user to the circulant channel matrixes between receiving terminal m root antennas, its 1st is classified asWherein 01×(N-L)Indicate that element is all the row vector that 0, length is N-L, F ∈ CN×NIt is to return The one FFT matrixes changed, its j-th of element of the i-th row aredk=[dk,1,dk,2,…,dk,N]TIt is The data or pilot frequency sequence that k-th of user sends,It is the white complex gaussian noise sequence of time domain,
It is decomposed into form below:
Wherein Hm,k=diag {s [Hm,k,1,Hm,k,2,…,Hm,k,N]T,
And(2) are substituted into (1) to obtain
(3) are rewritten as
Indicate non-normalized FFT matrixes, its i-th row jth column element isTable Show byPreceding L row composition matrix;Note
(4) are rewritten as
The unified vector of phase noise θ receiving terminalr,m=[θr,m,1,θr,m,2,…,θr,m,N]TWith the vector of phase noise of transmitting terminal θt,k=[θt,k,1,θt,k,2,…,θt,k,N]TUniformly it is denoted as θ=s [θ1,θ2,…,θN]T,WithUniformly it is denoted asDue to θnValue very little, utilize approximation relationThen there are p ≈ 1+j θ, wherein 1 expression element is all 1 N-dimensional column vector;
θ=s [θ1,θ2,…,θN]TFor the vector of phase noise of real Gaussian Profile, i.e. θ=N (0, Φ);Due to the covariance matrix of θ Φ is real symmetric matrix, and characteristic value is real number, and can carry out similarity diagonalization with orthogonal matrix:
Φ=U Λ UT (6)
Wherein Λ=diag {s [λ1,λ2,…,λN]TIt is diagonal matrix, the characteristic value that the descending that diagonal element is Φ arranges, U is just Matrix is handed over, its each row are the feature vectors of the characteristic value of Λ respective columns;By can be calculated, before the diagonal element in Λ only has Several values are larger, and other elements compare very little with preceding several items, thus before only taking I it is approximate, i.e.,
Φ≈VΓVT (7)
Γ=diag {s [λ1,λ2,…,λI]TIt is the V ∈ C using preceding I characteristic value in Λ as the diagonal matrix of diagonal elementN×IBe by The matrix of the preceding I row compositions of preceding U;Linear transformation is made to vector of phase noise θ
θ=Ux'≈Vx (8)
By the property of Gaussian Profile it is found that x=N (0, Γ), since Γ is diagonal matrix, so being mutual between each component of x It is independent;It is P by transmitting terminal phase noise approximate matrixt,k=diag { 1+jVxt,k, receiving terminal phase noise approximate matrix is Pr,m=diag { 1+jVxr,m, substituting into (5) can obtain
Wherein,It is by close As matrix;
Receiving terminal antenna is divided into G groups, then has M/G=S root antennas, every group of S root antennas to use same oscillation for every group Device, then the value for organizing the phase noise on interior each antenna is identical, i.e., for the antenna in g (g=1,2 ..., G) group, has Priori probability density function be
hmObey the prior distribution of multiple Gauss
Wherein covariance matrix
Under the conditions of phase noise and channel are all known, the reception signal on m root antennasObey following multiple Gauss point Cloth
WhereinRounding in expression.
ThenJoint probability density function be
Logarithm is taken to (13), is obtained
It, can be successively to receiving terminal end vector of phase noise according to the principle of variational BayesianTransmitting terminal phase noise Vector xt,kWith channel vector hmIt is updated, other variables can be obtained every as constant successively when updating each variable The posterior probability density function of a variable;
It is characterized by comprising the following steps:
S1, in the starting stage, it is assumed that phase noise is not present, and carries out rough channel estimation;
S2, it is iterated using variational Bayesian algorithm:
S21, channel vector h is calculatedmPosterior distrbutionp mean value and variance:
Wherein, Mm=diag { Mm,1,Mm,2,…,Mm,K, i.e., with Mm,1,Mm,2,…,Mm,KFor the block diagonal matrix of block diagonal element,Nm= diag{Nm,1,Nm,2,…,Nm,K}, Diag { } indicates one A diagonal matrix, the diagonal element of diagonal matrix are the diagonal element of the symbol internal matrix;
S22, receiving terminal phase noise expansion vector is calculatedPosterior distrbutionp mean value and variance:
WhereinThe corresponding element of representing matrix is multiplied,It is circulant matrixes, its 1st is classified asWhereinIt isPosterior probability mean value, Indicate handleAfter being divided into the matrix in block form of K × K, jth row The corresponding submatrix of kth row block;
S23, transmitting terminal phase noise expansion vector x is calculatedt,kPosterior distrbutionp mean value and variance:
S24, the priori covariance matrix D of channel vector is updated:
WhereinIndicate vectorFirst of element,Representing matrixFirst of diagonal element;
S25, circulation step S21-S24, under conditions of known reception signal, channel vector will converge on a stable value.
CN201811045597.6A 2018-09-07 2018-09-07 Extensive mimo system uplink channel estimation method with both-end phase noise Pending CN108696465A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110649953A (en) * 2019-08-19 2020-01-03 江苏大学 Channel estimation method based on variational Bayesian learning under condition of impulse noise

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Publication number Priority date Publication date Assignee Title
CN107370693A (en) * 2017-08-07 2017-11-21 电子科技大学 Multi-user channel estimation method under extensive mimo system and DP priori
CN107947839A (en) * 2017-11-27 2018-04-20 电子科技大学 Phase noise compensation suppressing method for extensive mimo system
CN108111441A (en) * 2018-01-12 2018-06-01 电子科技大学 Channel estimation methods based on variational Bayesian

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107370693A (en) * 2017-08-07 2017-11-21 电子科技大学 Multi-user channel estimation method under extensive mimo system and DP priori
CN107947839A (en) * 2017-11-27 2018-04-20 电子科技大学 Phase noise compensation suppressing method for extensive mimo system
CN108111441A (en) * 2018-01-12 2018-06-01 电子科技大学 Channel estimation methods based on variational Bayesian

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110649953A (en) * 2019-08-19 2020-01-03 江苏大学 Channel estimation method based on variational Bayesian learning under condition of impulse noise

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Application publication date: 20181023