CN104717003B - A kind of mobile telecommunication channel error modeling method - Google Patents
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Abstract
The present invention proposes a kind of mobile telecommunication channel error modeling method, is specially：For in the practical application of multi-user's multiple-input, multiple-output MIMO FDD systems, mobile subscriber feeds back to the channel condition information CSI of base station BS while estimation be present, quantifies and the practical problem of delay time error, proposes combined channel error modeling method that is a kind of while considering above-mentioned three kinds of channel errors.This method can be used for reduction FDD GSM channel true to nature, and design basis is provided for the optimization of multiuser MIMO BS downlink transfers.
Description
Technical Field
The invention relates to a multi-user Multiple Input Multiple Output (MIMO) Frequency Division Duplex (FDD) mobile communication system, in particular to the field of mobile communication channel error modeling.
Background
Multiple Input Multiple Output (MIMO) technology can effectively improve spectrum efficiency and transmission rate, and has been widely applied in modern communication systems, such as multi-user wireless communication systems. In a multi-user MIMO downlink system, a Base Station (BS) communicates with a plurality of users on the same time-frequency resource, and the users are inevitably interfered by the multi-users. To eliminate this interference, beamforming is usually chosen at the BS side.
Currently, existing beamforming algorithms all assume that the BS can obtain ideal Channel State Information (CSI). However, in practical communication applications, the BS cannot obtain ideal CSI: for example, in a Time Division Duplex (TDD) system, a BS may perform channel estimation through an uplink channel according to reciprocity of uplink and downlink to obtain CSI, but channel estimation errors may be caused due to inaccuracy of estimation; in a Frequency Division Duplex (FDD) system, the BS generally obtains the CSI through channel estimation and limited feedback of the ue, where the limited feedback causes quantization error and the channel feedback process causes delay error. From the existing Chinese and foreign literature research, a plurality of modeling methods for comprehensively analyzing one or two of the three errors exist, but the number of the modeling methods is not large, and the modeling methods for comprehensively analyzing the three errors do not exist. However, in an actual communication system, channel errors have a great influence on the performance of the mobile communication system, so that the research on the comprehensive factors of different errors has great significance in communication application.
Based on the method, the error modeling method of the mobile communication channel is provided by combining channel estimation, quantization and delay errors, the estimation, quantization and delay errors of the CSI fed back to the BS by the mobile user are comprehensively considered, and the realistic actual channel state is effectively realized.
Disclosure of Invention
The purpose of the invention is as follows: a modeling method of an estimation error and a quantization error generated by estimating a channel when a BS cannot acquire ideal CSI (channel state information) in a multi-user MIMO (multiple input multiple output) communication system and a delay error generated in a channel feedback process is provided, and a design basis is provided for actual multi-user MIMO downlink transmission optimization.
The technical scheme of the invention is as follows:
the system model is a multi-user downlink MIMO system, in which a BS serves K users on the same time-frequency resource, the BS configures M transmit antennas, and each user configures N receive antennas, as shown in fig. 1.
The signal received by the kth user can be expressed as:
wherein,representing the channel matrix from BS to user k, whose elements are independent of each other and are complex gaussian random variables of zero mean, unit variance,representing the white gaussian noise vector received by user k, whose elements are independent and complex gaussian random variables with zero mean and unit variance,andprecoding matrix and transmit signal vector, respectively, (-)HRepresenting the conjugate transpose of the matrix.
The signals received by K users are:
y＝GWx+n (2)
wherein,G＝(G1,...,GK)H，(·)Trepresenting the transpose of the matrix. Note E [ nn ]H]＝1，E[xxH]Let 1 be the BS transmit power limit P, then there is tr (WW)H) P ≦ tr (-) denotes the trace of the matrix.
The process of channel estimation, quantization feedback and data transmission between the BS and the user is shown in fig. 2. The method mainly comprises the following three steps:
firstly, a BS sends a channel training sequence to a user;
receiving a training sequence by a user, estimating a channel matrix according to a Minimum Mean Square Error (MMSE) criterion, quantizing the channel matrix by adopting a Random Vector Quantization (RVQ) method after the user obtains the estimated channel matrix, selecting a code word with the minimum distance as a quantization matrix of the CSI, and sending an index number corresponding to the code word to the BS;
and finding a corresponding code word from the codebook according to the index number by the BS so as to obtain a channel matrix, namely the CSI.
In order to design a modeling method with channel estimation, quantization and delay errors, three channel errors are established first, and a combined channel error modeling method is provided on the basis.
(1) Estimating channel error model
Suppose HkFor user k to original channel matrix GkThe estimated channel matrix obtained after channel estimation has the following channel estimation models:
Gk＝Hk+Δk(3)
wherein,is a channel estimation error matrix whose elements are independent of each other and are all zero mean values with variance ofComplex gaussian random variables. Note that ΔkThe noise vector n is independent of the transmit signal vector x.
(2) Quantized channel error model
After the user estimates the channel, the RVQ quantization method is adopted, and the size of the codebook is 2BSelects code words according to the principle of minimum distance in the codebook, quantizes error channel matrixAnd the channel error matrix HkIs expressed as:
wherein,is formed by HkOf column space composition, i.e. orthogonal basisΛkIs composed ofAnd satisfies E (Λ)k)＝MIN；Is uniformly distributed inA spatially unitary matrix;is an upper triangular matrix, and delta is a quantization error, which is related only to the number of feedback bits and the number of transmit and receive antennas;is an upper triangular matrix and satisfiesIs composed ofA unitary matrix composed of the left null space of (a); skAnd ZkAre independent of each other. HandleSubstituting formula (4) to obtain:
(3) delayed channel error model
The delay channel modeling usually adopts a smooth traversal gaussian markov block fading method, and the mathematical form of the method is expressed as follows:
Hk[n]＝ρkHk[n-1]+Ek[n](6)
wherein Hk[n]And Hk[n-1]A channel matrix representing time n and time n-1, respectively; ek[n]Is a delay error matrix with elements of zero mean and varianceAnd (4) complex gaussian random variables, and are independent of each other. Ginseng radix (Panax ginseng C.A. Meyer)Number rhok＝J0(2πfdkTs)，J0(. h) is a Bessel function of order 0, fdkFor the Doppler shift of user k, TsIs a symbol interval.
(4) Joint channel error model
In the case of channel estimation, quantization and delay errors, the channel matrix G from the nth time BS to the user k is determined according to equations (3), (5) and (6)k[n]Modeling is as follows:
writing equation (7) in matrix form, assuming that G ═ G (G)1,.....,GK)HObtaining:
wherein,E＝[E1[n],...,EK[n]]H，D＝diag(ρ1,...,ρK)，S＝[S1[n-1],...,SK[n-1]]H，a ═ D Λ C, B ═ D Λ Z, diag (·) denotes the diagonal matrix.
Drawings
Fig. 1 is a block diagram of a multi-user downlink MIMO system;
FIG. 2 is a process of channel estimation, quantization feedback and data transmission between a BS and a user;
fig. 3 is a flow chart of joint channel error modeling.
Detailed Description
The specific flow of joint channel error modeling is as follows, as shown in fig. 3. Suppose the study object is the kth user in the cell:
step 10, channel modeling begins.
And 20, establishing a model of the channel estimation error of the kth user, wherein the estimation channel matrix contains an estimation error matrix as shown in the formula (3).
And step 30, after finishing the estimation of the channel, according to the RVQ quantization mode, giving a quantization error matrix after quantization as formula (4) and a quantization channel matrix as formula (5).
And step 40, establishing a delay error model as the formula (6) by adopting a smooth traversal Gaussian Markov block fading method.
And step 50, under the condition that channel estimation, quantization and delay errors exist simultaneously, obtaining a joint channel error matrix of the user k according to each error model expression as shown in the formula (7).
And step 60, finishing channel modeling.
As described above, the invention fully considers the problem of channel errors in the FDD multi-user MIMO communication system, and the estimation, quantization and delay errors of the actual channel can be presented by a mathematical model by adopting the invention, so that the real MIMO wireless channel is restored as realistically as possible, and a design basis is provided for the downlink transmission optimization of the multi-user MIMO communication system.
In this specification, the invention has been described with reference to specific embodiments. It will, however, be evident that various modifications and changes may be made thereto without departing from the broader spirit and scope of the invention. The specification and drawings are, accordingly, to be regarded in an illustrative rather than a restrictive sense.
Claims (1)
1. A method for modeling errors in a mobile communication channel, comprising the steps of:
s1, BS serves K users on the same time frequency resource, BS configures M sending antennas, each user configures N receiving antennas, the signal received by K user is expressed as:
<mrow> <msub> <mi>y</mi> <mi>k</mi> </msub> <mo>=</mo> <msubsup> <mi>G</mi> <mi>k</mi> <mi>H</mi> </msubsup> <mi>W</mi> <mi>x</mi> <mo>+</mo> <msub> <mi>n</mi> <mi>k</mi> </msub> <mo>,</mo> </mrow>
wherein,representing the channel matrix from BS to user k, whose elements are independent of each other and are complex gaussian random variables of zero mean, unit variance,representing the white gaussian noise vector received by user k, whose elements are independent and complex gaussian random variables with zero mean and unit variance,andprecoding matrix and transmit signal vector, respectively, (-)HRepresents a conjugate transpose of the matrix;
the signals received by K users are:
y＝GWx+n，
wherein,G＝(G1,...,GK)H，(·)Trepresenting transposes of matrices, E [ nn ]H]＝1，E[xxH]Let 1 be the BS transmit power limit P, then there is tr (WW)H) P, tr (-) denotes the trace of the matrix,
s2, the channel estimation, quantization feedback and data transmission process between the BS and the user are three steps:
(1) BS sends channel training sequence to user;
(2) a user receives a training sequence and estimates a channel matrix according to a minimum mean square error criterion, after obtaining the estimated channel matrix, the user quantizes by adopting a random vector quantization method, selects a code word with the minimum distance as a quantization matrix of CSI, and sends an index number corresponding to the code word to a BS;
(3) the BS finds out corresponding code words from the codebook according to the index numbers so as to obtain a channel matrix, wherein the processes of estimation, quantization and feedback respectively bring channel estimation, quantization and delay errors, and the BS determines a precoding matrix W according to the CSI and simultaneously sends signals to K users;
s3, in order to design a modeling method with channel estimation, quantization and delay errors, firstly establishing three channel errors, and then providing a joint channel error modeling method on the basis;
(1) estimating channel error model
Suppose HkFor user k to original channel matrix GkThe estimated channel matrix obtained after channel estimation has the following channel estimation models:
Gk＝Hk+Δk
wherein,is a channel estimation error matrix whose elements are independent of each other and are all zero mean values with variance ofComplex gaussian random variables of (a); note that ΔkIndependent of the transmitted signal vector x, the noise vector n;
(2) quantized channel error model
After the user estimates the channel, the random vector quantization RVQ quantization method is adopted, and the size of the codebook is 2BSelects code words according to the principle of minimum distance in the codebook, quantizes error channel matrixAnd the channel error matrix HkIs onThe system, superscript B is that codebook occupies B bits, superscript M configures M transmitting antennas for BS, superscript N configures N antennas for each user, and the formula is:
<mrow> <msub> <mover> <mi>H</mi> <mo>~</mo> </mover> <mi>k</mi> </msub> <mo>=</mo> <msub> <mover> <mi>H</mi> <mo>^</mo> </mover> <mi>k</mi> </msub> <msub> <mi>U</mi> <mi>k</mi> </msub> <msub> <mi>V</mi> <mi>k</mi> </msub> <mo>+</mo> <msub> <mi>S</mi> <mi>k</mi> </msub> <msub> <mi>Z</mi> <mi>k</mi> </msub> </mrow>
wherein,is formed by HkOf column space composition, i.e. orthogonal basisΛkIs composed ofAnd satisfies E (Λ)k)＝MIN；Is uniformly distributed inA spatially unitary matrix;is an upper triangular matrix, and delta is a quantization error, which is related only to the number of feedback bits and the number of transmit and receive antennas;is an upper triangular matrix and satisfiesIs composed ofA unitary matrix composed of the left null space of (a); skAnd ZkAre independent of each other; handleSubstitution intoObtaining:
<mrow> <msub> <mi>H</mi> <mi>k</mi> </msub> <mo>=</mo> <msub> <mover> <mi>H</mi> <mo>^</mo> </mover> <mi>k</mi> </msub> <msub> <mi>U</mi> <mi>k</mi> </msub> <msub> <mi>V</mi> <mi>k</mi> </msub> <msqrt> <msub> <mi>&Lambda;</mi> <mi>k</mi> </msub> </msqrt> <mo>+</mo> <msub> <mi>S</mi> <mi>k</mi> </msub> <msub> <mi>Z</mi> <mi>k</mi> </msub> <msqrt> <msub> <mi>&Lambda;</mi> <mi>k</mi> </msub> </msqrt> </mrow>
(3) delayed channel error model
The delay channel modeling usually adopts a smooth traversal gaussian markov block fading method, and the mathematical form of the method is expressed as follows:
Hk[n]＝ρkHk[n-1]+Ek[n]
wherein Hk[n]And Hk[n-1]A channel matrix representing time n and time n-1, respectively; ek[n]Is a delay error matrix with elements of zero mean and varianceThe complex Gaussian random variables are independent of each other; parameter pk＝J0(2πfdkTs)，J0(. h) is a Bessel function of order 0, fdkFor the Doppler shift of user k, TsIs a symbol interval;
(4) joint channel error model
In the presence of channel estimation, quantization and delay errors, according to formula Gk＝Hk+Δk、Hk[n]＝ρkHk[n-1]+Ek[n]Channel matrix G from the nth time BS to the user kk[n]Modeling is as follows:
<mfenced open = "" close = ""> <mtable> <mtr> <mtd> <mrow> <msub> <mi>G</mi> <mi>k</mi> </msub> <mo>&lsqb;</mo> <mi>n</mi> <mo>&rsqb;</mo> <mo>=</mo> <msub> <mi>&rho;</mi> <mi>k</mi> </msub> <mrow> <mo>(</mo> <msub> <mover> <mi>H</mi> <mo>^</mo> </mover> <mi>k</mi> </msub> <mo>&lsqb;</mo> <mi>n</mi> <mo>-</mo> <mn>1</mn> <mo>&rsqb;</mo> <msub> <mi>U</mi> <mi>k</mi> </msub> <mo>&lsqb;</mo> <mi>n</mi> <mo>-</mo> <mn>1</mn> <mo>&rsqb;</mo> <mi>V</mi> <msub> <mrow> <mo>&lsqb;</mo> <mi>n</mi> <mo>-</mo> <mn>1</mn> <mo>&rsqb;</mo> </mrow> <mi>k</mi> </msub> <mo>+</mo> <msub> <mi>S</mi> <mi>k</mi> </msub> <mo>&lsqb;</mo> <mi>n</mi> <mo>-</mo> <mn>1</mn> <mo>&rsqb;</mo> <msub> <mi>Z</mi> <mi>k</mi> </msub> <mo>&lsqb;</mo> <mi>n</mi> <mo>-</mo> <mn>1</mn> <mo>&rsqb;</mo> <mo>)</mo> </mrow> <msqrt> <mrow> <msub> <mi>&Lambda;</mi> <mi>k</mi> </msub> <mo>&lsqb;</mo> <mi>n</mi> <mo>-</mo> <mn>1</mn> <mo>&rsqb;</mo> </mrow> </msqrt> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mo>+</mo> <msub> <mi>E</mi> <mi>k</mi> </msub> <mo>&lsqb;</mo> <mi>n</mi> <mo>&rsqb;</mo> <mo>+</mo> <msub> <mi>&Delta;</mi> <mi>k</mi> </msub> <mo>&lsqb;</mo> <mi>n</mi> <mo>-</mo> <mn>1</mn> <mo>&rsqb;</mo> </mrow> </mtd> </mtr> </mtable> </mfenced>
the above equation is written in matrix form, assuming that G ═ G (G)1,.....,GK)HObtaining:
<mrow> <mi>G</mi> <mo>=</mo> <mi>A</mi> <mover> <mi>H</mi> <mo>^</mo> </mover> <mo>+</mo> <mi>B</mi> <mi>S</mi> <mo>+</mo> <mi>E</mi> <mo>+</mo> <mi>&Delta;</mi> </mrow>
wherein,E＝[E1[n],...,EK[n]]H，D＝diag(ρ1,...,ρK)，S＝[S1[n-1],...,SK[n-1]]H，a ═ D Λ C, B ═ D Λ Z, diag (·) denotes the diagonal matrix;
s4, the specific procedure of joint channel error modeling is as follows, assuming that the study object is the kth user in the cell,
(1) starting channel modeling;
(2) establishing a model of the channel estimation error of the kth user, wherein the estimation channel matrix comprises an estimation error matrix: gk＝Hk+Δk；
(3) After the estimation of the channel is finished, an RVQ quantization method is adopted to give a quantized quantization error matrix:quantizing the channel matrix:
(4) adopting a smooth traversal Gauss Markov block fading method to establish a delay error model: hk[n]＝ρkHk[n-1]+Ek[n]；
(5) Under the condition that channel estimation, quantization and delay errors exist simultaneously, a joint channel error matrix of a user k is obtained according to each error model expression:
<mrow> <mtable> <mtr> <mtd> <mrow> <msub> <mi>G</mi> <mi>k</mi> </msub> <mo>&lsqb;</mo> <mi>n</mi> <mo>&rsqb;</mo> <mo>=</mo> <msub> <mi>&rho;</mi> <mi>k</mi> </msub> <mrow> <mo>(</mo> <msub> <mover> <mi>H</mi> <mo>^</mo> </mover> <mi>k</mi> </msub> <mo>&lsqb;</mo> <mi>n</mi> <mo>-</mo> <mn>1</mn> <mo>&rsqb;</mo> <msub> <mi>U</mi> <mi>k</mi> </msub> <mo>&lsqb;</mo> <mi>n</mi> <mo>-</mo> <mn>1</mn> <mo>&rsqb;</mo> <mi>V</mi> <msub> <mrow> <mo>&lsqb;</mo> <mi>n</mi> <mo>-</mo> <mn>1</mn> <mo>&rsqb;</mo> </mrow> <mi>k</mi> </msub> <mo>+</mo> <msub> <mi>S</mi> <mi>k</mi> </msub> <mo>&lsqb;</mo> <mi>n</mi> <mo>-</mo> <mn>1</mn> <mo>&rsqb;</mo> <msub> <mi>Z</mi> <mi>k</mi> </msub> <mo>&lsqb;</mo> <mi>n</mi> <mo>-</mo> <mn>1</mn> <mo>&rsqb;</mo> <mo>)</mo> </mrow> <msqrt> <mrow> <msub> <mi>&Lambda;</mi> <mi>k</mi> </msub> <mo>&lsqb;</mo> <mi>n</mi> <mo>-</mo> <mn>1</mn> <mo>&rsqb;</mo> </mrow> </msqrt> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mo>+</mo> <msub> <mi>E</mi> <mi>k</mi> </msub> <mo>&lsqb;</mo> <mi>n</mi> <mo>&rsqb;</mo> <mo>+</mo> <msub> <mi>&Delta;</mi> <mi>k</mi> </msub> <mo>&lsqb;</mo> <mi>n</mi> <mo>-</mo> <mn>1</mn> <mo>&rsqb;</mo> </mrow> </mtd> </mtr> </mtable> <mo>;</mo> </mrow>
(6) the channel modeling is ended.
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Effective date of registration: 20171220 Address after: 330114 Jiangxi city of Nanchang province Xinjian County Lehua Zhen Ling Bei forest town No. 90 Applicant after: Qiu Deliang Address before: 400044 Shapingba District Sha Street, No. 174, Chongqing Applicant before: Chongqing University |
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Inventor after: Yue Kangjun Inventor after: Meng Weizhong Inventor before: Liao Yong Inventor before: Zhou Cuan Inventor before: Li Yufeng Inventor before: Cao Jie Inventor before: Li Dong |
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Effective date of registration: 20171229 Address after: 410003 Hunan province Changsha Kaifu District Station Road, No. 302 Applicant after: Hunan Provincial Communication Construction Co., Ltd. Address before: 330114 Jiangxi city of Nanchang province Xinjian County Lehua Zhen Ling Bei forest town No. 90 Applicant before: Qiu Deliang |
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