CN111464217A - Improved SVD precoding algorithm for MIMO-OFDM - Google Patents
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- 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
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- H04L27/2601—Multicarrier modulation systems
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- H04L5/0005—Time-frequency
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Abstract
The invention belongs to the technical field of MIMO communication, and particularly relates to an improved SVD precoding algorithm for MIMO-OFDM (multiple input multiple output-orthogonal frequency division multiplexing) under the background of a frequency domain selective fading channel. The core of the invention aims to optimize the SVD of the MIMO channel matrix under each subcarrier so as to avoid the smoothness of the channel in the frequency domain from being damaged by the traditional pre-coding matrix based on the SVD. The invention provides a modeling method for quantizing channel smoothness, and an algorithm for designing and improving an SVD pre-coding matrix. The optimization simulation result shows that the smoothness of an equivalent channel in the MIMO-OFDM system on the frequency domain can be obviously improved by the algorithm, and the channel estimation is facilitated.
Description
Technical Field
The invention belongs to the technical field of MIMO communication, and particularly relates to an improved SVD precoding algorithm for MIMO-OFDM.
Background
In 5G wireless systems, MIMO or massive MIMO and Orthogonal Frequency Division Multiplexing (OFDM) are often combined for frequency selective fading channels. Each subcarrier corresponds to a respective independent fading coefficient, but different SVD precoding is used for each subcarrier, which deteriorates the smoothness of the channel and makes channel estimation difficult.
Aiming at MIMO-OFDM, an improved SVD (singular value decomposition) precoding algorithm is designed to improve the smoothness of an equivalent channel in a frequency domain, and the method is a problem with practical significance. The existing related work can be seen [1] [2 ]. However, the focus of these efforts is to interpolate the precoding matrix, or to reduce the feedback amount of the precoding matrix [2], or to make the precoding matrix somewhat smooth [1 ]. The method of the invention is directly oriented to the equivalent channel matrix, improves the smoothness of the equivalent channel by utilizing the degree of freedom provided by the non-uniqueness of the SVD, and brings convenience to channel estimation.
Disclosure of Invention
The invention aims to provide a method for optimizing SVD (singular value decomposition) so as to avoid the phenomenon that the smoothness of a channel in a frequency domain is damaged by the traditional SVD precoding.
The core of the invention is that a plurality of orthogonal sub-channels are obtained by respectively using SVD pre-coding on MIMO channels on different sub-carriers, the phases of the orthogonal sub-channels are optimized on different sub-carriers, and each corresponding orthogonal sub-channel on all sub-carriers is transformed to a time domain through discrete inverse Fourier transform. By utilizing the dual property of time domain and frequency domain, the energy of each subchannel in the time domain is concentrated at the head of the time domain by optimizing the phase on different subcarriers, thereby improving the smoothness of the subchannel in the frequency domain. The method has low algorithm complexity, can obviously improve the smoothness of the channel in the frequency domain, and brings convenience to channel estimation.
The invention provides an improved SVD pre-coding algorithm, which comprises the following specific steps:
(1) problem resolution
(1.1) consideration of NfMIMO-OFDM system of a subcarrier with M transmitting antennastThe number of receiving antennas is MrOn the ith subcarrier, the received signal may be represented as:
y(i)=H(i)s(i)+z(i)(1)
wherein the content of the first and second substances,is a frequency domain channel system of rank KA matrix of numbers is formed by a matrix of numbers,it is indicated that the signal is transmitted,is the received signal.Is zero-mean circularly symmetric complex gaussian noise. When the channel state is known at the transmitting end, it can be calculatedAnd use of ViOrthogonalizing a channel [1] as a coding matrix on the ith pre-subcarrier]Thus obtaining an equivalent channelHowever, precoding each subcarrier will destroy the smoothness of the channel in the frequency domain, and bring difficulties for the subsequent channel estimation. The current solution is to use the same precoding matrix for multiple adjacent subcarriers, but this results in performance loss. We propose an improved SVD algorithm for this as follows.
First, note that SVD decomposition is not unique. In fact, SVD [4] is performed on H:
H=UΛVH(2)
if order:
using diagonal matrices but reciprocity, H ═ UD Λ D-1VH(ii) a After uptake of D into U and V gives:comprises the following steps:
at this time, the ith subcarrier (i ═ 1, …, N)f) The equivalent channels above are:
therefore, we can adjust D(i)To improve the equivalent channel by the phase of the diagonal elements ofAnd the smoothness in the frequency domain brings convenience for subsequent channel estimation.
(1.2) in the formula (6)There are K orthogonal subchannels available for transmitting the K data streams. All N arefThe k-th sub-channel on the sub-carrier is arranged together to obtain an Mr×NfOf (2) matrix
Wherein the content of the first and second substances, whileRepresents U(i)The kth column of (1);represents H(i)The k-th singular value of (a);represents D(i)The kth diagonal element of (1).
According to Fourier transform properties
Where F is an inverse Fourier transform (IDFT) matrix:
according to the duality of time domain and frequency domain, in order to letIt is smoother in the frequency domain and should be made such that its energy in the time domain is concentrated at the head. Without loss of generality, toPerforming N-point inverse Fourier transform (N is more than or equal to N)f). The number of points N determines the density of the sampling in the frequency domain.
The problem is solved by a diagonal matrix D(i)The optimization problem of (2) is specifically as follows:
the first optimization problem is to extract P rows representing time domain headers from the F matrix and take the first N rowsfRow, composition of
FL=F(1:P,1:Nf) (10)
Solving a maximization function:
the function is equivalent frequency domain channel, after inverse Fourier transform, the first P time samples of energy;
the second optimization problem is to extract the M-N-P rows of the F matrix excluding the head P row and take the first NfColumn, composition FH,
FH=F(S+1:N,1:Nf) (12)
Solving the minimization function corresponding to equation (11):
the function is the energy of the tail M time samples of the equivalent frequency domain channel after the inverse Fourier transform.
(2) Solving of problems
I.e. solve the problems (11) and (13).
For problem (11), the property of vectorization according to the matrix is exploited:
is the Kronecker product, fnIs FLThe column vector of (2). To simplify the representation, A is represented as:
the optimization function (11) can be equivalently converted into:
wherein the content of the first and second substances,according to the definition of F-norm, the following results are obtained:
the solving method comprises the following steps: random initializationPerforming a number of iterations on x, using xiRepresenting the result of the ith iteration, then the solution for the (i + 1) th iteration is:
where ∠ represents the operation of taking the phase element by element on the vector, iterate until convergence.
For problem (13):
similar to (16), the objective function is further expressed as
Wherein A is as defined for (13), but fnIs FH(instead of F)L) A column vector of (a);
the solution of equation (20) is more complicated than equation (16). The solution method is explained as follows:
using the coordinate descent method, x is split into:
resolving the above formula (21) into:
thus, we obtain:
(3)AHsimplified operation of A
For solving the objective function algorithm, A can be simplifiedHAnd A, operation, namely reducing the operation amount, and the specific method comprises the following steps:
by using the basic properties of the Kronecker product, the following are obtained:
wherein (F)HF) Which may be calculated in advance, ⊙ is the hadamard product of the matrix.
Compared with the prior art, the invention has the advantages that: the algorithm is suitable for an MIMO-OFDM system, and system simulation shows that the smoothness of a channel pre-coded by using the improved SVD is obviously superior to that of a channel pre-coded by using the common SVD.
Drawings
Fig. 1 is a time domain response of an element of an equivalent channel.
Fig. 2 shows the variation of the imaginary part of the element at a specific position of the equivalent frequency domain channel with the label of the subcarrier.
Detailed Description
The technical solution of the present invention will be described in detail with reference to the accompanying drawings and embodiments.
As an embodiment, the invention simulates the subcarrier number N by a computerf272 for 4 × 192 massive MIMO channel modified SVD decomposition is performed and the diagonal matrix D is calculated according to the given formula(i)To obtain 4 × 4 equivalent channel Obtaining 4 × 4 equivalent channel by ordinary SVD decomposition
In FIG. 1, without loss of generality, at eachAnd eachAll take elements with a certain fixed position to form two elements with the length of NfRespectively performing inverse Fourier transform on the sequences to obtain time domain graphs. These were compared. The results show that the channel using the improved SVD is seen to be significantly more energy concentrated at the head of the time domain than the channel obtained by the normal SVD decomposition, while the amplitudes at the middle and tail are significantly suppressed. Thus, the improved SVD is shown to make the equivalent channel smoother in the frequency domain.
In FIG. 2, again without loss of generality, at eachAnd eachTaking some fixed position element, in each original channel H without precoding(i)Also taking the same fixed position element. The three subgraphs respectively show the real part and the imaginary part of an original channel, an equivalent channel pre-coded by using the traditional SVD and the equivalent channel pre-coded by using the improved SVD provided by the invention in the frequency domain, and the horizontal axis is the serial number of the subcarrier. As can be seen from the figure, original H(i)The method has certain smoothness in a frequency domain, the smoothness is completely destroyed by ordinary SVD decomposition, and after the improved SVD algorithm is adopted, a point line graph presented by an obtained equivalent channel is smoother than an original channel, and the smoothness is beneficial to channel estimation.
Reference to the literature
[1]K.Schober,R.-A.Pitaval,and R.Wichman,“Improved User-SpecificChannel Estimation Using Geodesical Interpolation at the Transmitter,”IEEEWireless Communications Letters,vol.4,no.2,pp.165–168,Apr.2015.
[2]Jihoon Choi and R.W.Heath,"Interpolation based transmitbeamforming for MIMO-OFDM with limited feedback,"in IEEE Transactions onSignal Processing,vol.53,no.11,pp.4125-4135,Nov.2005.
[3]Palomar,D.P.,et al.“Joint Tx-Rx Beamforming Design forMulticarrier MIMO Channels:A Unified Framework for Convex Optimization.”IEEETransactions on Signal Processing,vol.51,no.9,2003,pp.2381–2401.
[4]G.H.Golub and C.F.Van Loan,“Matrix Computation.”ThirdEdition,TheJohn Hopkins University Press,1996。
Claims (2)
1. An improved SVD precoding matrix algorithm for MIMO-OFDM is characterized by comprising the following specific steps:
(1) problem resolution
(1.1) for having NfMIMO-OFDM system of a subcarrier with M transmitting antennastThe number of receiving antennas is MrOn the ith subcarrier, the received signal is represented as:
y(i)=H(i)s(i)+z(i)(1)
wherein the content of the first and second substances,is a frequency domain channel coefficient matrix of rank K,it is indicated that the signal is transmitted,is a received signal;is zero-mean circularly symmetric complex gaussian noise; when the channel state is known at the transmitting end, it can be calculatedAnd use of ViOrthogonalizing the channel as a coding matrix on the ith subcarrier, thus obtaining an equivalent channelThe algorithm of the improved SVD precoding matrix is as follows;
first, note that SVD decomposition is not unique; in fact, the SVD decomposition is performed on H:
H=UΛVH(2)
let the diagonal matrix:
using the reciprocity of diagonal matrices, H ═ UD Λ D-1VH(ii) a After uptake of D into U and V gives:
at this time, the ith subcarrier (i ═ 1, …, N)f) The equivalent channels above are:
therefore, we can adjust D(i)To improve the equivalent channel by the phase of the diagonal elements ofSmoothness in the frequency domain, thereby bringing convenience to subsequent channel estimation;
1.2) in the formula (6)There are K orthogonal subchannels available for transmitting the K data streams; all N arefThe k-th sub-channel on the sub-carrier is arranged together to obtain an Mr×NfOf (2) matrix
Wherein the content of the first and second substances,whileRepresents U(i)The kth column of (1);represents H(i)The k-th singular value of (a);represents D(i)The kth diagonal element of (1);
according to the fourier transform property:
where F is an inverse Fourier transform (IDFT) matrix:
according to the duality of time domain and frequency domain, in order to letThe energy in the time domain is concentrated on the head part by being smoother in the frequency domain; to pairPerforming N-point inverse Fourier transform (N is more than or equal to N)f) (ii) a The point number N determines the sampling density on the frequency domain;
diagonal matrix D(i)The problem is solved by the following two equivalent optimization problems:
the first optimization problem is to extract P rows representing time domain headers from the F matrix and take the first N rowsfColumn, composition FL,
FL=F(1:P,1:Nf) (10)
Solving a maximization function:
the function is equivalent frequency domain channel, after inverse Fourier transform, the first P time samples of energy;
the second optimization problem is to extract the M-N-P rows of the F matrix excluding the head P row and take the first NfColumn, composition FH,
FH=F(S+1:N,1:Nf) (12)
Solving the minimization function corresponding to equation (11):
the function is the energy of tail M time samples of the equivalent frequency domain channel after inverse Fourier transform;
(2) solving of problems
For problem (11), the property of vectorization according to the matrix is exploited:
the optimization function (11) is equivalently transformed into:
wherein the content of the first and second substances,according to the definition of F-norm, the following results are obtained:
the solving method comprises the following steps: random initializationPerforming a number of iterations on x, using xiRepresenting the result of the ith iteration, then the solution for the (i + 1) th iteration is:
wherein ∠ represents the operation of taking the phase element by element for the vector;
for problem (13):
similar to (16), the objective function is further expressed as
Wherein A is as defined for formula (15), but fnIs FHA column vector of (a);
the solution method of equation (20) is explained as follows:
using the coordinate descent method, x is split into:
resolving the above formula (21) into:
thus, we obtain:
2. the improved SVD precoding matrix algorithm of claim 1, wherein for AHA, carrying out simplified operation, wherein the specific method comprises the following steps: by using the basic properties of the Kronecker product, the following are obtained:
wherein (F)HF) Which may be calculated in advance, ⊙ is the hadamard product of the matrix.
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Cited By (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN112910818A (en) * | 2021-01-28 | 2021-06-04 | 河北经贸大学 | Iterative diversity combining and demodulating method and device and terminal equipment |
CN115065579A (en) * | 2022-07-26 | 2022-09-16 | 新华三技术有限公司 | Channel estimation method, device, electronic equipment and storage medium |
CN115189734A (en) * | 2022-06-16 | 2022-10-14 | 复旦大学 | Phase rotation UCD precoding algorithm for MIMO-OFDM |
Citations (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN104702543A (en) * | 2013-12-04 | 2015-06-10 | 华为技术有限公司 | Precoding method and device |
US20150229374A1 (en) * | 2003-11-05 | 2015-08-13 | Sony Corporation | Wireless communications system, wireless communications apparatus, wireless communications method and computer program for wireless communication |
CN105681009A (en) * | 2015-12-29 | 2016-06-15 | 厦门大学 | Pilot frequency optimization and allocation combined pre-coding method for multi-user multiple-input multiple-output |
CN108933745A (en) * | 2018-07-16 | 2018-12-04 | 北京理工大学 | A kind of broad-band channel estimation method estimated based on super-resolution angle and time delay |
US20180359120A1 (en) * | 2015-12-18 | 2018-12-13 | Orange | Precompensation of interference induced by an ofdm/oqam modulation that is faster than nyquist |
CN109412983A (en) * | 2018-10-25 | 2019-03-01 | 哈尔滨工程大学 | A kind of extensive mimo channel algorithm for estimating of mesh freeization based on the domain DFT |
CN110022274A (en) * | 2018-12-24 | 2019-07-16 | 深圳先进技术研究院 | A kind of combined channel and carrier frequency deviation estimating method of millimeter wave MIMO-OFDM system |
CN110166401A (en) * | 2019-07-12 | 2019-08-23 | 电子科技大学 | The phase noise inhibition method of extensive MIMO ofdm system |
-
2020
- 2020-03-08 CN CN202010154734.0A patent/CN111464217B/en active Active
Patent Citations (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20150229374A1 (en) * | 2003-11-05 | 2015-08-13 | Sony Corporation | Wireless communications system, wireless communications apparatus, wireless communications method and computer program for wireless communication |
CN104702543A (en) * | 2013-12-04 | 2015-06-10 | 华为技术有限公司 | Precoding method and device |
US20180359120A1 (en) * | 2015-12-18 | 2018-12-13 | Orange | Precompensation of interference induced by an ofdm/oqam modulation that is faster than nyquist |
CN105681009A (en) * | 2015-12-29 | 2016-06-15 | 厦门大学 | Pilot frequency optimization and allocation combined pre-coding method for multi-user multiple-input multiple-output |
CN108933745A (en) * | 2018-07-16 | 2018-12-04 | 北京理工大学 | A kind of broad-band channel estimation method estimated based on super-resolution angle and time delay |
CN109412983A (en) * | 2018-10-25 | 2019-03-01 | 哈尔滨工程大学 | A kind of extensive mimo channel algorithm for estimating of mesh freeization based on the domain DFT |
CN110022274A (en) * | 2018-12-24 | 2019-07-16 | 深圳先进技术研究院 | A kind of combined channel and carrier frequency deviation estimating method of millimeter wave MIMO-OFDM system |
CN110166401A (en) * | 2019-07-12 | 2019-08-23 | 电子科技大学 | The phase noise inhibition method of extensive MIMO ofdm system |
Non-Patent Citations (2)
Title |
---|
XIA LIU; MAREK E. BIALKOWSKI: "SVD-Based Blind Channel Estimation for a MIMO OFDM System Employing a Simple Block Pre-coding Scheme", 《 EUROCON 2007 - THE INTERNATIONAL CONFERENCE ON "COMPUTER AS A TOOL"》 * |
赵雁妍: "LTE下行链路中的MIMO-OFDM传输系统关键技术的仿真研究", 《中国优秀博硕士学位论文全文数据库(硕士)信息科技辑》 * |
Cited By (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN112910818A (en) * | 2021-01-28 | 2021-06-04 | 河北经贸大学 | Iterative diversity combining and demodulating method and device and terminal equipment |
CN112910818B (en) * | 2021-01-28 | 2022-08-23 | 河北经贸大学 | Iterative diversity combining and demodulating method and device and terminal equipment |
CN115189734A (en) * | 2022-06-16 | 2022-10-14 | 复旦大学 | Phase rotation UCD precoding algorithm for MIMO-OFDM |
CN115189734B (en) * | 2022-06-16 | 2023-11-21 | 复旦大学 | Phase rotation UCD precoding algorithm for MIMO-OFDM |
CN115065579A (en) * | 2022-07-26 | 2022-09-16 | 新华三技术有限公司 | Channel estimation method, device, electronic equipment and storage medium |
CN115065579B (en) * | 2022-07-26 | 2022-11-01 | 新华三技术有限公司 | Channel estimation method, device, electronic equipment and storage medium |
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