CN107017929B - MIMO system signal transmitting and receiving method - Google Patents

MIMO system signal transmitting and receiving method Download PDF

Info

Publication number
CN107017929B
CN107017929B CN201710293082.7A CN201710293082A CN107017929B CN 107017929 B CN107017929 B CN 107017929B CN 201710293082 A CN201710293082 A CN 201710293082A CN 107017929 B CN107017929 B CN 107017929B
Authority
CN
China
Prior art keywords
mark
symbol
symbols
matrix
reconstructed
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.)
Active
Application number
CN201710293082.7A
Other languages
Chinese (zh)
Other versions
CN107017929A (en
Inventor
梁应敞
黄雨迪
张倩倩
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.)
University of Electronic Science and Technology of China
Original Assignee
University of Electronic Science and Technology of China
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 University of Electronic Science and Technology of China filed Critical University of Electronic Science and Technology of China
Priority to CN201710293082.7A priority Critical patent/CN107017929B/en
Publication of CN107017929A publication Critical patent/CN107017929A/en
Application granted granted Critical
Publication of CN107017929B publication Critical patent/CN107017929B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • 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
    • H04L1/00—Arrangements for detecting or preventing errors in the information received
    • H04L1/004—Arrangements for detecting or preventing errors in the information received by using forward error control
    • H04L1/0045—Arrangements at the receiver end
    • H04L1/0054—Maximum-likelihood or sequential decoding, e.g. Viterbi, Fano, ZJ algorithms

Abstract

The invention belongs to the technical field of communication, and particularly relates to a signal sending and receiving method of an MIMO system. In order to solve the problem that the traditional MIMO system based on channel detection needs long pilot frequency to carry out channel estimation, the scheme of the invention directly clusters the received signals and carries out class Labeling (Labeling) by using labeled Symbols (labeled Symbols), thereby recovering the transmitted Symbols. The method has the advantages that compared with the traditional technology, the method does not need channel estimation and realizes ML detection with lower complexity.

Description

MIMO system signal transmitting and receiving method
Technical Field
The invention belongs to the technical field of communication, and particularly relates to a signal sending and receiving method of an MIMO system.
Background
MIMO (Multi-input Multi-output, MIMO, multiple input multiple output) systems have been widely used in wireless communication systems, such as WiFi, LTE, etc. In a conventional MIMO system, a transmitting end needs to transmit pilot signals (PilotSymbols) to enable a receiving end to perform channel estimation, so that a receiver can recover a transmission symbol by using channel information and a linear detection method (such as LMMSE, ZF, and the like) or a detection method with high complexity (such as ML detection). Due to the existence of noise, the channel information can be estimated more accurately only by using a longer pilot signal. However, even if the channel information is completely known, the complexity of performing ML detection is extremely high.
Disclosure of Invention
In view of the above problems, an object of the present invention is to provide a MIMO system signal transmission and reception method that does not require channel estimation at the receiving end.
The technical scheme of the invention is as follows:
the MIMO system signal sending and receiving method is characterized in that:
at the transmitting end: the transmitter inserts M marker symbols before transmitting data information, wherein M is the number of possible modulation symbol combinations, namely the possible total class number of transmitted symbol vectors, M is a natural number, and the content of the marker symbols is known by a receiving end;
at the receiving end: clustering all signals received in a period of time through a clustering algorithm to form M categories, marking each category through the obtained mark symbols, and judging the data signals according to the clustering result and the mark symbols to recover the sending symbols.
In order to solve the problem that the traditional MIMO system based on channel detection needs long pilot frequency to carry out channel estimation, the general technical scheme of the invention directly clusters the received signals and carries out category Labeling (Labeling) by using label symbols (labeled symbols), thereby recovering the sending symbols.
The invention also provides another signal transmitting and receiving method of the MIMO system, which is characterized in that,
at the transmitting end: the transmitter inserts N before transmitting data informationt<M mutually uncorrelated tokens, the content of which is known to the receiving end;
at the receiving end: using N transmitted by the transmitting endtReconstructing a mark matrix consisting of M mark symbols by using the mark symbol vectors which are not related to each other, clustering all signals received in a period of time by using a clustering algorithm to form M categories, labeling each category by using the mark symbol matrix obtained by reconstruction, and judging the data signals according to a clustering result and the mark symbol matrix to recover to send symbols;
the N sent by the transmitting terminaltThe specific method for reconstructing the mark matrix of M mark symbols by the mark symbol vectors which are not related to each other is as follows:
let the mark matrix formed by M mark symbols be:
with a rank of NtSo that the transmitting end only needs to send NtA linearly independent mark symbolThe complete marker symbol matrix can be reconstructed by linear combination, i.e. L ═ LsA, where A is the reconstruction matrix, AijRepresents the reconstructed ithiWhen a symbol is marked lsjThe weight coefficient of (2).
Similar to the above-described scheme, the present embodiment is different in that the present embodiment clusters the received signals and performs class labeling using the label symbols, thereby recovering the transmitted symbolsThe method of Label Reconstruction (Label Reconnection) is adopted, so that only N is required to be sent in each time periodt<M marking symbols are used for marking the transmitting symbols corresponding to the M categories.
The method has the advantages that compared with the traditional technology, the method does not need channel estimation and realizes ML detection with lower complexity.
Drawings
Fig. 1 illustrates a first symbol detection method proposed by the present invention;
fig. 2 shows a second symbol detection method proposed by the present invention;
FIG. 3 illustrates a conventional pilot design approach;
FIG. 4 illustrates a first method of token design of the present invention;
FIG. 5 shows a first labeling method after received signal clustering;
FIG. 6 illustrates a second method of token design of the present invention;
FIG. 7 illustrates a third method of token design of the present invention;
FIG. 8 shows an algorithm flow of a clustering algorithm used by the present invention as an example;
fig. 9 shows a comparison between the performance of the second method of designing a marker symbol according to the present invention and the performance of the conventional method.
Fig. 10 shows a comparison between the performance of the proposed symbol design method three and the performance of the conventional method.
Detailed Description
The technical solution of the present invention will be described in detail below with reference to the accompanying drawings.
The signal transmitted during a period of time passes through the same channel on each receive antenna, assuming that the channel remains constant for that period of time. Since the noise follows the CSCG distribution, the signal received at the receiving antenna follows the multidimensional CSCG distribution given the transmitted symbol. Considering all signals received in the short time, they are subject to Gaussian Mixture Model (GMM), and received signals corresponding to the same transmitted symbol can be clustered into the same category by clustering the gaussian mixture Model formed by the received signals. And indicating the sending symbol corresponding to each category by using the corresponding relation between the mark information and the cluster categories, and further judging the received signal to recover the corresponding sending symbol.
As a special type of MIMO system, a Single-input Multiple-output (SIMO) model, a signal received by a receiver can be expressed as:
Figure BDA0001282371750000031
wherein
Figure BDA0001282371750000032
Is the received signal, p is the transmit power, h is the channel coefficient vector from the transmit antenna to the receiver, s (n) is the symbol transmitted by the transmit antenna, w (n) is the noise following a Circularly Symmetric Complex Gaussian (CSCG) distribution, i.e.
Figure BDA0001282371750000033
Σ＝E[(w-μ)(w-μ)H]W (n) and s (n) are independent of each other. For a general MIMO model, let H be the channel coefficient matrix,
Figure BDA0001282371750000035
is NtThe signals transmitted by the transmitting antennas together, the signals received by the receiving antennas are expressed as:
Figure BDA0001282371750000034
the signal transmitted during a period of time passes through the same channel on each receive antenna, assuming that the channel remains constant for that period of time. Let H (r,: denote the r-th row vector, C of the channel matrix HkIndicating that the transmitted symbol is the k-th modulation state, C at a given s (n) since the noise follows the CSCG distributionkOn condition that the signal y received on antenna r is receivedr(n) obeys a mean value of H (r:) · CkVariance is ΣkOf CSCG distribution, i.e.
Figure BDA0001282371750000041
Because the noises of different receiving antennas are independent of each other, the distribution of the signals received by all the antennas of the receiver in the period satisfies
Wherein M is CkPossible species,. pikIs each of CkThe probability of occurrence. Take 2 x 2 antenna array, QPSK modulation as an example, Ck＝[a+jb,p+jq]TWhere, a { ± 1}, b { ± 1}, p { ± 1}, and q { ± 1}, so M { ± 4 { ± 1}, respectively2. It is apparent that (3) is a Gaussian Mixture Model (GMM) consisting of M Gaussian distribution components.
The invention provides a clustering-based symbol detection method of an MIMO system. Received signals corresponding to the same transmitted symbol can be clustered into the same category by clustering the received signals. And recovering the transmitting symbol corresponding to the received signal by utilizing the corresponding relation between the preset mark symbol and the cluster category. The process is shown in figure 1, and comprises the following steps:
1. all signals received within a time period are clustered into M classes,
2. marking the corresponding transmitting symbol of each category by the marking symbol transmitted by the transmitting end,
3. and judging the data signal according to the clustering result and the corresponding relation between the category and the transmission symbol so as to recover the corresponding transmission symbol.
Using a 4 x 4 antenna array, QPSK modulation as an example, the transmitter inserts M-4 symbols before transmitting the data symbols4For example, if four antennas transmit 1+ j, 3+ j,1+ j and 3+3j respectively, after clustering, the received signal corresponding to the symbol will be labeled as [1+ j, 3+ j,1+ j, 3+3j [ ], or]TThe received signal can be decided upon, with the marker symbols in the same category.
The invention further relates toA method for Label Reconstruction (Label Reconstruction) is provided, so that each time interval only needs to send NtAnd labeling the sending symbols corresponding to the M categories by using a label symbol. The flow is shown in fig. 3, and the specific steps are as follows:
1. using N transmitted by the transmitting endtReconstructing a Label Matrix (Label Matrix) consisting of M Label symbols by utilizing linear combination of the Label symbol vectors which are mutually uncorrelated
2. Clustering the data signals received in a time interval and the signals corresponding to the reconstructed mark symbols to form M categories,
4. marking the sending symbols corresponding to each category through the mark symbol matrix obtained by reconstruction,
3. and judging the data signal according to the clustering result and the corresponding relation between the category and the transmission symbol so as to recover the corresponding transmission symbol.
Take 4 × 4 antenna array, 16QAM modulation as an example, where M is 164The mark symbols form a mark matrix
Figure BDA0001282371750000051
The Rank (Rank) is NtSo only N needs to be transmittedt4 linearly independent symbols
Figure BDA0001282371750000052
I.e. the complete marking matrix L can be reconstructed using linear combinations, i.e. L ═ LsA, where A is the reconstruction matrix, AijIndicating the l-th in the reconstructed mark matrixiWhen an individual markssjThe weight coefficient of (2).
The invention further provides a mark design method with error correction capability. This method transmits a vN at each time intervaltA vector of marker symbols, of which there is NtAnd each mark symbol vector is repeated v times, and the same mark symbol vector is averaged before mark reconstruction so as to reduce noise interference.
The invention further provides a hierarchical clustering-based MIMO system. Taking 2 × 2 antenna array and QPSK modulation as an example, the received signals form 16 categories in total, and all the received signals can be clustered into 4 categories, and then each category is clustered. The method can greatly improve the decoding speed.
The invention takes Expectation Maximization (EM) algorithm in a Gaussian mixture model clustering method as an example to illustrate the main idea of a clustering-based symbol detection system. Meanwhile, the invention takes the modulation symbol equiprobability and noise variance invariance as examples to explain the main idea of taking the inherent characteristics of the communication system as a priori to accelerate the detection algorithm.
The likelihood function of the received signal is:
Figure BDA0001282371750000053
wherein psi [ { pi [ ]1,θ1},{π2,θ2},...,{πM,θM}],θk＝{μk,ΣkSince the communication system modulates symbols with equal probability and the noise covariance matrices of multiple antennas are the same, there is Ψ [ { π }0,θ1},{π0,θ2},…,{π0,θM}],θk＝{μk,Σ0},π 01/M. Since each data point y (n) necessarily belongs to a certain gaussian component, an implicit variable z is introducedn∈{0,1}M,
Figure BDA0001282371750000061
Wherein z isnkIs znElement m of (2), i.e. znOnly one element is 1 and the others are 0. The expectation of the joint distribution of the received signal and the hidden variable is:
given the parameter Ψ of the gaussian mixture distribution, y (n) ═ ckThe posterior probability of (a) is:
assuming that gamma is knownnkIn the case of (2), by deriving (5) with respect to the mean and covariance matrices and taking 0, an updated formula can be obtained:
Figure BDA0001282371750000064
and
Figure BDA0001282371750000065
by iterating equations (6) and (7) and (8), the parameters of the gaussian mixture distribution can be obtained and used for subsequent classification. If further consideration is given to the mutual independence of the noise of the different receiving antennas, i.e. the noise covariance matrix is the diagonal matrix sigma2I, the updated formula of the covariance matrix can be written as: sigma0＝σ2I,
Figure BDA0001282371750000066
The calculation speed can be greatly improved by using the updating formula.
The invention is not limited to the utilization of EM algorithm to obtain the parameters of Gaussian mixture distribution, and is not limited to the utilization of modulation symbol equiprobability and noise variance invariance to accelerate the detection process. The invention can use the uncorrelated and equal variance of each receiving antenna noise to accelerate the detection process; a multi-stage clustering approach may also be utilized to speed up the detection process.
FIG. 3 illustrates a conventional pilot design method where gray is the antenna transmitting 0 and black is the antenna transmitting 1, each antenna transmits 1 once, at least N need be transmittedtEach channel is estimated per symbol. Because a single pilot is affected by noise with a large variance, the pilot needs to be repeatedly transmitted for accurate estimation.
FIG. 4 illustrates a method of designing a token according to the present invention. The method inserts M marker symbols before transmitting data symbols, each marker symbol vector being one possible transmitted symbol vector. At 2X 2 daysLinear array, QPSK modulation for example, for a total of 42Possible transmit symbol vectors. With the transmitted data symbol being [1+ j,1+ j ]]TFor example, since the data symbol and the mark symbol pass through the same channel during the period, all the transmitted symbols are [1+ j,1+ j ]]TData symbols and marker symbols [1+ j,1+ j ]]TMay be clustered into the same category. The relation between the cluster class and the transmission symbol is established by the marker symbol, and then all the data signals of the class are decided to be the same transmission symbol as the marker symbol. Wherein the results after clustering are shown by figure 5.
Fig. 5 shows the results of clustering and the locations of the markers for the method shown in fig. 4.
FIG. 6 illustrates another proposed scheme of the present invention to only send N during a time periodt<A method of designing a mark with M mark symbols instead of M mark symbols. Taking a 4 × 4 antenna array and 16QAM modulation as an example, M marker symbols form a marker matrix
Figure BDA0001282371750000071
The Rank (Rank) is NtSo only N needs to be transmittedtA linearly independent mark symbol
Figure BDA0001282371750000072
I.e. the complete marking matrix can be reconstructed using linear combinations, i.e. L ═ LsA, wherein AijRepresents the reconstructed ithiTime of received signal l corresponding to symbolsjThe weight coefficient of (2).
Fig. 7 shows another design method of a mark symbol with error correction function proposed by the present invention. Due to noise effects, the markers may appear at a distance from the corresponding class mean, thereby affecting performance. By repeating the same tag v times, a total of vNs need to be senttThe symbols are marked, thereby improving the noise immunity.
Fig. 8 shows an algorithm flow of a clustering algorithm used as an example of the present invention. Firstly, parameters of Gaussian mixture distribution are initialized, and then y (n) -c is calculated in an iterative waykA posteriori ofProbability of
Figure BDA0001282371750000073
And updating parameters of the Gaussian mixture distribution, namely:
and
Figure BDA0001282371750000082
through the iterative formula, the parameters of the gaussian mixture distribution can be obtained and used for subsequent classification.
Fig. 9 shows the performance comparison between the second method for designing the mark of the present invention and the conventional method. The simulation scene is that two transmitting antennas cooperate with Rayleigh channels of two receiving antennas, 1600 data symbols are sent in each time period, the modulation mode is QPSK, and the error rate is averaged by 3000 experiments. The scheme provided by the invention adopts a mark symbol design method based on mark reconstruction, and mark symbols are not repeated, as shown in fig. 6. The channel estimation uses the same number of pilot symbols as the present scheme. The performance of the scheme provided by the invention is obviously based on the maximum likelihood estimation of the channel estimation and two MMSE detection algorithms, and has a convergence property, namely the maximum likelihood estimation performance of accurately knowing the channel information is achieved under the condition of not knowing the channel information after the SNR is higher than a certain threshold.
Fig. 10 shows the performance comparison of the proposed marker design method three with the conventional method. The simulation scene is that two transmitting antennas cooperate with Rayleigh channels of two receiving antennas, 1600 data symbols are sent in each time period, the modulation mode is QPSK, and the error rate is averaged by 3000 experiments. The scheme provided by the invention adopts a mark symbol design method based on mark reconstruction with an error correction function, and as shown in fig. 7, mark symbols are repeated twice. The channel estimation uses the same number of pilot symbols as the present scheme. The performance of the scheme provided by the invention is obviously based on the maximum likelihood estimation of the channel estimation and two MMSE detection algorithms, and has a convergence property, namely the maximum likelihood estimation performance of accurately knowing the channel information is achieved under the condition of not knowing the channel information after the SNR is higher than a certain threshold.
The EM algorithm utilized by the invention is not limited to equal probability sending symbol scene, and meanwhile, the variance of the noise can also change along with the time. In addition, the invention is not limited to the parameter updating of the clustering algorithm by using the EM algorithm.

Claims (1)

  1. A signal transmitting and receiving method of a MIMO system, characterized in that,
    at the transmitting end: the transmitter inserts N before transmitting data informationtA plurality of mutually uncorrelated tokens, the content of which is known to the receiving end;
    at the receiving end: using N transmitted by the transmitting endtReconstructing a mark matrix consisting of M mark symbols by utilizing linear combination of the mark symbol vectors which are not related to each other, clustering data signals received in a period of time and signals corresponding to the reconstructed mark symbols to form M categories, labeling a sending symbol corresponding to each category through the reconstructed mark matrix, and recovering the sending symbol according to a clustering result and the corresponding relation between the categories and the sending symbol;
    the N sent by the transmitting terminaltThe specific method for reconstructing the mark matrix of M mark symbols by the mark symbol vectors which are not related to each other is as follows:
    let the mark matrix formed by M mark symbols be:
    Figure FDA0002298375020000011
    with a rank of NtSo that the transmitting end only needs to send NtA linearly independent mark symbol
    Figure FDA0002298375020000012
    I.e. the complete marking matrix can be reconstructed, i.e. L ═ LsA, where the elements A in the matrix A are reconstructedijIndicating a reconstructed marker symbol liHour lsjThe weight coefficient of (2).
CN201710293082.7A 2017-04-28 2017-04-28 MIMO system signal transmitting and receiving method Active CN107017929B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201710293082.7A CN107017929B (en) 2017-04-28 2017-04-28 MIMO system signal transmitting and receiving method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201710293082.7A CN107017929B (en) 2017-04-28 2017-04-28 MIMO system signal transmitting and receiving method

Publications (2)

Publication Number Publication Date
CN107017929A CN107017929A (en) 2017-08-04
CN107017929B true CN107017929B (en) 2020-02-18

Family

ID=59447445

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201710293082.7A Active CN107017929B (en) 2017-04-28 2017-04-28 MIMO system signal transmitting and receiving method

Country Status (1)

Country Link
CN (1) CN107017929B (en)

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107682289B (en) * 2017-09-30 2019-12-10 电子科技大学 Mark symbol design method for mark auxiliary clustering receiver
CN107689847B (en) * 2017-09-30 2019-11-01 电子科技大学 Mark auxiliary radio communication system signal method of sending and receiving

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101513093A (en) * 2006-09-08 2009-08-19 高通股份有限公司 Recovery from resource mismatch in a wireless communication system
CN104079524A (en) * 2014-07-24 2014-10-01 电子科技大学 Method for identifying OFDM (orthogonal frequency division multiplexing)-based distorted communication signals under QAM (quadrature amplitude modulation)

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2016150488A1 (en) * 2015-03-24 2016-09-29 Sony Corporation Pilot signal resource allocation for a cellular mimo system

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101513093A (en) * 2006-09-08 2009-08-19 高通股份有限公司 Recovery from resource mismatch in a wireless communication system
CN104079524A (en) * 2014-07-24 2014-10-01 电子科技大学 Method for identifying OFDM (orthogonal frequency division multiplexing)-based distorted communication signals under QAM (quadrature amplitude modulation)

Also Published As

Publication number Publication date
CN107017929A (en) 2017-08-04

Similar Documents

Publication Publication Date Title
Guo et al. Exploiting multiple antennas for cognitive ambient backscatter communication
CN103701513B (en) Generalized spatial modulation system transmission antenna system of selection under correlated channels
CN106059972B (en) A kind of Modulation Identification method under MIMO correlated channels based on machine learning algorithm
CN107359921B (en) Mixed precoding method of large-scale MIMO system based on standard orthogonalization
CN100499395C (en) Space-time transmit diversity (STTD) for multiple antennas in radio communications
CN107135017B (en) Signal transmitting and receiving method of backscattering communication system
CN104113398B (en) MIMO blind Channel Estimation fuzziness minimizing technologies based on Orthogonal Space-Time Block Code
CN109861731B (en) Hybrid precoder and design method thereof
CN108540185B (en) Differential space modulation method combined with space-time block code
CN100571098C (en) The maximum likelihood detecting method of low complex degree and device in the communication system
Marseet et al. Application of complex-valued convolutional neural network for next generation wireless networks
Huang et al. A machine learning approach to MIMO communications
Somekh et al. Detecting the number of transmit antennas with unauthorized or cognitive receivers in MIMO systems
CN107017929B (en) MIMO system signal transmitting and receiving method
CN111628833A (en) MIMO antenna number estimation method based on convolutional neural network
CN113114603B (en) Information recovery method and device for MIMO-OFDM system
CN107070603B (en) Space-time block code system signal method of sending and receiving
CN109981151A (en) Improved Gauss tree approximation message transmission detection algorithm in extensive mimo system
CN107659348B (en) SLNR and THP mixed adaptive precoding design method
CN105119860B (en) A kind of signal detecting method of generalized spatial modulation system
CN109698741B (en) A kind of novel full-duplex communication transmitter and receiver design method
CN107689847B (en) Mark auxiliary radio communication system signal method of sending and receiving
CN107395542B (en) Signal transmitting and receiving method of bidirectional relay communication system
CN103326825A (en) Low complexity decoding method of quasi-orthogonal space-time block codes
CN104734756A (en) MIMO system detection method and device

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
GR01 Patent grant
GR01 Patent grant