CN103873411A - Method and device for maximum likelihood frequency offset estimation based on joint pilot frequency - Google Patents

Abstract

The invention discloses a method and a device for maximum likelihood frequency offset estimation based on a joint pilot frequency. The method and the device are used for reception synchronization of a multi-input multi-output (MIMO)-orthogonal frequency division multiplexing (OFDM) system. The method comprises the following steps: a receiving end preprocesses a corresponding nth transmission pilot symbol matrix D(n), a discrete Fourier transform (DFT) matrix F, and a matrix W(wavy line) composed of the first L columns of the matrix F to obtain a matrix P(n); the receiving end respectively and correspondingly stores a receiving pilot symbol matrix R(n) and the matrix P(n) in first-in-first-out sliding window memories with the length being M; and M frequency offset matrixes are constructed, the receiving pilot symbol matrix R(n) and the matrix P(n) are acquired from the sliding window memories, a likelihood function is calculated according to the receiving pilot symbol matrix R(n), the matrix P(n) and the M frequency offset matrixes, and frequency offset estimation values to be selected are substituted in the likelihood function to obtain a frequency offset estimation value, which makes the accumulation of values of the likelihood function minimum, as the final maximum likelihood frequency offset estimation value.

Description

Maximum likelihood frequency deviation estimating method and device based on joint pilot

Technical field

The present invention relates to field of mobile communication, particularly relate to a kind of maximum likelihood frequency deviation estimating method and device based on joint pilot.

Background technology

For any digital communication system, be synchronously prerequisite and the important guarantee of reliable data transmission.The quality of net synchronization capability will directly affect the performance of whole communication system.But in wireless communication system, due to the frequency difference between sending ending equipment and receiving device, and ustomer premises access equipment moves the impacts such as brought Doppler frequency-shift, makes to exist frequency deviation between carrier frequency and the frequency of local crystal oscillator.In order to guarantee the transmitting of data, must carry out accurately estimating and being compensated to the frequency deviation of signal.

In addition third generation cooperative programme (3rd Generation Partnership Project, referred to as 3GPP) Long Term Evolution (Long Term Evolution, referred to as LTE) the descending OFDM that the availability of frequency spectrum is higher (the Orthogonal Frequency Division Multiplexing that adopted, referred to as OFDM) modulation technique, OFDM symbol is to be formed by multiple sub-carrier signal stacks, utilize the orthogonality between subcarrier to carry out demodulation at receiving terminal, thereby the orthogonality of ofdm system antithetical phrase intercarrier propose strict requirement.In actual transmissions, owing to not mating the frequency departure bringing between Doppler frequency shift and transceiver local oscillator, can destroy the orthogonality between ofdm system subcarrier, cause and between subcarrier, disturb (ICI).Timing slip can cause intersymbol interference (ISI), reduces the validity of Cyclic Prefix (CP).Therefore, synchronously very important for ofdm system.

In the prior art, the frequency synchronization method in ofdm system roughly can be divided into two classes, i.e. blind synchronized algorithm and the synchronized algorithm based on training sequence.The known pilot symbols that synchronized algorithm utilization based on training sequence is inserted realizes synchronously.Blind synchronized algorithm has mainly utilized the peculiar Cyclic Prefix character of ofdm system to complete synchronous estimation.Extensively adopt all kinds of training sequences in view of comprising in the practical communication system such as TD-LTE standard, therefore often more had in actual applications actual using value around the auxiliary frequency synchronization algorithm of pilot tone.Under the precondition of receiving terminal known training sequence, the frequency deviation of maximum likelihood is estimated often to obtain better frequency deviation estimated performance, and becomes the emphasis that all kinds of research institutes pay close attention to.Maximum likelihood frequency deviation estimating method under MIMO-OFDM condition is a lot, these methods of estimation have been selected essentially identical likelihood function, and how the difference of different frequency deviation algorithm for estimating is derived and to be obtained concrete frequency deviation algorithm for estimating by given likelihood function if being mainly reflected in.

Comprehensive analysis at present both at home and abroad around the frequency deviation estimation under MIMO-OFDM and ofdm system condition or the achievement of frequency deviation and the research of channel joint Estimation, on the basis of given frequency deviation and channel joint likelihood function, at present existing a large amount of practicable frequency deviations and channel estimation methods can be for using for reference, and these frequency deviations and channel estimation method have proposed a large amount of feasible solutions from pilot frequency sequence design, the analysis of OFDM symbol characteristic and utilization, pilot tone OFDM Design of Symbols, concrete frequency deviation algorithm for estimating equal angles.But meanwhile, be not difficult to find, the performance of frequency deviation estimating method of the prior art is also lower, therefore, is badly in need of a kind of high-performance frequency offset estimation technique solution of the TD-LTE of meeting system requirements.

Summary of the invention

The invention provides a kind of maximum likelihood frequency deviation estimating method and device based on joint pilot, to solve the low problem of offset frequency estimated performance in prior art.

The invention provides a kind of maximum likelihood frequency deviation estimating method based on joint pilot, synchronous for the reception of multiple-input and multiple-output MIMO-orthogonal frequency division multiplex OFDM system, comprising: receiving terminal is to corresponding n frequency domain pilot symbol transmitted matrix D ⁽ⁿ⁾, discrete Fourier transform (DFT) DFT matrix F and matrix F the matrix that forms of front L row carry out preliminary treatment, obtain matrix P ⁽ⁿ⁾; Receiving terminal will receive frequency pilot sign matrix R ⁽ⁿ⁾with matrix P ⁽ⁿ⁾storing respectively length into is in the sliding window memory of first-in first-out of M; Build M frequency deviation matrix, from sliding window memory, obtain and receive frequency pilot sign matrix R ⁽ⁿ⁾with matrix P ⁽ⁿ⁾, according to receiving frequency pilot sign matrix R ⁽ⁿ⁾, matrix P ⁽ⁿ⁾go out likelihood function with M frequency deviation matrix computations, by frequency deviation estimated value substitution likelihood function to be selected, obtaining and making the frequency deviation estimated value of cumulative likelihood function value minimum is final maximum likelihood frequency deviation estimated value.

Preferably, receiving terminal is to corresponding pilot symbol transmitted matrix D ⁽ⁿ⁾, discrete Fourier transform (DFT) DFT matrix F and matrix F the matrix that forms of front L row

carry out preliminary treatment, obtain matrix P ⁽ⁿ⁾specifically comprise:

Receiving terminal is according to formula 1, to corresponding pilot symbol transmitted matrix D ⁽ⁿ⁾, discrete Fourier transform (DFT) DFT matrix F and matrix F the matrix that forms of front L row

carry out preliminary treatment, obtain matrix P ⁽ⁿ⁾;

Formula 1;

Wherein,

n _tfor the number of transmit antennas of mimo system, N is OFDM sub-carrier number,

represent n the pilot tone sign matrix that p root transmitting antenna sends,

represent n the pilot tone symbol that p root transmitting antenna sends; F ∈ C ^{n × N}for DFT matrix, the individual element of its (l, m) is $F_{l,m}=\frac{1}{\sqrt{N}}{e}^{-j(2\mathrm{\π}(l-1)(m-1)/N)};$ $\tilde{W}={(W\⊗I_{N_T})}_{{\mathrm{NN}}_T\×{\mathrm{LN}}_T},$ W represents that the front L row of DFT matrix F are F=[W|V], W ∈ C ^{n × L}, V ∈ C ^{n × (N-L)}, and have W ^hv=0, WW ^h+ VV ^h=I, H is that conjugation turns order.

Preferably, receiving terminal will receive frequency pilot sign matrix R ⁽ⁿ⁾with matrix P ⁽ⁿ⁾store respectively length into and be in the sliding window memory of first-in first-out of M and specifically comprise:

Receiving terminal will receive frequency pilot sign matrix R ⁽ⁿ⁾with matrix P ⁽ⁿ⁾storing respectively length into is in the sliding window memory of first-in first-out of M, obtains following two groups of data: $\begin{array}{c}{\{P}^{(n-k)},k=\mathrm{0,1},\·\·\·,M-1\}\\ \{R^{(n-k)},k=\mathrm{0,1},\·\·\·,M-1\}\end{array},$ Wherein,

n _rfor the reception antenna number of mimo system, N is OFDM sub-carrier number, and under the condition of system receiving terminal sign synchronization, n the reception frequency pilot sign that q root reception antenna receives is: $r_q^{\left(n\right)}=\underset{p=1}{\overset{N_T}{\mathrm{\&Sigma;}}}{E}^{\left(n\right)}{F}^H{D}_p^{\left(n\right)}{\mathrm{Wg}}_{q,p}^{\left(n\right)}+n_q,$ Wherein, $E^{\left(n\right)}={\mathrm{<figure-callout\; id="2"\; label="e\; j"\; filenames="HDA00002579101000031.png,HDA00002579101000041.png"\; state="\{\{state\}\}">e</figure-callout>}}^{j}E$ Represent n the frequency deviation matrix that frequency pilot sign is corresponding, E=diag ([1, e ^{j2 π ε/N}..., e ^{j2 π (N-1) ε/N}] ^t), ε is normalization frequency deviation value, L _nrepresent n the corresponding sequence number of first sampling time of frequency pilot sign;

represent the L footpath time domain channel gain that n frequency pilot sign experiences while transmission between (q, p) antenna pair, T represents to turn order, n _qrepresent that N × 1 dimension zero-mean, every one dimension variance that q root reception antenna receives are

multiple Gaussian noise, j ²=-1.

Preferably, building M frequency deviation matrix specifically comprises:

Build M frequency deviation matrix according to formula 2; $E^{(n-k)}={\mathrm{<figure-callout\; id="2"\; label="e\; j"\; filenames="HDA00002579101000031.png,HDA00002579101000041.png"\; state="\{\{state\}\}">e</figure-callout>}}^j\&CenterDot;\mathrm{diag}\left({[1,{\mathrm{<figure-callout\; id="2"\; label="e\; j"\; filenames="HDA00002579101000031.png,HDA00002579101000041.png"\; state="\{\{state\}\}">e</figure-callout>}}^j,\&CenterDot;\&CenterDot;\&CenterDot;,{\mathrm{<figure-callout\; id="2"\; label="e\; j"\; filenames="HDA00002579101000031.png,HDA00002579101000041.png"\; state="\{\{state\}\}">e</figure-callout>}}^j]}^T\right)$ Formula 2;

Wherein, k=0,1 ..., M-1, ε is normalization frequency deviation value, N is OFDM sub-carrier number, j ²=-1, L _n-krepresent n-k the corresponding sequence number of first sampling time of frequency pilot sign, T represents to turn order.

Preferably, from sliding window memory, obtain and receive frequency pilot sign matrix R ⁽ⁿ⁾with matrix P ⁽ⁿ⁾, according to receiving frequency pilot sign matrix R ⁽ⁿ⁾, matrix P ⁽ⁿ⁾go out likelihood function with M frequency deviation matrix computations, frequency deviation estimated value substitution likelihood function to be selected specifically comprised:

From sliding window memory, obtain and receive frequency pilot sign matrix R ⁽ⁿ⁾with matrix P ⁽ⁿ⁾, according to receiving frequency pilot sign matrix R ⁽ⁿ⁾, matrix P ⁽ⁿ⁾with M frequency deviation matrix, and calculate likelihood function according to formula 3;

λ ⁽ⁿ⁾(ε)=‖ (R ⁽ⁿ⁾) ^he ⁽ⁿ⁾(P ⁽ⁿ⁾-I) (E ⁽ⁿ⁾) ^hr ⁽ⁿ⁾‖ _f formula 3;

Wherein, ‖ ‖ _frepresent the F norm of getting matrix, H is that conjugation turns order,

represent n the frequency deviation matrix that frequency pilot sign is corresponding, E=diag ([1, e ^{j2 π ε/N}..., e ^{j2 π (N-1) ε/N}] ^t), N is OFDM sub-carrier number, λ ⁽ⁿ⁾(ε) likelihood function of estimating for maximum likelihood frequency deviation;

By frequency deviation estimated value substitution formula 3 to be selected.

Preferably, obtain that to make the frequency deviation estimated value of cumulative likelihood function value minimum be that final maximum likelihood frequency deviation estimated value specifically comprises:

Adopt the method for substep search, calculate according to formula 4 frequency offset estimation that makes following cumulative likelihood function value minimum

and by minimum frequency offset estimation as final maximum likelihood frequency deviation estimated value; ${\widehat{\mathrm{\&epsiv;}}}^{\left(n\right)}=\underset{\mathrm{\&epsiv;}}{\arg \min }\left\{\underset{k=0}{\overset{M-1}{\mathrm{\&Sigma;}}}{\mathrm{\&lambda;}}^{(n-k)}\left(\mathrm{\&epsiv;}\right)\right\}$ Formula 4;

Wherein, M is the length of sliding window memory.

Preferably, the method for substep search specifically comprises:

Step 1, supposes that initial frequency deviation scope is for (ε _max, ε _max);

Step 2 is all divided into frequency deviation hunting zone P interval in the time of each search, then calculates successively relatively likelihood function

in the value size of each interval endpoint;

Step 3, according to likelihood function

the process that changes from big to small and change from small to big with the selection value of different normalization frequency deviation values, determines the frequency deviation region (ε that next round frequency deviation is searched for ₁, ε ₂), wherein ε ₁, ε ₂choose with likelihood function value variation relation meet $\underset{n=0}{\overset{M-1}{\mathrm{\&Sigma;}}}{\mathrm{\&lambda;}}^{\left(n\right)}\left({\mathrm{\&epsiv;}}_1\right)>\underset{n=1}{\overset{M-1}{\mathrm{\&Sigma;}}}{\mathrm{\&lambda;}}^{\left(n\right)}\left({\mathrm{\&epsiv;}}_0\right),$ $\underset{n=1}{\overset{M-1}{\mathrm{\&Sigma;}}}{\mathrm{\&lambda;}}^{\left(n\right)}\left({\mathrm{\&epsiv;}}_2\right)>\underset{n=1}{\overset{M-1}{\mathrm{\&Sigma;}}}{\mathrm{\&lambda;}}^{\left(n\right)}\left({\mathrm{\&epsiv;}}_0\right),$ Wherein ε ₀=(ε ₁+ ε ₂)/2;

Step 4, at the new frequency deviation region (ε of calculative determination ₁, ε ₂) basis on, repeat above-mentioned steps 2-3, until current frequency deviation step-size in search has met the predetermined frequency offset estimation accuracy requirement of system, export current frequency deviation hunting zone (ε ₁, ε ₂) intermediate value ε ₀for the final valuation of this search

The present invention also provides a kind of maximum likelihood frequency deviation estimation device based on joint pilot, reception for multiple-input and multiple-output MIMO-orthogonal frequency division multiplex OFDM system is synchronous, comprise: pretreatment module, for to corresponding n frequency domain pilot symbol transmitted matrix D ⁽ⁿ⁾, discrete Fourier transform (DFT) DFT matrix F and matrix F the matrix that forms of front L row

carry out preliminary treatment, obtain matrix P ⁽ⁿ⁾; Memory module, for receiving frequency pilot sign matrix R ⁽ⁿ⁾with matrix P ⁽ⁿ⁾storing respectively length into is in the sliding window memory of first-in first-out of M; Frequency deviation estimating modules for building M frequency deviation matrix, is obtained and is received frequency pilot sign matrix R from sliding window memory ⁽ⁿ⁾with matrix P ⁽ⁿ), according to receiving frequency pilot sign matrix R ⁽ⁿ⁾, matrix P ⁽ⁿ⁾go out likelihood function with M frequency deviation matrix computations, by frequency deviation estimated value substitution likelihood function to be selected, obtaining and making the frequency deviation estimated value of cumulative likelihood function value minimum is final maximum likelihood frequency deviation estimated value.

Preferably, pretreatment module specifically for: according to formula 1, to corresponding pilot symbol transmitted matrix D ⁽ⁿ⁾, discrete Fourier transform (DFT) DFT matrix F and matrix F the matrix that forms of front L row

carry out preliminary treatment, obtain matrix P ⁽ⁿ⁾;

Formula 1;

Wherein,

n _tfor the number of transmit antennas of mimo system, N is OFDM sub-carrier number, represent n the pilot tone sign matrix that p root transmitting antenna sends,

represent n the pilot tone symbol that p root transmitting antenna sends; F ∈ C ^{n × N}for DFT matrix, the individual element of its (l, m) is $F_{l,m}=\frac{1}{\sqrt{N}}{e}^{-j(2\mathrm{\&pi;}(l-1)(m-1)/N)};$ $\tilde{W}={(W\&CircleTimes;I_{N_T})}_{{\mathrm{NN}}_T\&times;{\mathrm{LN}}_T},$ W represents that the front L row of DFT matrix F are F=[W|V], W ∈ C ^{n × L}, V ∈ C ^n*(N-L), and have W ^hv=0, WW ^h+ VV ^h=I, H is that conjugation turns order;

Memory module specifically for: will receive frequency pilot sign matrix R ⁽ⁿ⁾with matrix P ⁽ⁿ⁾storing respectively length into is in the sliding window memory of first-in first-out of M, obtains following two groups of data: $\begin{array}{c}{\{P}^{(n-k)},k=\mathrm{0,1},\&CenterDot;\&CenterDot;\&CenterDot;,M-1\}\\ \{R^{(n-k)},k=\mathrm{0,1},\&CenterDot;\&CenterDot;\&CenterDot;,M-1\}\end{array},$ Wherein,

n _rfor the reception antenna number of mimo system, N is OFDM sub-carrier number, and under the condition of system receiving terminal sign synchronization, n the reception frequency pilot sign that q root reception antenna receives is: $r_q^{\left(n\right)}=\underset{p=1}{\overset{N_T}{\mathrm{\&Sigma;}}}{E}^{\left(n\right)}{F}^H{D}_p^{\left(n\right)}{\mathrm{Wg}}_{q,p}^{\left(n\right)}+n_q,$ Wherein, $E^{\left(n\right)}={\mathrm{<figure-callout\; id="2"\; label="e\; j"\; filenames="HDA00002579101000031.png,HDA00002579101000041.png"\; state="\{\{state\}\}">e</figure-callout>}}^{j}E$ Represent n the frequency deviation matrix that frequency pilot sign is corresponding, E=diag ([1, e ^{j2 π ε/N}..., e ^{j2 π (N-1) ε/N}] ^t), ε is normalization frequency deviation value, L _nrepresent n the corresponding sequence number of first sampling time of frequency pilot sign;

represent the L footpath time domain channel gain that n frequency pilot sign experiences while transmission between (q, p) antenna pair, T represents to turn order, n _qrepresent that N × 1 dimension zero-mean, every one dimension variance that q root reception antenna receives are multiple Gaussian noise, j ²-1;

Frequency deviation estimating modules specifically for: build M frequency deviation matrix according to formula 2; $E^{(n-k)}={\mathrm{<figure-callout\; id="2"\; label="e\; j"\; filenames="HDA00002579101000031.png,HDA00002579101000041.png"\; state="\{\{state\}\}">e</figure-callout>}}^j\&CenterDot;\mathrm{diag}\left({[1,{\mathrm{<figure-callout\; id="2"\; label="e\; j"\; filenames="HDA00002579101000031.png,HDA00002579101000041.png"\; state="\{\{state\}\}">e</figure-callout>}}^j,\&CenterDot;\&CenterDot;\&CenterDot;,{\mathrm{<figure-callout\; id="2"\; label="e\; j"\; filenames="HDA00002579101000031.png,HDA00002579101000041.png"\; state="\{\{state\}\}">e</figure-callout>}}^j]}^T\right)$ Formula 2;

Wherein, k=0,1 ..., M-1, ε is normalization frequency deviation value, N is OFDM sub-carrier number, j ²=-1, L _n-krepresent n-k the corresponding sequence number of first sampling time of frequency pilot sign, T represents to turn order;

From sliding window memory, obtain and receive frequency pilot sign matrix R ⁽ⁿ⁾with matrix P ⁽ⁿ⁾, according to receiving frequency pilot sign matrix R ⁽ⁿ⁾, matrix P ⁽ⁿ⁾with M frequency deviation matrix, and calculate likelihood function according to formula 3;

λ ⁽ⁿ⁾(ε)=‖ (R ⁽ⁿ⁾) ^he ⁽ⁿ⁾(P ⁽ⁿ⁾-I) (E ⁽ⁿ⁾) ^hr ⁽ⁿ⁾‖ _f formula 3;

Wherein, ‖ ‖ _frepresent the F norm of getting matrix, H is that conjugation turns order, represent n the frequency deviation matrix that frequency pilot sign is corresponding, E=diag ([1, e ^{j2 π ε/N}..., e ^{j2 π (N-1) ε/N}] ^t), N is OFDM sub-carrier number, λ ⁽ⁿ⁾(ε) likelihood function of estimating for maximum likelihood frequency deviation;

By frequency deviation estimated value substitution formula 3 to be selected;

Adopt the method for substep search, calculate according to formula 4 frequency offset estimation that makes following cumulative likelihood function value minimum and by minimum frequency offset estimation

as final maximum likelihood frequency deviation estimated value; ${\widehat{\mathrm{\&epsiv;}}}^{\left(n\right)}=\underset{\mathrm{\&epsiv;}}{\arg \min }\left\{\underset{k=0}{\overset{M-1}{\mathrm{\&Sigma;}}}{\mathrm{\&lambda;}}^{(n-k)}\left(\mathrm{\&epsiv;}\right)\right\}$ Formula 4;

Wherein, M is the length of sliding window memory.

Preferably, frequency deviation estimating modules specifically comprises:

Submodule is set, for supposing that initial frequency deviation scope is for (ε _max, ε _max);

Calculating sub module, interval for all frequency deviation hunting zone is divided into P in the time searching at every turn, then calculate successively relatively likelihood function

in the value size of each interval endpoint;

Determine submodule, for according to likelihood function

the process that changes from big to small and change from small to big with the selection value of different normalization frequency deviation values, determines the frequency deviation region (ε that next round frequency deviation is searched for ₁, ε ₂), wherein ε ₁, ε ₂choose with likelihood function value variation relation meet

$\underset{n=1}{\overset{M-1}{\mathrm{\&Sigma;}}}{\mathrm{\&lambda;}}^{\left(n\right)}\left({\mathrm{\&epsiv;}}_2\right)>\underset{n=1}{\overset{M-1}{\mathrm{\&Sigma;}}}{\mathrm{\&lambda;}}^{\left(n\right)}\left({\mathrm{\&epsiv;}}_0\right),$ Wherein ε ₀=(ε ₁+ ε ₂)/2;

Output sub-module, at the new frequency deviation region (ε of calculative determination ₁, ε ₂) basis on, call calculating sub module and definite submodule, until current frequency deviation step-size in search has met the predetermined frequency offset estimation accuracy requirement of system, export current frequency deviation hunting zone (ε ₁, ε ₂) intermediate value ε ₀for the final valuation of this search

Beneficial effect of the present invention is as follows:

By according to revised reception pilot signal (R ⁽ⁿ⁾) ^hr ⁽ⁿ⁾in derive the maximum likelihood estimator of frequency deviation, and based on revising measuring-signal (R ⁽ⁿ⁾) ^hr ⁽ⁿ⁾derive on the basis of maximal possibility estimation relation of frequency deviation, adopt the method for sliding window that adjacent multiple subframe frequency pilot signs are combined and utilized significantly to improve frequency deviation estimated performance, solve the low problem of offset frequency estimated performance in prior art, it is simple that the frequency deviation estimating method of the technical scheme of the embodiment of the present invention has calculating, the advantage that frequency offset estimation scope is large, frequency deviation estimated performance is good.

Above-mentioned explanation is only the general introduction of technical solution of the present invention, in order to better understand technological means of the present invention, and can be implemented according to the content of specification, and for above and other objects of the present invention, feature and advantage can be become apparent, below especially exemplified by the specific embodiment of the present invention.

Accompanying drawing explanation

By reading below detailed description of the preferred embodiment, various other advantage and benefits will become cheer and bright for those of ordinary skills.Accompanying drawing is only for the object of preferred implementation is shown, and do not think limitation of the present invention.And in whole accompanying drawing, represent identical parts by identical reference symbol.In the accompanying drawings:

Fig. 1 is the flow chart of the maximum likelihood frequency deviation estimating method based on joint pilot of the embodiment of the present invention;

Fig. 2 is the schematic diagram of the TD-LTE standard Uplink MIMO SC-FDMA system transmitting terminal of the embodiment of the present invention;

Fig. 3 is the schematic diagram of the TD-LTE standard Uplink MIMO SC-FDMA system receiving terminal of the embodiment of the present invention;

Fig. 4 be the embodiment of the present invention be the descending MIMO-OFDM system of TD-LTE standard sending and receiving end schematic diagram;

Fig. 5 is the processing schematic diagram of the enhancing maximum likelihood frequency deviation algorithm for estimating of the embodiment of the present invention;

Fig. 6 is that the Sliding window data of the embodiment of the present invention upgrades schematic diagram;

Fig. 7 is that the frequency deviation likelihood estimating searching scope of the embodiment of the present invention is adjusted schematic diagram;

Fig. 8 is that under the TD-LTE standard of the embodiment of the present invention, frequency deviation is estimated mean square deviation performance schematic diagram;

Fig. 9 is the cumulative probability function schematic diagram one that under the TD-LTE standard of the embodiment of the present invention, frequency deviation is estimated;

Figure 10 is the cumulative probability function schematic diagram two that under the TD-LTE standard of the embodiment of the present invention, frequency deviation is estimated;

Figure 11 is the cumulative probability function schematic diagram three that under the TD-LTE standard of the embodiment of the present invention, frequency deviation is estimated;

Figure 12 is the schematic diagrames of the mean square deviation performance that under the TD-LTE standard of the embodiment of the present invention, under different sliding window length, frequency deviation is estimated;

Figure 13 is the accumulated probability distribution function schematic diagrames that under the TD-LTE standard of the embodiment of the present invention, under different sliding window length, frequency deviation is estimated;

Figure 14 is the structural representation of the maximum likelihood frequency deviation estimation device based on joint pilot of the embodiment of the present invention.

Embodiment

Exemplary embodiment of the present disclosure is described below with reference to accompanying drawings in more detail.Although shown exemplary embodiment of the present disclosure in accompanying drawing, but should be appreciated that and can realize the disclosure and the embodiment that should do not set forth limits here with various forms.On the contrary, it is in order more thoroughly to understand the disclosure that these embodiment are provided, and can be by the those skilled in the art that conveys to complete the scope of the present disclosure.

Existing frequency synchronization algorithm research under MIMO-OFDM condition seldom comes from this angle of effectively utilizing of frequency pilot sign effective means and method that Improvement frequency deviation is estimated, in order to solve the low problem of offset frequency estimated performance in prior art, the invention provides a kind of maximum likelihood frequency deviation estimating method and device based on joint pilot, being applicable to the maximum likelihood frequency deviation based on joint pilot in the reception synchronously of MIMO-OFDM system estimates, do not revising under the condition of transmitting terminal frequency pilot sign, pilot tone layout, significantly promoting and improving frequency deviation estimated performance; With conventional maximum likelihood frequency deviation algorithm for estimating directly from receiving pilot signal R ⁽ⁿ⁾derive the maximal possibility estimation of frequency deviation and compare, the maximum likelihood frequency deviation based on revising measuring-signal of the embodiment of the present invention estimates it is from revised reception pilot signal (R ⁽ⁿ⁾) ^hr ⁽ⁿ⁾in derive the maximal possibility estimation of frequency deviation.In addition, based on revising measuring-signal (R ⁽ⁿ⁾) ^hr ⁽ⁿ⁾derive on the basis of maximal possibility estimation relation of frequency deviation, the embodiment of the present invention adopts the method for sliding window that adjacent multiple subframe frequency pilot signs are combined and utilized significantly to improve frequency deviation estimated performance.Therefore, it is simple that the frequency deviation estimating method of the embodiment of the present invention has calculating, the advantage that frequency offset estimation scope is large, frequency deviation estimated performance is good.

Below in conjunction with accompanying drawing and embodiment, the present invention is further elaborated.Should be appreciated that specific embodiment described herein, only in order to explain the present invention, does not limit the present invention.

Embodiment of the method

According to embodiments of the invention, a kind of maximum likelihood frequency deviation estimating method based on joint pilot is provided, Fig. 1 is the flow chart of the maximum likelihood frequency deviation estimating method based on joint pilot of the embodiment of the present invention, as shown in Figure 1, comprise following processing according to the maximum likelihood frequency deviation estimating method based on joint pilot of the embodiment of the present invention:

Step 101, receiving terminal is to corresponding n pilot symbol transmitted matrix D ⁽ⁿ⁾, discrete Fourier transform (DFT) DFT matrix F and matrix F the matrix that forms of front L row

carry out preliminary treatment, obtain matrix P ⁽ⁿ⁾;

Step 101 specifically comprises following processing:

Receiving terminal is according to formula 1, to corresponding pilot symbol transmitted matrix D ⁽ⁿ⁾, discrete Fourier transform (DFT) DFT matrix F and matrix F the matrix that forms of front L row

carry out preliminary treatment, obtain matrix P ⁽ⁿ⁾;

Formula 1;

Wherein,

n _tfor the number of transmit antennas of mimo system, N is OFDM sub-carrier number,

represent n the pilot tone sign matrix that p root transmitting antenna sends, represent n the pilot tone symbol that p root transmitting antenna sends; F ∈ C ^{n × N}for DFT matrix, the individual element of its (l, m) is $F_{l,m}=\frac{1}{\sqrt{N}}{e}^{-j(2\mathrm{\&pi;}(l-1)(m-1)/N)};$ $\tilde{W}={(W\&CircleTimes;I_{N_T})}_{{\mathrm{NN}}_T\&times;{\mathrm{LN}}_T},$ W represents that the front L row of DFT matrix F are F=[W|V], W ∈ C ^{n × L}, V ∈ C ^{n × (N-L)}, and have W ^hv=0, WW ^h+ VV ^h=I, H is that conjugation turns order.

Step 102, receiving terminal will receive frequency pilot sign matrix R ⁽ⁿ⁾with matrix P ⁽ⁿ⁾storing respectively length into is in the sliding window memory of first-in first-out of M;

Step 102 specifically comprises following processing:

Receiving terminal will receive frequency pilot sign matrix R ⁽ⁿ⁾with matrix P ⁽ⁿ⁾storing respectively length into is in the sliding window memory of first-in first-out of M, obtains following two groups of data: $\begin{array}{c}{\{P}^{(n-k)},k=\mathrm{0,1},\&CenterDot;\&CenterDot;\&CenterDot;,M-1\}\\ \{R^{(n-k)},k=\mathrm{0,1},\&CenterDot;\&CenterDot;\&CenterDot;,M-1\}\end{array},$ Wherein,

n _rfor the reception antenna number of mimo system, N is OFDM sub-carrier number, and under the condition of system receiving terminal sign synchronization, n the reception frequency pilot sign that q root reception antenna receives is: $r_q^{\left(n\right)}=\underset{p=1}{\overset{N_T}{\mathrm{\&Sigma;}}}{E}^{\left(n\right)}{F}^H{D}_p^{\left(n\right)}{\mathrm{Wg}}_{q,p}^{\left(n\right)}+n_q,$ Wherein, $E^{\left(n\right)}=e^{j2\mathrm{\&pi;}{L}_n\mathrm{\&epsiv;}/N}E$ Represent n the frequency deviation matrix that frequency pilot sign is corresponding, E=diag ([1, e ^{j2 π ε/N}..., e ^{j2 π (N-1) ε/N}] ^t), ε is normalization frequency deviation value, L _nrepresent n the corresponding sequence number of first sampling time of frequency pilot sign;

represent the L footpath time domain channel gain that n frequency pilot sign experiences while transmission between (q, p) antenna pair, T represents to turn order, n _qrepresent the imaginary part of n frequency pilot sign, j ²-1.

Step 103, builds M frequency deviation matrix, obtains and receive frequency pilot sign matrix R from sliding window memory ⁽ⁿ⁾with matrix P ⁽ⁿ⁾, according to receiving frequency pilot sign matrix R ⁽ⁿ⁾, matrix P ⁽ⁿ⁾go out likelihood function with M frequency deviation matrix computations, by frequency deviation estimated value substitution likelihood function to be selected, obtaining and making the frequency deviation estimated value of cumulative likelihood function value minimum is final maximum likelihood frequency deviation estimated value.

In step 103, build M frequency deviation matrix and specifically comprise:

Build M frequency deviation matrix according to formula 2; $E^{(n-k)}={\mathrm{<figure-callout\; id="2"\; label="e\; j"\; filenames="HDA00002579101000031.png,HDA00002579101000041.png"\; state="\{\{state\}\}">e</figure-callout>}}^j\&CenterDot;\mathrm{diag}\left({[1,{\mathrm{<figure-callout\; id="2"\; label="e\; j"\; filenames="HDA00002579101000031.png,HDA00002579101000041.png"\; state="\{\{state\}\}">e</figure-callout>}}^j,\&CenterDot;\&CenterDot;\&CenterDot;,{\mathrm{<figure-callout\; id="2"\; label="e\; j"\; filenames="HDA00002579101000031.png,HDA00002579101000041.png"\; state="\{\{state\}\}">e</figure-callout>}}^j]}^T\right)$ Formula 2;

Wherein, k=0,1 ..., M-1, ε is normalization frequency deviation value, N is OFDM sub-carrier number, j ²=-1, L _n-krepresent n-k the corresponding sequence number of first sampling time of frequency pilot sign, T represents to turn order.

In step 103, from sliding window memory, obtain and receive frequency pilot sign matrix R ⁽ⁿ⁾with matrix P ⁽ⁿ⁾, according to receiving frequency pilot sign matrix R ⁽ⁿ⁾, matrix P ⁽ⁿ⁾go out likelihood function with M frequency deviation matrix computations, frequency deviation estimated value substitution likelihood function to be selected specifically comprised:

From sliding window memory, obtain and receive frequency pilot sign matrix R ⁽ⁿ⁾with matrix P ⁽ⁿ⁾, according to receiving frequency pilot sign matrix R ⁽ⁿ⁾, matrix P ⁽ⁿ⁾with M frequency deviation matrix, and calculate likelihood function according to formula 3;

λ ⁽ⁿ⁾(ε)=‖ (R ⁽ⁿ⁾) ^he ⁽ⁿ⁾(P ⁽ⁿ⁾-I) (E ⁽ⁿ⁾) ^hr ⁽ⁿ⁾‖ _f formula 3;

Wherein, ‖ ‖ _frepresent the F norm of getting matrix, H is that conjugation turns order,

represent n the frequency deviation matrix that frequency pilot sign is corresponding, E=diag ([1, e ^{j2 π ε/N}..., e ^{j2 π (N-1) ε/N}] ^t), N is OFDM sub-carrier number, λ ⁽ⁿ⁾(ε) likelihood function of estimating for maximum likelihood frequency deviation;

By frequency deviation estimated value substitution formula 3 to be selected.

Wherein, obtaining and making the frequency deviation estimated value of cumulative likelihood function value minimum is that final maximum likelihood frequency deviation estimated value specifically comprises: adopt the method for substep search, calculate according to formula 4 frequency offset estimation that makes following cumulative likelihood function value minimum

and by minimum frequency offset estimation

as final maximum likelihood frequency deviation estimated value; ${\widehat{\mathrm{\&epsiv;}}}^{\left(n\right)}=\underset{\mathrm{\&epsiv;}}{\arg \min }\left\{\underset{k=0}{\overset{M-1}{\mathrm{\&Sigma;}}}{\mathrm{\&lambda;}}^{(n-k)}\left(\mathrm{\&epsiv;}\right)\right\}$ Formula 4;

Wherein, M is the length of sliding window memory.

Preferably, the method for substep search specifically comprises:

Step 1, supposes that initial frequency deviation scope is for (ε _max, ε _max);

Step 2 is all divided into frequency deviation hunting zone P interval in the time of each search, then calculates successively relatively likelihood function

in the value size of each interval endpoint;

Step 3, according to likelihood function

the process that changes from big to small and change from small to big with the selection value of different normalization frequency deviation values, determines the frequency deviation region (ε that next round frequency deviation is searched for ₁, ε ₂), wherein ε ₁, ε ₂choose with likelihood function value variation relation meet $\underset{n=0}{\overset{M-1}{\mathrm{\&Sigma;}}}{\mathrm{\&lambda;}}^{\left(n\right)}\left({\mathrm{\&epsiv;}}_1\right)>\underset{n=1}{\overset{M-1}{\mathrm{\&Sigma;}}}{\mathrm{\&lambda;}}^{\left(n\right)}\left({\mathrm{\&epsiv;}}_0\right),$ $\underset{n=1}{\overset{M-1}{\mathrm{\&Sigma;}}}{\mathrm{\&lambda;}}^{\left(n\right)}\left({\mathrm{\&epsiv;}}_2\right)>\underset{n=1}{\overset{M-1}{\mathrm{\&Sigma;}}}{\mathrm{\&lambda;}}^{\left(n\right)}\left({\mathrm{\&epsiv;}}_0\right),$ Wherein ε ₀=(ε ₁+ ε ₂)/2;

Step 4, at the new frequency deviation region (ε of calculative determination ₁, ε ₂) basis on, repeat above-mentioned steps 2-3, until current frequency deviation step-size in search has met the predetermined frequency offset estimation accuracy requirement of system, export current frequency deviation hunting zone (ε ₁, ε ₂) intermediate value ε ₀for the final valuation of this search

Below in conjunction with the substandard Uplink MIMO SC-FDMA system of TD-LTE and descending MIMO-OFDM system, the concrete implementation step of the embodiment of the present invention is elaborated.

Fig. 2 is the schematic diagram of the TD-LTE standard Uplink MIMO SC-FDMA system transmitting terminal of the embodiment of the present invention, Fig. 3 is the schematic diagram of the TD-LTE standard Uplink MIMO SC-FDMA system receiving terminal of the embodiment of the present invention, Fig. 4 be the embodiment of the present invention be the descending MIMO-OFDM system of TD-LTE standard sending and receiving end schematic diagram, as in Figure 2-4, under the condition of hypothesis receiving terminal sign synchronization, for Uplink MIMO SC-FDMA system and descending MIMO-OFDM system, although on LTE, descending pilot frequency number of symbols, the regularity of distribution in each subframe, on, descending transmitting terminal number of antennas is not quite similar, but it is identical that the time domain after reception antenna removal Cyclic Prefix receives characteristics of signals, for simplified characterization, the embodiment of the present invention is take MIMO SC-FDMA system as example, suppose N _t, N _rbe respectively number of transmit antennas and the reception antenna number of mimo system, the sub-carrier number that N adopts for system.

Under the condition of system receiving terminal sign synchronization, the time-domain expression of n the symbol that q root reception antenna receives is: $r_q^{\left(n\right)}=\underset{p=1}{\overset{N_T}{\mathrm{\&Sigma;}}}{E}^{\left(n\right)}{F}^H{D}_p^{\left(n\right)}W{g}_{q,p}^{\left(n\right)}+{\mathrm{<figure-callout\; id="5"\; label="n\; q\; Formula"\; filenames="HDA00002579101000041.png"\; state="\{\{state\}\}">n</figure-callout>}}_q$ Formula 5

Wherein,

represent n carrier wave frequency deviation (CFO) matrix that frequency pilot sign is corresponding, E=diag ([1, e _{j2 π ε/N}..., e _{j2 π (N-1) ε/N}] _t), ε is normalization frequency deviation value, L _nrepresent n the corresponding sequence number of first sampling time of frequency pilot sign; F ∈ C ^{n × N}for DFT matrix, wherein (l, m) individual element is $F_{l,m}=\frac{1}{\sqrt{N}}{e}^{-j(2\mathrm{\&pi;}(l-1)(m-1)/N)};$ $D_p^{\left(n\right)}=\mathrm{diag}\left(d_p^{\left(n\right)}\right)=\mathrm{diag}\left({[d_{p,0}^{\left(n\right)},\&CenterDot;\&CenterDot;\&CenterDot;,d_{p,N-1}^{\left(n\right)}]}^T\right)$ Represent n the pilot tone sign matrix that p root transmitting antenna sends;

the L footpath time domain channel that represents corresponding n frequency pilot sign between (q, p) antenna pair gains.W represents the front L row of DFT matrix F, i.e. F=[W|V], W ∈ C ^{n × L}, V ∈ C ^{n × (N-L)}, and have W ^hv=0, WW ^h+ VV ^h=I.N _qrepresent that N × 1 dimension zero-mean, every one dimension variance that q root reception antenna receives are

multiple Gaussian noise.Order

$g_q^{\left(n\right)}={[{\mathrm{<figure-callout\; id="1"\; label="g"\; filenames="HDA00002579101000012.png,HDA00002579101000021.png"\; state="\{\{state\}\}">g</figure-callout>}}_{1,q}^{\left(n\right)},\&CenterDot;\&CenterDot;\&CenterDot;,g_{N_R,q}^{\left(n\right)}]}_{L\&times;N_R},$ ${\mathrm{<figure-callout\; id="1"\; label="g"\; filenames="HDA00002579101000012.png,HDA00002579101000021.png"\; state="\{\{state\}\}">g</figure-callout>}}_{1,q}^{\left(n\right)}={{\left[{\mathrm{<figure-callout\; id="1"\; label="g"\; filenames="HDA00002579101000012.png,HDA00002579101000021.png"\; state="\{\{state\}\}">g</figure-callout>}}_{1,q}^{\left(n\right)}\right[0],\&CenterDot;\&CenterDot;\&CenterDot;,{\mathrm{<figure-callout\; id="1"\; label="g"\; filenames="HDA00002579101000012.png,HDA00002579101000021.png"\; state="\{\{state\}\}">g</figure-callout>}}_{1,q}^{\left(n\right)}[L-1\left]\right]}^T}_{L\&times;1},$ $N=[{\mathrm{<figure-callout\; id="1"\; label="n"\; filenames="HDA00002579101000012.png,HDA00002579101000021.png"\; state="\{\{state\}\}">n</figure-callout>}}_1,\&CenterDot;\&CenterDot;\&CenterDot;,n_{N_R}]$

$\tilde{W}={(W\&CircleTimes;I_{N_T})}_{{\mathrm{NN}}_T\&times;{\mathrm{LN}}_T},$ $D^{\left(n\right)}=\frac{1}{\sqrt{N_T}}{[D_1^{\left(n\right)},\&CenterDot;\&CenterDot;\&CenterDot;,D_{N_T}^{\left(n\right)}]}_{N\&times;{\mathrm{NN}}_T},$ $R^{\left(n\right)}={[r_1^{\left(n\right)},\&CenterDot;\&CenterDot;\&CenterDot;,r_{N_R}^{\left(n\right)}]}_{N\&times;N_R}$

N _rthe matrix form that the reception signal of root reception antenna can be expressed as: $R^{\left(n\right)}=E^{\left(n\right)}{F}^H{D}^{\left(n\right)}\tilde{W}{G}^{\left(n\right)}+\mathrm{<figure-callout\; id="6"\; label="N\; Formula"\; filenames="HDA00002579101000041.png"\; state="\{\{state\}\}">N</figure-callout>}$ Formula 6

Wherein, G ⁽ⁿ⁾represent time domain channel gain, according to maximal possibility estimation theory, adopt and the similar analytical method of mimo system, it be easy to show that maximum likelihood channel estimating and frequency deviation estimation meet following relation:

formula 7

Wherein,

represent pseudoinverse.Can obtain following correction measuring-signal according to the reception signal in formula 6: ${\left(R^{\left(n\right)}\right)}^H{R}^{\left(n\right)}={\left(G^{\left(n\right)}\right)}^H{\tilde{W}}^H{\left(D^{\left(n\right)}\right)}^H{D}^{\left(n\right)}\tilde{W}{G}^{\left(n\right)}+{\tilde{N}}^{\left(n\right)}$ Formula 8

According to revising measuring-signal, be not difficult to obtain N _rroot reception antenna receives the conditional probability density function of signal: $\mathrm{\&Lambda;}\left({\left(R^{\left(n\right)}\right)}^H{R}^{\left(n\right)}\right|G^{\left(n\right)})=\frac{1}{{\left(\mathrm{\&pi;}{\mathrm{\&sigma;}}_n^2\right)}^{N\&times;N_R}}\exp \{-\frac{1}{{\mathrm{\&sigma;}}_{\mathrm{<figure-callout\; id="2"\; label="n"\; filenames="HDA00002579101000031.png,HDA00002579101000041.png"\; state="\{\{state\}\}">n</figure-callout>}}^2}\mathrm{tr}\left[\begin{array}{c}{({\left(R^{\left(n\right)}\right)}^H{R}^{\left(n\right)}-{\left(G^{\left(n\right)}\right)}^H{\tilde{W}}^H{D}^{\left(n\right)H}{D}^{\left(n\right)}\tilde{W}{G}^{\left(n\right)})}^H\&CenterDot;\\ ({\left(R^{\left(n\right)}\right)}^H{R}^{\left(n\right)}-{\left(G^{\left(n\right)}\right)}^H{\tilde{W}}^H{\left(D^{\left(n\right)}\right)}^H{D}^{\left(n\right)}\tilde{W}{G}^{\left(n\right)})\end{array}\right]\}$ Formula 9

According to maximal possibility estimation theory, can construct and obtain following channel estimating likelihood function:

$L\left({\left(R^{\left(n\right)}\right)}^H{R}^{\left(n\right)}\right|G^{\left(n\right)})=-\mathrm{tr}{[({\left(R^{\left(n\right)}\right)}^H{R}^{\left(n\right)}-G^{\left(n\right)H}{\tilde{W}}^H{D}^{\left(n\right)H}{D}^{\left(n\right)}{\tilde{W}}^{\left(n\right)})}^{H}G({\left(R^{\left(n\right)}\right)}^H{R}^{\left(n\right)}-G^{\left(n\right)H}{\tilde{W}}^H{D}^{\left(n\right)H}{D}^{\left(n\right)}{\tilde{W}G}^{\left(n\right)})]$

$=-\mathrm{tr}\left[\begin{array}{c}{\left(R^{\left(n\right)}\right)}^H{R}^{\left(n\right)}{\left(R^{\left(n\right)}\right)}^H{R}^{\left(n\right)}-{\left(G^{\left(n\right)}\right)}^H{\tilde{W}}^H{\left(D^{\left(n\right)}\right)}^H{D}^{\left(n\right)}\tilde{W}{G}^{\left(n\right)}{\left(R^{\left(n\right)}\right)}^H{R}^{\left(n\right)}\\ -{\left(R^{\left(n\right)}\right)}^H{R}^{\left(n\right)}{\left(G^{\left(n\right)}\right)}^H{\tilde{W}}^H{\left(D^{\left(n\right)}\right)}^H{D}^{\left(n\right)}\tilde{W}{G}^{\left(n\right)}+{\left(G^{\left(n\right)}\right)}^H{\tilde{W}}^H{\left(D^{\left(n\right)}\right)}^H{D}^{\left(n\right)}\tilde{W}{G}^{\left(n\right)}{\left(G^{\left(n\right)}\right)}^H{\tilde{W}}^H{\left(D^{\left(n\right)}\right)}^H{D}^{\left(n\right)}\tilde{W}{G}^{\left(n\right)}\end{array}\right]$

Formula 10

Because

$\frac{\&PartialD;\mathrm{tr}\left({\left(G^{\left(n\right)}\right)}^H{\tilde{W}}^H{\left(D^{\left(n\right)}\right)}^H{D}^{\left(n\right)}\tilde{W}{G}^{\left(n\right)}{\left(R^{\left(n\right)}\right)}^H{R}^{\left(n\right)}\right)}{\&PartialD;G^{{\left(n\right)}^{*}}}={\tilde{W}}^H{\left(D^{\left(n\right)}\right)}^H{D}^{\left(n\right)}{\tilde{W}G}^{\left(n\right)}{\left(R^{\left(n\right)}\right)}^H{R}^{\left(n\right)},$

$\frac{\&PartialD;\mathrm{tr}\left({\left(R^{\left(n\right)}\right)}^H{R}^{\left(n\right)}{\left(G^{\left(n\right)}\right)}^H{\tilde{W}}^H{\left(D^{\left(n\right)}\right)}^H{D}^{\left(n\right)}\tilde{W}{G}^{\left(n\right)}\right)}{\&PartialD;G^{{\left(n\right)}^{*}}}={\tilde{W}}^H{\left(D^{\left(n\right)}\right)}^H{D}^{\left(n\right)}\tilde{W}{G}^{\left(n\right)}{\left(R^{\left(n\right)}\right)}^H{R}^{\left(n\right)}$

$\frac{\&PartialD;\mathrm{tr}\left({\left(G^{\left(n\right)}\right)}^H{\tilde{W}}^H{\left(D^{\left(n\right)}\right)}^H{D}^{\left(n\right)}\tilde{W}{G}^{\left(n\right)}{\left(G^{\left(n\right)}\right)}^H{\tilde{W}}^H{\left(D^{\left(n\right)}\right)}^H{D}^{\left(n\right)}\tilde{W}{G}^{\left(n\right)}\right)}{\&PartialD;G^{{\left(n\right)}^{*}}}=2{\tilde{W}}^H{\left(D^{\left(n\right)}\right)}^H{D}^{\left(n\right)}\tilde{W}{G}^{\left(n\right)}{\left(G^{\left(n\right)}\right)}^H{\tilde{W}}^H{\left(D^{\left(n\right)}\right)}^H{D}^{\left(n\right)}\tilde{W}{G}^{\left(n\right)}$

By can obtain maximum likelihood channel estimating and meet following restriction relation:

${\tilde{W}}^H{\left(D^{\left(n\right)}\right)}^H{D}^{\left(n\right)}\tilde{W}{\stackrel{^^}{G}}^{\left(\stackrel{^^}{n}\right)}\left(\mathrm{\&epsiv;}\right){\left(R^{\left(n\right)}\right)}^H{R}^{\left(n\right)}={\tilde{W}}^H{\left(D^{\left(n\right)}\right)}^H{D}^{\left(n\right)}\tilde{W}{G}^{\left(n\right)}\left(\mathrm{\&epsiv;}\right){\left(G^{\left(n\right)}\right)}^H\left(\mathrm{\&epsiv;}\right){\tilde{W}}^H{\left(D^{\left(n\right)}\right)}^H{D}^{\left(n\right)}\tilde{W}{G}^{\left(n\right)}\left(\mathrm{\&epsiv;}\right)$

$\&DoubleRightArrow;{\left(R^{\left(n\right)}\right)}^H{R}^{\left(n\right)}={\left({\stackrel{^^}{G}}^{\left(n\right)}\right)}^H\left(\mathrm{\&epsiv;}\right){\tilde{W}}^H{\left(D^{\left(n\right)}\right)}^H{D}^{\left(n\right)}\tilde{W}{G}^{\left(n\right)}\left(\mathrm{\&epsiv;}\right)$

Formula 11

In order to estimate in the satisfied restriction relation of the maximum likelihood channel estimating by formula 11 that obtaining maximum likelihood frequency deviation estimates, we can be by the maximum likelihood channel estimating in formula 7

bring formula 11 into and obtain following relation:

$\&DoubleRightArrow;{\left(R^{\left(n\right)}\right)}^H{R}^{\left(n\right)}={\left(R^{\left(n\right)}\right)}^H{E}^{\left(n\right)}{P}^{\left(n\right)}{\left(E^{\left(n\right)}\right)}^H{R}^{\left(n\right)}$

$\&DoubleRightArrow;{\left(R^{\left(n\right)}\right)}^H{E}^{\left(n\right)}(P^{\left(n\right)}-I){\left(E^{\left(n\right)}\right)}^H{R}^{\left(n\right)}=0$

Formula 12

The likelihood function that maximum likelihood frequency deviation is estimated is: λ ⁽ⁿ⁾(ε)=‖ (R ⁽ⁿ⁾) ^he ⁽ⁿ⁾(P ⁽ⁿ⁾-I (E ⁽ⁿ⁾) ^hr ⁽ⁿ⁾‖ _fformula 13

Wherein, ‖ ‖ _frepresent the F norm of getting matrix.In order further to improve frequency deviation estimated performance, can combine the many groups frequency pilot sign in adjacent sub-frame, adopt sliding window to realize improved maximum likelihood frequency deviation and estimate.If sliding window length is M, the maximum likelihood frequency deviation based on sliding window estimates that likelihood function is:

${\widehat{\mathrm{\&epsiv;}}}^{\left(n\right)}=\underset{\mathrm{\&epsiv;}}{\arg \min }\left\{\underset{k=0}{\overset{M-1}{\mathrm{\&Sigma;}}}{\mathrm{\&lambda;}}^{(n-k)}\left(\mathrm{\&epsiv;}\right)\right\}$

$=\underset{\mathrm{\&epsiv;}}{\arg \min }\left\{\underset{k=0}{\overset{M-1}{\mathrm{\&Sigma;}}}{\left|\right|{\left(R^{(n-k)}\right)}^H{E}^{(n-k)}(P^{(n-k)}-I){\left(E^{(n-k)}\right)}^H{R}^{(n-k)}\left|\right|}_F\right\}$ Formula 14

Fig. 5 is the processing schematic diagram of the enhancing maximum likelihood frequency deviation algorithm for estimating of the embodiment of the present invention, and as shown in Figure 5, the concrete steps that the MIMO-OFDM system under LTE system condition strengthens maximum likelihood frequency deviation estimating method are as follows:

Step 1, data pre-treatment step:

Receiving terminal is first by according to corresponding pilot symbol transmitted D ⁽ⁿ⁾, the matrix that the front L row of DFT matrix F and DFT matrix F form

carry out by the following method preliminary treatment and obtain P ⁽ⁿ⁾:

Step 2, upgrade sliding window buffer:

Fig. 6 is that the Sliding window data of the embodiment of the present invention upgrades schematic diagram, as shown in Figure 6, and by the matrix P of the frequency pilot sign receiving and the generation of data pre-treatment step ⁽ⁿ⁾deposit in the sliding window memory of the first-in first-out that corresponding length is M (FIFO).

Step 3, the appraising frequency bias step based on cumulative likelihood function:

Build by the following method M frequency deviation matrix

$E^{(n-k)}={\mathrm{<figure-callout\; id="2"\; label="e\; j"\; filenames="HDA00002579101000031.png,HDA00002579101000041.png"\; state="\{\{state\}\}">e</figure-callout>}}^j\&CenterDot;\mathrm{diag}\left({[1,{\mathrm{<figure-callout\; id="2"\; label="e\; j"\; filenames="HDA00002579101000031.png,HDA00002579101000041.png"\; state="\{\{state\}\}">e</figure-callout>}}^j,\&CenterDot;\&CenterDot;\&CenterDot;,{\mathrm{<figure-callout\; id="2"\; label="e\; j"\; filenames="HDA00002579101000031.png,HDA00002579101000041.png"\; state="\{\{state\}\}">e</figure-callout>}}^j]}^T\right),k=\mathrm{0,1},\&CenterDot;\&CenterDot;\&CenterDot;,M-1$

The reception frequency pilot sign R of receiving terminal buffer memory from sliding window memory ⁽ⁿ⁾with the matrix P generating through data pre-treatment step 1 ⁽ⁿ⁾, by the following likelihood function of frequency offset estimation ε to be selected substitution:

λ ⁽ⁿ⁾(ε)=‖ (R ⁽ⁿ⁾) ^he ⁽ⁿ⁾(P ⁽ⁿ⁾-I) (E ⁽ⁿ⁾) ^hr ⁽ⁿ⁾‖ _fadopt the method calculative determination of substep search to make the frequency offset estimation of following cumulative likelihood function value minimum

${\widehat{\mathrm{\&epsiv;}}}^{\left(n\right)}=\underset{\mathrm{\&epsiv;}}{\arg \min }\left\{\underset{k=0}{\overset{M-1}{\mathrm{\&Sigma;}}}{\mathrm{\&lambda;}}^{(n-k)}\left(\mathrm{\&epsiv;}\right)\right\}$

Fig. 7 is that the frequency deviation likelihood estimating searching scope of the embodiment of the present invention is adjusted schematic diagram, as shown in Figure 7, frequency offset estimation searching method below seeking in the process of the frequency offset estimation that makes cumulative likelihood function value minimum, adopting: suppose that frequency offset estimation range is (ε _max, ε _max), in the time of each search, all frequency deviation hunting zone is divided into P interval, then calculate successively relatively likelihood function

in the value size of each interval endpoint, the process of selecting value to change from big to small, then change from small to big again with different frequency offset estimations according to likelihood function, calculative determination goes out the frequency deviation region (ε of next round frequency deviation search ₁, ε ₂), wherein ε ₁, ε ₂choose with likelihood function value variation relation meet $\underset{n=0}{\overset{M-1}{\mathrm{\&Sigma;}}}{\mathrm{\&lambda;}}^{\left(n\right)}\left({\mathrm{\&epsiv;}}_1\right)>\underset{n=1}{\overset{M-1}{\mathrm{\&Sigma;}}}{\mathrm{\&lambda;}}^{\left(n\right)}\left({\mathrm{\&epsiv;}}_0\right),$ $\underset{n=1}{\overset{M-1}{\mathrm{\&Sigma;}}}{\mathrm{\&lambda;}}^{\left(n\right)}\left({\mathrm{\&epsiv;}}_2\right)>\underset{n=1}{\overset{M-1}{\mathrm{\&Sigma;}}}{\mathrm{\&lambda;}}^{\left(n\right)}\left({\mathrm{\&epsiv;}}_0\right),$ Wherein ε ₀=(ε ₁+ ε ₂)/2.Then repeat above-mentioned search step, if current frequency deviation step-size in search meets the required precision of the predetermined frequency offset estimation of system, exportable as Fig. 7 frequency deviation hunting zone (ε ₁, ε ₂) intermediate value ε ₀for the final valuation of this search

Fig. 8 is that under the TD-LTE standard of the embodiment of the present invention, frequency deviation is estimated mean square deviation performance schematic diagram, as shown in Figure 8, Fig. 8 has provided the improvement frequency deviation algorithm for estimating based on revising measuring-signal under TD-LTE standard uplink condition and has slided mean square error (the Mean Square Error) performance in window length situation in difference, channel is standard test channel EVA70Hz under TD-LTE standard, ETU300HZ, the normalization frequency deviation of default is ε=0.05, MIMO dual-mode antenna is configured to 2 × 1, OFDM sub-carrier number is 512, and pilot sub-carrier is counted N _pbe 240, sliding window length is set to respectively 1,2,4,8.As seen from Figure 8, by utilizing the cumulative likelihood function of adjacent reception signal can significantly improve frequency deviation estimated performance, and along with sliding the increase of window length M, the mean square deviation performance that frequency deviation is estimated is better, this explanation adopts the frequency deviation algorithm for estimating of cumulative likelihood function can significantly promote and improve frequency deviation estimated performance, so the frequency deviation algorithm for estimating based on revising the cumulative likelihood function of measuring-signal is the high-performance frequency offset estimation technique scheme that a class is applicable to TD-LTE system.

Fig. 9 is the cumulative probability function schematic diagram one that under the TD-LTE standard of the embodiment of the present invention, frequency deviation is estimated, Figure 10 is the cumulative probability function schematic diagram two that under the TD-LTE standard of the embodiment of the present invention, frequency deviation is estimated, Figure 11 is the cumulative probability function schematic diagram three that under the TD-LTE standard of the embodiment of the present invention, frequency deviation is estimated, as shown in Fig. 9-11, the channel type ETU 300Hz of Fig. 9, M=1, 8,-5dB, the channel type ETU 300Hz of Figure 10, M=1, 8, 5dB, the channel type ETU 300Hz of Figure 11, M=1, 8, 15dB, Fig. 9-11 have provided the accumulated probability distribution function of the improvement frequency deviation algorithm for estimating frequency offset estimation result under standard test channel ETU300HZ under TD-LTE standard based on correction measuring-signal under TD-LTE standard uplink condition, the normalization frequency deviation of default is ε=0.05, MIMO dual-mode antenna is configured to 2 × 1, OFDM sub-carrier number is 512, pilot sub-carrier is counted N _pbe 240, sliding window length is 1 and 8.As seen from the figure, by utilizing the cumulative likelihood function of adjacent reception signal can significantly improve frequency deviation estimated performance, reduce the dynamic range of frequency offset estimation result.

Figure 12 is the schematic diagrames of the mean square deviation performance that under the TD-LTE standard of the embodiment of the present invention, under different sliding window length, frequency deviation is estimated, as shown in figure 12, the channel type of Figure 12 is ETU 300Hz, Figure 12 has provided the mean square deviation performance of the improvement frequency deviation algorithm for estimating frequency offset estimation result under standard test channel ETU300HZ under TD-LTE standard based on correction measuring-signal under TD-LTE standard uplink condition, the normalization frequency deviation of default is ε=0.05, MIMO dual-mode antenna is configured to 1 × 2, OFDM sub-carrier number is 512, and pilot sub-carrier is counted N _pbe 240, signal to noise ratio is fixed as 5dB, and the long M of sliding window is respectively 8,10,12,14,16 and 18.As seen from Figure 9, even under the fast ETU300HZ channel condition becoming of channel, along with the increase of sliding window length, the mean square deviation performance that frequency deviation is estimated is also improved thereupon, but this improvement is more and more less.Figure 13 is the accumulated probability distribution function schematic diagrames that under the TD-LTE standard of the embodiment of the present invention, under different sliding window length, frequency deviation is estimated, the channel type of Figure 13 is ETU 300Hz, Figure 13 has provided the accumulated probability distribution function performance of the improvement frequency deviation algorithm for estimating frequency offset estimation result under standard test channel ETU300HZ under TD-LTE standard based on correction measuring-signal under MIMO-OFDM system condition, the normalization frequency deviation of default is ε=0.05, MIMO dual-mode antenna is configured to 2 × 1, OFDM sub-carrier number is 512, and pilot sub-carrier is counted N _pbe 240, signal to noise ratio is fixed as 5dB, and the long M of sliding window is respectively 8,10,16 and 18.As shown in figure 13, even under the fast ETU300HZ channel condition becoming of channel, along with the increase of sliding window length, the accumulated probability distribution function performance that frequency deviation is estimated is also improved thereupon, but this improvement is more and more less.Above-mentioned analysis result shows, will increase the implementation complexity of system cache expense, increase algorithm in view of sliding the increase of window length M, and in actual applications, the selection of sliding window length should be according to application requirements choose reasonable.

In sum, the technical scheme of the embodiment of the present invention, by according to revised reception pilot signal (R ⁽ⁿ⁾) ^hr ⁽ⁿ⁾in derive the maximum likelihood estimator of frequency deviation, and based on revising measuring-signal (R ⁽ⁿ⁾) ^hr ⁽ⁿ⁾derive on the basis of maximal possibility estimation relation of frequency deviation, adopt the method for sliding window that adjacent multiple subframe frequency pilot signs are combined and utilized significantly to improve frequency deviation estimated performance, solve the low problem of offset frequency estimated performance in prior art, it is simple that the frequency deviation estimating method of the technical scheme of the embodiment of the present invention has calculating, the advantage that frequency offset estimation scope is large, frequency deviation estimated performance is good.

Device embodiment

According to embodiments of the invention, a kind of maximum likelihood frequency deviation estimation device based on joint pilot is provided, reception for multiple-input and multiple-output MIMO-orthogonal frequency division multiplex OFDM system is synchronous, Figure 14 is the structural representation of the maximum likelihood frequency deviation estimation device based on joint pilot of the embodiment of the present invention, as shown in figure 14, comprise according to the maximum likelihood frequency deviation estimation device based on joint pilot of the embodiment of the present invention: pretreatment module 140, memory module 142, and frequency deviation estimating modules 144, below the modules of the embodiment of the present invention is described in detail.

Pretreatment module 140, for to corresponding n pilot symbol transmitted matrix D ⁽ⁿ⁾, discrete Fourier transform (DFT) DFT matrix F and matrix F the matrix that forms of front L row

carry out preliminary treatment, obtain matrix P ⁽ⁿ⁾;

Pretreatment module 140 specifically for: according to formula 1, to corresponding pilot symbol transmitted matrix D ⁽ⁿ⁾, discrete Fourier transform (DFT) DFT matrix F and matrix F the matrix that forms of front L row

carry out preliminary treatment, obtain matrix P ⁽ⁿ⁾;

Formula 1;

Wherein,

n _tfor the number of transmit antennas of mimo system, N is OFDM sub-carrier number,

represent n the pilot tone sign matrix that p root transmitting antenna sends,

represent n the pilot tone symbol that p root transmitting antenna sends; F ∈ C ^{n × N}for DFT matrix, the individual element of its (l, m) is $F_{l,m}=\frac{1}{\sqrt{N}}{e}^{-j(2\mathrm{\&pi;}(l-1)(m-1)/N)};$ $\tilde{W}={(W\&CircleTimes;I_{N_T})}_{{\mathrm{NN}}_T\&times;{\mathrm{LN}}_T},$ W represents that the front L row of DFT matrix F are F=[W|V], W ∈ C ^{n × L}, V ∈ C ^{n × (N-L)}, and have W ^hv=0, WW ^h+ VV ^h=I, H is that conjugation turns order.

Memory module 142, for receiving frequency pilot sign matrix R ⁽ⁿ⁾with matrix P ⁽ⁿ⁾storing respectively length into is in the sliding window memory of first-in first-out of M;

Memory module 142 specifically for: will receive frequency pilot sign matrix R ⁽ⁿ⁾with matrix P ⁽ⁿ⁾storing respectively length into is in the sliding window memory of first-in first-out of M, obtains following two groups of data: $\begin{array}{c}{\{P}^{(n-k)},k=\mathrm{0,1},\&CenterDot;\&CenterDot;\&CenterDot;,M-1\}\\ \{R^{(n-k)},k=\mathrm{0,1},\&CenterDot;\&CenterDot;\&CenterDot;,M-1\}\end{array},$ Wherein,

n _rfor the reception antenna number of mimo system, N is OFDM sub-carrier number, and under the condition of system receiving terminal sign synchronization, n the reception frequency pilot sign that q root reception antenna receives is:

wherein, E ⁽ⁿ⁾represent n the frequency deviation matrix that frequency pilot sign is corresponding,

represent carrier wave frequency deviation matrix, E=diag ([1, e ^{j2 π ε/N}..., e ^{j2 π (N-1) ε/N}] ^t), ε is normalization frequency deviation value, L _nrepresent n the corresponding sequence number of first sampling time of frequency pilot sign;

represent the L footpath time domain channel gain that n frequency pilot sign experiences while transmission between (q, p) antenna pair, T represents to turn order, n _qrepresent that N × 1 dimension zero-mean, every one dimension variance that q root reception antenna receives are

multiple Gaussian noise, j ²=-1.

Frequency deviation estimating modules 144 for building M frequency deviation matrix, is obtained and is received frequency pilot sign matrix R from sliding window memory ⁽ⁿ⁾with matrix P ⁽ⁿ⁾, according to receiving frequency pilot sign matrix R ⁽ⁿ⁾, matrix P ⁽ⁿ⁾go out likelihood function with M frequency deviation matrix computations, by frequency deviation estimated value substitution likelihood function to be selected, obtaining and making the frequency deviation estimated value of cumulative likelihood function value minimum is final maximum likelihood frequency deviation estimated value.

Frequency deviation estimating modules 144 specifically for: build M frequency deviation matrix according to formula 2; $E^{(n-k)}={\mathrm{<figure-callout\; id="2"\; label="e\; j"\; filenames="HDA00002579101000031.png,HDA00002579101000041.png"\; state="\{\{state\}\}">e</figure-callout>}}^j\&CenterDot;\mathrm{diag}\left({[1,{\mathrm{<figure-callout\; id="2"\; label="e\; j"\; filenames="HDA00002579101000031.png,HDA00002579101000041.png"\; state="\{\{state\}\}">e</figure-callout>}}^j,\&CenterDot;\&CenterDot;\&CenterDot;,{\mathrm{<figure-callout\; id="2"\; label="e\; j"\; filenames="HDA00002579101000031.png,HDA00002579101000041.png"\; state="\{\{state\}\}">e</figure-callout>}}^j]}^T\right)$ Formula 2;

Wherein, k=0,1 ..., M-1, ε is normalization frequency deviation value, N is OFDM sub-carrier number, j ²=-1, L _n-krepresent n-k the corresponding sequence number of first sampling time of frequency pilot sign, T represents to turn order;

From sliding window memory, obtain and receive frequency pilot sign matrix R ⁽ⁿ⁾with matrix P ⁽ⁿ⁾, according to receiving frequency pilot sign matrix R ⁽ⁿ⁾, matrix P ⁽ⁿ⁾with M frequency deviation matrix, and calculate likelihood function according to formula 3;

λ ⁽ⁿ⁾(ε)=‖ (R ⁽ⁿ⁾) ^he ⁽ⁿ⁾(P ⁽ⁿ⁾-I) (E ⁽ⁿ⁾) ^hr ⁽ⁿ) ‖ _f formula 3;

Wherein, ‖ ‖ _frepresent the F norm of getting matrix, H is that conjugation turns order, represent n the frequency deviation matrix that frequency pilot sign is corresponding, E=diag ([1, e ^{j2 π ε/N}..., e ^{j2 π (N-1) ε/N}] ^t), N is OFDM sub-carrier number, λ ⁽ⁿ⁾(ε) likelihood function of estimating for maximum likelihood frequency deviation;

By frequency deviation estimated value substitution formula 3 to be selected;

Adopt the method for the search that distributes, calculate according to formula 4 frequency offset estimation that makes following cumulative likelihood function value minimum

and by minimum frequency offset estimation

as final maximum likelihood frequency deviation estimated value; ${\widehat{\mathrm{\&epsiv;}}}^{\left(n\right)}=\underset{\mathrm{\&epsiv;}}{\arg \min }\left\{\underset{k=0}{\overset{M-1}{\mathrm{\&Sigma;}}}{\mathrm{\&lambda;}}^{(n-k)}\left(\mathrm{\&epsiv;}\right)\right\}$ Formula 4;

Wherein, M is the length of sliding window memory.

Preferably, frequency deviation estimating modules 144 specifically comprises:

Submodule is set, for supposing that initial frequency deviation scope is for (ε _max, ε _max);

Calculating sub module, interval for all frequency deviation hunting zone is divided into P in the time searching at every turn, then calculate successively relatively likelihood function

in the value size of each interval endpoint;

Determine submodule, for according to likelihood function

the process that changes from big to small and change from small to big with the selection value of different normalization frequency deviation values, determines the frequency deviation region (ε that next round frequency deviation is searched for ₁, ε ₂), wherein ε ₁, ε ₂choose with likelihood function value variation relation meet

$\underset{n=1}{\overset{M-1}{\mathrm{\&Sigma;}}}{\mathrm{\&lambda;}}^{\left(n\right)}\left({\mathrm{\&epsiv;}}_2\right)>\underset{n=1}{\overset{M-1}{\mathrm{\&Sigma;}}}{\mathrm{\&lambda;}}^{\left(n\right)}\left({\mathrm{\&epsiv;}}_0\right),$ Wherein ε ₀=(ε ₁+ ε ₂)/2;

Output sub-module, at the new frequency deviation region (ε of calculative determination ₁, ε ₂) basis on, call calculating sub module and definite submodule, until current frequency deviation step-size in search has met the predetermined frequency offset estimation accuracy requirement of system, export current frequency deviation hunting zone (ε ₁, ε ₂) intermediate value ε ₀for the final valuation of this search

The concrete processing of the above-mentioned modules of the embodiment of the present invention can, with reference to the related content of said method embodiment, not repeat them here.

In sum, by means of the technical scheme of the embodiment of the present invention, by according to revised reception pilot signal (R ⁽ⁿ⁾) ^hr ⁽ⁿ⁾in derive the maximum likelihood estimator of frequency deviation, and based on revising measuring-signal (R ⁽ⁿ⁾) ^hr ⁽ⁿ⁾derive on the basis of maximal possibility estimation relation of frequency deviation, adopt the method for sliding window that adjacent multiple subframe frequency pilot signs are combined and utilized significantly to improve frequency deviation estimated performance, solve the low problem of offset frequency estimated performance in prior art, it is simple that the frequency deviation estimating method of the technical scheme of the embodiment of the present invention has calculating, the advantage that frequency offset estimation scope is large, frequency deviation estimated performance is good.

The algorithm providing at this is intrinsic not relevant to any certain computer, virtual system or miscellaneous equipment with demonstration.Various general-purpose systems also can with based on using together with this teaching.According to description above, it is apparent constructing the desired structure of this type systematic.In addition, the present invention is not also for any certain programmed language.It should be understood that and can utilize various programming languages to realize content of the present invention described here, and the description of above language-specific being done is in order to disclose preferred forms of the present invention.

In the specification that provided herein, a large amount of details are described.But, can understand, embodiments of the invention can be put into practice in the situation that there is no these details.In some instances, be not shown specifically known method, structure and technology, so that not fuzzy understanding of this description.

Similarly, be to be understood that, in order to simplify the disclosure and to help to understand one or more in each inventive aspect, in the above in the description of exemplary embodiment of the present invention, each feature of the present invention is grouped together into single embodiment, figure or sometimes in its description.But, the method for the disclosure should be construed to the following intention of reflection: the present invention for required protection requires than the more feature of feature of clearly recording in each claim.Or rather, as reflected in claims below, inventive aspect is to be less than all features of disclosed single embodiment above.Therefore, claims of following embodiment are incorporated to this embodiment thus clearly, and wherein each claim itself is as independent embodiment of the present invention.

Those skilled in the art are appreciated that and can the module in the equipment in embodiment are adaptively changed and they are arranged in one or more equipment different from this embodiment.Module in embodiment or unit or assembly can be combined into a module or unit or assembly, and can put them in addition multiple submodules or subelement or sub-component.At least some in such feature and/or process or unit are mutually repelling, and can adopt any combination to combine all processes or the unit of disclosed all features in this specification (comprising claim, summary and the accompanying drawing followed) and disclosed any method like this or equipment.Unless clearly statement in addition, in this specification (comprising claim, summary and the accompanying drawing followed) disclosed each feature can be by providing identical, be equal to or the alternative features of similar object replaces.

In addition, those skilled in the art can understand, although embodiment more described herein comprise some feature rather than further feature included in other embodiment, the combination of the feature of different embodiment means within scope of the present invention and forms different embodiment.For example, in the following claims, the one of any of embodiment required for protection can be used with compound mode arbitrarily.

All parts embodiment of the present invention can realize with hardware, or realizes with the software module of moving on one or more processor, or realizes with their combination.It will be understood by those of skill in the art that and can use in practice microprocessor or digital signal processor (DSP) to realize according to the some or all functions of the some or all parts in the maximum likelihood frequency deviation estimation device based on joint pilot of the embodiment of the present invention.The present invention can also be embodied as part or all equipment or the device program (for example, computer program and computer program) for carrying out method as described herein.Realizing program of the present invention and can be stored on computer-readable medium like this, or can there is the form of one or more signal.Such signal can be downloaded and obtain from internet website, or provides on carrier signal, or provides with any other form.

It should be noted above-described embodiment the present invention will be described rather than limit the invention, and those skilled in the art can design alternative embodiment in the case of not departing from the scope of claims.In the claims, any reference symbol between bracket should be configured to limitations on claims.Word " comprises " not to be got rid of existence and is not listed as element or step in the claims.Being positioned at word " " before element or " one " does not get rid of and has multiple such elements.The present invention can be by means of including the hardware of some different elements and realizing by means of the computer of suitably programming.In the unit claim of having enumerated some devices, several in these devices can be to carry out imbody by same hardware branch.The use of word first, second and C grade does not represent any order.Can be title by these word explanations.

Claims (10)

1. the maximum likelihood frequency deviation estimating method based on joint pilot, synchronous for the reception of multiple-input and multiple-output MIMO-orthogonal frequency division multiplex OFDM system, it is characterized in that, comprising:

Receiving terminal is to corresponding n pilot symbol transmitted matrix D ⁽ⁿ⁾, discrete Fourier transform (DFT) DFT matrix F and described matrix F the matrix that forms of front L row

carry out preliminary treatment, obtain matrix P ⁽ⁿ⁾;

Receiving terminal will receive frequency pilot sign matrix R ⁽ⁿ⁾with described matrix P ⁽ⁿ⁾storing respectively length into is in the sliding window memory of first-in first-out of M;

Build M frequency deviation matrix, from described sliding window memory, obtain described reception frequency pilot sign matrix R ⁽ⁿ⁾with described matrix P ⁽ⁿ⁾, according to described reception frequency pilot sign matrix R ⁽ⁿ⁾, described matrix P ⁽ⁿ⁾go out likelihood function with described M frequency deviation matrix computations, by likelihood function described in frequency deviation estimated value to be selected substitution, obtaining and making the frequency deviation estimated value of cumulative likelihood function value minimum is final maximum likelihood frequency deviation estimated value.

2. the method for claim 1, is characterized in that, receiving terminal is to corresponding pilot symbol transmitted matrix D ⁽ⁿ⁾, discrete Fourier transform (DFT) DFT matrix F and described matrix F the matrix that forms of front L row carry out preliminary treatment, obtain matrix P ⁽ⁿ⁾specifically comprise:

Described receiving terminal is according to formula 1, to corresponding pilot symbol transmitted matrix D ⁽ⁿ⁾, discrete Fourier transform (DFT) DFT matrix F and described matrix F the matrix that forms of front L row

carry out preliminary treatment, obtain matrix P ⁽ⁿ⁾;

formula 1;

Wherein,

n _tfor the number of transmit antennas of mimo system, N is OFDM sub-carrier number,

represent n the pilot tone sign matrix that p root transmitting antenna sends,

represent n the pilot tone symbol that p root transmitting antenna sends; F ∈ C ^{n × N}for DFT matrix, the individual element of its (l, m) is $F_{l,m}=\frac{1}{\sqrt{N}}{e}^{-j(2\mathrm{\&pi;}(l-1)(m-1)/N)};$ $\tilde{W}={(W\&CircleTimes;I_{N_T})}_{{\mathrm{NN}}_T\&times;{\mathrm{LN}}_T},$ W represents that the front L row of DFT matrix F are F=[W|V], W ∈ C ^{n × L}, V ∈ C ^{n × (N-L)}, and have W ^hv=0, WW ^h+ VV ^h=I, H represents that conjugation turns order.

3. the method for claim 1, is characterized in that, receiving terminal will receive frequency pilot sign matrix R ⁽ⁿ⁾with described matrix P ⁽ⁿ⁾store respectively length into and be in the sliding window memory of first-in first-out of M and specifically comprise:

Receiving terminal will receive frequency pilot sign matrix R ⁽ⁿ⁾with described matrix P ⁽ⁿ⁾storing respectively length into is in the sliding window memory of first-in first-out of M, obtains following two groups of data: $\begin{array}{c}{\{P}^{(n-k)},k=\mathrm{0,1},\&CenterDot;\&CenterDot;\&CenterDot;,M-1\}\\ \{R^{(n-k)},k=\mathrm{0,1},\&CenterDot;\&CenterDot;\&CenterDot;,M-1\}\end{array},$ Wherein,

n _rfor the reception antenna number of mimo system, N is OFDM sub-carrier number, and under the condition of system receiving terminal sign synchronization, n the reception frequency pilot sign that q root reception antenna receives is: $r_q^{\left(n\right)}=\underset{p=1}{\overset{N_T}{\mathrm{\&Sigma;}}}{E}^{\left(n\right)}{F}^H{D}_p^{\left(n\right)}{\mathrm{Wg}}_{q,p}^{\left(n\right)}+n_q,$ Wherein, $E^{\left(n\right)}=e^{j2\mathrm{\&pi;}{L}_n\mathrm{\&epsiv;}/N}E$ Represent n the frequency deviation matrix that frequency pilot sign is corresponding, E=diag ([1, e ^{j2 π ε/N}..., e ^{j2 π (N-1) ε/N}] ^t), ε is normalization frequency deviation value, L _nrepresent n the corresponding sequence number of first sampling time of frequency pilot sign;

represent the L footpath time domain channel gain that n frequency pilot sign experiences while transmission between (q, p) antenna pair, T represents to turn order, n _qrepresent that N × 1 dimension zero-mean and every one dimension variance that q root reception antenna receives are multiple Gaussian noise, j ²=-1.

4. the method for claim 1, is characterized in that, builds M frequency deviation matrix and specifically comprises:

Build M frequency deviation matrix according to formula 2; $E^{(n-k)}=e^{j2\mathrm{\&pi;\&epsiv;}{L}_{n-k}/N}\&CenterDot;\mathrm{diag}\left({[1,e^{j2\mathrm{\&pi;\&epsiv;}/N},\&CenterDot;\&CenterDot;\&CenterDot;,e^{j2\mathrm{\&pi;}(N-1)\mathrm{\&epsiv;}/N}]}^T\right)$ Formula 2;

Wherein, k=0,1 ..., M-1, ε is normalization frequency deviation value, N is OFDM sub-carrier number, j ²=-1, L _n-krepresent the corresponding sequence number of first sampling time of (n-k) individual frequency pilot sign, T represents to turn order.

5. the method for claim 1, is characterized in that, obtains described reception frequency pilot sign matrix R from described sliding window memory ⁽ⁿ⁾with described matrix P ⁽ⁿ⁾, according to described reception frequency pilot sign matrix R ⁽ⁿ⁾, described matrix P ⁽ⁿ⁾go out likelihood function with described M frequency deviation matrix computations, likelihood function described in frequency deviation estimated value to be selected substitution specifically comprised:

From described sliding window memory, obtain described reception frequency pilot sign matrix R ⁽ⁿ⁾with described matrix P ⁽ⁿ⁾, according to described reception frequency pilot sign matrix R ⁽ⁿ⁾, described matrix P ⁽ⁿ⁾with described M frequency deviation matrix, and calculate likelihood function according to formula 3;

λ ⁽ⁿ⁾(ε)=‖ (R ⁽ⁿ⁾) ^he ⁽ⁿ⁾(P ⁽ⁿ⁾-I) (E ⁽ⁿ⁾) ^hr ⁽ⁿ⁾‖ _fformula 3;

Wherein, ‖ ‖ _frepresent the F norm of getting matrix, H is that conjugation turns order,

represent n the frequency deviation matrix that frequency pilot sign is corresponding,

n is OFDM sub-carrier number, λ ⁽ⁿ⁾(ε) likelihood function of estimating for maximum likelihood frequency deviation;

By formula 3 described in the substitution of described frequency deviation estimated value to be selected.

6. the method for claim 1, is characterized in that, obtaining and making the frequency deviation estimated value of cumulative likelihood function value minimum is that final maximum likelihood frequency deviation estimated value specifically comprises:

Adopt the method for substep search, calculate according to formula 4 frequency offset estimation that makes following cumulative likelihood function value minimum

and by the frequency offset estimation of described minimum

as final maximum likelihood frequency deviation estimated value; ${\widehat{\mathrm{\&epsiv;}}}^{\left(n\right)}=\underset{\mathrm{\&epsiv;}}{\arg \min }\left\{\underset{k=0}{\overset{M-1}{\mathrm{\&Sigma;}}}{\mathrm{\&lambda;}}^{(n-k)}\left(\mathrm{\&epsiv;}\right)\right\}$ Formula 4;

Wherein, M is the length of sliding window memory.

7. method as claimed in claim 6, is characterized in that, the method for described substep search specifically comprises:

Step 1, supposes that initial frequency deviation scope is for (ε _max, ε _max);

Step 2 is all divided into frequency deviation hunting zone P interval in the time of each search, then calculates successively relatively likelihood function in the value size of each interval endpoint;

Step 3, according to likelihood function

the process that changes from big to small and change from small to big with the selection value of different normalization frequency deviation values, determines the frequency deviation region (ε that next round frequency deviation is searched for ₁, ε ₂), wherein ε ₁, ε ₂choose with likelihood function value variation relation meet $\underset{n=0}{\overset{M-1}{\mathrm{\&Sigma;}}}{\mathrm{\&lambda;}}^{\left(n\right)}\left({\mathrm{\&epsiv;}}_1\right)>\underset{n=1}{\overset{M-1}{\mathrm{\&Sigma;}}}{\mathrm{\&lambda;}}^{\left(n\right)}\left({\mathrm{\&epsiv;}}_0\right),$ $\underset{n=1}{\overset{M-1}{\mathrm{\&Sigma;}}}{\mathrm{\&lambda;}}^{\left(n\right)}\left({\mathrm{\&epsiv;}}_2\right)>\underset{n=1}{\overset{M-1}{\mathrm{\&Sigma;}}}{\mathrm{\&lambda;}}^{\left(n\right)}\left({\mathrm{\&epsiv;}}_0\right),$ Wherein ε ₀=(ε ₁+ ε ₂)/2;

Step 4, at the new frequency deviation region (ε of calculative determination ₁, ε ₂) basis on, repeat above-mentioned steps 2-3, until current frequency deviation step-size in search has met the predetermined frequency offset estimation accuracy requirement of system, export current frequency deviation hunting zone (ε ₁, ε ₂) intermediate value ε ₀for the final valuation of this search

8. the maximum likelihood frequency deviation estimation device based on joint pilot, synchronous for the reception of multiple-input and multiple-output MIMO-orthogonal frequency division multiplex OFDM system, it is characterized in that, comprising:

Pretreatment module, for to corresponding n pilot symbol transmitted matrix D ⁽ⁿ⁾, discrete Fourier transform (DFT) DFT matrix F and described matrix F the matrix that forms of front L row

carry out preliminary treatment, obtain matrix P ⁽ⁿ⁾;

Memory module, for receiving frequency pilot sign matrix R ⁽ⁿ⁾with described matrix P ⁽ⁿ⁾storing respectively length into is in the sliding window memory of first-in first-out of M;

Frequency deviation estimating modules for building M frequency deviation matrix, is obtained described reception frequency pilot sign matrix R from described sliding window memory ⁽ⁿ⁾with described matrix P ⁽ⁿ⁾, according to described reception frequency pilot sign matrix R ⁽ⁿ⁾, described matrix P ⁽ⁿ⁾go out likelihood function with described M frequency deviation matrix computations, by likelihood function described in frequency deviation estimated value to be selected substitution, obtaining and making the frequency deviation estimated value of cumulative likelihood function value minimum is final maximum likelihood frequency deviation estimated value.

9. device as claimed in claim 8, is characterized in that,

Described pretreatment module specifically for: according to formula 1, to corresponding pilot symbol transmitted matrix D ⁽ⁿ⁾, discrete Fourier transform (DFT) DFT matrix F and described matrix F the matrix that forms of front L row

carry out preliminary treatment, obtain matrix P ⁽ⁿ⁾;

formula 1;

Wherein,

n _tfor the number of transmit antennas of mimo system, N is OFDM sub-carrier number,

represent n the pilot tone sign matrix that p root transmitting antenna sends,

represent n the pilot tone symbol that p root transmitting antenna sends; F ∈ C ^{n × N}for DFT matrix, the individual element of its (l, m) is $F_{l,m}=\frac{1}{\sqrt{N}}{e}^{-j(2\mathrm{\&pi;}(l-1)(m-1)/N)};$ $\tilde{W}={(W\&CircleTimes;I_{N_T})}_{{\mathrm{NN}}_T\&times;{\mathrm{LN}}_T},$ W represents that the front L row of DFT matrix F are F=[W|V], W ∈ C ^{n × L}, V ∈ C ^{n × (N-L)}, and have W ^hv=0, WW ^h+ VV ^h=I, H is that conjugation turns order;

Described memory module specifically for: will receive frequency pilot sign matrix R ⁽ⁿ⁾with described matrix P ⁽ⁿ⁾storing respectively length into is in the sliding window memory of first-in first-out of M, obtains following two groups of data: $\begin{array}{c}{\{P}^{(n-k)},k=\mathrm{0,1},\&CenterDot;\&CenterDot;\&CenterDot;,M-1\}\\ \{R^{(n-k)},k=\mathrm{0,1},\&CenterDot;\&CenterDot;\&CenterDot;,M-1\}\end{array},$ Wherein,

n _rfor the reception antenna number of mimo system, N is OFDM sub-carrier number, and under the condition of system receiving terminal sign synchronization, n the reception frequency pilot sign that q root reception antenna receives is: $r_q^{\left(n\right)}=\underset{p=1}{\overset{N_T}{\mathrm{\&Sigma;}}}{E}^{\left(n\right)}{F}^H{D}_p^{\left(n\right)}{\mathrm{Wg}}_{q,p}^{\left(n\right)}+n_q,$ Wherein, $E^{\left(n\right)}=e^{j2\mathrm{\&pi;}{L}_n\mathrm{\&epsiv;}/N}E$ Represent n the frequency deviation matrix that frequency pilot sign is corresponding, E=diag ([1, e ^{j2 π ε/N}..., e ^{j2 π (N-1) ε/N}] ^t), ε is normalization frequency deviation value, L _nrepresent n the corresponding sequence number of first sampling time of frequency pilot sign;

represent the L footpath time domain channel gain that n frequency pilot sign experiences while transmission between (q, p) antenna pair, T represents to turn order, n _qrepresent that N × 1 dimension zero-mean and every one dimension variance that q root reception antenna receives are

multiple Gaussian noise, j ²=-1;

Described frequency deviation estimating modules specifically for: build M frequency deviation matrix according to formula 2; $E^{(n-k)}=e^{j2\mathrm{\&pi;\&epsiv;}{L}_{n-k}/N}\&CenterDot;\mathrm{diag}\left({[1,e^{j2\mathrm{\&pi;\&epsiv;}/N},\&CenterDot;\&CenterDot;\&CenterDot;,e^{j2\mathrm{\&pi;}(N-1)\mathrm{\&epsiv;}/N}]}^T\right)$ Formula 2;

Wherein, k=0,1 ..., M-1, ε is normalization frequency deviation value, N is OFDM sub-carrier number, j ²=-1, L _n-krepresent the corresponding sequence number of first sampling time of (n-k) individual frequency pilot sign, T represents to turn order;

From described sliding window memory, obtain described reception frequency pilot sign matrix R ⁽ⁿ⁾with described matrix P ⁽ⁿ⁾, according to described reception frequency pilot sign matrix R ⁽ⁿ⁾, described matrix P ⁽ⁿ⁾with described M frequency deviation matrix, and calculate likelihood function according to formula 3;

λ ⁽ⁿ⁾(ε)=‖ (R ⁽ⁿ⁾) ^he ⁽ⁿ⁾(P ⁽ⁿ⁾-I) (E ⁽ⁿ⁾) ^hr ⁽ⁿ⁾‖ _fformula 3;

Wherein, ‖ ‖ _frepresent the F norm of getting matrix, H is that conjugation turns order, E ⁽ⁿ⁾represent n the frequency deviation matrix that frequency pilot sign is corresponding, represent carrier wave frequency deviation matrix, E=diag ([1, e ^{j2 π ε/N}..., e ^{j2 π (N-1) ε/N}] ^t), N is OFDM sub-carrier number, λ ⁽ⁿ⁾(ε) likelihood function of estimating for maximum likelihood frequency deviation;

By formula 3 described in the substitution of described frequency deviation estimated value to be selected;

Adopt the method for the search that distributes, calculate according to formula 4 frequency offset estimation that makes following cumulative likelihood function value minimum

and by the frequency offset estimation of described minimum

as final maximum likelihood frequency deviation estimated value; ${\widehat{\mathrm{\&epsiv;}}}^{\left(n\right)}=\underset{\mathrm{\&epsiv;}}{\arg \min }\left\{\underset{k=0}{\overset{M-1}{\mathrm{\&Sigma;}}}{\mathrm{\&lambda;}}^{(n-k)}\left(\mathrm{\&epsiv;}\right)\right\}$ Formula 4;

Wherein, M is the length of sliding window memory.

10. device as claimed in claim 9, is characterized in that, described frequency deviation estimating modules specifically comprises:

Submodule is set, for supposing that initial frequency deviation scope is for (ε _max, ε _max);

Calculating sub module, interval for all frequency deviation hunting zone is divided into P in the time searching at every turn, then calculate successively relatively likelihood function

in the value size of each interval endpoint;

Determine submodule, for according to likelihood function

the process that changes from big to small and change from small to big with the selection value of different normalization frequency deviation values, determines the frequency deviation region (ε that next round frequency deviation is searched for ₁, ε ₂), wherein ε ₁, ε ₂choose with likelihood function value variation relation meet

$\underset{n=1}{\overset{M-1}{\mathrm{\&Sigma;}}}{\mathrm{\&lambda;}}^{\left(n\right)}\left({\mathrm{\&epsiv;}}_2\right)>\underset{n=1}{\overset{M-1}{\mathrm{\&Sigma;}}}{\mathrm{\&lambda;}}^{\left(n\right)}\left({\mathrm{\&epsiv;}}_0\right),$ Wherein ε ₀=(ε ₁+ ε ₂)/2;

Output sub-module, at the new frequency deviation region (ε of calculative determination ₁, ε ₂) basis on, call described calculating sub module and described definite submodule, until current frequency deviation step-size in search has met the predetermined frequency offset estimation accuracy requirement of system, export current frequency deviation hunting zone (ε ₁, ε ₂) intermediate value ε ₀for the final valuation of this search

Images