CN107566307A - Blind equalizing apparatus and method, data modulation system and method - Google Patents
Blind equalizing apparatus and method, data modulation system and method Download PDFInfo
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Abstract
The present invention relates to blind equalizing apparatus and method, digital modulation system and method, blind equalizing apparatus includes：Balance module, for receiving the data from carrier recovery block, balanced adjustment is carried out to data；Judging module, for being made decisions to the data after balanced adjust, with the data after being adjudicated；Control errors module, for error signal value, and whether the absolute value of error in judgement signal value is less than threshold value C1；Tap coefficient adjusting module, for calculating tap coefficient according to the relation of error signal value and thresholding, when the absolute value of error signal value is less than threshold value C1When, the tap coefficient of next sampled point is calculated, when the absolute value of error signal value is not less than threshold value C1When, the tap coefficient of sampled point keeps constant.
Description
Technical field
The present invention relates to data to modulate field, more particularly to blind equalizing apparatus and method, data modulation system and method.
Background technology
It is proposed the self- recoverage algorithm for not needing training sequence to realize channel equalization first from Sato in 1975.So far, channel
The research in blind equalization field is very active always, and achieves substantial amounts of theory and practice achievement in research.Blind equalization algorithm is overall
On can be divided into three classes：Nonlinear blind equalization algorithm based on neutral net and Volterra series, based on high-order statistic or
Blind equalization algorithm, the Bussgang races blind equalization algorithm of cyclo-stationary statistic.
Nonlinear blind equalization algorithm is mainly used in the more serious occasion of non-linear distortion.Neutral net can approach arbitrarily
Complicated nonlinear system, strong robustness, parallel and distributed process method can be used to carry out a large amount of computings, in blind equalization field
There is good application prospect, shortcoming is that system is excessively complicated, and cost is high.Volterra series is to memoryless and memory nonlinear
System has powerful modeling ability, and for there is the very strong ability of equalization during balanced field, shortcoming is that convergence process is too slow.Letter
Number higher-order spectrum include the amplitude information and phase information of signal simultaneously, High Order Moment algorithm can be merely with reception signal sequence
The characteristic of channel is recognized, and then realizes channel equalization.But there are two subject matters in it：When it is most based on high-order statistic from
Adapt to blind equalization algorithm and local convergence be present；And convergence rate is very slow, it is necessary to which the sample point of observation is excessive.Based on second order
The blind equalization algorithm of Cyclic Statistics is directed to the shortcomings that higher order statistical quantity algorithm and proposes that it reduces observation data, improve from
The speed of adaptive algorithm.Bussgang classes blind equalization algorithm carries out certain nonlinear transformation in the output end of balanced device, produces the phase
The estimate of signal is hoped, and designs appropriate cost function, the minimum point of cost function is found using stochastic gradient algorithm, it is real
Existing adaptive equalization process.Several famous special cases of Bussgang algorithms have：
Sato algorithms, BGR algorithms, Stop and Go algorithms, decision-making direction calculation and Godard algorithms.Sato algorithms are
Earliest blind equalization algorithm, proposed for pulse amplitude modulation (Pulse Amplitude Modulation, PAM).With
Afterwards, Sato algorithms are generalized to quadrature amplitude modulation (Quadrature Amplitude by Benveniste and Goursat
Modulation, QAM) in, propose BGR algorithms.After blind equalization algorithm is restrained, channel eye pattern opens, it is possible to utilizes decision-making
Direction calculation control balanced device work, Stop and go algorithms are proposed that decision-making is pointed to and calculated by the algorithm by Picchi and Prati
Method and Sato algorithms are combined, and whether the weight coefficient for determining balanced device according to decision error carries out adaptive updates.Godard is calculated
A special case in method is constant modulus algorithm (Constant Modulus Algorithm, CMA), by Godard and Treichter
Independently propose, its cost function is constructed by the higher order statistical characteristic of transmission signal.
Digital communication must carry out equilibrium due to more influences through with channel strip limit.When communication occurs over just single emitter
When between single receiver, it is possible that they, which establish certain agreement training balanced device,.But for multi-point or wide
Broadcast system, training are unacceptable must to appoint blind equalization.In various blind equalization algorithms, constant modulus algorithm (CMA) is a kind of
Important approach for blind channel equalization, has been widely used in digital communication system, and this algorithm impliedly make use of reception
The high-order statistic of signal.CMA is only relevant with the amplitude of reception signal, and insensitive to carrier phase offset, robustness is good；Steady
Under the conditions of state, its mean square error is less than other Bussgang algorithms.There are many innovatory algorithms after CMA algorithms, it is main
To reduce the steady residual error of balanced device from convergence of algorithm speed is improved and make it have carrier phase recovery ability etc.
Aspect is improved.In order that algorithm has carrier phase recovery ability, it is proposed that simplifies constellation nomography (Reduced
Constellation Algorithm, RCA) and amendment constant modulus algorithm (Modefied Constant Modulus
Algorithm, MCMA), cost function is constructed respectively to the real and imaginary parts of signal, while realizing balanced, it may have carry
Wave phase recovers function.For steady-state error after CMA algorithm equilibriums it is big the shortcomings that, it is proposed that multimode blind equalization algorithm (Multi-
modulus Algorithm,MMA).Planisphere is divided into several regions by MMA according to certain rule, improves and high-order QAM is believed
Number equalization performance.Due to real and imaginary parts separate computations error term, algorithm are had into the function of phase recovery simultaneously.
A kind of existing patent " improved time-domain adaptive blind balance method ", application number：201410406416.3 belong to
Domain equalizing technology field, more particularly to a kind of improved time-domain adaptive blind balance method.The improved time-domain adaptive is blind
Weighing apparatus method comprises the following steps：Received information sequence [x (0), x (1) ..., x (N-1)], total road corresponding to described information sequence
Footpath number is L；The multi-path channel parameters in i-th of path are expressed as h (i), then the signal z (n) of n receptions is：Z (n)=y
(n)+w (n), w (n) represent sampled value of the additive white Gaussian noise at the n moment of setting；By the tap coefficient of transversal filter
It is expressed as f (n'), n'=-q,-q+1 ..., -1,0,1 ..., p, q and p represent the forward direction exponent number of transversal filter and backward respectively
Exponent number；Mean square error Es [e2 (n)] of the z (n) through the filtered signal yeq (n) of transversal filter Yu x (n) is drawn, with E [e2
(n)] minimum criterion, the normal equation of transversal filter tap coefficient is drawn；According to the transversal filter tap coefficient
Normal equation, the tap coefficient of transversal filter is solved using recursion mode.Although this patent effect is reduced, algorithm
Calculate more complicated, it is necessary to calculate specific multipath number, difficulty is also bigger.
Dual Mode Blind Equalization Algorithm is to improve the effective ways of blind equalization convergence rate and steady-state behaviour.Double mode blind equalization
CMA modes are selected when equilibrium starts, when balanced device output meets that eye pattern opens condition, go to decision-directed balanced way.
An existing frequently-used algorithm is CMA algorithms, and this convergence of algorithm speed is slow, and steady-state error is high, and is needed special
The phase recovery system of door recovers carrier phase.For these shortcomings, many scholars are improved constant modulus algorithm, compare work
The innovatory algorithm of name includes：Correct constant modulus algorithm (MCMA).MCMA algorithms separately handle the real and imaginary parts of signal, i.e., simultaneously
Using the amplitude information and phase information of signal, while channel eye pattern is opened, the phase of signal can also be overcome to deflect, collected
Balanced and phase recovery function is.
CMA approaches the output of the balanced device circle of radii fixus into figure, only considered the amplitude information of signal, balanced device
Steady-state error is big after convergence；MCMA operation principle, MCMA draw close real part of output signal or so two lines, and imaginary part is to upper and lower
Two lines are approached, and can overcome phase misalignment caused by multipath channel characteristic and carrier shift to a certain extent compared to CMA algorithms
Very；Therefore after equalizer convergence, the steady-state error of minimum can be obtained.
MCMA is the blind equalization algorithm being widely used at present, and CMA cost function is divided into real and imaginary parts two by it
Point, it is defined as：
Algorithm update mode is as follows, and the signal X. of input is corrected by W
Y (k)=XT(k)W(k)
Input vector X is equal to：X (k)=[x (k) ..., x (k-N+1)]T
Weighing vector W is equal to：W (k)=[w0..., w (k)N-1(k)]T
In MCMA algorithms, cost function is divided into real and imaginary parts two parts, modification cost function is
J (k)=JR(k)+JI(k)
E [] represents mathematic expectaion, and subscript R is expressed as the real part of signal, and I is expressed as the imaginary part of signal, wherein, norm
R2,RAnd R2,IIt is as follows
To cost function J derivation, error function e is obtained, is divided into real and imaginary parts,
E (k)=eR(k)+jeI(k)
Fig. 1 is the schematic diagram of target value of the CMA algorithms under perfect balance, and Fig. 2 is MCMA algorithms under perfect balance
Target value schematic diagram.Referring to Fig. 1 and Fig. 2, for ideally CMA signal e (k)=0, preferable equation | y (k) |2-
R2=0, CMA approach the output of the balanced device circle of radii fixus into figure, only considered the amplitude information of signal, and balanced device is received
It is big to hold back rear steady-state error, and MCMA real and imaginary parts separate,With
So the average of MCMA output signal y solid part signals make great efforts toIt is close, imaginary signals y average make great efforts toIt is close.So signal real and imaginary parts each meet to be correctly oriented so that deviation will not occur for signal phase.
MCMA algorithms separately handle the real and imaginary parts of signal, i.e., utilize amplitude information and the phase letter of signal simultaneously
Breath, while channel eye pattern is opened, can also overcome the phase of signal to deflect, and integrate balanced and phase recovery function.
CMA convergence rate is slow, and steady-state error is high, and needs special phase recovery system to recover carrier phase.Lacked for these
Point, many scholars are improved constant modulus algorithm, and more famous innovatory algorithm includes：Correct constant modulus algorithm (MCMA).CMA
Operation principle, approach the output of the balanced device circle of radii fixus into figure, only considered the amplitude information of signal, balanced device
Steady-state error is big after convergence；MCMA operation principle, MCMA draw close real part of output signal or so two lines, and imaginary part is to upper and lower
Two lines are approached, and can overcome phase misalignment caused by multipath channel characteristic and carrier shift to a certain extent compared to CMA algorithms
Very；Therefore after equalizer convergence, the steady-state error of minimum can be obtained.The constant modulus algorithm (MCMA) of amendment makes revised error
Function is minimum, and autoadapted learning rate is adjusted immediately by receiving sequence.Performance comparision is carried out to two kinds of algorithms with QAM signals,
For extensive analog result amendment constant modulus algorithm than the fast convergence rate of common constant modulus algorithm, intersymbol interference (ISI) is small, calculates in addition
Method convergence post-equalizer output has the advantages that both without phase place or without delay.Obviously, compared with common constant modulus algorithm herein
The blind equalization performance for correcting constant modulus algorithm is more excellent.
Because finite length transversal filter is the truncation of ideal equalizer, thus it can not possibly be completely eliminated intersymbol (or
Person's intersymbol) interference, intersymbol or intersymbol interference can only be reduced to a certain extent.In actual applications, it is further to subtract
Small intersymbol interference, generally use DFF are realized.DFF is substantially a kind of non-linear equal
Weighing apparatus, typically by two parts wave filter group into：One is feedforward filter, and another is feedback filter.Two wave filters
Tap interval is mark space T, and the input for part of feedovering is reception signal sequence { x (k) }.In this regard, feedforward filtering
Equivalent to one transversal filter of device.Feedback filter is used as by the use of the previous judgement sequence for being detected symbol and inputted.Functionally
Say, feedback filter is used for removing previously part intersymbol interference caused by detected symbol from current estimate.It is assumed that
Weighing apparatus has N1 tap in feedforward filter, there is N2 tap in feedback filter, but this algorithm is excessively complicated, and
The resource of consumption is more.
Fig. 3 is the data modulation system of prior art, as shown in figure 3, the data modulation system mainly includes three keys
Module, it is respectively：Sign synchronization module, carrier recovery block and balance module, sign synchronization module eliminate timing error so that
Data modulation system is adjudicated in symbol the best time, and carrier recovery block is the equilibrium in order to eliminate frequency departure and phase deviation
Module eliminates the intersymbol caused by transmitting and receiving filter, time delay and Multipath Transmission, coupling effect and multi-access inference and done
Disturb.
Gardner algorithms are that each symbol period calculates once, are a kind of typical unbound nucleus timing error inspections
Method of determining and calculating, the algorithm proposed in 1986 by Gardner, initially just for the Timing Error Detection of BPSK/QPSK signals, still
It is equally applicable to QAM signals.
Therefore, it is necessary to the blind equalizing apparatus and method of a kind of combination MCMA algorithms and DD-LMS algorithms for QAM signals,
Data modulation system and method.
The content of the invention
According to an aspect of the present invention, blind equalizing apparatus provided by the invention, including：
Balance module, for receiving the data from carrier recovery block, pass through taking out from tap coefficient adjusting module
Head coefficient carries out balanced adjustment to data；
Judging module, for being made decisions to the data after balanced adjust, with the data after being adjudicated；
Control errors module, for error signal value, and whether the absolute value of error in judgement signal value is less than thresholding
Value；
Tap coefficient adjusting module, for calculating tap coefficient according to the relation of error signal value and thresholding：
When the absolute value of error signal value is less than threshold value, according to the tap coefficient of k-th of sampled point, DD-LMS algorithms
Step-length, the data of error signal value and k-th sampled point calculate the tap coefficient of+1 sampled point of kth, and by kth+1 adopt
The tap coefficient of sampling point is transmitted to the balance module, and k is positive integer,
When the absolute value of error signal value is not less than threshold value, the tap coefficient of sampled point keeps constant.
In balance module, the calculating of balanced adjustment is carried out by below equation：
Y (k)=XT(k)W(k)
Wherein, X (k) is the data from analog-to-digital conversion module, XT(k) transposition for being X (k), W (k) is tap coefficient, y
(k) it is the data after balanced adjustment.
In control errors module, the calculating of error signal value is carried out by below equation：
E (k)=eR(k)+jeI(k)
eRAnd e (k)I(k) calculated by below equation：
R2,RWith R2,ICalculated by below equation：
Wherein, e (k) is error signal value, and y (k) is the data after adjustment, the data after dec (y (k)) judgements, R2To be normal
Mould, E [] represent mathematic expectaion, and a (k) is the data that balance module receives, and C is y (k) limitation scope, and subscript R is expressed as letter
Number real part, I is expressed as the imaginary part of signal.
In tap coefficient adjusting module, the tap coefficient of+1 sampled point of kth is calculated by below equation：
w(k+1,:)=w (k,:)+2*μ_lms*e(k)*X(k,:)
Wherein, w (k+1,:) be+1 sampled point of kth tap coefficient, w (k,:) for the tap coefficient of k-th sampled point,
μ _ lms is the step-length of DD-LMS algorithms, and e (k) is error signal value, and X (k) is the data of k-th of sampled point.
According to another aspect of the present invention, the data modulation system provided by the invention for including above-mentioned blind equalizing apparatus,
Also include：
Down conversion module, for receiving the data from analog-to-digital conversion module, frequency-conversion processing is carried out to received data,
And the data after frequency-conversion processing are sent to sign synchronization module；
Sign synchronization module, for receiving the data after frequency-conversion processing, the timing error of data is eliminated, and will be without timing by mistake
The data of difference are sent to carrier recovery block；
Carrier recovery block, for receiving the data without timing error, the frequency deviation and skew of data are eliminated, and will be without frequency deviation
Sent with the data of skew to balance module.
In sign synchronization module, the timing error of data is eliminated by Gardner algorithms.
In carrier recovery block, the frequency deviation and skew of data are eliminated by the way of COS and SIN table look-up.
According to a further aspect of the invention, the blind balance method provided by the invention based on above-mentioned blind equalizing apparatus, bag
Include following steps：
S141, the data from carrier recovery block are received, pass through the tap coefficient pair from tap coefficient adjusting module
Data carry out balanced adjustment；
S142, the data after balanced adjust are made decisions, with the data after being adjudicated；
Whether S143, error signal value, and the absolute value of error in judgement signal value are less than threshold value；
S144, tap coefficient is calculated according to error signal value and the relation of thresholding：
When the absolute value of error signal value is less than threshold value, according to the tap coefficient of k-th of sampled point, DD-LMS algorithms
Step-length, the data of error signal value and k-th sampled point calculate the tap coefficient of+1 sampled point of kth, and by kth+1 adopt
The tap coefficient of sampling point is transmitted to the balance module, and k is positive integer,
When the absolute value of error signal value is not less than threshold value, the tap coefficient of sampled point keeps constant.
In step S143, the calculating of error signal value is carried out by below equation：
E (k)=eR(k)+jeI(k)
eRAnd e (k)I(k) calculated by below equation：
R2,RWith R2,ICalculated by below equation：
Wherein, e (k) is error signal value, and y (k) is the data after adjustment, the data after dec (y (k)) judgements, R2To be normal
Mould, E [] represent mathematic expectaion, and a (k) is the data that balance module receives, and C is y (k) limitation scope, and subscript R is expressed as letter
Number real part, I is expressed as the imaginary part of signal.
According to a further aspect of the invention, it is provided by the invention to be based on above-mentioned blind balance method, it is further comprising the steps of：
S110, the data from analog-to-digital conversion module are received, frequency-conversion processing is carried out to received data；
S120, eliminate the timing error of the data after frequency-conversion processing；
S130, eliminate the frequency deviation and skew of the data of no timing error.
The present invention compared with prior art, has advantages below：
1. in the present invention, clock synchronous calibration is carried out before frequency calibration and phase alignment, can effectively collect
Optimum sampling point, and system frequency deviation does not influence on optimum sampling point；
2. in the present invention, the suitching type double mode DD-LMS-MCMA being combined using MCMA algorithms and DD-LMS algorithms is become
Step length algorithm can reach fast convergence rate and the small effect of remainder error；
3. in the present invention, the suitching type double mode DD-LMS-MCMA being combined using MCMA algorithms and DD-LMS algorithms is become
Step length algorithm, it is only necessary to a forward direction filtering process, the filtering multiplier of half can be saved, performance, which truly has, substantially to be carried
Rise.
Described above is only the general introduction of technical solution of the present invention, in order to better understand the technological means of the present invention,
And can be practiced according to the content of specification, and in order to allow above and other objects of the present invention, feature and advantage can
Become apparent, below especially exemplified by the embodiment of the present invention.
Brief description of the drawings
By reading the detailed description of hereafter preferred embodiment, it is various other the advantages of and benefit it is common for this area
Technical staff will be clear understanding.Accompanying drawing is only used for showing the purpose of preferred embodiment, and is not considered as to the present invention
Setting.And in whole accompanying drawing, identical part is denoted by the same reference numerals.In the accompanying drawings：
Fig. 1 is the schematic diagram of target value of the CMA algorithms under perfect balance；
Fig. 2 is the schematic diagram of target value of the MCMA algorithms under perfect balance；
Fig. 3 is the data modulation system of prior art；
Fig. 4 is the data modulation system of the present invention；
Fig. 5 is the fundamental diagram of the blind equalizing apparatus of the present invention；
Fig. 6 is Gardner algorithm schematic diagrames；
Fig. 7 is the data modulation method of the present invention；
Fig. 8 is the schematic diagram of the down-conversion signal of collection；
Fig. 9 is the schematic diagram of signal after Timing Error Detection circuit synchronization timing；
Figure 10 is the schematic diagram by correcting constant modulus algorithm signal after equalization；
After Figure 11 is combines decision-directed least mean square algorithm progress equilibrium by the amendment constant modulus algorithm of the present invention
Constellation schematic diagram；
Figure 12 is the constellation schematic diagram by correcting constant modulus algorithm signal after equalization.
Embodiment
The exemplary embodiment of the disclosure is more fully described below with reference to accompanying drawings.Although the disclosure is shown in accompanying drawing
Exemplary embodiment, it being understood, however, that may be realized in various forms the disclosure without should be by embodiments set forth here
It is set.On the contrary, these embodiments are provided to facilitate a more thoroughly understanding of the present invention, and can be by the limitation of the disclosure
Scope is completely communicated to those skilled in the art.
Those skilled in the art of the present technique are appreciated that unless expressly stated, singulative " one " used herein, " one
It is individual ", " described " and "the" may also comprise plural form.It is to be further understood that what is used in the specification of the present invention arranges
Diction " comprising " refer to the feature, integer, step, operation, element and/or component be present, but it is not excluded that in the presence of or addition
One or more other features, integer, step, operation, element, component and/or their groups.
Those skilled in the art of the present technique are appreciated that unless otherwise defined, all terms used herein (including technology art
Language and scientific terminology), there is the general understanding identical meaning with the those of ordinary skill in art of the present invention.Should also
Understand, those terms defined in such as general dictionary, it should be understood that have with the context of prior art
The consistent meaning of meaning, and unless by specific definitions, otherwise will not be explained with the implication of idealization or overly formal.
Fig. 4 is the data modulation system of the present invention, as shown in figure 4, the data modulation system of the present invention fills including blind equalization
Put, blind equalizing apparatus provided by the invention, including：
Balance module, for receiving the data from carrier recovery block, pass through taking out from tap coefficient adjusting module
Head coefficient carries out balanced adjustment to data；
Judging module, for being made decisions to the data after balanced adjust, with the data after being adjudicated；
Control errors module, for error signal value, and whether the absolute value of error in judgement signal value is less than thresholding
Value C1, such as C1For 0.18；
Tap coefficient adjusting module, for calculating tap coefficient according to the relation of error signal value and thresholding：
When the absolute value of error signal value is less than threshold value, according to the tap coefficient of k-th of sampled point, DD-LMS algorithms
Step-length, the data of error signal value and k-th sampled point calculate the tap coefficient of+1 sampled point of kth, and by kth+1 adopt
The tap coefficient of sampling point is transmitted to the balance module, and k is positive integer,
When the absolute value of error signal value is not less than threshold value, the tap coefficient of sampled point keeps constant.
In balance module, the calculating of balanced adjustment is carried out by below equation：
Y (k)=XT(k)W(k)
Wherein, X (k) is the data from analog-to-digital conversion module, XT(k) transposition for being X (k), W (k) is tap coefficient, y
(k) it is the data after balanced adjustment.
In control errors module, the calculating of error signal value is carried out by below equation：
E (k)=eR(k)+jeI(k)
eRAnd e (k)I(k) calculated by below equation：
R2,RWith R2,ICalculated by below equation：
Wherein, e (k) is error signal value, and y (k) is the data after adjustment, the data after dec (y (k)) judgements, R2To be normal
Mould, E [] represent mathematic expectaion, and a (k) is the data that balance module receives, and C is y (k) limitation scope, and subscript R is expressed as letter
Number real part, I is expressed as the imaginary part of signal.
In tap coefficient adjusting module, the tap coefficient of+1 sampled point of kth is calculated by below equation：
w(k+1,:)=w (k,:)+2*μ_lms*e(k)*X(k,:)
Wherein, w (k+1,:) be+1 sampled point of kth tap coefficient, w (k,:) for the tap coefficient of k-th sampled point,
μ _ lms is the step-length of DD-LMS algorithms, and e (k) is error signal value, and X (k) is the data of k-th of sampled point.
LMS algorithm is DD-LMS, and after equalizer convergence, yd (k) and y (k) error is zero, then the tap system of balanced device
Number also just stops renewal, and the steady residual error after convergence is smaller.Due to being believed in DD-LMS algorithms with yd (k) instead of preferable
Number, therefore DD-LMS application, on condition that now most of ISI in channel has been eliminated, the output of decision device is in very big ratio
It is correct in example, so just can guarantee that estimation direction of the algorithm to cost function gradient is statistically correct.Therefore,
DD-LMS can be only applied to the tracking to channel, can so make full use of its excellent remainder error characteristic.Fig. 5 is of the invention
The fundamental diagram of blind equalizing apparatus, as shown in figure 5, error function is divided into real and imaginary parts point by the blind equalizing apparatus of the present invention
Other calculation error, and threshold sets are carried out to error, to further determine whether to update weight coefficient, so as to which obtain can be balanced
The weight coefficient of input signal is adjusted, the suitching type double mode blind equalization that the present invention is combined using MCMA and DD-LMS algorithms is calculated
Method DD-LMS-MCMA calculates equalizing coefficient, and compared to single MCMA, filtering resource does not increase, but algorithm has larger improvement.
Referring to Fig. 4, data modulation system provided by the invention, except above-mentioned blind equalizing apparatus, in addition to：
Down conversion module, for receiving the data from analog-to-digital conversion module, frequency-conversion processing is carried out to received data,
And the data after frequency-conversion processing are sent to sign synchronization module；
Sign synchronization module, for receiving the data after frequency-conversion processing, pass through Gardner algorithms and eliminate the timings of data and miss
Difference, and the data without timing error are sent to carrier recovery block,
The reason for selecting Gardner algorithms is as follows：
Gardner algorithms are unrelated with carrier phase, i.e., the timing error that Gardner algorithms detect does not have with carrier phase
There is relation, therefore, can be independently of carrier synchronization come the sign synchronization module realized using Gardner Timing Error Detections algorithm
Module, reduce the coupling between loop.The complexity of system realization can so be simplified.
Error function e (r) also has low pass filter below, and some high fdrequency components are filtered out.So clock synchronous calibration
It can be carried out before frequency calibration and phase alignment.Using synchronization timing preceding, optimum sampling point can be effectively collected,
System frequency deviation does not influence on optimum sampling point.
Gardner algorithms require that each symbol has two sampled points, one be symbol optimum sampling point, another is two
Point between individual continuous symbol optimum sampling point.Fig. 6 is Gardner algorithm schematic diagrames, wherein, error calculation formula is as follows：
Carrier recovery block, for receiving the data without timing error, data are eliminated by the way of COS and SIN table look-up
Frequency deviation and skew, and the data without frequency deviation and skew are sent to balance module.
Fig. 7 is the data modulation method of the present invention, as shown in fig. 7, the data modulation method of the present invention is included based on above-mentioned
The blind balance method of blind equalizing apparatus, comprises the following steps：
S141, the data from carrier recovery block are received, pass through the tap coefficient pair from tap coefficient adjusting module
Data carry out balanced adjustment；
S142, the data after balanced adjust are made decisions, with the data after being adjudicated；
Whether S143, error signal value, and the absolute value of error in judgement signal value are less than threshold value；
S144, tap coefficient is calculated according to error signal value and the relation of thresholding：
When the absolute value of error signal value is less than threshold value, according to the tap coefficient of k-th of sampled point, DD-LMS algorithms
Step-length, the data of error signal value and k-th sampled point calculate the tap coefficient of+1 sampled point of kth, and by kth+1 adopt
The tap coefficient of sampling point is transmitted to the balance module, and k is positive integer,
When the absolute value of error signal value is not less than threshold value, the tap coefficient of sampled point keeps constant.
In step S143, the calculating of error signal value is carried out by below equation：
E (k)=eR(k)+jeI(k)
eRAnd e (k)I(k) calculated by below equation：
R2,RWith R2,ICalculated by below equation：
Wherein, e (k) is error signal value, and y (k) is the data after adjustment, the data after dec (y (k)) judgements, R2To be normal
Mould, E [] represent mathematic expectaion, and a (k) is the data that balance module receives, and C is y (k) limitation scope, and subscript R is expressed as letter
Number real part, I is expressed as the imaginary part of signal.
Referring to Fig. 7, the data modulation method provided by the invention based on above-mentioned data modulation system, except including above-mentioned blind
It is further comprising the steps of outside equalization methods：
S110, the QAM data from analog-to-digital conversion module are received, frequency-conversion processing is carried out to the QAM data received；
S120, the timing error of the QAM data after frequency-conversion processing is eliminated by Gardner algorithms；
S130, the frequency deviation and skew of the QAM data of no timing error are eliminated by the way of COS and SIN table look-up, specifically
Ground, current frequency and corresponding Sin and Cos values are respectively obtained to the data lookup table without timing error, to the IQ two-way of QAM signals
Complex signal and Sin＆Cos results carry out complex multiplication, obtain the signal after frequency offset correction processing.
Fig. 8 is the schematic diagram of the down-conversion signal of collection, and Fig. 9 is that signal shows after Timing Error Detection circuit synchronization timing
It is intended to, Figure 10 is the schematic diagram by correcting constant modulus algorithm signal after equalization, referring to Fig. 8 to Figure 10, the down-conversion signal of collection
After synchronization timing and amendment constant modulus algorithm equilibrium, hence it is evident that similar to the distribution in Fig. 2.
Figure 11 is to combine decision-directed least mean square algorithm (DD- by the amendment constant modulus algorithm (MCAM) of the present invention
LMS the constellation schematic diagram after equilibrium) is carried out, Figure 12 is to illustrate by correcting the constellation of constant modulus algorithm (MCAM) signal after equalization
Figure, it is 13 for signal to noise ratio referring to Figure 11 and Figure 12, the signal that error vector magnitude is 31.93 carries out equilibrium, passes through DD-
After LMS-MCAM algorithm equilibriums, error vector magnitude is down to 7.6873, and after MCAM algorithm equilibriums, error vector magnitude is down to
10.7174, illustrate that DD-LMS-MCAM algorithms are better than the portfolio effect of MCAM algorithm.
Device embodiment described above is only schematical, wherein the unit illustrated as separating component can
To be or may not be physically separate, it can be as the part that unit is shown or may not be physics list
Member, you can with positioned at a place, or can also be distributed on multiple NEs.It can be selected according to the actual needs
In some or all of module realize the purpose of this embodiment scheme.Those of ordinary skill in the art are not paying creativeness
Work in the case of, you can to understand and implement.
Through the above description of the embodiments, those skilled in the art can be understood that each embodiment can
Realized by the mode of software plus required general hardware platform, naturally it is also possible to pass through hardware.Based on such understanding, on
The part that technical scheme substantially in other words contributes to prior art is stated to embody in the form of software product, should
Computer software product can store in a computer-readable storage medium, such as ROM/RAM, magnetic disc, CD, including some fingers
Make to cause a computer equipment (can be personal computer, server, or network equipment etc.) to perform each implementation
Method described in some parts of example or embodiment.
In addition, it will be appreciated by those of skill in the art that although some embodiments in this include institute in other embodiments
Including some features rather than further feature, but the combination of the feature of different embodiments mean in the present invention limitation
Within the scope of and form different embodiments.For example, in the following claims, embodiment claimed is appointed
One of meaning mode can use in any combination.
Finally it should be noted that：The above embodiments are merely illustrative of the technical solutions of the present invention, rather than its limitations；Although
The present invention is described in detail with reference to the foregoing embodiments, it will be understood by those within the art that：It still may be used
To be modified to the technical scheme described in foregoing embodiments, or equivalent substitution is carried out to which part technical characteristic；
And these modification or replace, do not make appropriate technical solution essence depart from various embodiments of the present invention technical scheme spirit and
Limit scope.
Claims (10)
- A kind of 1. blind equalizing apparatus, it is characterised in that including：Balance module, for receiving the data from carrier recovery block, pass through the tap system from tap coefficient adjusting module It is several that balanced adjustment is carried out to data；Judging module, for being made decisions to the data after balanced adjust, with the data after being adjudicated；Control errors module, for error signal value, and whether the absolute value of error in judgement signal value is less than threshold value；Tap coefficient adjusting module, for calculating tap coefficient according to the relation of error signal value and thresholding：When the absolute value of error signal value is less than threshold value, according to the step of the tap coefficient of k-th of sampled point, DD-LMS algorithms Long, error signal value and the data of k-th of sampled point calculate the tap coefficient of+1 sampled point of kth, and by+1 sampled point of kth Tap coefficient transmit to the balance module, k is positive integer,When the absolute value of error signal value is not less than threshold value, the tap coefficient of sampled point keeps constant.
- 2. blind equalizing apparatus according to claim 1, it is characterised in that in the balance module, entered by below equation The calculating of the balanced adjustment of row：Y (k)=XT(k)W(k)Wherein, X (k) is the data from analog-to-digital conversion module, XT(k) transposition for being X (k), W (k) is tap coefficient, and y (k) is Data after equilibrium adjustment.
- 3. blind equalizing apparatus according to claim 2, it is characterised in that in the control errors module, pass through following public affairs Formula carries out the calculating of error signal value：E (k)=eR(k)+jeI(k)eRAnd e (k)I(k) calculated by below equation：R2,RWith R2,ICalculated by below equation：<mrow> <msub> <mi>R</mi> <mrow> <mn>2</mn> <mo>,</mo> <mi>R</mi> </mrow> </msub> <mo>=</mo> <mfrac> <mrow> <mi>E</mi> <mo>&lsqb;</mo> <msubsup> <mi>a</mi> <mi>R</mi> <mn>4</mn> </msubsup> <mrow> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> <mo>&rsqb;</mo> </mrow> <mrow> <mi>E</mi> <mo>&lsqb;</mo> <msubsup> <mi>a</mi> <mi>R</mi> <mn>2</mn> </msubsup> <mrow> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> <mo>&rsqb;</mo> </mrow> </mfrac> </mrow><mrow> <msub> <mi>R</mi> <mrow> <mn>2</mn> <mo>,</mo> <mi>I</mi> </mrow> </msub> <mo>=</mo> <mfrac> <mrow> <mi>E</mi> <mo>&lsqb;</mo> <msubsup> <mi>a</mi> <mi>I</mi> <mn>4</mn> </msubsup> <mrow> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> <mo>&rsqb;</mo> </mrow> <mrow> <mi>E</mi> <mo>&lsqb;</mo> <msubsup> <mi>a</mi> <mi>I</mi> <mn>2</mn> </msubsup> <mrow> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> <mo>&rsqb;</mo> </mrow> </mfrac> </mrow>Wherein, e (k) is error signal value, and y (k) is the data after adjustment, the data after dec (y (k)) judgements, R2For norm, E [] represents mathematic expectaion, and a (k) is the data that balance module receives, and C is y (k) limitation scope, and subscript R is expressed as signal Real part, I are expressed as the imaginary part of signal.
- 4. blind equalizing apparatus according to claim 3, it is characterised in that in the tap coefficient adjusting module, pass through Below equation calculates the tap coefficient of+1 sampled point of kth：w(k+1,:)=w (k,:)+2*μ_lms*e(k)*X(k,:)Wherein, w (k+1,:) be+1 sampled point of kth tap coefficient, w (k,:) for the tap coefficient of k-th sampled point, μ _ Lms is the step-length of DD-LMS algorithms, and e (k) is error signal value, and X (k) is the data of k-th of sampled point.
- 5. a kind of data modulation system for including blind equalizing apparatus described in claim 4, it is characterised in that also include：Down conversion module, for receiving the data from analog-to-digital conversion module, frequency-conversion processing is carried out to received data, and will Data after frequency-conversion processing are sent to sign synchronization module；Sign synchronization module, for receiving the data after frequency-conversion processing, the timing error of data is eliminated, and by without timing error Data are sent to carrier recovery block；Carrier recovery block, for receiving the data without timing error, the frequency deviation and skew of data are eliminated, and will be without frequency deviation and phase Inclined data are sent to balance module.
- 6. data modulation system according to claim 5, it is characterised in that in the sign synchronization module, pass through Gardner algorithms eliminate the timing error of data.
- 7. data modulation system according to claim 5, it is characterised in that in the carrier recovery block, using COS The frequency deviation and skew of data are eliminated with the mode that SIN tables look-up.
- 8. a kind of blind balance method based on blind equalizing apparatus described in claim 1, it is characterised in that comprise the following steps：S141, the data from carrier recovery block are received, by the tap coefficient from tap coefficient adjusting module to data Carry out balanced adjustment；S142, the data after balanced adjust are made decisions, with the data after being adjudicated；Whether S143, error signal value, and the absolute value of error in judgement signal value are less than threshold value；S144, tap coefficient is calculated according to error signal value and the relation of thresholding：When the absolute value of error signal value is less than threshold value, according to the step of the tap coefficient of k-th of sampled point, DD-LMS algorithms Long, error signal value and the data of k-th of sampled point calculate the tap coefficient of+1 sampled point of kth, and by+1 sampled point of kth Tap coefficient transmit to the balance module, k is positive integer,When the absolute value of error signal value is not less than threshold value, the tap coefficient of sampled point keeps constant.
- 9. blind balance method according to claim 8, it is characterised in that in step S143, carried out by below equation The calculating of error signal value：E (k)=eR(k)+jeI(k)eRAnd e (k)I(k) calculated by below equation：R2,RWith R2,ICalculated by below equation：<mrow> <msub> <mi>R</mi> <mrow> <mn>2</mn> <mo>,</mo> <mi>R</mi> </mrow> </msub> <mo>=</mo> <mfrac> <mrow> <mi>E</mi> <mo>&lsqb;</mo> <msubsup> <mi>a</mi> <mi>R</mi> <mn>4</mn> </msubsup> <mrow> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> <mo>&rsqb;</mo> </mrow> <mrow> <mi>E</mi> <mo>&lsqb;</mo> <msubsup> <mi>a</mi> <mi>R</mi> <mn>2</mn> </msubsup> <mrow> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> <mo>&rsqb;</mo> </mrow> </mfrac> </mrow><mrow> <msub> <mi>R</mi> <mrow> <mn>2</mn> <mo>,</mo> <mi>I</mi> </mrow> </msub> <mo>=</mo> <mfrac> <mrow> <mi>E</mi> <mo>&lsqb;</mo> <msubsup> <mi>a</mi> <mi>I</mi> <mn>4</mn> </msubsup> <mrow> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> <mo>&rsqb;</mo> </mrow> <mrow> <mi>E</mi> <mo>&lsqb;</mo> <msubsup> <mi>a</mi> <mi>I</mi> <mn>2</mn> </msubsup> <mrow> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> <mo>&rsqb;</mo> </mrow> </mfrac> </mrow>Wherein, e (k) is error signal value, and y (k) is the data after adjustment, the data after dec (y (k)) judgements, R2For norm, E [] represents mathematic expectaion, and a (k) is the data that balance module receives, and C is y (k) limitation scope, and subscript R is expressed as signal Real part, I are expressed as the imaginary part of signal.
- 10. a kind of data modulation method based on data modulation system described in claim 8, it is characterised in that also including following Step：S110, the data from analog-to-digital conversion module are received, frequency-conversion processing is carried out to received data；S120, eliminate the timing error of the data after frequency-conversion processing；S130, eliminate the frequency deviation and skew of the data of no timing error.
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Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108989260A (en) * | 2018-08-01 | 2018-12-11 | 清华大学 | The digital time synchronization method of modified and device based on Gardner |
| CN112468419A (en) * | 2020-11-23 | 2021-03-09 | 中国科学院国家空间科学中心 | Self-adaptive dual-mode blind equalization method and system |
| CN112583433A (en) * | 2020-12-15 | 2021-03-30 | 四川灵通电讯有限公司 | Apparatus for timing recovery error detection in digital receiver and method of use |
| CN113285900A (en) * | 2021-07-15 | 2021-08-20 | 天津讯联科技有限公司 | Double-antenna fusion receiving method with time domain equalization function |
| CN115001645A (en) * | 2022-06-13 | 2022-09-02 | 北京邮电大学 | Clock recovery method and device, electronic equipment and computer storage medium |
| CN116346552A (en) * | 2023-02-21 | 2023-06-27 | 西安宇画智测通信科技有限公司 | A fast search method for pre-equalization coefficients based on scanning matrix |
| CN116827735A (en) * | 2023-07-06 | 2023-09-29 | 电子科技大学 | A coefficient update method for switching dual modes of Ethernet decision feedback structure |
| CN117040976A (en) * | 2023-08-22 | 2023-11-10 | 中国人民解放军国防科技大学 | Blind equalizer under non-cooperative communication condition and dual-mode joint blind equalization method |
| CN118972211A (en) * | 2024-07-22 | 2024-11-15 | 北京理工大学 | A signal recovery method combining weighted channel equalization and carrier synchronization |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2004075469A2 (en) * | 2003-02-19 | 2004-09-02 | Dotcast Inc. | Joint, adaptive control of equalization, synchronization, and gain in a digital communications receiver |
| CN102404012A (en) * | 2010-09-10 | 2012-04-04 | 上海明波通信技术有限公司 | Training sequence using method in digital communication receiver and adaptive equalizer |
| CN103973614A (en) * | 2013-01-31 | 2014-08-06 | 富士通株式会社 | Adaptive equalizer tap coefficient correction method and optical receiver |
| US20150270921A1 (en) * | 2014-03-19 | 2015-09-24 | Zte Corporation | Techniques for blind equalization of high-order quadrature amplitude modulation signals |
| CN105245303A (en) * | 2015-08-28 | 2016-01-13 | 北京理工大学 | High-speed burst demodulation synchronization system |
| CN106878211A (en) * | 2017-02-16 | 2017-06-20 | 南京大学 | A kind of satellite channel multimode blind equalization algorithm |
-
2017
- 2017-08-31 CN CN201710774028.4A patent/CN107566307A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2004075469A2 (en) * | 2003-02-19 | 2004-09-02 | Dotcast Inc. | Joint, adaptive control of equalization, synchronization, and gain in a digital communications receiver |
| CN102404012A (en) * | 2010-09-10 | 2012-04-04 | 上海明波通信技术有限公司 | Training sequence using method in digital communication receiver and adaptive equalizer |
| CN103973614A (en) * | 2013-01-31 | 2014-08-06 | 富士通株式会社 | Adaptive equalizer tap coefficient correction method and optical receiver |
| US20150270921A1 (en) * | 2014-03-19 | 2015-09-24 | Zte Corporation | Techniques for blind equalization of high-order quadrature amplitude modulation signals |
| CN105245303A (en) * | 2015-08-28 | 2016-01-13 | 北京理工大学 | High-speed burst demodulation synchronization system |
| CN106878211A (en) * | 2017-02-16 | 2017-06-20 | 南京大学 | A kind of satellite channel multimode blind equalization algorithm |
Non-Patent Citations (1)
| Title |
|---|
| 姜晖: "一种基于MMMA的双模式变步长盲均衡算法", 《火力与指挥控制》 * |
Cited By (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108989260A (en) * | 2018-08-01 | 2018-12-11 | 清华大学 | The digital time synchronization method of modified and device based on Gardner |
| CN112468419A (en) * | 2020-11-23 | 2021-03-09 | 中国科学院国家空间科学中心 | Self-adaptive dual-mode blind equalization method and system |
| CN112468419B (en) * | 2020-11-23 | 2021-08-31 | 中国科学院国家空间科学中心 | An adaptive dual-mode blind equalization method and system |
| CN112583433A (en) * | 2020-12-15 | 2021-03-30 | 四川灵通电讯有限公司 | Apparatus for timing recovery error detection in digital receiver and method of use |
| CN112583433B (en) * | 2020-12-15 | 2022-03-25 | 四川灵通电讯有限公司 | Apparatus for timing recovery error detection in digital receiver and method of use |
| CN113285900A (en) * | 2021-07-15 | 2021-08-20 | 天津讯联科技有限公司 | Double-antenna fusion receiving method with time domain equalization function |
| CN115001645A (en) * | 2022-06-13 | 2022-09-02 | 北京邮电大学 | Clock recovery method and device, electronic equipment and computer storage medium |
| CN115001645B (en) * | 2022-06-13 | 2023-12-26 | 北京邮电大学 | Clock recovery method, clock recovery device, electronic equipment and computer storage medium |
| CN116346552A (en) * | 2023-02-21 | 2023-06-27 | 西安宇画智测通信科技有限公司 | A fast search method for pre-equalization coefficients based on scanning matrix |
| CN116346552B (en) * | 2023-02-21 | 2025-05-16 | 西安宇画智测通信科技有限公司 | A fast search method for pre-equalization coefficients based on scanning matrix |
| CN116827735A (en) * | 2023-07-06 | 2023-09-29 | 电子科技大学 | A coefficient update method for switching dual modes of Ethernet decision feedback structure |
| CN117040976A (en) * | 2023-08-22 | 2023-11-10 | 中国人民解放军国防科技大学 | Blind equalizer under non-cooperative communication condition and dual-mode joint blind equalization method |
| CN117040976B (en) * | 2023-08-22 | 2024-05-24 | 中国人民解放军国防科技大学 | Dual-mode joint blind equalization method and blind equalizer under non-cooperative communication condition |
| CN118972211A (en) * | 2024-07-22 | 2024-11-15 | 北京理工大学 | A signal recovery method combining weighted channel equalization and carrier synchronization |
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