CN103220253A - OFDM (Orthogonal Frequency Division Multiplexing) symbol synchronizing method based on chaotic mapping and particle swarm optimization algorithm - Google Patents

Abstract

The invention discloses an OFDM (Orthogonal Frequency Division Multiplexing) symbol synchronizing method based on chaotic mapping and a particle swarm optimization algorithm. The method comprises the design and implementation of a universal search scheme and the design and implementation of a local search scheme, wherein the design and implementation of the universal search scheme comprises the step of obtaining a coarsely estimated value of an OFDM symbol synchronizing parameter by utilizing the PSO (particle swarm optimization) algorithm; and the design and implementation of the local search scheme comprises the step of obtaining a finely-estimated value of the OFDM symbol synchronizing parameter by utilizing chaotic mapping. According to the symbol synchronizing method, the defect that an OFDM symbol timing and frequency-deviation joint estimation method based on the PSO algorithm is easily trapped in a local extreme point is overcome, the estimating accuracy of the OFDM symbol synchronizing parameter is enhanced, simultaneously the iteration time number of the algorithm is reduced, the OFDM symbol synchronizing time is shortened, and the method is effective for rapidly and accurately synchronizing the OFDM symbols of a packet bursting wireless transmission system.

Description

OFDM symbol timing synchronization method based on chaotic maps and particle swarm optimization algorithm

Technical field

The present invention relates to a kind of OFDM symbol timing synchronization method, belong to the wireless mobile telecommunication technology field based on chaotic maps and particle swarm optimization algorithm.

Background technology

Chaotic motion comes from the nonlinear kinetics system, and the signal that adopts chaotic maps to produce has randomness, ergodic, certainty and to characteristics such as initial value sensitivities.Britain mathematician Robert A.J.Matthews had proposed the chaos DEA by Logistic mapping and distortion thereof in 1989, learned development for chaos cipher and had established solid foundation.The Logistic mapping is a typical non linear chaos equation, and it can embody all fundamental characteristics of chaotic motion.Obtained in fields such as image processing, secure communication, neural nets at present using widely.

Particle group optimizing (PSO) algorithm is proposed in nineteen ninety-five by Eberhart and Kennedy two people, and its basic thought is the inspiration that is subjected to birds colony predation modeling method and simulation result thereof.It is from RANDOM SOLUTION, seeks the optimal solution of problem by iteration repeatedly, by the quality that fitness evaluation is separated, is a kind of evolution based on the colony intelligence method (EA) computing technique that occurs in recent years.Compare with genetic algorithm, simple, easily realize there is not " variation " and " intersection " computing, and the precision height, restrain soon, be widely used in applications such as neural metwork training, fuzzy system control, function optimization.If lack the diversity of population, particle swarm optimization algorithm is absorbed in locally optimal solution easily, so-called " precocity " phenomenon occurs but in actual applications.Based on the characteristic of chaotic motion, chaotic maps is combined with particle group optimizing, can effectively avoid particle swarm optimization algorithm to be absorbed in the generation of local solution, make this algorithm have more performance.

OFDM (OFDM) is a kind of multi-carrier transmission technology in the wireless communication system, has obtained extensive use in wireless lan (wlan), back 3G mobile communication LTE (Long Term Evolution), digital audio broadcasting (DAB) and digital video broadcasting many fields such as (DVB) at present.The basic principle of OFDM is the subchannel (subcarrier) that transmission band is divided into some quadratures, makes the serial high speed rheology change a plurality of parallel low rate data streams parallel transmission on each subcarrier into.It has availability of frequency spectrum height, anti-multipath interference, anti-burst noise and overcomes frequency effectively selects advantages such as decline, but also exist some obvious defects, as very responsive to symbol timing error and frequency shift (FS), timing error can cause the phase place rotation of signal in frequency domain.The frequency shift (FS) meeting destroys the orthogonality between the subcarrier, cause the serious decline of systematic function, so the net synchronization capability of ofdm system is very important.

OFDM sign synchronization algorithm generally can be divided into the synchronous two parts of Domain Synchronous and frequency domain.Domain Synchronous is to determine OFDM symbol timing position.The symbol timing offset will influence the effective range of receiving terminal FFT window value.Though in the effective range of this window value, the symbol timing offset only can cause the phase place rotation of received signal, can not destroy the orthogonality of subcarrier, can reduce anti-many tolerance limits through effect.Frequency domain is wanted the frequency deviation of first estimating OFDM symbol synchronously, carries out corresponding compensation then.Usually frequency offset estimating is divided into respect to the integer frequency offset and the fractional part of frequency offset of subcarrier spacing and estimates that the frequency deviation of integral multiple only causes the cyclic shift of subcarrier, fractional part of frequency offset will destroy the orthogonality between subcarrier.Because the integer frequency offset estimated result can affect to the result of timing estimation, therefore, accurate timing estimation generally will could be determined after integer frequency offset is estimated.

The algorithm of realizing the OFDM sign synchronization generally can be divided into two big classes at present: a class is based on auxiliary data, mainly comprise and utilize pilot tone and utilize Cyclic Prefix (CP) method, the better performances of this homochronousness algorithm, but can cause to a certain degree bandwidth and power loss.Another kind of is to need not auxiliary data, as blind synchronously etc., though blind simple synchronously, easily realize that its locking range is less.

Utilize the synchronized algorithm of pilot tone,, can reduce the efficiency of transmission of system, but computation complexity is relatively low owing to need to insert extra data (also claiming training sequence), the synchronization accuracy height, practicality is stronger.The Schmidl algorithm is the classical synchronized algorithm based on training sequence, it utilizes two training sequences to finish synchronously, realize symbol timing estimation and fractional part of frequency offset estimation with previous sequence earlier, realize the frequency offset estimating of integral multiple again according to the relation of former and later two sequences, frequency offset estimation range is bigger, and the time spent is longer.Seo improves the Schmidl algorithm for this reason, still use two training sequences, but the subcarrier to each training sequence carries out the differential phase modulation, phase difference estimation integer frequency offset by adjacent sub-carrier, need not to search for whole frequency band, reduced computation complexity, and performance is suitable with the Schmidl algorithm.There is " plateau effect " at symbol timing estimation function in the Schmidl algorithm, have a strong impact on regularly synchronization accuracy problem, the Minn algorithm is further improved, adopt the PN sequence of optimizing, and design new training sequence, make symbol timing estimation peak of function comparatively sharp-pointed, improved symbol timing estimation precision.The Ren algorithm utilizes permanent envelope zero auto-correlation (CAZAC) sequence construct training sequence, makes timing synchronization functional value peak value sharp-pointed, and secondary lobe is zero substantially, and hour algorithm performance is good for frequency deviation.

The OFDM technology both can be used for broadcast system, also can be used for packet switching network, and as wireless office city net (WLAN), the OFDM symbol timing synchronization method of the two employing is different.Broadcast system generally can send data continuously, and therefore corresponding receiver can utilize the expense of several symbols to obtain the accurate estimation of sign synchronization position, converts tracing mode afterwards again to.And it is higher in the wlan system owing to message transmission rate, adopt packet switching again, therefore be fit to select for use the method for synchronization based on training sequence, promptly begin to send the back in grouped data and will reach synchronously in very short time, the WLAN receiver also can not be obtained OFDM sign synchronization position outside pilot tone.

OFDM sign synchronization algorithm based on training sequence generally will be by estimate symbol timing position, little several times and integer frequency offset are realized respectively.Receiving terminal earlier carries out computing cross-correlation with the training sequence that receives with known training sequence, calculates and the symbol amplitude of the cross-correlation coefficient of all possible positions regularly relatively, and the position of getting the maximum correspondence is the symbol timing value.Try to achieve the fractional part of frequency offset value by the phase difference between the calculation training sequence then.Secondly carry out related calculation with known training sequence again according to the symbol timing value and with the signal after the fractional part of frequency offset compensation, getting the corresponding frequency deviation of amplitude maximum is system's integer frequency offset estimated value, do the integer frequency offset compensation again, finally realize symbol essence regularly synchronously.Whole synchronizing process is complicated, and amount of calculation is big, and is time-consuming more.

The timing and the frequency deviation value that particle swarm optimization algorithm are applied to the OFDM sign synchronization are estimated, can simplify the implementation procedure of whole sign synchronization greatly, and function admirable.But it is improper that translational speed initial change scope of upgrading when 2 dimension particle iteration and weight etc. are provided with, timing of OFDM symbol and diviation combined estimation method based on the PSO algorithm, be absorbed in locally optimal solution easily, reduce the estimated accuracy of symbol timing and frequency deviation value, influence OFDM sign synchronization performance.

Summary of the invention

The purpose of this invention is to provide a kind of OFDM symbol timing synchronization method based on chaotic maps and particle swarm optimization algorithm, this method at first can obtain the timing of OFDM sign synchronization and the rough estimate evaluation of frequency deviation simultaneously by global search, be absorbed in the problem of local solution easily at the PSO algorithm, utilize the Logistic chaotic maps, and further obtain the timing of OFDM sign synchronization and the smart estimated value of frequency deviation by Local Search.Can improve the timing of OFDM sign synchronization and the estimated accuracy of frequency deviation value, reduce the iterations of synchronized algorithm, shorten the lock in time of OFDM symbol, be a kind of effective ways of realizing the quick precise synchronization of packet burst wireless transmitting system OFDM symbol.

The present invention realizes that the technical scheme of above-mentioned purpose is, a kind of OFDM symbol timing synchronization method based on chaotic maps and particle swarm optimization algorithm, and it is characterized in that: this method may further comprise the steps:

A1, according to OFDM sign synchronization and the requirement of PSO algorithm, be configured to 2 dimension particles based on the OFDM sign synchronization of chaotic maps and PSO algorithm, i particle establishing population is x _i, this particle is a two-dimentional variable, comprises the timing estimation value x of OFDM sign synchronization _I1With Frequency offset estimation value x _I2

A2, according to the requirement of OFDM sign synchronization, be configured to the 2 dimension particle moving velocity vector v that upgrade based on chaotic maps and PSO algorithm iteration _i, the component of this 2 n dimensional vector n comprises the translational speed value v of the timing estimation that is used for OFDM sign synchronization algorithm _I1Translational speed value v with frequency offset estimating _I2

A3, be designed for fitness function based on the OFDM sign synchronization of chaotic maps and PSO algorithm; The training sequence of transmitting terminal adopts the CAZAC sequences Design, receiving terminal will known training sequence and the computing cross-correlation of the training sequence that receives as the fitness function of OFDM sign synchronization; The historical optimum position of single particle is present position when making the adaptive value of fitness function reach maximum in the synchronized algorithm iterative process, the historical optimum position of colony is present position when making the adaptive value of fitness function reach maximum in the whole population, above-mentioned particle historical optimum position individual and colony all is a two Dimension Numerical Value, and each optimum position is made up of the optimal estimation value of OFDM symbol timing and the optimal estimation value of frequency deviation;

A4, carry out global search program, the optimum position of the individual and colony of new particle more, and the fundamental formular of utilizing the PSO the algorithm more translational speed and the position of new particle based on the OFDM sign synchronization algorithm of chaotic maps and PSO; And calculate and upgrade the pairing adaptive value of each particle position of back, the adaptive value size of after the renewal of each particle and historical optimum position relatively, and each particle current overall optimum position of adaptive value and colony adaptive value size after upgrading, the position when choosing adaptive value and reaching maximum is the more optimum position of new particle individuality and the optimum position of colony respectively;

Particle optimum position in A5, the colony that obtains for steps A 4, utilize logistic chaotic maps function, with between the chaotic region [0,1] is mapped to the timing of OFDM sign synchronization and the interval of frequency offset estimating value, execution is based on the Local Search program of the OFDM sign synchronization of chaotic maps and PSO algorithm, and upgrades the optimum position of particle colony once more;

After A6, k iteration of OFDM sign synchronization algorithm process based on chaotic maps and PSO algorithm, check stopping criterion for iteration: reached maximum iteration time or obtained enough good adaptive value, perhaps optimal solution is stagnated no longer and is changed, and satisfies as if above-mentioned condition, then termination of iterations; From 2 dimension variablees of the historical optimal location of population, obtain the timing position and the frequency deviation value of needed OFDM sign synchronization, carry out corresponding compensation after, it is synchronous fully to reach the OFDM symbol.

In steps A 1, setting is based on the iterations T of the OFDM sign synchronization algorithm of chaotic maps and PSO and the scale M of population, and the basic parameter in the PSO algorithm carried out initialization, each particle timing initial position and the initial frequency deviation value that are used for the OFDM sign synchronization all are set to random value, and excursion is set to (x respectively _I1min, x _I1max) and (x _I2min, x _I2max).

In steps A 2, be provided for based on each particle of the OFDM sign synchronization of chaotic maps and PSO algorithm regularly and the translational speed scope of frequency offset estimating be respectively (v _I1min, v _I1max) and (v _I2min, v _I2max).

In steps A 3, transmitting terminal adopts CAZAC project training sequence z (n), and L is a training sequence length

J is a complex symbol in the formula (1), down together.

The training sequence that receives is y (n), and μ is the estimated value of OFDM symbol timing initial position, and ε is the frequency offset estimating value, based on the fitness function of the OFDM synchronized algorithm of chaotic maps and PSO is:

Z* (n) is the conjugate complex number of z (n) in the formula (2).

According to the above-mentioned fitness function formula (2) that is used for the OFDM sign synchronization, calculate the initial adaptive value of i particle

The individual best initial position of corresponding particle is The initial adaptive value size of all particles relatively, selecting adaptive value institute's correspondence position of maximum time is the initial optimal location of colony.

In steps A 4, particle position and translational speed are upgraded in fundamental formular (3), (4) and (5) according to the PSO algorithm, and calculate and upgrade the pairing adaptive value of each particle position of back, relatively after the renewal of each particle and historical optimal location adaptive value size, and the current global optimum of adaptive value and the colony position adaptive value size after each particle renewal, choose the maximum position of adaptive value correspondence respectively, more the optimal location of new particle individuality

And the optimal location p of colony _g ^k, in the calculating process OFDM symbol timing optimal location is rounded operation,

${x_{\mathrm{id}}}^{k+1}=x_{\mathrm{id}}^k+v_{\mathrm{id}}^{k+1}---\left(4\right)$

Wherein, k is the current iteration number of times, and d is the dimension of particle, r ₁And r ₂Be the random number between [0,1], c ₁And c ₂Be the study factor, w is the weight of the k time iteration of particle cluster algorithm, w _MaxBe the initial weight of this algorithm, w _MinBe the final weight of this algorithm, T is the maximum iteration time of this algorithm, v _Id, x _IdD dimension translational speed and the location estimation value of representing i particle respectively.

In steps A 5, adopt logistic chaotic maps function as the formula (6):

u _n+1＝λ□u _n(1-u _n)，n＝0，1，2，… (6)

In the formula (6), λ is the Logistic parameter, and λ ∈ (0,4], work as u _n∈ [0,1], the Logistic mapping is in chaos state.

May further comprise the steps based on the OFDM symbol timing of chaotic maps and the Local Search of frequency offset estimating value:

B1, make m=1, with the population optimum position Be mapped to the Chaos Variable between [0,1]

Wherein

X in the formula (7) _{Id, max}, x _{Id, min}Be respectively the upper and lower boundary of search of d dimension variable.

B2, calculate the Chaos Variable of the m+1 time iteration according to formula (6)

λ=4 wherein, promptly

As λ〉3.57 the time, the sequence motion form that is produced by the Logistic mapping has typical chaos characteristic.

B3, with Chaos Variable

Be converted into the population optimum position

Wherein,

$x_{\mathrm{id}}^{m+1}=x_{\mathrm{id},\min }+{\mathrm{cx}}_{\mathrm{id}}^{m+1}(x_{\mathrm{id},\max }-x_{\mathrm{id},\min }),---\left(9\right)$

B4, according to the population optimum position after the chaotic maps

The adaptation functional value of the OFDM synchronized algorithm shown in the calculating formula (2);

B5, reached predefined greatest iteration step number, upgraded the optimum position of population, otherwise make m=m+1, and return above-mentioned steps B2 if adapt to adaptive value or the Chaos Search that functional value is better than before the chaotic maps.

In steps A 6, if the adaptive value of colony's optimal location correspondence satisfies the threshold value R that is provided with for OFDM sign synchronization algorithm _ThThen stop iteration, this moment, colony's optimal location was the regularly best and frequency offset estimating value of OFDM sign synchronization, if do not satisfy, then return steps A 4, continue iteration, till meeting the demands after certain iteration or reaching maximum iteration time, colony's global optimum of this moment is the regularly best and frequency offset estimating value of OFDM sign synchronization.

The beneficial effect that the present invention reached:

The present invention is according to the relevant theory of chaotic motion and two-dimentional particle swarm optimization algorithm, utilize the good chaotic characteristic of Logistic Nonlinear Mapping, a kind of OFDM sign synchronization algorithm based on chaotic maps and particle group optimizing has been proposed, it can realize the quick precise synchronization of OFDM symbol, has avoided traditional PS O algorithm to be absorbed in the problem of Local Extremum easily effectively.By the global search step, can obtain the rough estimate evaluation of OFDM sign synchronization parameter fast.By the Local Search step, can further obtain the smart estimated value of OFDM sign synchronization parameter.Improve the estimated accuracy of OFDM sign synchronization parameter, reduced the iterations of synchronized algorithm simultaneously, shortened the lock in time of OFDM symbol.

Description of drawings

Fig. 1 is the OFDM transmission system block diagram of tape symbol timing and frequency synchronization module;

Fig. 2 is timing of OFDM symbol and Frequency Synchronization process schematic diagram;

Fig. 3 is the variation diagram of each particle adaptive value in the OFDM sign synchronization iterative process;

Fig. 4 is based on the OFDM sign synchronization algorithm realization flow figure of chaotic maps and PSO;

Fig. 5 be have, the iterations comparison curves of the OFDM sign synchronization algorithm of no chaotic maps;

Table 1 be have, the OFDM symbol of no chaotic maps regularly and the MSE simulation result of Frequency Synchronization.

Embodiment

Below in conjunction with accompanying drawing, provide the specific embodiment of the present invention, be used for that the present invention is described further.

Be used in the embodiment of the invention in the OFDM transmission system shown in Figure 1, transmitting terminal passes through after the MSK digital modulation to the data that will transmit, insert pilot frequency sequence, carry out serial/parallel conversion then, the OFDM symbol is divided into N parallel subsignal, by inverse fast fourier transform (IFFT) each subsignal is modulated N subcarrier respectively, realize multi-carrier modulation, signal is converted on the time domain by frequency domain.Pass through parallel/serial conversion again, add Cyclic Prefix (CP), constitute the digital form of complete OFDM symbol, carry out digital-to-analogue (D/A) conversion and low-pass filtering (LPF) again, send to wireless channel after multiplying each other with radio-frequency carrier.The digital signal s (n) that sends can be expressed as:

$s\left(n\right)=\frac{1}{N}\underset{k=0}{\overset{N-1}{\mathrm{\Σ}}}S\left(k\right)\exp \left(\frac{j2\mathrm{\π}}{N}\mathrm{kn}\right),---\left(10\right)$

n＝-G,-(G-1),...0...N-1

Wherein N is the total number of sub-carriers of ofdm system, and S (k) is the signal of k sub-carrier transmission, and G is a circulating prefix-length.The digital signal y (n) that receives can be expressed as:

$y\left(n\right)=\underset{l=0}{\overset{L-1}{\mathrm{\Σ}}}{h}_{l}s(n-l)\exp \left(\frac{j2\mathrm{\π}}{N}\mathrm{\ϵn}\right)+\mathrm{\ω}\left(n\right)---\left(11\right)$

Wherein ε represents the frequency shift (FS) after the normalization, h _lBe the channel gain of channel l, Lindenmayer system multipath channel number of path, ω (n) represents additive white Gaussian noise.

The signal processing of receiving terminal is opposite with transmitting terminal, the signal of coming in from antenna multiplies each other with radio-frequency carrier earlier, after modulus (A/D) conversion and low-pass filtering (LPF), carry out symbol timing and Frequency Synchronization again, the correcting system frequency shift (FS) is eliminated in receiving terminal and transmitting terminal oscillator (VCO) frequency departure and the transmission course because the influence of the frequency shift (FS) that Doppler effect produces.Remove the Cyclic Prefix of OFDM symbol then, carry out a series of anti-operations corresponding, finally obtain the data that send with transmitting terminal.

The synchronizing process of timing of receiving terminal OFDM symbol and frequency as shown in Figure 2.Usually the Frequency Synchronization process is divided into two steps, the one, integer frequency offset is estimated and compensation, the 2nd, fractional part of frequency offset is estimated and compensation, because integer frequency offset generally only causes the cyclic shift of subcarrier, and therefore broken orthogonality of changing between subcarrier can be placed on the FFT computing and estimate afterwards and correction.Received signal at first estimates to remove Cyclic Prefix by the symbol timing coarse, then the signal after the thick timing is synchronously carried out the frequency offset estimating and the compensation of little several times, OFDM symbol behind the compensate of frequency deviation is carried out serial/parallel conversion and FFT computing, estimate the frequency deviation of integral multiple then, and carry out corresponding compensation, realize that at last symbol is regularly synchronously smart, finish whole symbol synchronization process.

The training sequence method for designing is as follows:

The present invention's employing is inserted pilot tone and is come estimating OFDM symbol timing position in the data flow that transmits, and utilizes CAZAC sequences Design training sequence in an embodiment, and the training sequence of transmission can be expressed as:

Wherein L is a training sequence length.The symbol timing estimation mainly is the autocorrelation performance that utilizes the CAZAC sequence good.

Peak value is sharp-pointed because the auto-correlation coefficient of CAZAC sequence has, secondary lobe is zero characteristics, therefore the training sequence that utilizes the training sequence that receives and receiving terminal to deposit among the embodiment carries out related operation, and structure is based on the fitness function of the OFDM synchronized algorithm of particle cluster algorithm.

The method for designing of fitness function is as follows:

If receive the signal that contains training sequence is y (n), can be expressed as based on the fitness function of the OFDM synchronized algorithm of particle cluster algorithm:

Wherein μ is the estimated value of OFDM symbol timing position, and ε is the estimated value of the frequency departure of system.

By PSO algorithm and global search, the timing position to the OFDM sign synchronization in is on a large scale constantly estimated and adjustment, and frequency deviation value is estimated and corresponding compensation.By chaotic maps, avoid the PSO algorithm to be absorbed in Local Extremum, by Local Search, (μ ε) reaches maximum best particle to find out the adaptation functional value R that makes synchronized algorithm after the chaotic maps.By global search and Local Search, when constantly being carried out, the training sequence that receives reaches correcting frequency deviation partially, finally can obtain the tram of OFDM symbol Time and Frequency Synchronization.For the OFDM sign synchronization algorithm that adopts PSO, really obtained globally optimal solution this moment.Fig. 3 has provided the variation situation of each particle fitness function value in iterative process among the embodiment.As shown in Figure 3, because the effect of chaotic maps, the fitness function value seems disorderly and unsystematic, but along with the increase of iterations, fitness function approaches to the maximum adaptive value of the overall situation, and correspondingly population is approaching to the global optimum position.

For reducing amount of calculation, shorten the OFDM sign synchronization time, can be the default threshold value R of iterative algorithm according to channel quality and training sequence character _Th, when algorithm iteration to k (k=0,1,2 ... when T) inferior, if when the adaptive value of the global optimum position of population meets or exceeds threshold value, expression present position point has satisfied the synchronous requirement of OFDM symbol, has obtained required separating, and then stops iteration.If no adaptive value surpasses threshold value in the algorithm iteration process, then iteration just stops to maximum iteration time always, and the global optimum position that get population this moment is the sync bit of OFDM symbol.

The chaotic maps design for scheme:

The mathematic(al) representation of the chaotic maps that adopts is as follows:

u _n+1＝λ□u _n(1-u _n)，n＝0，1，2，…

Wherein, λ is called the Logistic parameter, and λ ∈ (0,4], u _n∈ [0,1].After the λ value was determined, by any initial value between 0 and 1, but iteration went out definite random sequence a: u ₁, u ₂..., u _n, for different λ values, this sequence presents different characteristics.As λ〉3.57 the time, this sequence has typical chaos characteristic, and λ gets 4 in the present embodiment.

Implementation design based on the OFDM symbol timing synchronization method of chaotic maps and PSO:

The flow process that realizes best particle global search is shown in the block diagram of Fig. 4 left side.Concrete steps are described as follows:

1), is configured to 2 dimension particles based on the OFDM sign synchronization of chaotic maps and PSO algorithm according to PSO algorithm and the requirement of OFDM sign synchronization.If i particle of population is x _i, the two-dimentional variable of this particle is respectively the timing estimation value x of sign synchronization _I1With Frequency offset estimation value x _I2, each particle regularly initial position and initial frequency deviation value all is set to random value, and excursion be set to respectively ( x _I1max) and (

x _I2max), corresponding to the step 401 among Fig. 4.

2) be configured to the 2 dimension particle moving velocity vector v that chaotic maps and PSO algorithm iteration upgrade _i, the component of this 2 n dimensional vector n is respectively the translational speed value v of the timing estimation of OFDM sign synchronization algorithm _I1Translational speed value v with frequency offset estimating _I2, the translational speed scope of each particle timing and frequency offset estimating is respectively (v _I1min, v _I1max) and (v _I2min, v _I2max), corresponding to step 402.

3) utilize the CAZAC sequences Design to be used for the training sequence of OFDM sign synchronization, training sequence length is N, and the transmitting terminal that carries out is as shown in Figure 1 handled, corresponding to step 403.

4) be configured to the fitness function of OFDM sign synchronization, the cross-correlation function of training sequence that the receiving terminal utilization receives and known training sequence is as the fitness function of OFDM symbol timing estimation, corresponding to step 404.

5), calculate the adaptive value of each particle, corresponding to step 405 according to the fitness function of formula (1) generation training sequence and formula (2) structure.If p _iBe i particle in iterative process according to the adaptive value of formula (2) gained present position when maximum, i.e. the historical optimal location of this particle, p _gPresent position, the i.e. historical optimal location of colony when making the adaptive value of fitness function maximum for whole particle colony.The initial adaptive value of i particle is designated as

Corresponding optimal location The initial adaptive value size of all particles relatively, selecting adaptive value institute's correspondence position of maximum time is the initial optimal location of colony.

6) establishing maximum iteration time is T, upgrade particle position and movement velocity according to formula (3), (4) and (5), and calculate and upgrade the pairing adaptive value of each particle position of back, relatively after the renewal of each particle and historical optimal location adaptive value size, and adaptive value and the current global optimum of population position adaptive value size after each particle renewal, the position of correspondence, the more optimal location of new particle individuality when choosing adaptive value respectively and reaching maximum And the optimal location p of colony _g ^k, corresponding to step 406.

Because the symbol timing position of ofdm system should be integer, and may be decimal according to the location point that formula (4) obtains, so tackle x in the iterative process _I1(i=1,2,3 ... M) carry out rounding operation, promptly get near x _I1Integer.

7) with the optimal location p of population _g ^kCarry out the logistic chaotic maps, carry out best particle Local Search program, and upgrade the optimum position of population once more, corresponding to step 407 based on the OFDM sign synchronization of chaotic maps.

8) if the adaptive value of population optimal location meets or exceeds the threshold value R of OFDM synchronized algorithm _Th, then stopping iteration, colony's optimal location of this moment is the regularly best and frequency offset estimating value of OFDM sign synchronization.If do not satisfy, then return step 405, continue iteration, till reaching maximum iteration time T, can obtain the needed regularly best and frequency offset estimating value of OFDM sign synchronization this moment by global optimum of particle colony position, corresponding to step 408 and 409.

The flow process that realizes best particle Local Search is shown in the block diagram of Fig. 4 the right, and detailed process is described as follows:

At first preset the maximum times of Local Search, and to the initialization of logistic chaotic maps function, corresponding to step 501.Secondly with the population optimum position

Be mapped to the Chaos Variable between [0,1] (corresponding to step 502), the logistic chaotic maps shown in the execution formula (8) obtains new Chaos Variable

(corresponding to step 503) is with new Chaos Variable

Be converted into the population optimum position

(corresponding to step 504).

According to formula (2), calculate

The fitness function value (corresponding to step 505) of corresponding OFDM synchronized algorithm if this adaptive value is better than the preceding adaptive value of chaotic maps, is then upgraded the optimum position (corresponding to step 506) of population.Check local searching times (step 507), if Local Search has reached predefined maximum times, then enter global search flow process (step 508), otherwise return step 502.

Embodiment and simulation result:

Adopt the simulated environment of 3GPP LTE mobile communication in the embodiments of the invention, the sub-carrier number of system is 1024, and the FFT/IFFT computing is counted identical with sub-carrier number, and Cyclic Prefix length is got 128 sampled points, channel width is 10MHz, and sample frequency is 20.48M sampling point/second.The transmission path number is L=6(l=0,1,2 ... 5), each paths all is independent Rayleigh fading, and the time delay in each path is respectively 2,3,4,5,9,13 sampled points, and corresponding gain is respectively 0.9,0.85,0.8,0.7,0.5,0.4.With the performance parameter of mean square error (MSE) as algorithm simulating.The scale M of population gets 50, the timing position initial value of sign synchronization is got the random number between 50～250, the initial value of frequency offset estimating value is the random value in-30～30, it is integer between 0～5 that the translational speed of the timing position of sign synchronization takes absolute value, and the rate travel scope of frequency estimation gets-2～2.The Logistic parameter lambda of chaotic maps gets 4.

Under the condition of given different signal to noise ratio (snr)s, the performance based on the OFDM symbol timing synchronization method of chaotic maps and PSO is carried out Computer Simulation.Fig. 5 has provided the comparison curves that adopts iterations when reaching identical adaptive value with the OFDM sign synchronization algorithm that does not adopt chaotic maps respectively, and table 1 has provided the MSE simulation result with corresponding OFDM symbol timing of different iterationses shown in Figure 5 and Frequency Synchronization respectively simultaneously.As can be seen from Figure 5, compare with no chaotic maps, adopt the iterations of chaotic maps will lack a lot, along with the increase of signal to noise ratio, both have approaching trend.And from table 1(a), (b) OFDM symbol of providing respectively regularly and the MSE of Frequency Synchronization as can be known, adopt the synchronization accuracy of chaotic maps to want obviously height.The OFDM symbol timing synchronization method based on chaotic maps and PSO algorithm that the present invention that hence one can see that proposes can reduce the iterations of synchronized algorithm, shortens the lock in time of OFDM symbol, can improve the OFDM symbol simultaneously regularly and the synchronization accuracy of frequency.

Table 1 has, the OFDM symbol of no chaotic maps regularly and the MSE simulation result of Frequency Synchronization

SNR（dB）	7	8	9	10	11	12	13	14	15	16	17	18
													No chaotic maps	0.4	0.4	0.3	0.3	0.3	0.2	0.2	0.2	0.2	0.1	0.1	0.1
Chaotic maps is arranged	0.3	0.3	0.2	0.2	0.2	0.2	0.2	0.1	0.1	0.1	0	0

(a) OFDM timing synchronization MSE

(b) the synchronous MSE of OFDM symbol frequency

More than show and described basic principle of the present invention, principal character and advantage of the present invention.The technical staff of the industry should understand; the present invention is not restricted to the described embodiments; that describes in the foregoing description and the specification just illustrates principle of the present invention; without departing from the spirit and scope of the present invention; the present invention also has various changes and modifications, and these changes and improvements all fall in the claimed scope of the invention.The claimed scope of the present invention is defined by appending claims and equivalent thereof.

Claims (9)

1. OFDM symbol timing synchronization method based on chaotic maps and particle swarm optimization algorithm, it is characterized in that: this method may further comprise the steps:

A1, according to OFDM sign synchronization and the requirement of PSO algorithm, be configured to 2 dimension particles based on the OFDM sign synchronization of chaotic maps and PSO algorithm, i particle establishing population is x _i, this particle is a two-dimentional variable, comprises the timing estimation value x of OFDM sign synchronization _I1With Frequency offset estimation value x _I2

A2, according to the requirement of OFDM sign synchronization, be configured to the 2 dimension particle moving velocity vector v that upgrade based on chaotic maps and PSO algorithm iteration _i, the component of this 2 n dimensional vector n comprises the translational speed value v of the timing estimation that is used for OFDM sign synchronization algorithm _I1Translational speed value v with frequency offset estimating _I2

A3, be designed for fitness function based on the OFDM sign synchronization of chaotic maps and PSO algorithm; The training sequence of transmitting terminal adopts the CAZAC sequences Design, receiving terminal will known training sequence and the computing cross-correlation of the training sequence that receives as the fitness function of OFDM sign synchronization; The historical optimum position of single particle is present position when making the adaptive value of fitness function reach maximum in the synchronized algorithm iterative process, the historical optimum position of colony is present position when making the adaptive value of fitness function reach maximum in the whole population, above-mentioned particle historical optimum position individual and colony all is a two Dimension Numerical Value, and each optimum position is made up of the optimal estimation value of OFDM symbol timing and the optimal estimation value of frequency deviation;

A4, carry out global search program, the optimum position of the individual and colony of new particle more, and the fundamental formular of utilizing the PSO the algorithm more translational speed and the position of new particle based on the OFDM sign synchronization algorithm of chaotic maps and PSO; And calculate and upgrade the pairing adaptive value of each particle position of back, the adaptive value size of after the renewal of each particle and historical optimum position relatively, and each particle current overall optimum position of adaptive value and colony adaptive value size after upgrading, the position when choosing adaptive value and reaching maximum is the more optimum position of new particle individuality and the optimum position of colony respectively;

Particle optimum position in A5, the colony that obtains for steps A 4, utilize logistic chaotic maps function, with between the chaotic region [0,1] is mapped to the timing of OFDM sign synchronization and the interval of frequency offset estimating value, execution is based on the Local Search program of the OFDM sign synchronization of chaotic maps and PSO algorithm, and upgrades the optimum position of particle colony once more;

After A6, k iteration of OFDM sign synchronization algorithm process based on chaotic maps and PSO algorithm, check stopping criterion for iteration: reached maximum iteration time or obtained enough good adaptive value, perhaps optimal solution is stagnated no longer and is changed, and satisfies as if above-mentioned condition, then termination of iterations; From 2 dimension variablees of the historical optimal location of population, obtain the timing position and the frequency deviation value of needed OFDM sign synchronization, carry out corresponding compensation after, it is synchronous fully to reach the OFDM symbol.

2. the OFDM symbol timing synchronization method based on chaotic maps and particle swarm optimization algorithm according to claim 1, it is characterized in that, in steps A 1, setting is based on the iterations T of the OFDM sign synchronization algorithm of chaotic maps and PSO and the scale M of population, and the basic parameter in the PSO algorithm carried out initialization, each particle timing initial position and the initial frequency deviation value that are used for the OFDM sign synchronization all are set to random value, and excursion is set to (x respectively _I1min, x _I1max) and (x _I2min, x _I2max).

3. the OFDM symbol timing synchronization method based on chaotic maps and particle swarm optimization algorithm according to claim 1, it is characterized in that, in steps A 2, be provided for based on each particle of the OFDM sign synchronization of chaotic maps and PSO algorithm regularly and the translational speed scope of frequency offset estimating be respectively (v _I1min, v _I1max) and (

v _I2max).

4. the OFDM symbol timing synchronization method based on chaotic maps and particle swarm optimization algorithm according to claim 1 is characterized in that, in steps A 3, transmitting terminal adopts CAZAC project training sequence z (n), and L is a training sequence length

$z\left(n\right)=\exp \left(\frac{j(L-1)\mathrm{\π}{n}^2}{L}\right),n\∈\left[\begin{array}{cc}0& L-1\end{array}\right]---\left(1\right)$

The training sequence that receives is y (n), and μ is the estimated value of OFDM symbol timing initial position, and ε is the frequency offset estimating value, based on the fitness function of the OFDM synchronized algorithm of chaotic maps and PSO is:

$R(\mathrm{\μ},\mathrm{\ϵ})=\underset{n=0}{\overset{L-1}{\mathrm{\Σ}}}\left[y(n+\mathrm{\μ})\exp \left(\frac{-j2\mathrm{\π}}{L}\mathrm{\ϵn}\right)\right]z^{*}\left(n\right),n=\mathrm{0,1,2}\·\·\·L-1.---\left(2\right)$

5. the OFDM symbol timing synchronization method based on chaotic maps and particle swarm optimization algorithm according to claim 1 is characterized in that, according to the designed fitness function that is used for the OFDM sign synchronization of steps A 3, calculates the initial adaptive value of i particle

The individual best initial position of corresponding particle is

The initial adaptive value size of all particles relatively, selecting adaptive value institute's correspondence position of maximum time is the initial optimal location of colony.

6. the OFDM symbol timing synchronization method based on chaotic maps and particle swarm optimization algorithm according to claim 1, it is characterized in that, in steps A 4, fundamental formular (3) according to the PSO algorithm, (4) and (5) upgrade particle position and translational speed, and calculate and upgrade the pairing adaptive value of each particle position of back, relatively after the renewal of each particle and historical optimal location adaptive value size, and the current global optimum of adaptive value and the colony position adaptive value size after each particle renewal, choose the maximum position of adaptive value correspondence respectively, more the optimal location of new particle individuality

And the optimal location p of colony _g ^k, in the calculating process OFDM symbol timing optimal location is rounded operation,

${x_{\mathrm{id}}}^{k+1}=x_{\mathrm{id}}^k+v_{\mathrm{id}}^{k+1}---\left(4\right)$

Wherein, k is the current iteration number of times, and d is the dimension of particle, r ₁And r ₂Be the random number between [0,1], c ₁And c ₂Be the study factor, w is the weight of the k time iteration of particle cluster algorithm, w _MaxBe the initial weight of this algorithm, w _MinBe the final weight of this algorithm, T is the maximum iteration time of this algorithm, v _Id, x _IdD dimension translational speed and the location estimation value of representing i particle respectively.

7. the OFDM symbol timing synchronization method based on chaotic maps and particle swarm optimization algorithm according to claim 1 is characterized in that, in steps A 5, adopts logistic chaotic maps function as the formula (6):

u _N+1Two λ u _n(1-u _n), n=0,1,2 ... (6)

In the formula (6), λ is the Logistic parameter, and λ ∈ (0,4], work as u _n∈ [0,1], the Logistic mapping is in chaos state.

8. the OFDM symbol timing synchronization method based on chaotic maps and particle swarm optimization algorithm according to claim 7 is characterized in that, comprises based on the OFDM symbol timing of chaotic maps and the Local Search step of frequency offset estimating value:

B1, make m=1, with the population optimum position

Be mapped to the Chaos Variable between [0,1]

Wherein

X in the formula (7) _{Id, max}, x _{Id, min}Be respectively the upper and lower boundary of search of d dimension variable;

B2, calculate the Chaos Variable of the m+1 time iteration according to formula (6)

λ=4 wherein, that is,

B3, with Chaos Variable Be converted into the population optimum position

Wherein,

$x_{\mathrm{id}}^{m+1}=x_{\mathrm{id},\min }+{\mathrm{cx}}_{\mathrm{id}}^{m+1}(x_{\mathrm{id},\max }-x_{\mathrm{id},\min }),---\left(9\right)$

B4, according to the population optimum position after the chaotic maps The fitness function value of the OFDM synchronized algorithm shown in the calculating formula (2);

B5, if adaptive value or Chaos Search that the fitness function value is better than before the chaotic maps have reached predefined greatest iteration step number, upgrade the optimum position of particle colony, otherwise make m=m+1, and return above-mentioned steps B2.

9. the OFDM symbol timing synchronization method based on chaotic maps and particle swarm optimization algorithm according to claim 1 is characterized in that, in steps A 6, if the adaptive value of colony's optimal location correspondence satisfies the threshold value R that is provided with for OFDM sign synchronization algorithm _ThThen stop iteration, colony's optimal location of this moment is the estimated value of timing of OFDM symbol and frequency deviation, if do not satisfy, then return steps A 4, continue iteration, till meeting the demands after certain iteration or reaching maximum iteration time, colony's global optimum of this moment be the OFDM symbol regularly and the estimated value of frequency deviation.

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