CN103338169A - Self-adapting minimum entropy blind equalization method for optimizing one-dimensional harmonic oscillator quantum artificial fish-swarm - Google Patents

Abstract

The invention discloses a self-adapting minimum entropy blind equalization method for optimizing a one-dimensional harmonic oscillator quantum artificial fish-swarm. The one-dimensional harmonic oscillator quantum artificial fish-swarm is an artificial fish-swarm in which all artificial fishes are placed in a one-dimensional harmonic oscillator quantum potential well. The method comprises the following steps: randomly initializing a position vector of the one-dimensional harmonic oscillator quantum artificial fish-swarm to be taken as a decision variable of the one-dimensional harmonic oscillator quantum artificial fish-swarm method, taking input signals of a balancer as input signals of the one-dimensional harmonic oscillator quantum artificial fish-swarm method, determining the food concentration at the position where the one-dimensional harmonic oscillator quantum artificial fishes are located by a cost function of the self-adapting minimum entropy blind equalization method, searching the optimal position vector of the one-dimensional harmonic oscillator quantum artificial fish-swarm by utilizing the one-dimensional harmonic oscillator quantum artificial fish-swarm method, and taking the position vector as the initialized weight vector of the self-adapting minimum entropy blind equalization method. When balancing high order QAM (Quadrature Amplitude Modulation) signals, the method is high in convergence rate and small in steady-state error.

Description

One-dimensional Harmonic Oscillator quantum artificial fish school optimization self adaptation minimum entropy blind balance method

Technical field

The present invention relates to a kind of underwater acoustic channel blind balance method, especially a kind of One-dimensional Harmonic Oscillator quantum artificial fish school optimization self adaptation minimum entropy blind balance method.

Background technology

The complex communication channel constantly changes in T/F-space, has low, the strong multipath fading of carrier frequency, strong noise, characteristics such as bandwidth is limited and propagation delay time is big, and intersymbol interference is serious, influences communication quality.In order to suppress intersymbol interference (ISI), conserve bandwidth, raising communication quality, the traditional constant mould blind balance method of normal employing (Constant Modulus Algorithm, and CMA) (see document [1] Shahzad A S.New Blind Equalization Techniques Based on Improved Square Contour Algorithm[J] .Digital Signal Processing.2008 (18) 4:680-693; Document [2] Mikael S, Lienven D L.On Jacobi-type Methods for Blind Equalization of Paraunitary Channels[J] .Signal Processing.2012 (92) 1:617-624.).But because unusual digital and analogue signals, as quadrature amplitude modulation and amplitude-phase frequency shift keyed signals, can be modulated onto on the different circle of several radiuses, when with balanced these signals of traditional digital-to-analogue blind balance method, then equalizer output signal is tending towards on a certain fixedly circle, not only the stable state mean square error is big, and phase place rotation appears, even cause can't be balanced (see that document [3] Ouyang copies. two recurrence least square second-order statistics blind Channel identifications and equalization algorithm [J]. Chinese science F collects (information science) .2009 (39) 6:654-662; Document [4] Li X.L, Zhang X D.A Family of Generalized Constant Modulus Algorithm for Blind Equalization[J] .IEEE Transaction on Communications.2006 (54) 11:1913-1917.).Be the unusual digital and analogue signals and overcome phase place rotation problem of balanced high-order, document [5-6] (document [5] S.Abrar, A.Nandi.Blind Equalization of Square-QAM Signals:A Multimodulus Approach[J] .IEEE Transaction on Communications.2010 (58) 6:1674-1685; Document [6] J.Yang, G Dumont.The Multimodulus Blind equalization and Its Generalized Algorithms.2002 (20) 5:997-1015.) provided a kind of multimode method, this method utilizes PHASE-LOCKED LOOP PLL TECHNIQUE to correct the phase place rotation, improved the very portfolio effect of digital and analogue signals of high-order, compare with traditional constant mould blind balance method, operand is big, complex structure, be difficult for real-time implementation.Document [7] (S.Abrar, A.Nandi.Adaptive Minimum Entropy Equalization Algorithm[J] .IEEE Communications Letters.2010 (14) 10:966-968.) provided a kind of self adaptation self adaptation minimum entropy algorithm (β CMA), this method can be corrected the phase place rotation, but convergence rate is slow, steady-state error is big.

For overcoming the shortcoming of β CMA, after the present invention was incorporated into self adaptation minimum entropy blind balance method with One-dimensional Harmonic Oscillator quantum artificial fish school optimization method, the performance of convergence rate and steady-state error aspect obviously improved.

Summary of the invention

The present invention seeks under the prerequisite that increases less computing cost, to have invented One-dimensional Harmonic Oscillator quantum artificial fish school optimization self adaptation minimum entropy blind balance method for improving the very portfolio effect of digital and analogue signals of high-order.

The ability of searching optimum that the inventive method takes full advantage of One-dimensional Harmonic Oscillator quantum artificial fish-swarm method is strong, the characteristics of fast convergence rate, and the equalizer weight vector is optimized, and fast convergence rate, mean square error are little.

The present invention adopts following technical scheme for achieving the above object:

One-dimensional Harmonic Oscillator quantum artificial fish school optimization self adaptation minimum entropy blind balance method of the present invention, described method is as follows:

One-dimensional Harmonic Oscillator quantum artificial fish-swarm refers to all artificial fishes in the artificial fish-swarm are placed the artificial fish-swarm of One-dimensional Harmonic Oscillator quantum well, the position vector of one group of artificial fish of One-dimensional Harmonic Oscillator quantum of random initializtion is as the decision variable of One-dimensional Harmonic Oscillator quantum artificial fish-swarm method, with the input signal of the equalizer input signal as One-dimensional Harmonic Oscillator quantum artificial fish-swarm method, determined the food concentration of the artificial fish of One-dimensional Harmonic Oscillator quantum position by the cost function of self adaptation minimum entropy blind balance method, utilize One-dimensional Harmonic Oscillator quantum artificial fish-swarm method to seek the optimal location vector of One-dimensional Harmonic Oscillator quantum artificial fish-swarm, with the initialization weight vector of this position vector as self adaptation minimum entropy blind balance method.

The food concentration of described One-dimensional Harmonic Oscillator quantum artificial fish-swarm determines that method is as follows: with the inverse of the self adaptation minimum entropy blind balance method β CMA cost function food concentration as One-dimensional Harmonic Oscillator quantum artificial fish-swarm, namely have

F(X _i)=1/J _βCMA(X _i),i=1,2,…,M

Wherein, J _{β CMA}(X _i) be the cost function of self adaptation minimum entropy blind balance method β CMA, M is the quantity of the artificial fish of One-dimensional Harmonic Oscillator quantum, M is positive integer, down together;

X _iBe the position vector of the artificial fish of i bar One-dimensional Harmonic Oscillator quantum, a weight vector of corresponding equalizer; F is the position vector X with the artificial fish of i bar One-dimensional Harmonic Oscillator quantum _iBe the food concentration function of independent variable, to determine its position vector X _iCorresponding food concentration;

Described One-dimensional Harmonic Oscillator quantum artificial fish school optimization weight vector method is as follows:

Step 1: the initialization of One-dimensional Harmonic Oscillator quantum artificial fish-swarm: the quantity of establishing the artificial fish of One-dimensional Harmonic Oscillator quantum is M, and the position dimension of every artificial fish is q, and q is positive integer, down together; The initial position vector X of the artificial fish of i bar One-dimensional Harmonic Oscillator quantum _i=(X _I1, X _I2..., X _Iq), X _IqIt is the q dimension position of the artificial fish of i bar One-dimensional Harmonic Oscillator quantum;

Step 2: the food concentration that calculates One-dimensional Harmonic Oscillator quantum artificial fish-swarm: by the food concentration F (X of the artificial fish of the described i bar of claim 1 One-dimensional Harmonic Oscillator quantum _i) determine method, when the cost function of self adaptation minimum entropy blind balance method β CMA hour, the food concentration F (X of the artificial fish of i bar One-dimensional Harmonic Oscillator quantum _i) be maximum, the position of this artificial fish is the optimal location of artificial fish-swarm, this position vector is called the optimal location vector of artificial fish-swarm;

Step 3: bulletin board and initialization thereof: with the food concentration maximum of One-dimensional Harmonic Oscillator quantum artificial fish-swarm and with it corresponding optimal location vector preserve, this is called bulletin board to the value that is saved; Calculate in the One-dimensional Harmonic Oscillator quantum artificial fish-swarm the manually food concentration F (X of fish of institute _i), therefrom select the food concentration maximum F of the artificial fish of One-dimensional Harmonic Oscillator quantum _BestReach corresponding with it optimal location vector X _Best, as the initial value of bulletin board, this process is called the bulletin board initialization;

Step 4: bulletin board upgrades: to the three kinds of operations of looking for food, bunch, knock into the back of all artificial fishes in the One-dimensional Harmonic Oscillator quantum artificial fish-swarm; Each operation all obtains position vector and the food concentration of the artificial fish of each bar in the One-dimensional Harmonic Oscillator quantum artificial fish-swarm, and from each operating result, select food concentration maximum and corresponding optimal location vector with it, obtain three kinds of food concentration maximums and corresponding three kinds of optimal location vectors with it; From three kinds of food concentration maximums, select the maximum again and reach corresponding with it optimal location vector, as the new maximum of food concentration of One-dimensional Harmonic Oscillator quantum artificial fish-swarm and the new optimal location vector of correspondence, the food concentration of preserving in the new maximum of this food concentration and the bulletin board is compared; If the new maximum of this food concentration greater than the food concentration of preserving in the bulletin board, then reaches the value that corresponding with it new optimal location vector is replaced the original preservation of bulletin board with the new maximum of this food concentration, this process is called bulletin board and upgrades; Otherwise, jump procedure 5;

Step 5: judge whether the bulletin board renewal finishes: when the food concentration value remains unchanged in the bulletin board, finish to upgrade; Otherwise, jump to step 4;

Step 6: equalizer weight vector initialization: after finish upgrading, the final optimal position vector that bulletin board is preserved is as the initial weight vector of equalizer.

The present invention combines self adaptation minimum entropy blind balance method with One-dimensional Harmonic Oscillator quantum artificial fish school optimization method, invented a kind of One-dimensional Harmonic Oscillator quantum artificial fish school optimization self adaptation minimum entropy blind balance method (QO-EI-β CMA).Compare with super index self adaptation minimum entropy blind balance method SEI-β CMA, self adaptation minimum entropy blind balance method β CMA method, the inventive method fast convergence rate, stable state mean square error are little.

Description of drawings

Fig. 1 is One-dimensional Harmonic Oscillator quantum artificial fish school optimization self adaptation minimum entropy blind balance method theory diagram of the present invention;

Fig. 2 a is the mean square error curve of three kinds of methods;

Fig. 2 b is the signal output map of β CMA;

Fig. 2 c is the signal output map of SEI-β CMA;

Fig. 2 d is the signal output map of QO-SEI-β CMA.

β CMA represents self adaptation minimum entropy blind balance method among Fig. 2 a, and SEI-β CMA represents super index self adaptation minimum entropy blind balance method, and QO-SEI-β CMA represents One-dimensional Harmonic Oscillator quantum artificial fish school optimization self adaptation minimum entropy blind balance method.

Embodiment

Self adaptation minimum entropy blind balance method β CMA

The cost function of self adaptation minimum entropy blind balance method β CMA is

J=max E[|z(k)| ²] (1)

f(R,|z(k)|)=R (2)

Formula (2) is the constraints of formula (1), and z (k) is k equalizer output signal constantly, | z (k) | the absolute value of expression z (k), R is the maximum amplitude of original series; E represents mathematic expectaion; Max represents to get maximum.For any a, b is constant, and function f is defined as

$f\left(\right|a|,|b\left|\right)=\frac{\left|\right|a|+|b\left|\right|}{2}+\frac{\left|\right|a|-|b\left|\right|}{2}=\left\{\begin{array}{cc}\left|a\right|& \left|a\right|\&GreaterEqual;\left|b\right|\\ \left|b\right|& \left|a\right|<\left|b\right|\end{array}\right.---\left(3\right)$

By formula (3), if | z (k) |＜R, constraints (2) is set up; If | z (k) |〉R, then constraints (2) is false.For constraints (2) is set up, utilize lagrange's method of multipliers that cost function and constraints are merged into new cost function, namely

J=max E[|z(k)| ²]+λ(f(R,|z(k)|)-R) (4)

In the formula, z (k)=wx (k), w are the weight vector of self adaptation minimum entropy blind balance method β CMA; X (k) is the k input signal of self adaptation minimum entropy blind balance method β CMA constantly; λ is Lagrangian; | z (k) | ²And f (R, | z (k) |) respectively to the differentiate of blind equalization weight vector w, get

$\frac{\&PartialD;{\left|z\left(k\right)\right|}^2}{\&PartialD;w}=\frac{\&PartialD;{\left|z\left(k\right)\right|}^2}{\&PartialD;z\left(k\right)}\frac{\&PartialD;z\left(k\right)}{\&PartialD;w}---\left(5\right)$

$\frac{\&PartialD;f(R,|z\left(k\right)\left|\right)}{\&PartialD;w}=\frac{\&PartialD;f(R,|z\left(k\right)\left|\right)}{\&PartialD;z\left(k\right)}\frac{\&PartialD;z\left(k\right)}{\&PartialD;w}=\frac{g\left(k\right)z^{*}\left(k\right)}{4\left|z\left(k\right)\right|}x\left(k\right)---\left(6\right)$

In the formula, * represents to get conjugation, down together; G (k)=1+sign (| z (k) |-R), sign represents sign function, with formula (4)～(6) simultaneous; The blind equalization weight vector more new formula be

w(k+1)=w(k)-μ(1+g(k)/4|z(k)|)z ^*(k)x(k) (7)

If | z (k) |＜R, g (k)=0 then, weight vector becomes w (k+1)=w (k)+μ z (k) x (k) with new-type (7);

If | z (k) |=R, g (k)=1 then, formula (2) is set up, and the adaptive updates process stops;

Be to guarantee w (k+1)=w (k), then λ=-4|z (k) |;

If | z (k) |〉R, then weight vector more new-type (7) becomes w (k+1)=w (k)+μ (1+ λ/2z (k)) x (k), and constraints (2) is false.For formula (2) is set up, calculate λ, make it satisfy the Bussgang process.Make λ=-2 (1+ β) | z (k) |, then the Bussgang process is

In the formula, τ represents time series, round numbers; α is real constant; Z represents set of integers.Be N for original series a (k) length, and each primary signal all drop on modulation set { R ₁..., R _KIn, R _K=R, R _KThe mould value of expression K contrast signal processed.N _lRepresent that l contrast signal processed is counted and For τ=0, by formula (9),

$N_1{R}_1^2+\&CenterDot;\&CenterDot;\&CenterDot;+N_{K-1}{R}_{K-1}^2+\frac{1}{2}{N}_K{R}_K^2-\frac{\mathrm{\&alpha;}}{2}{N}_K{R}_K^2=0---\left(10\right)$

By formula (10),

$\mathrm{\&alpha;}=2\frac{N}{N_K}\frac{P}{R^2}-1---\left(11\right)$

In the formula, P=E[|a (k) | ²] be the average energy of original series.With formula (11) substitution formula (7), the weight vector that gets self adaptation minimum entropy blind balance method β CMA more new formula is

w(k+1)=w(k)-μf[z(k)]z ^*(k)x(k) (12)

$f\left[z\left(k\right)\right]=\left\{\begin{array}{cc}1,& \left|z\left(k\right)\right|<R\\ -\mathrm{\&alpha;},& \left|z\left(k\right)\right|>R\\ 0,& \left|z\left(k\right)\right|=R\end{array}\right.---\left(13\right)$

Super index self adaptation minimum entropy blind balance method

Super index alternative manner constantly calculates the inverse matrix D (k) of input signal correlation matrix in equalizer weight vector iterative process, optimize step factor and albefaction equalizer input signal, thereby accelerates convergence rate.The iterative formula of D (k) matrix is

$D(k+1)=\frac{1}{1-{\mathrm{\&mu;}}_D}[D\left(k\right)-\frac{u_{D}D\left(k\right)x^{*}\left(k\right)x^{T}D\left(k\right)}{1-{\mathrm{\&mu;}}_D+{\mathrm{\&mu;}}_D{x}^T\left(k\right)D\left(k\right)x^{*}\left(k\right)}]---\left(14\right)$

In the formula, T represents transposition, μ _DIteration step length for matrix D (k); The initial value of D (k) is the contrary of input signal correlation matrix, namely

$R=C^{T}C+\frac{{\mathrm{\&sigma;}}_n^2}{{\mathrm{\&sigma;}}_a^2}I---\left(15\right)$

Wherein, C is the convolution matrix of channel,

With

The variance of representing original series and noise respectively, I is unit matrix.With formula (14), (15) substitution formula (12) obtain new weight vector more new formula be

w(k+1)=w(k)-μD(k)f[z(k)]z ^*(k)x(k) (16)

One-dimensional Harmonic Oscillator quantum artificial fish school optimization self adaptation minimum entropy blind balance method

Basic artificial fish-swarm AFSA model

In q dimension search volume, the artificial fish of M bar is formed a population;

X wherein _i=(X _I1, X _I2..., X _Iq) position vector of expression i bar artificial fish, q is the position dimension of every artificial fish, the food concentration of artificial fish current location is F (X _i); Between i bar and the artificial fish of s bar apart from d _{I, s}=| X _i-X _s|; Perceived distance, maximum moving step length are respectively Visual, step, and artificial fish is by looking for food, bunch and position vector being upgraded in the behavior of knocking into the back, namely

X _i-next=X _i+(2r _R-1)·Visual·ones(1,q) (17)

$X_{i-\mathrm{next}}=X_i+r_R\&CenterDot;\mathrm{step}\&CenterDot;\frac{X_c-X_i}{|X_s-X_i|}---\left(18\right)$

$X_{i-\mathrm{next}}=X_i+r_R\&CenterDot;\mathrm{step}\&CenterDot;\frac{X_{\mathrm{gbest}}-X_i}{|X_{\mathrm{gbest}}-X_i|}---\left(19\right)$

Wherein, r _RBe the random number between (0,1); X _sThe position vector of representing the artificial fish of s bar; (1, q) expression 1 row q row and element are 1 row vector to ones entirely; X _I-nextThe position vector of representing the artificial fish next iteration of i bar; X _cAnd X _GbestBe respectively the center vector sum global optimum position vector of artificial fish-swarm.

One-dimensional Harmonic Oscillator quantum well AFSA model

In quantum theory, the dynamic behaviour of quantum is described as with Schrodinger equation

$\mathrm{jh}\frac{\&PartialD;}{\&PartialD;t}\mathrm{\&psi;}(r,t)=(-\frac{h^2}{2m^{\&prime;}}{\&dtri;}^2+V\left(r\right))\mathrm{\&psi;}(r,t)---\left(20\right)$

In the formula, m' is the quality of quantum, and V (r) is the potential energy distribution function, and h is planck constant.In Schrodinger equation, according to wave function ψ (r, statistical interpretation t), its amplitude square be the probability that quantum occurs in a certain position, wherein, r is the gesture distance of quantum, t be the place constantly.

The design philosophy of One-dimensional Harmonic Oscillator quantum well AFSA: at first select certain not contain the One-dimensional Harmonic Oscillator potential well of time t, isolate wave function ψ (r) by finding the solution Schrodinger equation then, obtain the probability density function that the artificial fish of One-dimensional Harmonic Oscillator occurs in potential field | ψ (r) | ², control random number r in the artificial fish-swarm iteration by potential well parameter reasonable in design again _RThe artificial fish of i bar One-dimensional Harmonic Oscillator quantum is by looking for food, bunch and position vector being upgraded in the behavior of knocking into the back, obtain respectively position vector more new formula be

X _i-next=X _i+(2H-1)·Visual·ones(1,q) (21)

$X_{i-\mathrm{next}}=X_i+H\&CenterDot;\mathrm{step}\&CenterDot;\frac{X_c-X_i}{\left||X_s-X_i|\right|}---\left(22\right)$

$X_{i-\mathrm{next}}=X_i+H\&CenterDot;\mathrm{step}\&CenterDot;\frac{X_{\mathrm{gbest}}-X_i}{\left|\right|X_{\mathrm{gbest}}-X_i||}---\left(23\right)$

In the formula, H represents

U is the random number between (0,1), and p is real number and 0＜p＜1,

The expression square root, ln represents with e to be the natural logrithm at the end; Formula (21)～formula (23) constitutes One-dimensional Harmonic Oscillator quantum artificial fish-swarm method QOAFSA.

One-dimensional Harmonic Oscillator quantum artificial fish school optimization weight vector method

The QOAFSA system is a quantized system, presses the quantum theory, and is as follows with this system optimization equalizer weight vector process:

Step 1: One-dimensional Harmonic Oscillator quantum artificial fish-swarm initialization:

If the quantity of the artificial fish of One-dimensional Harmonic Oscillator quantum is M, the position dimension of every artificial fish is q, and q is positive integer, down together; The initial position vector X of the artificial fish of i bar One-dimensional Harmonic Oscillator quantum _i=(X _I1, X _I2..., X _Iq), X _IqIt is the q dimension position of the artificial fish of i bar One-dimensional Harmonic Oscillator quantum;

Step 2: the food concentration that calculates One-dimensional Harmonic Oscillator quantum artificial fish-swarm: press

Food concentration F (the X of the artificial fish of the described i bar of claim 1 One-dimensional Harmonic Oscillator quantum _i) determine method, when the cost function of self adaptation minimum entropy blind balance method β CMA hour, the food concentration F (X of the artificial fish of i bar One-dimensional Harmonic Oscillator quantum _i) be maximum, the position of this artificial fish is the optimal location of artificial fish-swarm, this position vector is called the optimal location vector of artificial fish-swarm;

Step 3: bulletin board and initialization thereof:

With the food concentration maximum of One-dimensional Harmonic Oscillator quantum artificial fish-swarm and with it corresponding optimal location vector preserve, this is called bulletin board to the value that is saved; Calculate in the One-dimensional Harmonic Oscillator quantum artificial fish-swarm the manually food concentration F (X of fish of institute _i), therefrom select the food concentration maximum F of the artificial fish of One-dimensional Harmonic Oscillator quantum _BestReach corresponding with it optimal location vector X _Best, as the initial value of bulletin board, this process is called the bulletin board initialization;

Step 4: bulletin board upgrades:

To the three kinds of operations of looking for food, bunch, knock into the back of all artificial fishes in the One-dimensional Harmonic Oscillator quantum artificial fish-swarm; Each operation all obtains position vector and the food concentration of the artificial fish of each bar in the One-dimensional Harmonic Oscillator quantum artificial fish-swarm, and from each operating result, select food concentration maximum and corresponding optimal location vector with it, obtain three kinds of food concentration maximums and corresponding three kinds of optimal location vectors with it; From three kinds of food concentration maximums, select the maximum again and reach corresponding with it optimal location vector, as the new maximum of food concentration of One-dimensional Harmonic Oscillator quantum artificial fish-swarm and the new optimal location vector of correspondence, the food concentration of preserving in the new maximum of this food concentration and the bulletin board is compared; If the new maximum of this food concentration greater than the food concentration of preserving in the bulletin board, then reaches the value that corresponding with it new optimal location vector is replaced the original preservation of bulletin board with the new maximum of this food concentration, this process is called bulletin board and upgrades; Otherwise, jump procedure 5;

Step 5: judge whether the bulletin board renewal finishes:

When the food concentration value remains unchanged in the bulletin board, finish to upgrade; Otherwise, jump to step 4;

Step 6: equalizer weight vector initialization:

After finish upgrading, the final optimal position vector that bulletin board is preserved is as the initial weight vector of equalizer.

Method with One-dimensional Harmonic Oscillator quantum artificial fish-swarm method optimization self adaptation minimum entropy blind equalization weight vector is referred to as One-dimensional Harmonic Oscillator quantum artificial fish school optimization self adaptation minimum entropy blind balance method, notes the CMA into QO-SEI-β by abridging.

Embodiment

In order to verify the performance of the inventive method QO-SEI-β CMA, adopt the 512-QAM signal to carry out the blind equalization experiment, and compare with self adaptation minimum entropy blind balance method β CMA and super index self adaptation minimum entropy blind balance method SEI-β CMA.

In the experiment, minimum phase underwater acoustic channel c=[0.9656-0.0906 0.0578 0.2368], signal to noise ratio is 44dB, equalizer length is that 16, β CMA and SEI-β CMA adopt the centre cap initialization; The quantity of artificial fish is 100 in the One-dimensional Harmonic Oscillator quantum artificial fish-swarm, and step-length step, field range Visual get 0.75,0.3 respectively; Step parameter, as shown in table 1.

400 Monte Carlo experiment results are shown in Fig. 2 a to Fig. 2 d.

Table 1 step parameter is selected

Fig. 2 a to Fig. 2 d shows that the convergence rate of the inventive method QO-SEI-β CMA fast about 10000 and 20000 goes on foot respectively than SEI-β CMA and β CMA; The steady-state error of the inventive method QO-SEI-β CMA has reduced about 3dB and 3.5dB respectively than SEI-β CMA and β CMA, and the eye pattern of the inventive method QO-SEI-β CMA is the most clear, compact.Hence one can see that: One-dimensional Harmonic Oscillator quantum artificial fish-swarm method of the present invention is optimized self adaptation minimum entropy blind balance method, and fast convergence rate, steady-state error are little, be applicable to the high-order QAM signal is carried out efficient balance.

Claims (3)

1. One-dimensional Harmonic Oscillator quantum artificial fish school optimization self adaptation minimum entropy blind balance method is characterized in that described method is as follows:

One-dimensional Harmonic Oscillator quantum artificial fish-swarm refers to all artificial fishes in the artificial fish-swarm are placed the artificial fish-swarm of One-dimensional Harmonic Oscillator quantum well, the position vector of the artificial fish of random initializtion One-dimensional Harmonic Oscillator quantum is as the decision variable of One-dimensional Harmonic Oscillator quantum artificial fish-swarm method, with the input signal of the equalizer input signal as One-dimensional Harmonic Oscillator quantum artificial fish-swarm method, determined the food concentration of the artificial fish of One-dimensional Harmonic Oscillator quantum position by the cost function of self adaptation minimum entropy blind balance method, utilize One-dimensional Harmonic Oscillator quantum artificial fish-swarm method to seek the optimal location vector of One-dimensional Harmonic Oscillator quantum artificial fish-swarm, with the initialization weight vector of this position vector as self adaptation minimum entropy blind balance method.

2. One-dimensional Harmonic Oscillator quantum artificial fish school optimization self adaptation minimum entropy blind balance method according to claim 1 is characterized in that the food concentration of described One-dimensional Harmonic Oscillator quantum artificial fish-swarm determines that method is as follows:

With the inverse of the self adaptation minimum entropy blind balance method β CMA cost function food concentration as One-dimensional Harmonic Oscillator quantum artificial fish-swarm, namely have

F(X _i)=1/J _βCMA(X _i),i=1,2,…,M

Wherein, J _{β CMA}(X _i) be the cost function of self adaptation minimum entropy blind balance method β CMA, M is the quantity of the artificial fish of One-dimensional Harmonic Oscillator quantum, M is positive integer, down together;

X _iBe the position vector of the artificial fish of i bar One-dimensional Harmonic Oscillator quantum, a weight vector of corresponding equalizer; F is the position vector X with the artificial fish of i bar One-dimensional Harmonic Oscillator quantum _iBe the food concentration function of independent variable, to determine its position vector X _iCorresponding food concentration.

3. One-dimensional Harmonic Oscillator quantum artificial fish school optimization self adaptation minimum entropy blind balance method according to claim 1 is characterized in that, described One-dimensional Harmonic Oscillator quantum artificial fish school optimization weight vector method is as follows:

Step 1: One-dimensional Harmonic Oscillator quantum artificial fish-swarm initialization:

If the quantity of the artificial fish of One-dimensional Harmonic Oscillator quantum is M, the position dimension of every artificial fish is q, and q is positive integer, down together; The initial position vector X of the artificial fish of i bar One-dimensional Harmonic Oscillator quantum _i=(X _I1, X _I2..., X _Iq), X _IqIt is the q dimension position of the artificial fish of i bar One-dimensional Harmonic Oscillator quantum;

Step 2: the food concentration that calculates One-dimensional Harmonic Oscillator quantum artificial fish-swarm:

Food concentration F (X by the artificial fish of the described i bar of claim 1 One-dimensional Harmonic Oscillator quantum _i) determine method, when the cost function of self adaptation minimum entropy blind balance method β CMA hour, the food concentration F (X of the artificial fish of i bar One-dimensional Harmonic Oscillator quantum _i) be maximum, the position of this artificial fish is the optimal location of artificial fish-swarm, this position vector is called the optimal location vector of artificial fish-swarm;

Step 3: bulletin board and initialization thereof:

With the food concentration maximum of One-dimensional Harmonic Oscillator quantum artificial fish-swarm and with it corresponding optimal location vector preserve, this is called bulletin board to the value that is saved; Calculate in the One-dimensional Harmonic Oscillator quantum artificial fish-swarm the manually food concentration F (X of fish of institute _i), therefrom select the food concentration maximum F of the artificial fish of One-dimensional Harmonic Oscillator quantum _BestReach corresponding with it optimal location vector X _Best, as the initial value of bulletin board, this process is called the bulletin board initialization;

Step 4: bulletin board upgrades:

To the three kinds of operations of looking for food, bunch, knock into the back of all artificial fishes in the One-dimensional Harmonic Oscillator quantum artificial fish-swarm; Each operation all obtains position vector and the food concentration of the artificial fish of each bar in the One-dimensional Harmonic Oscillator quantum artificial fish-swarm, and from each operating result, select food concentration maximum and corresponding optimal location vector with it, obtain three kinds of food concentration maximums and corresponding three kinds of optimal location vectors with it; From three kinds of food concentration maximums, select the maximum again and reach corresponding with it optimal location vector, as the new maximum of food concentration of One-dimensional Harmonic Oscillator quantum artificial fish-swarm and the new optimal location vector of correspondence, the food concentration of preserving in the new maximum of this food concentration and the bulletin board is compared; If the new maximum of this food concentration greater than the food concentration of preserving in the bulletin board, then reaches the value that corresponding with it new optimal location vector is replaced the original preservation of bulletin board with the new maximum of this food concentration, this process is called bulletin board and upgrades; Otherwise, jump procedure 5;

Step 5: judge whether the bulletin board renewal finishes: when the food concentration value remains unchanged in the bulletin board, finish to upgrade; Otherwise, jump to step 4;

Step 6: equalizer weight vector initialization: after finish upgrading, the final optimal position vector that bulletin board is preserved is as the initial weight vector of equalizer.

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