CN103475382A - Zero intermediate frequency Gaussian white noise adding method and device - Google Patents

Abstract

The invention discloses a zero intermediate frequency Gaussian white noise adding method and device. The method comprises the steps of (1) inputting zero intermediate frequency signals and calculating average energy of the signals, (2) generating additive Gaussian white noise with the matrix conversion designing method, (3) obtaining amplitude factors with the search approaching algorithm to further generate an imnoised zero intermediate frequency signal with a specific signal to noise ratio, and (4) inputting the imnoised zero intermediate frequency signal into a digital automatic gain module and controlling the output gain of the signal according to a set threshold value. Compared with the prior art, the zero intermediate frequency Gaussian white noise adding method and device have the advantages that consumption of a hardware logical unit is greatly reduced, the output efficiency of Gaussian white noise is improved, and the imnoised zero intermediate frequency signal with the gain controllable is realized. According to the zero intermediate frequency Gaussian white noise adding method, modular design can be achieved.

Description

A kind of zero intermediate frequency white Gaussian noise adding method and device

Technical field

The present invention relates to the communications field, particularly a kind of zero intermediate frequency white Gaussian noise adding method and device.

Background technology

Additive white Gaussian noise is that a kind of each spectrum component obedience is uniformly distributed (being white noise), and the noise signal of whose amplitude obeys Gaussian Profile.Because of its additive property, whose amplitude obeys Gaussian Profile and for a kind of of white noise, gain the name.

In communication and signal processing system, white Gaussian noise is very common noise signal, and is more useful noise signal, especially for use in the interference free performance of check communication system.In radio communication or satellite communication, according to actual conditions, can need the Gaussian white noise channel of different signal to noise ratios and gain-variable to check the interference free performance of receiving equipment.

Through the contrast to existing patent and document, find, there is following problem in existing white Gaussian noise adding method: one, the white Gaussian noise generation unit adopts logical block and shift register to realize at hardware aspect realizing, the cycle of this method is 2 ^m-1, the figure place that wherein m is shift register, a bit of each clock output.Suppose to require output nbit, need n * m trigger to realize.If cycle request is longer just larger to hardware resource consumption; Two,, when certain signal to noise ratio output is set, use multiplier and trigger to realize increasing the consumption to hardware resource.This just causes, when checking the interference free performance of radio communication or satellite receiving equipment in batches, can causing the consumption of high resource, poor efficiency.

Therefore, from realizing angle, select hardware realize simple, resource consumption is few, efficiency is high and can realize that the noise addition methods of signal to noise ratio and gain-variable and device are very significant.

Summary of the invention

The shortcoming that the object of the invention is to overcome prior art, with not enough, adopt and takies the smaller nonequivalence operation generation white Gaussian noise of hardware resource, and, in conjunction with searching algorithm and digital Auto Gain, realize a kind of efficient white Gaussian noise adding method.

Another object of the present invention is to, a kind of device of zero intermediate frequency white Gaussian noise adding method is provided.

In order to reach above-mentioned the first goal of the invention, the present invention by the following technical solutions:

A kind of zero intermediate frequency white Gaussian noise adding method, comprise the steps:

(1) zero intermediate frequency signals of input is averaging to energy power_avg: at first calculate the energy power of the zero intermediate frequency signals I+jQ of input, then the signal energy of a period of time is averaging to obtain to average signal energy power_avg;

(2) produced additive white Gaussian noise α and the β of unit energy by white Gaussian noise generation module, and then obtain the multiple noise α that energy is 2+j β;

(3) the multiple noise α that the energy that the average signal energy power_avg obtained according to given signal to noise ratio snr and step (1) and step (2) obtain is 2+j β, search for corresponding signal amplitude factors A and noise amplitude factor B;

Described signal to noise ratio

wherein b=(2 ^cnt_shift) ²; a=y ²1, and a<4; Cnt_shift is the multiple bit number that noise signal α+j β is moved to the left, and multiplication factor is 2 ^cnt_shift, the energy multiplication factor is b=(2 ^cnt_shift) ²; Y is the signal amplitude multiplication factor, and energy amplifies y ²;

Described signal amplitude factors A=y, noise amplitude factor B=2 ^cnt_shift;

(4) zero intermediate frequency input signal I+jQ is multiplied by signal amplitude factors A that step (3) the obtains signal SIG after adjusted; The multiple noise signal α obtained by step (2)+j β is multiplied by noise amplitude B that step (3) the obtains noise NOISE after adjusted; Signal SIG and noise NOISE obtain adding the signal SIG_NOISE made an uproar through the noise summation module;

(5) SIG_NOISE that digital Auto Gain module DAGC obtains step (4) according to given thresholding R automatically adjusts.

In step (2), the implementation method of white Gaussian noise random number is as follows:

(2-1) according to the Tausworthe method, use the algorithm of matrixing to produce the uniform random number t on (0,1) _n;

Described uniform random number t _nrandom sequence x with L length _nfollowing relational expression is arranged:

$t_n=\underset{i=1}{\overset{L}{\mathrm{\&Sigma;}}}{x}_{\mathrm{ns}+i-1}\&CenterDot;2^{-i}$

Wherein, L and s are the non-zero positive integers, and L is random number output bit wide, and s is the saltus step step-length; Obviously, random number t _nwith binary sequence T _n=X _ns=(x _ns, x _ns+1..., x _ns+L-2, x _ns+L-1) corresponding one by one, therefore t in the present invention _nwith T _ndo not do special differentiation;

Described random sequence x _nrecurrence Relation as follows:

$x_n=a_1{x}_{n-1}\&CirclePlus;a_2{x}_{n-2}\&CirclePlus;\&CenterDot;\&CenterDot;\&CenterDot;\&CirclePlus;a_k{x}_{n-k}$

Wherein, a ₁, a ₂a _kfor proper polynomial P (z)=z ^k-a ₁z ^k-1-...-a _kcoefficient;

(2-2) produced the combined Tausworthe random number u of period expansion by J Tausworthe uniform random number _n;

Described combined Tausworthe random number relational expression is:

$u_n=\underset{i=1}{\overset{L}{\mathrm{\&Sigma;}}}(x_{\mathrm{ns}+i-\mathrm{1,1}}\&CirclePlus;x_{\mathrm{ns}+i-\mathrm{1,2}}\&CirclePlus;...\&CirclePlus;x_{\mathrm{ns}+i-1,J})\&CenterDot;2^{-i}$

Wherein, be J independently random number sequence, when J=1, u _n=t _n;

(2-3) the random number called after produced according to two combined Tausworthe of step (2-2): u _{n, 1}and u _{n, 2}, produce thus gaussian random distribution number α and β; Relational expression is as follows:

$\mathrm{\&alpha;}=\sqrt{-2\&times;\ln \left(u_{n,1}\right)}\sin \left(2\mathrm{\&pi;}{u}_{n,2}\right)$

$\mathrm{\&beta;}=\sqrt{2-\&times;\ln \left(u_{n,1}\right)}\cos \left(2\mathrm{\&pi;}{u}_{n,2}\right)$

In step (2-1), described matrixing algorithm can produce a binary sequence T within a clock cycle _nthereby, improving execution efficiency, concrete shift step is as follows:

(2-1-1) calculate transfer matrix A, expression formula is:

$A=\left[\begin{array}{cc}{C}_{k\&times;1}& I_{(L-1)\&times;(L-1)}\\ 0_{(L-k)\&times;(L-1)}& 0_{1\&times;1}\end{array}\right],$

Wherein, C _{k * 1}=[a ₁a ₂a _k] be the coefficient vector of proper polynomial P (Z), 0 _{(L-K) * (L-1)}, 0 _{1 * 1}for null matrix, I _{(L-1) * (L-1)}for unit matrix;

Described proper polynomial is got trinomial P (Z)=z ^k-z ^q-1;

(2-1-2) the transfer matrix A that uses step (2-1-1) to obtain and the L position state information X of current time _n=(x _n+L-1, x _n+L-2..., x _n+1, x _n), calculate next L position state information X constantly _n+1, i.e. X _n+1=X _n* A;

(2-1-3) according to the relational expression of step (2-1-2) and (2-1) in random number t _nwith binary sequence T _n=X _nsone-to-one relationship, can recursion obtain following relational expression:

T _n+1=X _(n+1)s=X _(n+1)s-1×A=(X _(n+1)s-2×A)×A=…=X _ns×A ^s=T _n×A ^s

(2-1-4) relational expression obtained according to step (2-1-3) can obtain a new random number t _n+1, its saltus step step-length is s; The random number producing method can be used following matrix notation:

T _n+1=T _n×A ^s。

In step (3), described search approximate algorithm comprises the following steps:

(3-1) search amplitude factor B: at first making the signal amplitude factor is 1, i.e. a=y ²=1, then cnt_shift increases progressively by 0, until meet

till, obtain cnt_shift and B=2 ^cnt_shift;

(3-2) search signal amplitude factor A: a is started by fixing multiple x by 1 ²the mode increased progressively is searched for a, until meet $\mathrm{snr}\&le;\frac{a\&times;\mathrm{power}\_\mathrm{avg}}{2b},$ Complete and search for $A=y=\sqrt{a};$

The multiple that described fixedly multiple x is amplitude factor y, its choosing method is: the supposition fixedly quantizing bit number of multiple incremental change x is BITS, and the value after taken amount is X=2 ^bITS+ 1,

while getting BITS=11, x=1.00048828125, now energy increases progressively 0.004dB at every turn, and the energy error is in 0.004dB;

Described choosing method, when BITS is enough large, therefore the incremental change of signal energy can be expressed as $\mathrm{power}\_\mathrm{avg}\&CenterDot;x^2=\mathrm{power}\_\mathrm{avg}+\frac{\mathrm{power}\_\mathrm{avg}}{2^{(\mathrm{BITS}-1)}}.$

Described step (5) is specially:

(5-1) energy that carries out signal calculates E _y(n), and by logarithmic transformation energy value is converted into to logarithmic form E _y1(n); Pass through E _y1(n) with the comparison of threshold value R, and the subtraction that carries out logarithm obtains ε (n);

(5-2) through antilogarithm, computing obtains amplitude gain regulated value G;

(5-3) finally by crossing a multiplier and upper amplitude gain G constantly _n-1the gain G in the current moment multiplies each other to obtain _n.

In order to reach above-mentioned another purpose, the present invention by the following technical solutions:

Device based on above-mentioned a kind of zero intermediate frequency white Gaussian noise adding method, comprise zero intermediate frequency signals input module, average energy computing module, amplitude factor search module, additive white Gaussian noise generation module AWGN, noise summation module, digital Auto Gain module DAGC;

The zero intermediate frequency signals input module is for being connected with average energy computing module and amplitude factor search module, and input signal is obtained through A/D conversion or digital intermediate frequency input by analog signal;

Described average energy computing module is connected with zero intermediate frequency signals input module and amplitude factor search module respectively;

Described amplitude factor search module is connected with zero intermediate frequency signals input module and AWGN module respectively, according to the signal to noise ratio snr of input, produces signal amplitude factors A, noise amplitude factor B;

Described AWGN module is connected with the amplitude factor search module, and the white Gaussian noise unit energy of generation is 1, and then obtains the multiple noise α that energy is 2+j β;

Described noise summation module is connected with zero intermediate frequency signals I+jQ, white Gaussian noise signal alpha+j β and DAGC module after amplitude adjusted respectively;

Described DAGC module is connected with the noise summation module, certain gain and the zero intermediate frequency signals that contain white Gaussian noise of output.

The present invention has following advantage and effect with respect to prior art:

(1) the method can be used field programmable gate array (FPGA) to realize by digital form, and can be used as the standalone module use of signal source, is convenient to module and transplants;

(2) in the generation module of additive white Gaussian noise, use the method for matrixing to produce the random number of a L position within a clock cycle, and the exclusive or logic gate that only comprises s coordination in the hardware configuration of transition matrix, the method has improved the delivery efficiency of white Gaussian noise greatly;

(3) when the search amplitude factor, set while increasing progressively multiple, make the mode of calculating the signal energy increment value convert addition and displacement to by multiplication, reduced the consumption of logical resource;

(4) use pipelining to realize search approximate algorithm, the search efficiency of the increase rate factor.

The accompanying drawing explanation

The composition schematic diagram of the zero intermediate frequency white Gaussian noise adding method that Fig. 1 is embodiments of the invention;

Fig. 2 (a) is the white Gaussian noise α that embodiments of the invention AWGN module produces;

Fig. 2 (b) is the white Gaussian noise β that embodiments of the invention AWGN module produces;

The streamline schematic diagram that Fig. 3 is embodiments of the invention search approximate algorithm.

Fig. 4 is embodiments of the invention DAGC structural representation.

Embodiment

Below in conjunction with embodiment and accompanying drawing, the present invention is described in further detail, but embodiments of the present invention are not limited to this.

Embodiment

As shown in Figure 1, the present embodiment comprises: zero intermediate frequency input signal I+jQ, average energy computing module, amplitude factor search module, additive white Gaussian noise generation module (AWGN), noise summation module and digital Auto Gain module (DAGC).

Described zero intermediate frequency input signal I+jQ is connected with average energy computing module and amplitude factor search module respectively, and input signal can be obtained through A/D conversion or digital intermediate frequency input by analog signal;

Described average energy computing module is connected with zero intermediate frequency signals I+jQ and amplitude factor search module respectively;

Described amplitude factor search module is connected with zero intermediate frequency signals I+jQ input signal and AWGN module respectively, according to the signal to noise ratio snr of input, produces signal amplitude factors A, noise amplitude factor B;

Described AWGN module is connected with the amplitude factor search module, and the white Gaussian noise unit energy of generation is 1, and then obtains the multiple noise α that energy is 2+j β.

Described noise summation module is connected with zero intermediate frequency signals I+jQ, white Gaussian noise signal alpha+j β and DAGC module after amplitude adjusted respectively;

Described DAGC module is connected with the noise summation module, certain gain and the zero intermediate frequency signals that contain white Gaussian noise of output.

A kind of zero intermediate frequency white Gaussian noise of the present invention interpolation side comprises the following steps:

(1) zero intermediate frequency signals of input is averaging to energy power_avg: at first calculate the energy power of the zero intermediate frequency signals I+jQ of input, then the signal energy of a period of time is averaging to obtain to average signal energy power_avg.

(2) produced additive white Gaussian noise α and the β of unit energy by white Gaussian noise generation module, and then obtain the multiple noise α that energy is 2+j β.The implementation procedure of white Gaussian noise random number is as follows:

(2-1) according to the Tausworthe method, use the algorithm of matrixing to produce the uniform random number t on (0,1) _n.

Described uniform random number t _nrandom sequence x with L length _nfollowing relational expression is arranged:

$t_n=\underset{i=1}{\overset{L}{\mathrm{\&Sigma;}}}{x}_{\mathrm{ns}+i,1}\&CenterDot;2^{-i}$

Wherein, L and s are the non-zero positive integers, and L is random number output bit wide, and s is the saltus step step-length.Obviously, random number t _nwith binary sequence T _n=X _ns=(x _ns, x _ns+1..., x _ns+L-2, x _ns+L-1) corresponding one by one, therefore t in the present invention _nwith T _ndo not do special differentiation.

Described random sequence x _nrecurrence Relation as follows:

$x_n=a_1{x}_{n-1}\&CirclePlus;a_2{x}_{n-2}\&CirclePlus;\&CenterDot;\&CenterDot;\&CenterDot;\&CirclePlus;a_k{x}_{n-k}$

Wherein, a ₁, a ₂a _kfor proper polynomial P (z)=z ^k-a ₁z ^k-1-...-a _kcoefficient.

Described matrixing algorithm can produce a binary sequence T within a clock cycle _n,, thereby improving execution efficiency, concrete shift step is as follows:

(2-1-1) calculate transfer matrix A, expression formula is:

$A=\left[\begin{array}{cc}{C}_{k\&times;1}& I_{(L-1)\&times;(L-1)}\\ 0_{(L-k)\&times;(L-1)}& 0_{1\&times;1}\end{array}\right]$

Wherein, C _{k * 1}=[a ₁a ₂a _k] be the coefficient vector of proper polynomial P (Z), 0 _{(L-K) * (L-1)}, 0 _{1 * 1}for null matrix, I _{(L-1) * (L-1)}for unit matrix.

Described proper polynomial is got trinomial P (Z)=z ^k-z ^q-1.

(2-1-2) the transfer matrix A that uses step (2-1-1) to obtain and the L position state information X of current time _n=(x _n+L-1, x _n+L-2..., x _n+1, x _n), calculate next L position state information X constantly _n+1, i.e. X _n+1=X _n* A.

(2-1-3) according to the relational expression of step (2-1-2) and (2-1) in random number t _nwith binary sequence T _n=X _nsone-to-one relationship, can recursion obtain following relational expression:

T _n+1=X _(n+1)s=X _(n+1)s-1×A=(X _(n+1)s-2×A)×A=…=X _ns×A ^s=T _n×A ^s

(2-1-3) relational expression obtained according to step (2-1-3) can obtain a new random number t _n+1, its saltus step step-length is s.The random number producing method can be used following matrix notation:

T _n+1=T _n×A ^s

(2-2) produced the combined Tausworthe random number u of period expansion by J Tausworthe uniform random number _n.

Described combined Tausworthe random number relational expression is:

$u_n=\underset{i=1}{\overset{L}{\mathrm{\&Sigma;}}}(x_{\mathrm{ns}+i-\mathrm{1,1}}\&CirclePlus;x_{\mathrm{ns}+i-\mathrm{1,2}}\&CirclePlus;...\&CirclePlus;x_{\mathrm{ns}+i-1,J})\&CenterDot;2^{-i}$

Wherein,

be J independently random number sequence, when J=1, u _n=t _n.

(2-3) the random number called after produced according to two combined Tausworthe of step (2-2): u _{n, 1}and u _{n, 2}, produce thus gaussian random distribution number α and β.Relational expression is as follows:

$\mathrm{\&alpha;}=\sqrt{-2\&times;\ln \left(u_{n,1}\right)}\sin \left(2\mathrm{\&pi;}{u}_{n,2}\right)$

$\mathrm{\&beta;}=\sqrt{-2\&times;\ln \left(u_{n,1}\right)}\cos \left(2\mathrm{\&pi;}{u}_{n,2}\right)$

White Gaussian noise α and β that Fig. 2 (a) and Fig. 2 (b) produce for the present invention.

(3) the multiple noise α that the energy that the average signal energy power_avg obtained according to given signal to noise ratio snr and step (1) and step (2) obtain is 2+j β, search for corresponding signal amplitude factors A and noise amplitude factor B.

Described signal to noise ratio

wherein b=(2 ^cnt_shift) ²; a=y ²1, and a<4; Cnt_shift is the multiple bit number that noise signal α+j β is moved to the left, and multiplication factor is 2 ^cnt_shift, the energy multiplication factor is b=(2 ^cnt_shift) ²; Y is the signal amplitude multiplication factor, and energy amplifies y ².

Described signal amplitude factors A=y, noise amplitude factor B=2 ^cnt_shift.

Described search step is as follows:

(3-1) search amplitude factor B: at first making the signal amplitude factor is 1, i.e. a=y ²=1, then cnt_shift increases progressively by 0, until meet

till, obtain cnt_shift and B=2 ^cnt_shift.

(3-2) search signal amplitude factor A: a is started by fixing multiple x by 1 ²the mode increased progressively is searched for a, until meet $\mathrm{snr}\&le;\frac{a\&times;\mathrm{power}\_\mathrm{avg}}{2b},$ Complete and search for $A=y=\sqrt{a}.$

The multiple that described fixedly multiple x is amplitude factor y, its choosing method is: the supposition fixedly quantizing bit number of multiple incremental change x is BITS, and the value after taken amount is X=2 ^bITS+ 1,

while getting BITS=11, x=1.00048828125, now energy increases progressively 0.004dB at every turn, and the energy error is in 0.004dB.

Described choosing method, when BITS is enough large,

therefore the incremental change of signal energy can be expressed as $\mathrm{power}\_\mathrm{avg}\&CenterDot;x^2=\mathrm{power}\_\mathrm{avg}+\frac{\mathrm{power}\_\mathrm{avg}}{2^{(\mathrm{BITS}-1)}}.$ This method for expressing is only used adder and shift register just can complete multiplying, has saved the hardware logic resource.

As shown in Figure 3, the present invention has designed a line level aggregated(particle) structure for realizing the search approximate algorithm.

(4) zero intermediate frequency input signal I+jQ is multiplied by signal amplitude factors A that step (3) the obtains signal SIG after adjusted; The multiple noise signal α obtained by step (2)+j β is multiplied by noise amplitude B that step (3) the obtains noise NOISE after adjusted; Signal SIG and noise NOISE obtain adding the signal SIG_NOISE made an uproar through the noise summation module.

(5) SIG_NOISE that as shown in Figure 4, digital Auto Gain module DAGC obtains step (4) according to given thresholding R automatically adjusts.Step is as follows: the energy that at first carries out signal calculates E _y(n), and by logarithmic transformation energy value is converted into to logarithmic form E _y1(n); Pass through E _y1(n) with the comparison of threshold value R, and the subtraction that carries out logarithm obtains ε (n); Then through antilogarithm, computing obtains amplitude gain regulated value G; Finally by crossing a multiplier and upper amplitude gain G constantly _n-1the gain G in the current moment multiplies each other to obtain _n.The mean effort of integrator can be cut noise reduced interference.

Described step can mean by following calculating formula:

E _y(n)=I ²+Q ²

E _y1(n)=log ₂(I ²+Q ²)

$\mathrm{\&epsiv;}\left(n\right)=\frac{1}{2}[{\log }_{2}R-{\log }_2(I^2+Q^2)]$

G=2 ^ε(n)

G _n=G×G _n-1

The device of a kind of zero intermediate frequency white Gaussian noise adding method of the present invention, used the FPGA(field programmable logic array) realize that the throughput of generation random number is more than 400 times that software realization mode produces the random number throughput; The search approximate algorithm realizes that energy error is in 0.004dB; This device increases the automatic gain module, is convenient to transplant, and can be used for detecting in signal source system the interference free performance of Wireless Telecom Equipment or satellite broadcasting receiving equipment.

A kind ofly realize that the device of described zero intermediate frequency white Gaussian noise adding method comprises:

Zero intermediate frequency input signal I+jQ, average energy computing module, amplitude factor search module, additive white Gaussian noise generation module (AWGN), noise summation module, digital Auto Gain module (DAGC).

Described zero intermediate frequency input signal I+jQ is connected with average energy computing module and amplitude factor search module respectively, and input signal can be obtained through A/D conversion or digital intermediate frequency input by analog signal;

Described average energy computing module is connected with zero intermediate frequency signals I+jQ and amplitude factor search module respectively;

Described amplitude factor search module is connected with zero intermediate frequency signals I+jQ input signal and AWGN module respectively, according to the signal to noise ratio snr of input, produces signal amplitude factors A, noise amplitude factor B;

Described AWGN module is connected with the amplitude factor search module, and the white Gaussian noise unit energy of generation is 1, and then obtains the multiple noise α that energy is 2+j β.

Described noise summation module is connected with zero intermediate frequency signals I+jQ, white Gaussian noise signal alpha+j β and DAGC module after amplitude adjusted respectively;

Described DAGC module is connected with the noise summation module, certain gain and the zero intermediate frequency signals that contain white Gaussian noise of output.

Above-described embodiment is preferably execution mode of the present invention; but embodiments of the present invention are not restricted to the described embodiments; other any do not deviate from change, the modification done under Spirit Essence of the present invention and principle, substitutes, combination, simplify; all should be equivalent substitute mode, within being included in protection scope of the present invention.

Claims (6)

1. a zero intermediate frequency white Gaussian noise adding method, is characterized in that, comprises the steps:

(1) zero intermediate frequency signals of input is averaging to energy power_avg: at first calculate the energy power of the zero intermediate frequency signals I+jQ of input, then the signal energy of a period of time is averaging to obtain to average signal energy power_avg;

(2) produced additive white Gaussian noise α and the β of unit energy by white Gaussian noise generation module, and then obtain the multiple noise α that energy is 2+j β;

(3) the multiple noise α that the energy that the average signal energy power_avg obtained according to given signal to noise ratio snr and step (1) and step (2) obtain is 2+j β, search for corresponding signal amplitude factors A and noise amplitude factor B;

Described signal to noise ratio

wherein b=(2 ^cnt_shift) ²; a=y ²1, and a<4; Cnt_shift is the multiple bit number that noise signal α+j β is moved to the left, and multiplication factor is 2 ^cnt_shift, the energy multiplication factor is b=(2 ^cnt_shift) ²; Y is the signal amplitude multiplication factor, and energy amplifies y ²;

Described signal amplitude factors A=y, noise amplitude factor B=2 ^cnt_shift;

(4) zero intermediate frequency input signal I+jQ is multiplied by signal amplitude factors A that step (3) the obtains signal SIG after adjusted; The multiple noise signal α obtained by step (2)+j β is multiplied by noise amplitude B that step (3) the obtains noise NOISE after adjusted; Signal SIG and noise NOISE obtain adding the signal SIG_NOISE made an uproar through the noise summation module;

(5) SIG_NOISE that digital Auto Gain module DAGC obtains step (4) according to given thresholding R automatically adjusts.

2. a kind of zero intermediate frequency white Gaussian noise adding method according to claim 1, is characterized in that, in step (2), the implementation method of white Gaussian noise random number is as follows:

(2-1) according to the Tausworthe method, use the algorithm of matrixing to produce the uniform random number t on (0,1) _n;

Described uniform random number t _nrandom sequence x with L length _nfollowing relational expression is arranged:

$t_n=\underset{i=1}{\overset{L}{\mathrm{\&Sigma;}}}{x}_{\mathrm{ns}+i-1}\&CenterDot;2^{-i},$

Wherein, L and s are the non-zero positive integers, and L is random number output bit wide, and s is the saltus step step-length; Obviously, random number t _nwith binary sequence T _n=X _ns=(x _ns, x _ns+1..., x _ns+L-2, x _ns+L-1) corresponding one by one, therefore t in the present invention _nwith T _ndo not do special differentiation;

Described random sequence x _nrecurrence Relation as follows:

$x_n=a_1{x}_{n-1}\&CirclePlus;a_2{x}_{n-2}\&CirclePlus;\&CenterDot;\&CenterDot;\&CenterDot;\&CirclePlus;a_k{x}_{n-k}$

Wherein, a ₁, a ₂a _kfor proper polynomial P (z)=z ^k-a ₁z ^k-1-...-a _kcoefficient;

(2-2) produced the combinedTausworthe random number u of period expansion by J Tausworthe uniform random number _n;

Described combined Tausworthe random number relational expression is:

$u_n=\underset{i=1}{\overset{L}{\mathrm{\&Sigma;}}}(x_{\mathrm{ns}+i-\mathrm{1,1}}\&CirclePlus;x_{\mathrm{ns}+i-\mathrm{1,2}}\&CirclePlus;...x_{\mathrm{ns}+i-1,J})\&CenterDot;2^{-i}$

Wherein,

be J independently random number sequence, when J=1, u _n=t _n;

(2-3) the random number called after produced according to two combined Tausworthe of step (2-2): u _{n, 1}and u _{n, 2}, produce thus gaussian random distribution number α and β; Relational expression is as follows:

$\mathrm{\&alpha;}=\sqrt{-2\&times;\ln \left(u_{n,1}\right)}\sin \left(2\mathrm{\&pi;}{u}_{n,2}\right)$

$\mathrm{\&beta;}=\sqrt{-2\&times;\ln \left(u_{n,1}\right)}\cos \left(2\mathrm{\&pi;}{u}_{n,2}\right).$

3. a kind of zero intermediate frequency white Gaussian noise adding method according to claim 2, is characterized in that, it is characterized in that, in step (2-1), described matrixing algorithm can produce a binary sequence T within a clock cycle _nthereby, improving execution efficiency, concrete shift step is as follows:

(2-1-1) calculate transfer matrix A, expression formula is:

$A=\left[\begin{array}{cc}{C}_{k\&times;1}& I_{(L-1)\&times;(L-1)}\\ 0_{(L-k)\&times;(L-1)}& 0_{1\&times;1}\end{array}\right],$

Wherein, C _{k * 1}=[a ₁a ₂a _k] be the coefficient vector of proper polynomial P (Z), 0 _{(L-K) * (L-1)}, 0 _{1 * 1}for null matrix, I _{(L-1) * (L-1)}for unit matrix;

Described proper polynomial is got trinomial P (Z)=z ^k-z ^q-1;

(2-1-2) the transfer matrix A that uses step (2-1-1) to obtain and the L position state information X of current time _n=(x _n+L-1, x _n+L-2..., x _n+1, x _n), calculate next L position state information X constantly _n+1, i.e. X _n+1=X _n* A;

(2-1-3) according to the relational expression of step (2-1-2) and (2-1) in random number t _nwith binary sequence T _n=X _nsone-to-one relationship, can recursion obtain following relational expression:

T _n+1=X _(n+1)s=X _(n+1)s-1×A=(X _(n+1)s-2×A)×A=…=X _ns×A ^s=T _n×A ^s

(2-1-4) relational expression obtained according to step (2-1-3) can obtain a new random number t _n+1, its saltus step step-length is s; The random number producing method can be used following matrix notation:

T _n+1=T _n×A ^s。

4. a kind of zero intermediate frequency white Gaussian noise adding method according to claim 1, is characterized in that, it is characterized in that, the described search approximate algorithm of step (3) comprises the following steps:

(3-1) search amplitude factor B: at first making the signal amplitude factor is 1, i.e. a=y ²=1, then cnt_shift increases progressively by 0, until meet

till, obtain cnt_shift and B=2 ^cnt_shift;

(3-2) search signal amplitude factor A: a is started by fixing multiple x by 1 ²the mode increased progressively is searched for a, until meet $\mathrm{snr}\&le;\frac{a\&times;\mathrm{power}\_\mathrm{avg}}{2b},$ Complete and search for $A=y=\sqrt{a};$

The multiple that described fixedly multiple x is amplitude factor y, its choosing method is: the supposition fixedly quantizing bit number of multiple incremental change x is BITS, and the value after taken amount is X=2 ^bITS+ 1,

while getting BITS=11, x=1.00048828125, now energy increases progressively 0.004dB at every turn, and the energy error is in 0.004dB;

Described choosing method, when BITS is enough large,

therefore the incremental change of signal energy can be expressed as $\mathrm{power}\_\mathrm{avg}\&CenterDot;x^2=\mathrm{power}\_\mathrm{avg}+\frac{\mathrm{power}\_\mathrm{avg}}{2^{(\mathrm{BITS}-1)}}.$

5. a kind of zero intermediate frequency white Gaussian noise adding method according to claim 1, is characterized in that, it is characterized in that, described step (5) is specially:

(5-1) energy that carries out signal calculates Ey (n), and by logarithmic transformation, energy value is converted into to logarithmic form E _y1(n); Pass through E _y1(n) with the comparison of threshold value R, and the subtraction that carries out logarithm obtains ε (n);

(5-2) through antilogarithm, computing obtains amplitude gain regulated value G;

(5-3) finally by crossing a multiplier and upper amplitude gain G constantly _n-1the gain G in the current moment multiplies each other to obtain _n.

6. according to the device of the described a kind of zero intermediate frequency white Gaussian noise adding method of any one in claim 1-5, it is characterized in that, comprise zero intermediate frequency signals input module, average energy computing module, amplitude factor search module, additive white Gaussian noise generation module AWGN, noise summation module, digital Auto Gain module DAGC;

The zero intermediate frequency signals input module is for being connected with average energy computing module and amplitude factor search module, and input signal is obtained through A/D conversion or digital intermediate frequency input by analog signal;

Described average energy computing module is connected with zero intermediate frequency signals input module and amplitude factor search module respectively;

Described amplitude factor search module is connected with zero intermediate frequency signals input module and AWGN module respectively, according to the signal to noise ratio snr of input, produces signal amplitude factors A, noise amplitude factor B;

Described AWGN module is connected with the amplitude factor search module, and the white Gaussian noise unit energy of generation is 1, and then obtains the multiple noise α that energy is 2+j β;

Described noise summation module is connected with zero intermediate frequency signals I+jQ, white Gaussian noise signal alpha+j β and DAGC module after amplitude adjusted respectively;

Described DAGC module is connected with the noise summation module, certain gain and the zero intermediate frequency signals that contain white Gaussian noise of output.

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