CN101087281A - A measurement method and device of orthogonal range modulation N/S ratio and N/R ratio - Google Patents

Abstract

The invention provides measuring method of Signal-to-Noise and breadth-to-noise modulated by orthogonal extend, it includes steps: receive data and filter to output symbol; demodulate said symbol and output; output evaluating signal power and noise power based on said soft judge; obtain Signal-to-Noise and breadth-to-noise based on said signal power and noise power. Besides, the invention also provides measuring device which is used to realize said measuring method which includes matching filter unit which is used to match and filter said data received, output symbol; demodulation and soft judge unit which is used to demodulate said symbol to obtain soft judge to output; and evaluating unit which is used to output evaluating signal power and noise power based on said soft judge to obtain Signal-to-Noise and breadth-to-noise. The invention can avoid hard judge of constellation point, calculation can be simplified greatly.

Description

A kind of signal to noise ratio of quadrature amplitude modulation and the method for measurement of amplitude and noise ratio and device

Technical field

The present invention relates to communication field, relate in particular to a kind of signal to noise ratio of quadrature amplitude modulation and the method for measurement and the device of amplitude and noise ratio.

Background technology

Quadrature amplitude modulation (QAM, Quadrature Amplitude Modulation) be to use the two-way quadrature carrier to modulate the parallel signal of two-way respectively, can equivalence be that two-way is carried on the pulse amplitude modulation (PAM on the quadrature carrier respectively, pulse amplitude modulation), QAM has obtained using widely in the 3G system.

The QAM modulation that the 3G mobile communication system is used has two kinds of quaternary phase shift keying modulation QPSK and 16 rank quadrature amplitude modulation 16QAM.Wherein, 16QAM modulation is a kind of high order modulation, can the more efficient use bandwidth, support high rate data transmission, the high speed downlink packet of introducing in 3GPP R5 inserts (HSDPA, High Speed Downlink Packet Access) technology and has just used the 16QAM modulation system.

The standard that 3GPP formulates comprises TD-SCDMA and WCDMA.16QAM planisphere in TD-SCDMA system and the WCDMA system respectively as depicted in figs. 1 and 2.As can be seen from the figure, in the TD-SCDMA system in the planisphere of 16QAM and the WCDMA system planisphere of 16QAM slightly different, the planisphere of WCDMA can turn clockwise through the planisphere of TD-SCDMA 45 ° and obtain.In some cases, as obtain channel quality indication (CQI, the Channel Quality Indicator) feedback of HSDPA according to signal to noise ratio (SNR, Signal to Noise Ratio) measurement result; Or according to amplitude and noise ratio (ANR, Amplitude Noise Ratio) and SNR be soft inputting and soft judgement output (SISO, when SoftInput Soft Output) decoder generates soft-decision output, need quality to received signal to estimate, carry out the measurement of signal to noise ratio or amplitude and noise ratio.

The existing method of measuring SNR and ANR is to use matched filtering (MF, Matched Filter) or joint-detection (JD, Joint Detection) Shu Chu symbol (specific constellation point on this symbol constellation figure), utilize maximum likelihood (ML, Maximum likelihood) power and the noise of method difference estimating received signal, thereby the estimated value of calculating SNR and ANR.

The system block diagram of prior art scheme is shown in 3: chip (chip) the level data of the 31 pairs of receptions in matched filtering unit are carried out matched filtering, output symbol stream (Symbols).Symbol by the 32 pairs of inputs in hard decision unit carries out hard decision.Hard decision is sought the constellation point apart from the Euclidean distance minimum of receiving symbol, and the receiving symbol judgement is this constellation point.ML estimation unit 33 utilizes hard decision result and receiving symbol to estimate principle estimating received signal amplitude, power and noise power according to ML, thereby can obtain the estimated value of SNR and ANR.In addition, the symbol of matched filtering output also is used to separate mediation soft judge unit 34 to carry out demodulation and exports the soft-decision result, to be used for the decoding of follow-up decoder.

As shown in Figure 4, the basic process of existing method of measurement is:

Step 401: receive data through matched filtering, output symbol (symbols);

Step 402: output symbol process hard decision corresponds to the constellation point on the planisphere;

Step 403:, utilize the preceding symbol of judgement and its corresponding hard decision constellation point power estimator signal and noise power based on the ML criterion;

Step 404: obtain SNR/ANR according to estimated signals power and noise power calibration.

Wherein, method in common is to calculate the Euclidean distance of current sign and all constellation point respectively in the step 402, and the constellation point of choosing the Euclidean distance minimum then is as the hard decision constellation point, but realizes that like this amount of calculation is very big.For the 16QAM planisphere that the WCDMA system uses, also can carry out the hard decision on I road and Q road respectively, the hard decision result on comprehensive then I road and Q road obtains the constellation point of hard decision, and amount of calculation can be slightly smaller.And for the 16QAM planisphere of TD-SCDMA system, can by rotation transformation the 16QAM planisphere of WCDMA system, realize the hard decision of constellation point then according to the mode of the 16QAM of WCDMA system planisphere, but this scheme be rotated with the amount of calculation of hard decision operation very big to receiving symbol.Therefore, the prior art scheme realizes very complicatedly for the TD-SCDMA system, and amount of calculation is bigger.

Summary of the invention

The technical problem to be solved in the present invention is to provide a kind of signal to noise ratio of quadrature amplitude modulation and the method for measurement and the device of amplitude and noise ratio, can avoid the hard decision of constellation point, thereby amount of calculation is simplified greatly.

The technical scheme that realizes the object of the invention is as follows:

A kind of signal to noise ratio of quadrature amplitude modulation and the method for measurement of amplitude and noise ratio comprise step:

Receive data through the matched filtering output symbol;

Described symbol is carried out demodulation obtain soft-decision output;

Based on described soft-decision output power estimator signal and noise power;

Obtain signal to noise ratio and amplitude and noise ratio according to described signal power and noise power calibration.

Wherein, the soft-decision of described symbol i output S _iBe to obtain: S according to following expression formula _i=f (i), wherein f (i) is the computational process of log-likelihood ratio or logarithm maximum posteriori probability.

Obtain soft-decision output S according to two Most Significant Bits in the described output symbol _i

According to soft-decision output S _iCalculate acquisition

| H _I1|, and obtain signal power valuation SNR according to maximum likelihood method ^(UE)With noise power valuation ANR ^(UE)

Preferably, described quadrature amplitude modulation is 16QAM.

In addition, the present invention also provides a kind of signal to noise ratio of quadrature amplitude modulation and the measurement mechanism of amplitude and noise ratio, comprising:

The matched filtering unit is used for carrying out matched filtering, output symbol to receiving data;

Separate the mediation soft judge unit, be used for described symbol is carried out demodulation, obtain soft-decision output;

Estimation unit be used for based on described soft-decision output power estimator signal and noise power, thereby calibration obtains signal to noise ratio and amplitude and noise ratio.

Wherein, the described mediation soft judge unit of separating obtains soft-decision output Si:Si=f (i) to symbol i according to following expression formula, and wherein f (i) is the computational process of log-likelihood ratio or logarithm maximum posteriori probability.

Described separating is in harmonious proportion soft judge unit according to two Most Significant Bits acquisition soft-decision output S in the described output symbol _i

Wherein, described estimation unit is exported S to soft-decision _iCalculate acquisition

| H _I1|, and obtain signal power valuation SNR according to maximum likelihood method ^(UE)With noise power-value ANR ^(UE)

Preferably, described quadrature amplitude modulation is 16QAM.

Compared with prior art, technical scheme provided by the invention has following beneficial effect:

1, the present invention adopts and separates the soft-decision output that the mediation soft judge unit generates, and as the basis of ML criterion estimation, thereby avoids carrying out hard decision.Be mainly reflected on the hard decision owing to measure the operand that calculates acquisition signal to noise ratio and amplitude and noise ratio, and the hard decision realization is very complicated, based on the consideration of measurement accuracy, need again a large amount of receiving symbols are added up, so amount of calculation adds up considerable.Especially in the TD-SCDMA system,, realize that merely the amount of calculation (complex multiplication) of rotating is also very big even according to the mode of dividing the two-way judgement after the simplest rotation.Therefore, the present invention is by avoiding the hard decision process of constellation point, thereby amount of calculation is simplified greatly.

2, prior art adopts the symbol to matched filtering output to carry out hard decision, again hard decision output is carried out signal power and noise power estimation based on the ML criterion; And the soft-decision after the demodulation is exported the decoding that only is used for follow-up decoder in the prior art.The present invention makes full use of the output of the soft-decision after the demodulation in the prior art, and with this basis of directly estimating as ML, and need not to carry out hard decision.Its principle is that soft-decision output comprises the more information amount than hard decision output, can obtain any by the hard decision acquired information by soft-decision output.Therefore, the present invention directly utilizes the information of soft-decision output, is used for SNR/ANR and estimates, thereby avoid complicated calculation amount in the hard decision, realizes simplifying computation purpose.

3, core concept of the present invention is to utilize soft-decision output to realize the estimation of SNR/ANR, and then obtains SNR/ANR.Because it is also different that the planisphere difference of system's correspondence, prior art are directly carried out the complexity that SNR/ANR estimates according to output symbol; But by technical solution of the present invention, all be suitable for, and amount of calculation is all the same for any planisphere.

Below in conjunction with the drawings and specific embodiments the present invention is further described.

Description of drawings

Fig. 1 is the 16QAM planisphere of TD-SCDMA system;

Fig. 2 is the 16QAM planisphere of WCDMA system;

Fig. 3 is the system block diagram of prior art scheme;

Fig. 4 is the flow chart of prior art scheme;

Fig. 5 is the present invention program's a system block diagram;

Fig. 6 is the present invention program's a flow chart.

Embodiment

System block diagram of the present invention is shown in 5: matched filtering unit 51 or joint-detection unit carry out matched filtering, output symbol (Symbols) to chip (chip) the level data that receive.Carry out soft-decision by the symbol of separating 52 pairs of inputs of mediation soft judge unit, estimation unit 53 utilizes through the output of the soft-decision after the demodulation again, estimate principle estimating received signal power and noise power based on ML, obtaining the estimated value of SNR and ANR, thereby realize the measurement of SNR and ANR.Wherein, in the TD-SCDMA system, also carried out joint-detection after the matched filtering and disturbed to eliminate.

Wherein, matched filtering unit 51, separate that to be in harmonious proportion the realization of soft judge unit 52 identical with the prior art scheme.Different is that the input of SNR/ANR estimation unit no longer is the hard decision output to the matched filtering output symbol, but separates the soft-decision output that the mediation soft judge unit generates.

Below, describe the employing technical solution of the present invention in detail based on WCDMA and TD-SCDMA system in conjunction with Fig. 6, measure the process of SNR and ANR.

Step 601: receive data through matched filtering, output symbol (symbols);

Step 602: described symbol is carried out demodulation obtain soft-decision output;

Step 603: based on described soft-decision output power estimator signal and noise power;

Step 604: obtain signal to noise ratio and amplitude and noise ratio according to described signal power and noise power calibration.

In fact, method of measurement of the present invention just is equivalent to reduce the process of carrying out hard decision for symbol compared with prior art, but the soft-decision output that demodulation obtains is estimated.Soft-decision output is directly related with the position of the point of receiving symbol on planisphere, soft-decision output has comprised the positional information of all these receiving symbols on planisphere, therefore the SNR/ANR that carries out according to receiving symbol estimates, the output according to soft-decision of equivalence obtains fully.The present invention just is being based on this thinking, thereby realizes the signal to noise ratio of amount of calculation simplification and the measurement of amplitude and noise ratio.

Suppose that incoming symbol is i ₁q ₁i ₂q ₂Four bits, when then being mapped to planisphere shown in Figure 2, I road component be real part only and i ₁And i ₂Relevant, Q road component be imaginary part only and q ₁And q ₂Relevant.Suppose that received signal is expressed as R=x+jy on planisphere, its I branch component is x, and the Q branch component is y, by preceding analysis as can be known, and i ₁And i ₂Soft-decision output can obtain q according to I branch component x independently ₁And q ₂Soft-decision output can obtain according to Q branch component y independently.

With most significant bit i ₁Be example, the amplitude of transmitting terminal I component has four kinds of values $\{-\frac{3}{2}d,-\frac{1}{2}d,\frac{1}{2}d,\frac{3}{2}d\}$ (d represents the shortest Euclidean distance in the planisphere, for the standard 16QAM planisphere in the TD-SCDMA system $d=\frac{2}{\sqrt{10}}$ ), the prior probability of these four kinds of values equates, is 1/4.Work as i ₁Value is 1 o'clock, the value of transmitting terminal I component can for

Work as i ₁Value is 0 o'clock, the value of transmitting terminal I component can for

For the transmitting terminal I component that knows, the distribution of receiving terminal I component meets Gaussian Profile, and its probability density satisfies following formula:

$y=\frac{1}{\sqrt{2\mathrm{\π}}\mathrm{\σ}}\exp [-\frac{E_b}{{2\mathrm{\σ}}^2}{(x-\mathrm{\μ})}^2]$

Wherein, μ is an average, and σ is a standard deviation.

Four kinds of values for the transmitting terminal I component $\{-\frac{3}{2}d,-\frac{1}{2}d,\frac{1}{2}d,\frac{3}{2}d\},$ σ is identical, respectively corresponding these the four kinds of values of μ.

Received signal R=x+jy is designated as incident A, i ₁Value is 1 to be designated as incident B, i ₁Value is 0 to be designated as incident C.

Then posterior probability P (B|A) satisfies:

$P\left(B\right|A)=\frac{P\left(\mathrm{AB}\right)}{P\left(A\right)}$

$=\frac{\frac{1}{4}\left\{\frac{1}{\sqrt{2\mathrm{\&pi;}}\mathrm{\&sigma;}}\exp \right[-\frac{E_b}{{2\mathrm{\&sigma;}}^2}{(x+\frac{3}{2}d)}^2]+\frac{1}{\sqrt{2\mathrm{\&pi;}}\mathrm{\&sigma;}}\exp [-\frac{E_b}{{2\mathrm{\&sigma;}}^2}{(x+\frac{1}{2}d)}^2\left]\right\}\&CenterDot;\mathrm{\&Delta;<figure-callout\; id="1"\; label="d"\; filenames="A20061008354600151.png"\; state="\{\{state\}\}">d</figure-callout>}}{\frac{1}{4}\left\{\frac{1}{\sqrt{2\mathrm{\&pi;}}\mathrm{\&sigma;}}\exp \right[-\frac{E_b}{{2\mathrm{\&sigma;}}^2}{(x+\frac{3}{2}d)}^2+\frac{1}{\sqrt{2\mathrm{\&pi;}}\mathrm{\&sigma;}}\exp [-\frac{E_b}{{2\mathrm{\&sigma;}}^2}{(x+\frac{1}{2}d)}^2]+\frac{1}{\sqrt{2\mathrm{\&pi;}}\mathrm{\&sigma;}}\exp [-\frac{E_b}{{2\mathrm{\&sigma;}}^2}{(x-\frac{1}{2}d)}^2]+\frac{1}{\sqrt{2\mathrm{\&pi;}}\mathrm{\&sigma;}}\exp [-\frac{E_b}{{2\mathrm{\&sigma;}}^2}{(x-\frac{3}{2}d)}^2]\}\&CenterDot;\mathrm{\&Delta;d}}$

$=\frac{\exp [-\frac{E_b}{{2\mathrm{\&sigma;}}^2}{(x+\frac{3}{2}d)}^2]+\exp [-\frac{E_b}{{2\mathrm{\&sigma;}}^2}{(x+\frac{1}{2}d)}^2]}{\exp [-\frac{E_b}{2{\mathrm{\&sigma;}}^2}{(x+\frac{3}{2}d)}^2+\exp [-\frac{E_b}{{2\mathrm{\&sigma;}}^2}{(x+\frac{1}{2}d)}^2+\exp [-\frac{E_b}{{2\mathrm{\&sigma;}}^2}{(x-\frac{1}{2}d)}^2+\exp [-\frac{E_b}{{2\mathrm{\&sigma;}}^2}{(x-\frac{3}{2}d)}^2}---\left(1\right)$

Posterior probability P (C|A) satisfies:

$P\left(C\right|A)=\frac{P\left(\mathrm{CA}\right)}{P\left(A\right)}$

$=\frac{\frac{1}{4}\left\{\frac{1}{\sqrt{2\mathrm{\&pi;}}\mathrm{\&sigma;}}\exp \right[-\frac{E_b}{{2\mathrm{\&sigma;}}^2}{(x-\frac{3}{2}d)}^2]+\frac{1}{\sqrt{2\mathrm{\&pi;}}\mathrm{\&sigma;}}\exp [-\frac{E_b}{{2\mathrm{\&sigma;}}^2}{(x-\frac{1}{2}d)}^2\left]\right\}\&CenterDot;\mathrm{\&Delta;<figure-callout\; id="1"\; label="d"\; filenames="A20061008354600151.png"\; state="\{\{state\}\}">d</figure-callout>}}{\frac{1}{4}\left\{\frac{1}{\sqrt{2\mathrm{\&pi;}}\mathrm{\&sigma;}}\exp \right[-\frac{E_b}{{2\mathrm{\&sigma;}}^2}{(x+\frac{3}{2}d)}^2+\frac{1}{\sqrt{2\mathrm{\&pi;}}\mathrm{\&sigma;}}\exp [-\frac{E_b}{{2\mathrm{\&sigma;}}^2}{(x+\frac{1}{2}d)}^2]+\frac{1}{\sqrt{2\mathrm{\&pi;}}\mathrm{\&sigma;}}\exp [-\frac{E_b}{{2\mathrm{\&sigma;}}^2}{(x-\frac{1}{2}d)}^2]+\frac{1}{\sqrt{2\mathrm{\&pi;}}\mathrm{\&sigma;}}\exp [-\frac{E_b}{{2\mathrm{\&sigma;}}^2}{(x-\frac{3}{2}d)}^2]\}\&CenterDot;\mathrm{\&Delta;d}}$

$=\frac{\exp [-\frac{E_b}{{2\mathrm{\&sigma;}}^2}{(x-\frac{3}{2}d)}^2]+\exp [-\frac{E_b}{{2\mathrm{\&sigma;}}^2}{(x-\frac{1}{2}d)}^2]}{\exp [-\frac{E_b}{2{\mathrm{\&sigma;}}^2}{(x+\frac{3}{2}d)}^2+\exp [-\frac{E_b}{{2\mathrm{\&sigma;}}^2}{(x+\frac{1}{2}d)}^2+\exp [-\frac{E_b}{{2\mathrm{\&sigma;}}^2}{(x-\frac{1}{2}d)}^2+\exp [-\frac{E_b}{{2\mathrm{\&sigma;}}^2}{(x-\frac{3}{2}d)}^2}---\left(2\right)$

According to log-likelihood ratio algorithm (LLR, Logarithmic Likelihood Ratio), then i ₁Soft-decision output S _I1Can choose in accordance with the following methods:

${\mathrm{<figure-callout\; id="1"\; label="S\; i"\; filenames="A20061008354600151.png"\; state="\{\{state\}\}">S</figure-callout>}}_{i_{}}=\log \frac{P\left(C\right|A)}{P\left(B\right|A)}---\left(3\right)$

S _I1For on the occasion of the time, the expression i ₁Get 0 posterior probability greater than the posterior probability of getting 1, be worth greatly more, it is big more that 0 posterior probability is got in expression; During for negative value, expression i ₁Get 1 posterior probability greater than the posterior probability of getting 0, be worth more for a short time, it is big more that 1 posterior probability is got in expression.Need to prove that molecule and denominator can exchange in the formula, only need the sign map relation of corresponding modify decoding and hard decision to get final product, essence did not influence of the present invention.

Formula (1) and (2) substitution formula (3) can be got:

${\mathrm{<figure-callout\; id="1"\; label="S\; i"\; filenames="A20061008354600151.png"\; state="\{\{state\}\}">S</figure-callout>}}_{i_{}}=\log \left(\exp \right[-\frac{E_b}{{2\mathrm{\&sigma;}}^2}{(x-\frac{3}{2}d)}^2]+\exp [-\frac{E_b}{{2\mathrm{\&sigma;}}^2}{(x-\frac{1}{2}d)}^2\left]\right)-\log \left(\exp \right[-\frac{E_b}{{2\mathrm{\&sigma;}}^2}{(x+\frac{3}{2}d)}^2]+\exp [-\frac{E_b}{{2\mathrm{\&sigma;}}^2}{(x+\frac{1}{2}d)}^2\left]\right)---\left(4\right)$

Use the MAX-log mode approximate, formula (4) can abbreviation be

${\mathrm{<figure-callout\; id="1"\; label="S\; i"\; filenames="A20061008354600151.png"\; state="\{\{state\}\}">S</figure-callout>}}_{i_{}}\&ap;\mathrm{MAX}(-\frac{E_b}{{2\mathrm{\&sigma;}}^2}{(x-\frac{3}{2}d)}^2,-\frac{E_b}{{2\mathrm{\&sigma;}}^2}{(x-\frac{1}{2}d)}^2)-\mathrm{MAX}(-\frac{E_b}{{2\mathrm{\&sigma;}}^2}{(x+\frac{3}{2}d)}^2,-\frac{E_b}{{2\mathrm{\&sigma;}}^2}{(x+\frac{1}{2}d)}^2)$

$=\left\{\begin{array}{cc}\frac{E_b}{{2\mathrm{\&sigma;}}^2}(4d\&CenterDot;x+{2d}^2),& x<-d\\ \frac{E_b}{{2\mathrm{\&sigma;}}^2}\&CenterDot;2d\&CenterDot;x,& -d\&le;x\&le;d\\ \frac{E_b}{{2\mathrm{\&sigma;}}^2}(4d\&CenterDot;x-{2d}^2),& x>d\end{array}---\left(5\right)\right.$

Wherein,

2 and d be common positive coefficient, can cast out, simple table is shown:

${\mathrm{<figure-callout\; id="1"\; label="S\; i"\; filenames="A20061008354600151.png"\; state="\{\{state\}\}">S</figure-callout>}}_{i_{}}=\left\{\begin{array}{cc}2x+d,& x<-d\\ x,& -d\&le;x\&le;d\\ 2x-d,& x>d\end{array},---\left(6\right)\right.$

Same, can obtain i ₂Soft-decision is output as:

$S_{i_2}=\left\{\begin{array}{cc}d+x,& x<0\\ d-x,& x\&GreaterEqual;0\end{array}\right.---\left(7\right)$

Analysis for the Q branch component is identical with the I branch component, obtains q equally ₁And q ₂Soft-decision output value be respectively:

${\mathrm{<figure-callout\; id="1"\; label="S\; q"\; filenames="A20061008354600151.png"\; state="\{\{state\}\}">S</figure-callout>}}_{q_{}}=\left\{\begin{array}{cc}2y+d,& y<-d\\ y,& -d\&le;y\&le;d\\ 2y-d,& y>d\end{array}\right.---\left(8\right)$

$S_{q_2}=\left\{\begin{array}{cc}d+y,& y<0\\ d-y,& y\&GreaterEqual;0\end{array}---\left(9\right)\right.$

Because the 16QAM planisphere that uses in the TD-SCDMA system can rotate 45 ° of acquisitions by the 16QAM planisphere that uses among the WCDMA, so the conclusion that the output of the soft-decision of TD-SCDMA system can utilize preamble to release is obtained by coordinate transform.If the coordinate that the 16QAM planisphere in the WCDMA system uses is X, Y, the coordinate that the 16QAM planisphere in the TD-SCDMA system uses is x, y, then has:

$(x+\mathrm{jy})\&CenterDot;e^{-j\frac{\mathrm{\&pi;}}{4}}=X+\mathrm{jY}$

Thereby have

$\left\{\begin{array}{c}X=\frac{\sqrt{2}}{2}(x+y)\\ Y=\frac{\sqrt{2}}{2}(y-x)\end{array}\right.$

Substitution formula (6)～formula (9) and will export divided by

Carry out normalization, promptly can obtain the soft-decision output computing formula of the 16QAM demodulation among the TD-SCDMA:

${\mathrm{<figure-callout\; id="1"\; label="S\; i"\; filenames="A20061008354600151.png"\; state="\{\{state\}\}">S</figure-callout>}}_{i_{}}=\left\{\begin{array}{cc}2(x+y)+\sqrt{2}d,& x+y<-\sqrt{2}d\\ x+y,& -\sqrt{2}d\&le;x+y\&le;\sqrt{2}d\\ 2(x+y)-\sqrt{2}d,& x+y>\sqrt{2}d\end{array}\right.---\left(10\right)$

${\mathrm{<figure-callout\; id="1"\; label="S\; q"\; filenames="A20061008354600151.png"\; state="\{\{state\}\}">S</figure-callout>}}_{q_{}}=\left\{\begin{array}{cc}2(y-x)+\sqrt{2}d,& y-x<-\sqrt{2}d\\ y-x,& -d\sqrt{2}\&le;y-x\&le;\sqrt{2}d\\ 2(y-x)-\sqrt{2}d,& y-x>\sqrt{2}d\end{array}\right.---\left(11\right)$

$S_{i_2}=\left\{\begin{array}{cc}\sqrt{2}d+(x+y),& x+y<0\\ \sqrt{2}d-(x+y),& x+y\&GreaterEqual;0\end{array}\right.---\left(12\right)$

$S_{q_2}=\left\{\begin{array}{cc}\sqrt{2}d+(y-x),& (y-x)<0\\ \sqrt{2}d-(y-x),& (y-x)\&GreaterEqual;0\end{array}\right.---\left(13\right)$

In addition, the principle of general soft-decision can be used the Max_log_MAP of logarithm maximum posteriori probability (log_MAP, maximum a posteriori probability) criterion or simplification.

If the symbol of matched filtering output is

${\hat{r}}_k={\hat{r}}_{I.k}+j{\hat{r}}_{Q,k},$ K=1 wherein, 2,3 ..., N (14)

N represents to be used for the number of modulation symbols that SNR measures.The hard decision unit is to symbol

Carry out hard decision, obtain to be expressed as with the coordinate of the constellation point of received signal Euclidean distance minimum

M _k=m _{I, k}+ jm _{Q, k}, k=1 wherein, 2,3 ..., N (15)

The ML estimation unit is respectively the valuation of signal power and noise power:

The power valuation of received signal is:

$\hat{S}={\left[\frac{\frac{1}{N}\underset{k=0}{\overset{N-1}{\mathrm{\&Sigma;}}}({\hat{r}}_{I,k}{m}_{I,k}+{\hat{r}}_{Q,k}{m}_{Q,k})}{\frac{1}{N}\underset{k=0}{\overset{N-1}{\mathrm{\&Sigma;}}}\left[(m_{I,k}^2+m_{Q,k}^2)\right]}\right]}^2={\left[\frac{\frac{1}{N}\underset{k=0}{\overset{N-1}{\mathrm{\&Sigma;}}}\mathrm{Re}\left({\hat{r}}_k^{*}{M}_k\right)}{\frac{1}{N}\underset{k=0}{\overset{N-1}{\mathrm{\&Sigma;}}}{\left|M_k\right|}^2}\right]}^2---\left(16\right)$

The noise power valuation is:

$\hat{N}=\frac{1}{N}\underset{k=0}{\overset{N-1}{\mathrm{\&Sigma;}}}(r_{I,k}^2+r_{Q,k}^2)-\hat{S}\&CenterDot;\frac{1}{N}\underset{k=0}{\overset{N-1}{\mathrm{\&Sigma;}}}(m_{I,k}^2+m_{Q,k}^2)---\left(17\right)$

Then there are the SNR and the ANR of estimation to be respectively

${\mathrm{SNR}}^{\left(\mathrm{UE}\right)}=\frac{\hat{S}}{\hat{N}}---\left(18\right)$

${\mathrm{ANR}}^{\left(\mathrm{UE}\right)}=\frac{\sqrt{\hat{S}}}{\hat{N}}---\left(19\right)$

Estimate and the noise power estimation according to the signal power of above-mentioned expression formula (18) and (19), obtain the measured value of signal to noise ratio and amplitude and noise ratio by calibration.

If use formula (16) and (17) in the planisphere of WCDMA system then can be distinguished equivalence and are

${\hat{S}}_t={\left[\frac{\frac{1}{N}\underset{k=t\&CenterDot;N}{\overset{(t+1)\&CenterDot;N-1}{\mathrm{\&Sigma;}}}\left(\right|{\hat{d}}_{I,k}|\&CenterDot;|{\mathrm{<figure-callout\; id="1"\; label="H\; i"\; filenames="A20061008354600151.png"\; state="\{\{state\}\}">H</figure-callout>}}_{i_{}}|+|{\hat{d}}_{Q,k}|\&CenterDot;|{\mathrm{<figure-callout\; id="1"\; label="H\; q"\; filenames="A20061008354600151.png"\; state="\{\{state\}\}">H</figure-callout>}}_{q_{}}\left|\right)}{\frac{1}{N}\underset{k=t\&CenterDot;N}{\overset{(t+1)\&CenterDot;N-1}{\mathrm{\&Sigma;}}}\left[({\left|{\mathrm{<figure-callout\; id="1"\; label="H\; i"\; filenames="A20061008354600151.png"\; state="\{\{state\}\}">H</figure-callout>}}_{i_{}}\right|}^2+{\left|{\mathrm{<figure-callout\; id="1"\; label="H\; q"\; filenames="A20061008354600151.png"\; state="\{\{state\}\}">H</figure-callout>}}_{q_{}}\right|}^2)\right]}\right]}^2---\left(20\right)$

${\hat{N}}_t=\frac{1}{N}\underset{k=t\&CenterDot;N}{\overset{(t+1)\&CenterDot;(N-1)}{\mathrm{\&Sigma;}}}({{\hat{d}}_{I,k}}^2+{{\hat{d}}_{Q,k}}^2)-{\hat{S}}_t\&CenterDot;\frac{1}{N}\underset{k=t\&CenterDot;N}{\overset{(t+1)\&CenterDot;N-1}{\mathrm{\&Sigma;}}}\left[({\left|{\mathrm{<figure-callout\; id="1"\; label="H\; i"\; filenames="A20061008354600151.png"\; state="\{\{state\}\}">H</figure-callout>}}_{i_{}}\right|}^2+{\left|{\mathrm{<figure-callout\; id="1"\; label="H\; q"\; filenames="A20061008354600151.png"\; state="\{\{state\}\}">H</figure-callout>}}_{q_{}}\right|}^2)\right]---\left(21\right)$

Wherein, H _I1, H _Q1Be respectively I road and Q road after constellation point is declared firmly.

As can be seen, most significant byte (MSB, Most Significant Bit) i ₁And q ₁Soft-decision output, expression formula can reflect the amplitude information of reception constellation point apart from initial point, i for (10) and (11) ₁And q ₁Soft-decision output absolute value big more, illustrate that the reception constellation point is far away more apart from the distance of initial point, the SMR/ANR estimation unit can be according to i ₁And q ₁Soft-decision output obtain H _I1, H _Q1Thereby, realize the estimation of signal and noise power.Be appreciated that and utilize i ₁And q ₁Soft-decision output, can be easy to obtain | H _I1|, | H _Q1|, in addition,

With

Also be easy to obtain.

If in the planisphere of TD-SCDMA system in use formula (20) and (21), | H _I1| and

Correspond respectively to the S in the formula (10) _I1| H _Q1| and

Correspond respectively to the S in the formula (11) _Q1

How to obtain based on different soft-decision scheme explanations respectively below by soft-decision output | H _I1|, | H _Q1|,

With

In fact, be not limited to the soft-decision output of following mode, any soft-decision output and

| H _I1| all be (in general, being a piecewise function) that direct relation is arranged, can be general be expressed as:

$\left|{\hat{d}}_{I,k}\right|=f\left(S_{i_1}\right)$

$\left|{\mathrm{<figure-callout\; id="1"\; label="H\; i"\; filenames="A20061008354600151.png"\; state="\{\{state\}\}">H</figure-callout>}}_{i_{}}\right|=g\left(S_{i_1}\right)$

If use max log mode (MAX-Log, Maximum Logarithm), the approximate of this mode can be represented with this formula: (A, B) approximate soft-decision output is then for | H for log (A+B)=Max _I1|

If $\left|{\mathrm{<figure-callout\; id="1"\; label="S\; i"\; filenames="A20061008354600151.png"\; state="\{\{state\}\}">S</figure-callout>}}_{i_{}}\right|\&GreaterEqual;\sqrt{2}d$

$\left|{\mathrm{<figure-callout\; id="1"\; label="H\; i"\; filenames="A20061008354600151.png"\; state="\{\{state\}\}">H</figure-callout>}}_{i_{}}\right|=\frac{3}{2}d$

Otherwise

$\left|{\mathrm{<figure-callout\; id="1"\; label="H\; i"\; filenames="A20061008354600151.png"\; state="\{\{state\}\}">H</figure-callout>}}_{i_{}}\right|=\frac{1}{2}d$

In like manner can obtain | H _Q1| value;

For

Then have

If $\left|{\mathrm{<figure-callout\; id="1"\; label="S\; i"\; filenames="A20061008354600151.png"\; state="\{\{state\}\}">S</figure-callout>}}_{i_{}}\right|\&GreaterEqual;\sqrt{2}d$

$\left|{\hat{d}}_{I,k}\right|=\frac{\left|{\mathrm{<figure-callout\; id="1"\; label="S\; i"\; filenames="A20061008354600151.png"\; state="\{\{state\}\}">S</figure-callout>}}_{i_{}}\right|}{\sqrt{2}}$

Otherwise

$\left|{\hat{d}}_{I,k}\right|=\frac{\left|{\mathrm{<figure-callout\; id="1"\; label="S\; i"\; filenames="A20061008354600151.png"\; state="\{\{state\}\}">S</figure-callout>}}_{i_{}}\right|+d}{2\&CenterDot;\sqrt{2}}$

In like manner can obtain

Above-described embodiment of the present invention does not constitute the qualification to protection range of the present invention.Any any modification of being done within the spirit and principles in the present invention, be equal to and replace and improvement etc., all should be included within the claim protection range of the present invention.

Claims (10)

1, the method for measurement of a kind of signal to noise ratio of quadrature amplitude modulation and amplitude and noise ratio is characterized in that, comprises step:

Receive data through the matched filtering output symbol;

Described symbol is carried out demodulation obtain soft-decision output;

Based on described soft-decision output power estimator signal and noise power;

Obtain signal to noise ratio and amplitude and noise ratio according to described signal power and noise power calibration.

2, method of measurement as claimed in claim 1 is characterized in that, the soft-decision output S of described symbol i _iBe to obtain: S according to following expression formula _i=f (i), wherein f (i) is the computational process of log-likelihood ratio or logarithm maximum posteriori probability.

3, method of measurement as claimed in claim 2 is characterized in that, obtains soft-decision output S according to two Most Significant Bits in the described output symbol _i

4, as claim 2 or 3 described method of measurement, it is characterized in that, according to soft-decision output S _iCalculate acquisition

, | H _I1|, and obtain signal power valuation SNR according to maximum likelihood method ^(UE)With the noise power value of relying on ANR ^(UE)

5, method of measurement as claimed in claim 1 is characterized in that, described quadrature amplitude modulation is 16QAM.

6, the measurement mechanism of a kind of signal to noise ratio of quadrature amplitude modulation and amplitude and noise ratio is characterized in that, comprising:

The matched filtering unit is used for carrying out matched filtering, output symbol to receiving data;

Separate the mediation soft judge unit, be used for described symbol is carried out demodulation, obtain soft-decision output;

Estimation unit be used for based on described soft-decision output power estimator signal and noise power, thereby calibration obtains signal to noise ratio and amplitude and noise ratio.

7, measurement mechanism as claimed in claim 6, it is characterized in that, the described mediation soft judge unit of separating obtains soft-decision output Si:Si=f (i) to symbol i according to following expression formula, and wherein f (i) is the computational process of log-likelihood ratio or logarithm maximum posteriori probability.

8, measurement mechanism as claimed in claim 7 is characterized in that, described separating is in harmonious proportion soft judge unit according to two Most Significant Bits acquisition soft-decision output S in the described output symbol _i

9, as claim 7 or 8 described measurement mechanisms, it is characterized in that described estimation unit is exported S to soft-decision _iCalculate acquisition , | H _I1|, and obtain signal power valuation SNR according to maximum likelihood method ^(UE)With noise power-value ANR ^(UE)

10, measurement mechanism as claimed in claim 6 is characterized in that, described quadrature amplitude modulation is 16QAM.

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