CN100362755C - Method for evaluating sign - Google Patents
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Abstract
The present invention discloses a symbol estimating method. The present invention has the method that in the application of estimating symbols for many times, rear probabilities of various symbols, which are obtained from a previous symbol estimation, are used as prior information of the present symbol estimation; the prior information is integrated to obtain a present symbol estimate value in the present symbol estimation; the prior information is integrated to obtain a present symbol estimate value in the present symbol estimation. After the method of the present invention is used, because the prior information of the previous estimation is considered in the symbol estimation, the accuracy of the symbol estimation can be increased under the condition that estimation frequency is not changed or reduced; high accuracy can be achieved within short time, so the accuracy of data transmission in a network is increased, and the time of the data transmission can be shortened.
Description
Technical Field
The present invention relates to wireless communication technologies, and in particular, to a symbol estimation method.
Background
The Multiple Input Multiple Output (MIMO) technology is a major technological breakthrough in the field of wireless communication, and can improve the capacity and the spectrum utilization rate of a communication system by multiples without increasing the bandwidth. The MIMO technology simultaneously transmits and receives signals using multiple antennas (antenna arrays) at a transmitting end and a receiving end. Because the signals transmitted by the transmitting antennas simultaneously occupy the same frequency band, the communication bandwidth is not increased. There is a spatial channel between each transmit antenna and each receive antenna. The MIMO system can create a plurality of parallel independent spatial channels between a transmitting end and a receiving end through a plurality of transmitting antennas and a plurality of receiving antennas if channel impulse responses of each spatial channel are independent. By independently transmitting information through these parallel spatial channels, the transmission data rate of the MIMO system must be multiplied. The above conclusions were fully demonstrated in the literature by g.j.fosschini and m.j.gans in 1998 and quantitatively indicated: assuming that the MIMO system has M transmitting antennas and N receiving antennas, an nxm order channel matrix can be established under a narrow-band slow fading channel. The elements of the matrix are complex Gaussian random variables which are independently and identically distributed. The channel capacity that can be obtained by the MIMO system will be min (M, N) times that of a Single Input Single Output (SISO) system, and the total transmission power remains unchanged, where min (M, N) represents taking the minimum of M and N.
In view of the high spectral efficiency of the MIMO system, the demodulation method of the MIMO system is a focus of research. Since 1996, a number of demodulation methods for MIMO systems have emerged in sequence. The parallel vertical layered space-time detection method has the following characteristics:
and detecting the symbol of each data stream by adopting a multi-stage parallel interference cancellation structure. In each stage of parallel interference cancellation structure, firstly, the decision statistic of the symbol in each data stream is obtained, and then the decision statistic is measured to obtain the estimated value of the symbol. The interference of the symbol with other data streams can be obtained from the estimated value of the symbol. For a certain data stream, the interference of the symbols of other data streams is subtracted from the received signal, and then an updated received signal of the data stream is obtained. In this manner, an updated received signal for each data stream is obtained by parallel processing in each stage of the parallel interference cancellation structure. The updated received signals of all data streams are input signals of the next stage parallel interference cancellation structure, and are used for symbol detection of corresponding data streams in the next stage parallel interference cancellation structure. Since the interference of other data streams in the updated received signal has been eliminated, the performance of the estimation of the symbols of the data stream is better when the symbols of the data stream are detected again in the next stage of parallel interference cancellation structure.
The multi-user detection is the joint detection of a plurality of user signals, and the parallel interference cancellation multi-user detection method is an important multi-user detection method. The parallel interference cancellation multi-user detection method adopts a multi-stage parallel interference cancellation structure similar to the MIMO system to carry out the joint detection of a plurality of user signals. In each stage of parallel interference cancellation structure, firstly, the multipath combination result of each user transmission symbol is obtained, and then the estimation value of the symbol is obtained according to the multipath combination result of the symbol. The interference of the symbol to other users can be obtained from the estimated value of the symbol. For a user, the interference of the symbols of other users is subtracted from the received signal, and then the updated received signal of the user is obtained. Since the interference of the symbols of other users in the updated received signal has been eliminated, the performance of the estimation of the symbols of the user is better when the symbol detection of the user is performed again in the next-stage interference cancellation structure.
In summary, in MIMO system and multiuser detection, it is necessary to estimate symbols of one user of a data stream of the MIMO system or a multiuser detection system more than once, and each symbol estimation is performed after the last interference cancellation.
In the prior art, the symbols are estimated by the following method.
Let the transmitted symbol be a, and a e a = { A = 1 ，A 2 ，…，A K }。A＝{A 1 ，A 2 ，…，A K Is the set of all possible transmitted symbols.
When the symbol a is estimated i (i =1,2, \8230;, S is the number of times the symbol a is estimated) times, the observed signal for symbol a is:
y i ＝a+v i
wherein v is i Is gaussian white noise.
Observation of a Signal y in a MIMO System i Is a decision statistic of the symbol a, the observed signal y is in a multiuser detection system i Is the result of multipath combining of the symbol a.
The ith estimated value of the symbol a can be obtained by a hard decision mode and a soft decision mode
In the hard-decision mode of the system,satisfies the following formula:
wherein the expressionIs such that P (A) k |y i ) Maximum A k And then A is added k As a is i Is estimated value of
Expression formula
Is such that P (y) i |A k )P(A k ) Maximum A k (ii) a In formula (1), the expression P (A | B) represents the conditional probability that A occurs when B occurs, so P (A | B) k |y i ) Indicates that the receiving end gets y i When A is k Conditional probability of (A) k A posterior probability of (a) P (y) i |A k ) Denotes that the transmitted symbol is A k Time y i Conditional probability of (A), P (A) k ) Denotes that the transmitted symbol is A k A priori probability of.
Suppose that
And by formula derivation, the following results can be obtained:
The hard decision mode is simple to realize, the calculation amount is small, and the estimation accuracy is low.
In the soft-decision mode, the decision is made,
satisfies the following formula:
suppose that
And by formula derivation, the following results can be obtained:
wherein, beta i Called correction factor, for correcting the deviation of symbol estimation and the deviation of interference cancellation caused by non-ideal channel estimation; f (y) i |A k ) Denotes that the transmitted symbol is A k Time-of-flight received signal y i Is calculated as a function of the probability density of (c).
If the transmitted symbol is complex, then:
wherein, AR k 、AI k Are respectively A k The real and imaginary parts of (c); YR i 、YI i Are each y i Real and imaginary parts of (a).
Due to Gaussian white noise v i Real part of (VR) i And imaginary part VI i Obey a normal distribution, i.e. N (0, σ) i 2 ) Distribution, so σ can be determined as follows i : separately determine VR i And VI i The two variance values are averaged to obtain sigma i Or the variance of the complex noise is calculated and then divided by two to obtain sigma i 。
Beta in equation (2) when the transmitted symbols are complex i Also a plurality.
If the transmitted symbol is a real number:
wherein, AR k Is A k The numerical value of (1) is a real number; YR i Is y i The real part of (A), usually if A k Is a real number, then y i Are also real numbers.
If A is k For real numbers, then σ is determined i White gaussian noise v i Viewed as real, i.e. only v is considered i Real part VR of i 。VR i Obeying a normal distribution, i.e. N (0, σ) i 2 ) Distribution, so that VR is found i The variance of (c) can be obtained i 。
Beta in equation (2) when the transmitted symbol is a real number i Are also real numbers.
The following explains beta i Value of (a), beta i Signal-to-noise ratio of value and symbol (Signal-NoiseRate, SNR)Closely related, is a function of the SNR of the symbol.
Generally, at a receiving end, the SNR of a symbol needs to reach a certain value, so that the performance of a bit error rate (BLER) after demapping and decoding can meet the requirement of service quality. For a given BLER value, it can be determined by simulation: minimum SNR value S that a symbol needs to reach in order to achieve the required BLER performance after decoding MIN . And setting two SNR interval demarcation points which are respectively T according to specific conditions 1 ＝S MIN -δ 1 ，T 2 ＝S MIN -δ 2 . Wherein, delta 1 ＞0，δ 2 ＞δ 1 。
If the SNR of the symbol is greater than or equal to the threshold value T 1 ＝S MIN -δ 1 Then the channel estimation of the symbol is considered to be more accurate, and can be approximated as beta i =1, i.e. the effect of the deviation of the channel estimate on the interference cancellation is not taken into account in the interference cancellation.
If the SNR of the symbol is less than or equal to the threshold T 2 ＝S MIN -δ 2 The SNR of the symbol is considered to be too low, making the channel estimation very poor, and can be approximated as β i =0, that is, since the channel quality is too poor, the current estimation value is 0, and since the estimation value is 0, it is invalidSo that the estimation result does not participate in the interference cancellation process.
If the SNR of the symbol is greater than the threshold T 2 ＝S MIN -δ 2 And is less than a threshold value T 1 ＝S MIN -δ 1 The SNR interval (T) can be determined by COSSAP simulation or MATLAB simulation optimization 2 ，T 1 ) Lower beta i Specific value of (a) i . Note that δ 1 、δ 2 The value directly affects beta i The accuracy of the correction of the symbol estimation bias and the size of the SNR optimization interval. Can be determined according to the need for correction accuracy and optimization calculation amount.
In summary, β i The values are as follows:
the SNR is in the interval larger than T2 and smaller than T1, and the interval can be divided into a plurality of subintervals, and the [ SNR ] is used i-1 ，SNR i ]Is shown in which
I is the SNR optimization interval (T) determined as needed 2 ，T 1 ) In the case of dividing the subinterval, the number of the segments is set to [ SNR ] for each subinterval i-1 ，SNR i ]Respectively obtaining alpha of each interval by COSSAP simulation or MATLAB simulation optimization i The value is obtained.
From the above analysis, it can be seen that the implementation of estimating symbols in a hard decision manner is relatively simple and computationally intensiveRespectively obtaining alpha of each interval through COSSAP simulation or MATLAB simulation optimization i The value is obtained.
From the above analysis, the symbol estimation in a hard decision manner is relatively simple to implement and has small calculation amount; the symbol estimation in a soft decision manner is relatively complex to realize and has a large calculation amount. However, in practical applications, the accuracy of symbol estimation by the soft decision method is higher than that of symbol estimation by the hard decision method.
The hard decision formula and the soft decision formula for the estimation of the symbols are obtained under the assumption that the prior probabilities of all possible symbols being transmitted are equal, because in formula (1 a) and formula (2 a), the prior probabilities of all possible symbols being transmitted are not reflected, that is, the probabilities of all possible symbols being transmitted are the same. In the absence of any a priori information, it is assumed that the probability that symbol a takes all possible values is equal, and it is the most unfavorable case of symbol estimation to make the symbol estimation under this assumption. If some prior information of the value of the symbol a can be obtained, the symbol a can be estimated more accurately by using the prior information, the estimation accuracy is higher, and the number of times or the series of symbol estimation can be reduced.
Disclosure of Invention
The main object of the present invention is to provide a symbol estimation method, which improves the accuracy of symbol estimation when performing multiple symbol estimations.
The purpose of the invention is realized by the following technical scheme:
a method of estimating a symbol, comprising the steps of:
A. estimating the symbol according to the observation signal of the symbol and the prior probability of all the symbols in the symbol possibility set to obtain the estimation value of this time, and simultaneously determining the posterior probability of this time of all the symbols in the estimation;
B. judging whether the preset estimation times are reached, if so, outputting the estimation value of the symbol estimation, and then ending; otherwise, the posterior probability of all the symbols is used as the prior probability of the next symbol estimation, and then the step A is returned to carry out the next symbol estimation.
The observed signal of the symbol is a decision statistic or a multipath combination result of the symbol.
At the first estimation, the prior probabilities of all symbols in the symbol likelihood set are set to be the same.
The predetermined number of estimations is 2 to 4.
Step a the method for estimating a symbol according to an observed signal of the symbol and an a priori probability of all symbols in a symbol likelihood set is:
obtaining an estimated value of the ith estimation by adopting the following formula:
where i is the number of estimations, j has a value of 1 to i,is an estimated value of the i-th estimation, y j The decision statistic for the j-th estimation is the sum of the symbol value and white Gaussian noise, A' is the symbol probability set, A k For symbols in the set of symbol possibilities, σ j 2 Is the variance of gaussian white noise.
Step a the method of estimating a symbol according to its observed signal and the a priori probabilities of all symbols in the symbol likelihood set is:
obtaining an estimated value of the ith estimation by adopting the following formula:
where i is the estimated number of times, j has a value of 1 to i,
is an estimate of the i-th estimate, y j The decision statistic for the j-th estimate, whose value is the sum of the symbol value and Gaussian white noise, A k For symbols in a set of symbol possibilities, σ j 2 Is the variance of Gaussian white noise, beta i Is a channel correction factor determined by the signal-to-noise ratio SNR of the transmission channel.
Said determining a channel correction factor beta from the SNR of the transmission channel i Is at SThe NR interval is determined by COSSAP simulation or MATLAB simulation.
Variance σ of the Gaussian white noise j 2 Comprises the following steps: when Gaussian white noise is complex, σ j 2 Is the average of the variance of the real part and the variance of the imaginary part of the Gaussian white noise; when Gaussian white noise is a real number, σ j 2 Is the variance of the real part of gaussian white noise.
The method is used in the parallel interference cancellation technology of the multi-input multi-output system or multi-user detection.
The invention provides a symbol estimation method, which estimates symbols according to decision statistics of the symbols or multipath combination results of the symbols and prior probabilities of all symbols in a symbol possibility set to obtain a current estimation value, determines the current posterior probabilities of all symbols in the current estimation, and substitutes the current posterior probabilities of all symbols into the next estimation as the prior probabilities of the next estimation. In the method in the prior art, the prior probabilities of all symbols are assumed to be the same in each estimation, and the symbols are estimated only according to decision statistics of the symbols or multipath combination results. Compared with the prior art, the method of the invention obviously improves the accuracy of symbol estimation by taking the posterior probabilities of all symbols obtained by the previous estimation as the prior probabilities of all symbols estimated at this time, and can obviously reduce the time used by multiple symbol estimation on the premise of reaching the same estimation accuracy, thereby increasing the correctness of data transmission in the network and reducing the time required for transmitting data.
Drawings
Fig. 1 is a flow chart of a symbol estimation method according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described with reference to the accompanying drawings and detailed description.
The symbol estimation method of the invention considers the result of the previous estimation as prior information in hard decision and soft decision.
Fig. 1 is a flow chart of a symbol estimation method according to the present invention, and as can be seen from fig. 1, the method includes the following steps:
step 101: an estimated number value S is set, and an estimated number variable i =1 is set.
Step 102: the ith prior probabilities of all symbols in the symbol likelihood set are set to be the same.
Step 103: and estimating the symbol for the ith time according to the ith observation signal and the ith prior probability of the symbol to obtain an ith estimation value, and simultaneously determining the ith posterior probabilities of all the symbols in the ith estimation.
Step 104: set i = i +1.
Step 105: judging whether i is larger than S, if so, turning to step 107; if not, go to step
Step 106: the (i-1) th posterior probability of all symbols is taken as the prior probability of the ith estimation, and then the step 103 is returned.
Step 107: and (5) outputting the (i-1) th symbol estimation value as a detection result of the symbol, and ending the symbol estimation process.
Preferably, when the number of times S of symbol estimation is 2 to 4, good symbol estimation performance can be obtained, and the performance of symbol estimation is not improved significantly by increasing the value of S.
The following describes how the method of the present invention is applied specifically. As in the prior art, the method of the invention can also adopt two decision modes of hard decision and soft decision.
Let the transmitted symbol be a, and a ∈ A = { A = 1 ，A 2 ，…，A K }。A＝{A 1 ，A 2 ，…，A K Is thatPossibly a set of transmitted symbols.
When the symbol a is estimated i (i =1,2, \8230;, S is the number of times the symbol a is estimated) times, the observed signal for symbol a is:
y i ＝a+v i
wherein v is i Is gaussian white noise.
In a MIMO system, the observation signal y i Is a decision statistic of the symbol a, observed signal y in a multi-user detection system i Is the result of multipath combining of the symbol a.
In the hard decision mode, the i-th (i =1,2, \8230;, S is the number of symbol estimations) sub-estimate value of symbol a
Satisfies the following formula:
as can be seen from equation (3), with P (A) k |y i-1 ) Replaces P (A) in the formula (1) k ) That is, formula (3) considers that the transmission symbol obtained in the last symbol estimation is a k The posterior probability of (d). When estimating the symbol for the first time, it is still assumed that the transmitted symbol is A k Are equal in prior probability, i.e. areSymbol estimation is still performed according to equation (1 a); in the second and later symbol estimation, the symbol estimation is calculated from the previous symbol estimationTo the transmission symbol of A k The probability of (2), i.e. the posterior probability, is used as the prior information of the estimation.
By further deriving equation (3), the estimated value obtained when the ith symbol is estimated is:
wherein,
substituting equation (3 b) and equation (3 c) into equation (3 a) yields:
at the (i-1) th symbol estimation, sum (i-1, A) is calculated k ) K =1,2, \ 8230;, K. In the ith symbol estimation, the Sum (i-1, A) calculated in the previous symbol estimation is directly used k ) K =1,2, \ 8230;, K, calculated according to equation (3 b) to yield Sum (i, a) k ) K =1,2, \ 8230;, K, then the estimate of the symbol a is found according to equation (3 a)
. Wherein, sum (i, A) k ) K =1,2, \ 8230, K is also used at the (i + 1) th symbol estimation. Wherein Sum (i-1, A) in the formula (3 b) k ) K =1,2, \8230, K is the concrete embodiment of the posterior probability information in the (i-1) th estimation in the ith symbol estimation.
In the soft-decision mode of the system,
satisfies the following formula:
as can be seen from equation (4), with P (A) k |y i-1 ) Replaces P (A) in the formula (2) k ) That is, formula (4) considers that the transmission symbol obtained in the last symbol estimation is a k The posterior probability of (d). When estimating the symbol for the first time, it is still assumed that the transmitted symbol is A k Are equal in probability, i.e. are
And calculated according to the formula (2 a)A symbol estimation value; in the second and subsequent symbol estimation, the transmitted symbol obtained in the previous estimation is set as A k The probability of (2), i.e. the posterior probability, is used as the prior information of the estimation.
By further deriving equation (4), the estimated value obtained when the ith symbol is estimated is:
wherein,
substituting equation (4 b) and equation (4 c) into equation (4 a) yields:
at the (i-1) th symbol estimation, sum (i-1, A) is calculated k ) K =1,2, \ 8230;, K. In the ith symbol estimation, the Sum (i-1, A) calculated in the previous symbol estimation is directly used k ) K =1,2, \ 8230;, K, calculated according to equation (4 b) to yield Sum (i, a) k ) K =1,2, \ 8230;, K, then the estimate of the symbol a is found according to equation (4 a)
. Wherein, sum (i, A) k ) K =1,2, \ 8230, K is also used at the (i + 1) th symbol estimation.
As can be seen from equations (3) and (4), each symbol estimation adds information of the last symbol estimation as a priori information of the current symbol estimation. Wherein Sum (i-1, A) in the formula (4 b) k ) K =1,2, \ 8230, K is the concrete embodiment of the posterior probability information in the (i-1) th symbol estimation.
In the formula (4 a), β i Referred to as correction factors, for correcting the bias of the symbol estimation and the bias of the interference cancellation due to non-ideality of the channel estimation, in the method of the invention, beta i The determination of the values is the same as in the prior art method and is not described here.
The method according to the invention can be suitably modified in the specific implementation to suit the specific needs of the specific case. It will be appreciated that the embodiments according to the present invention are exemplary only, and are not intended to limit the scope of the present invention, for example, the present invention is not limited to application in MIMO systems or multi-user detection systems, but can be applied to any system or method that requires multiple levels or multiple symbol estimates.
Claims (9)
1. A method for estimating symbols, the method comprising the steps of:
A. estimating the symbol according to the observation signal of the symbol and the prior probability of all the symbols in the symbol possibility set to obtain the estimation value of this time, and simultaneously determining the posterior probability of this time of all the symbols in the estimation;
B. judging whether the preset estimation times are reached, if so, outputting the estimation value of the symbol estimation, and then ending; otherwise, the posterior probability of the time of all the symbols is used as the prior probability of the next symbol estimation, and then the step A is returned to carry out the next symbol estimation.
2. The method of estimating symbols of claim 1, wherein the observed signal of the symbol is a decision statistic of the symbol or a multipath combining result.
3. The method of estimating symbols according to claim 1, wherein the prior probabilities of all symbols in the symbol likelihood set are set to be the same at the time of the first estimation.
4. The symbol estimation method according to claim 1, wherein the predetermined number of estimations is 2 to 4.
5. The method for estimating symbols according to claim 1, wherein the method for estimating symbols according to the observed signal of symbols and the prior probabilities of all symbols in the symbol possibility set in step a is:
obtaining an estimated value of the ith estimation by adopting the following formula:
where i is the estimated degree and j has a value of 1 to i i Is an estimated value of the i-th estimation, y j The decision statistic for the j-th estimation is the sum of the symbol value and white Gaussian noise, A' is the symbol probability set, A k For symbols in the set of symbol possibilities, σ j 2 Is the variance of gaussian white noise.
6. The method of estimating symbols according to claim 1, wherein the method of estimating symbols according to their observed signals and prior probabilities of all symbols in the symbol likelihood set in step a is:
obtaining an estimated value of the ith estimation by adopting the following formula:
where i is the estimated degree and j has a value of 1 to i i Is an estimate of the i-th estimate, y j The decision statistic for the j-th estimate, whose value is the sum of the symbol value and Gaussian white noise, A k For symbols in a set of symbol possibilities, σ j 2 Is the variance of Gaussian white noise, beta i Is a channel correction factor determined by the signal-to-noise ratio SNR of the transmission channel.
7. Method for estimating symbols according to claim 6, characterized in that said channel correction factor β is determined from the SNR of the transmission channel i Is determined by cossa simulation or MATLAB simulation in the SNR interval.
8. Method for estimating symbols according to claim 5 or 6, characterized in that the variance σ of said Gaussian white noise j 2 Comprises the following steps: when Gaussian white noise is complex, σ j 2 Is the average of the variance of the real part and the variance of the imaginary part of the Gaussian white noise; when Gaussian white noise is a real number, σ j 2 Is the variance of the real part of gaussian white noise.
9. The method of estimating symbols of claim 1, wherein the method is used in a parallel interference cancellation technique for multi-input multi-output systems or multi-user detection.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1260927A (en) * | 1997-06-13 | 2000-07-19 | 西门子公司 | Source-controlled channel decoding using intra-frame correlation |
| WO2003075182A1 (en) * | 2002-03-02 | 2003-09-12 | Bl Systems Inc. | Apparatus and method for selecting an optimal decision tree for data mining |
| EP1379001A2 (en) * | 2002-07-03 | 2004-01-07 | Hughes Electronics Corporation | Method and system for decoding low density parity check (LDPC) codes |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1260927A (en) * | 1997-06-13 | 2000-07-19 | 西门子公司 | Source-controlled channel decoding using intra-frame correlation |
| WO2003075182A1 (en) * | 2002-03-02 | 2003-09-12 | Bl Systems Inc. | Apparatus and method for selecting an optimal decision tree for data mining |
| EP1379001A2 (en) * | 2002-07-03 | 2004-01-07 | Hughes Electronics Corporation | Method and system for decoding low density parity check (LDPC) codes |
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