CN108777604B - Wavelength division multiple access ultra wide band multi-user detection method based on Gaussian mixture model clustering - Google Patents

Wavelength division multiple access ultra wide band multi-user detection method based on Gaussian mixture model clustering Download PDF

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CN108777604B
CN108777604B CN201810486452.3A CN201810486452A CN108777604B CN 108777604 B CN108777604 B CN 108777604B CN 201810486452 A CN201810486452 A CN 201810486452A CN 108777604 B CN108777604 B CN 108777604B
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尹振东
沈涛
吴芝路
吴明阳
赵延龙
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Harbin Institute of Technology Shenzhen
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Abstract

一种基于高斯混合模型聚类的波分多址超宽带多用户检测方法，本发明涉及波分多址超宽带多用户检测方法。本发明为了解决现有波分多址超宽带多用户检测方法实时性差以及检测性能低的问题。本发明包括：一：获取高斯信道下波分多址超宽带信号，将超宽带信号输入至K个匹配滤波器中进行初步检测，得到匹配滤波结果y；二：将K个用户的匹配滤波结果y进行符号判决和码元映射，将匹配滤波结果y映射为符合高斯混合模型的码元映射结果；三：将码元映射结果

Figure DDA0001665551540000011
进行高斯混合模型聚类，将错误码元纠错后，输出波分多址超宽带多用户检测结果。本发明用于超宽带通讯领域。

Figure 201810486452

A wavelength division multiple access ultra-wideband multi-user detection method based on Gaussian mixture model clustering, and the invention relates to a wavelength division multiple access ultra-wideband multi-user detection method. The invention aims to solve the problems of poor real-time performance and low detection performance of the existing wavelength division multiple access ultra-wideband multi-user detection method. The present invention includes: firstly: acquiring a wavelength division multiple access ultra-wideband signal under a Gaussian channel, inputting the ultra-wideband signal into K matched filters for preliminary detection, and obtaining a matched filtering result y; two: converting the matched filtering results of the K users y performs symbol decision and symbol mapping, and maps the matched filtering result y to the symbol mapping result conforming to the Gaussian mixture model; 3: Convert the symbol mapping result

Figure DDA0001665551540000011
Gaussian mixture model clustering is performed to correct the error symbols, and output the UWB multi-user detection results of WDM multiple access. The invention is used in the field of ultra-wideband communication.

Figure 201810486452

Description

Wavelength division multiple access ultra wide band multi-user detection method based on Gaussian mixture model clustering
Technical Field
The invention relates to ultra-wideband communication, in particular to a wavelength division multiple access ultra-wideband multi-user detection method based on Gaussian mixture model clustering.
Background
The wave division multiple access ultra wide band is a modulation mode of ultra wide band wireless communication, and the wave division multiple access communication system is different from the traditional communication system in that the pulse waveform used in the pulse modulation is generated by orthogonal wavelets, and each user adopts mutually orthogonal wavelet functions. The signal source is subjected to BPSK modulation with orthogonal wavelets after being coded, is transmitted to a space channel through a pulse signal transmitter, and is subjected to matched filtering and multi-user detection after being synchronously captured by a pulse signal receiver to obtain a baseband signal.
Assuming that K users exist in the system, the transmitters of the users all use a Binary Phase Shift Keying (BPSK) method for modulation. In the K (K ═ 1,2, …, K) th user, the waveform transmitted by the transmitter is wk(T) the waveform has a period TpAnd using BPSK, using bk iModulation of symbols of { -1, +1}, bk iThe ith bit sent for the kth user with a pulse repetition period of TsAmplitude of the transmitted signal being ak. The information transmitted by the kth user transmitter in M bits can be expressed by the mathematical expression:
Figure BDA0001665551520000011
in practical use, however, since the waveforms between users are not strictly orthogonal, multiple access interference is generated, and especially in asynchronous transmission, the multiple access interference becomes more serious, and the detection performance and capacity of the system are seriously affected. The multi-user detection technology is a receiving-end technology capable of eliminating or weakening multiple access interference. The optimal multi-user detection algorithm can enable the detection performance of the system to be close to the situation of a single-user system, but the calculation amount of the algorithm is very large, so that the real-time performance of the system is poor, and the algorithm is generally rarely used in engineering. Although the suboptimal detection algorithm has low computational complexity, the detection performance of the suboptimal detection algorithm is greatly different from the optimal condition, and the suboptimal detection algorithm is not suitable for high-quality communication occasions.
Disclosure of Invention
The invention aims to solve the problems of poor real-time performance and low detection performance of the existing wavelength division multiple access ultra wide band multi-user detection method, and provides a wavelength division multiple access ultra wide band multi-user detection method based on Gaussian mixture model clustering.
A wavelength division multiple access ultra wide band multi-user detection method based on Gaussian mixture model clustering comprises the following steps:
the method comprises the following steps: obtaining the wave division multiple access ultra wide band signal under the Gaussian channel, and inputting the ultra wide band signal into K matched filtersThe primary detection is carried out to obtain a matched filtering result y ═ y1,y2,...,yK]TWherein y is1,y2,...,yKMatching filtering results for the 1 st user to the Kth user; k users correspond to K matched filters;
step two: carrying out symbol decision and code element mapping on the matched filtering results y of the K users, and mapping the matched filtering results y into code element mapping results conforming to a Gaussian mixture model
Figure BDA0001665551520000021
A result of performing symbol decision for the jth user matched filtering result y;
step three: mapping the symbols to results
Figure BDA0001665551520000022
And performing Gaussian mixture model clustering, correcting errors of the error code elements, and outputting a wavelength division multiple access ultra wide band multi-user detection result.
The invention has the beneficial effects that:
the multi-user detection method of the invention firstly carries out preliminary detection on the wave division multiple access ultra-wideband signal through a matched filter; and then, carrying out symbol error correction judgment by using Gaussian mixture model clustering, thereby approaching the performance of optimal multi-user detection. The invention is suitable for the detection of the wave division multiple access ultra wide band multi-user under the Gaussian channel. The method solves the problems of poor system real-time performance and low detection performance caused by overhigh calculation complexity of the existing multi-user detection method.
Under the condition that the number of users K is 10, the error rate performance of the method is simulated and analyzed, the error rate performance is obviously improved compared with matched filtering, and is superior to a classical algorithm of decorrelation multi-user detection, and is close to the error rate performance (theoretical lower limit) of an optimal multi-user detection algorithm, and under the condition that the signal-to-noise ratio is 8dB, the error rate performance of the method is improved by about 6.5 times compared with a matched filtering result, and is improved by about 24 percent compared with the classical algorithm of decorrelation multi-user detection.
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FIG. 1 is a flow chart of the present invention;
FIG. 2 is a block diagram of a multi-user detection algorithm error rate based on Gaussian mixture model clustering;
FIG. 3 is a schematic block diagram of a multi-user detection algorithm based on a Gaussian mixture model.
Detailed Description
The first embodiment is as follows: as shown in fig. 1, a method for detecting a wavelength division multiple access ultra wide band multi-user based on gaussian mixture model clustering comprises the following steps:
the method comprises the following steps: obtaining a wavelength division multiple access ultra wide band signal under a Gaussian channel, inputting the ultra wide band signal into K matched filters for preliminary detection, and obtaining a matched filtering result y ═ y1,y2,...,yK]TWherein y is1,y2,...,yKMatching filtering results for the 1 st user to the Kth user;
step two: carrying out symbol decision and code element mapping on the matched filtering results y of the K users, and mapping the matched filtering results y into code element mapping results conforming to a Gaussian mixture model
Figure BDA0001665551520000023
A result of performing symbol decision for the jth user matched filtering result y;
step three: mapping the symbols to results
Figure BDA0001665551520000031
And performing Gaussian mixture model clustering, correcting errors of the error code elements, and outputting a wavelength division multiple access ultra wide band multi-user detection result.
Under the condition that the number of users K is 10, the error rate performance of the multi-user detection algorithm based on gaussian mixture model clustering is simulated and analyzed, the error rate performance is higher than that of matched filtering, the multi-user detection is decorrelated, and the error rate performance (theoretical lower limit) of the optimal multi-user detection algorithm is approached, as shown in fig. 2.
The principle diagram of the invention is shown in fig. 3.
The second embodiment is as follows: the first difference between the present embodiment and the specific embodiment is: the step one is as followsThe cross correlation coefficient matrix R of K matched filters ═ R (R)ij)K×KIs satisfied with and rii＞＞|rij|，r ii1, i, j ≠ 1,2, …, K, i ≠ j; wherein r isiiIs the ith row and ith column element in the cross-correlation coefficient matrix R, RijIs the ith row and the jth column element in the cross-correlation coefficient matrix R.
Other steps and parameters are the same as those in the first embodiment.
The third concrete implementation mode: the present embodiment differs from the first or second embodiment in that: in the second step, the matched filtering results y of K users are obtainedjPerforming symbol decision and symbol mapping, and matching the filtering result yjMapping to symbol mapping results conforming to a Gaussian mixture model
Figure BDA0001665551520000032
The specific process comprises the following steps:
step two, firstly: let the initial mapping function
Figure BDA0001665551520000033
Wherein
Figure BDA0001665551520000034
Is the result of symbol decision of the matched filtering result y; wherein a is a diagonal matrix and a is a diagonal matrix,
Figure BDA0001665551520000035
a result of performing symbol decision for the matched filtering result y from the first user to the Kth user;
step two: to pair
Figure BDA0001665551520000036
And (3) calculating a partial derivative to obtain:
Figure BDA0001665551520000037
step two and step three: let the mapping equation be:
Figure BDA0001665551520000038
wherein A isjThe amplitude value of the received jth ultra-wideband signal is obtained;
according to
Figure BDA0001665551520000039
The mapping equation is rewritten as:
Figure BDA0001665551520000041
wherein b isiFor transmitting symbols, njIs white gaussian noise under gaussian channel;
step two, four: all user signals are set to 1, namely A, with uniform amplitude1＝A2＝...＝A K1, the mapping equation is simplified as:
Figure BDA0001665551520000042
b is tojThe following two cases are distinguished:
Figure BDA0001665551520000043
wherein H0Is composed of
Figure BDA0001665551520000044
Case of correct symbol, H1Is composed of
Figure BDA0001665551520000045
Is the case of an erroneous symbol;
if it is not
Figure BDA0001665551520000046
Is a correct code element
Figure BDA0001665551520000047
If it is
Figure BDA0001665551520000048
Is an error code element
Figure BDA0001665551520000049
Obtaining:
Figure BDA00016655515200000410
wherein N is a Gaussian distribution, σ2Is the variance of the gaussian distribution;
step two and step five: the cross correlation coefficient matrix R for K matched filters is (R)ij)K×KIs satisfied with and rii＞＞|rij|，r ii1, i, j ≠ 1,2, …, K, i ≠ j, yielding:
Figure BDA00016655515200000411
other steps and parameters are the same as those in the first or second embodiment.
The fourth concrete implementation mode: the difference between this embodiment mode and one of the first to third embodiment modes is: mapping the code element in the third step
Figure BDA00016655515200000412
Performing Gaussian mixture model clustering, wherein the specific process of correcting the error code element comprises the following steps:
whether or not
Figure BDA00016655515200000413
Whether a correct or an incorrect symbol, a symbol
Figure BDA00016655515200000414
Through
Figure BDA00016655515200000415
The result after the function mapping is a Gaussian-compliantRandom variables that are distributed and have the same variance. And (4) carrying out Gaussian mixture model clustering by using an EM (effective minimum) algorithm, and setting the initial value of the distribution mean value after correct code element mapping as 0, the initial value of the distribution mean value after error code element mapping as 2 and the category number as 2.
The clustering results of the Gaussian mixture model are divided into two categories: an error symbol and a correct symbol. And outputting the error code element after negation and the correct code element together to obtain a wavelength division multiple access ultra wide band multi-user detection result.
Step three, firstly: setting the initial value of the distribution mean value after the correct code element mapping as 0 and the initial value of the distribution mean value after the error code element mapping as 2, and carrying out Gaussian mixture model clustering;
step three: the clustering results of the Gaussian mixture model are divided into two categories: an error symbol and a correct symbol; and outputting the error code element after negation and the correct code element together to obtain a wavelength division multiple access ultra wide band multi-user detection result.
Other steps and parameters are the same as those in one of the first to third embodiments.
The present invention is capable of other embodiments and its several details are capable of modifications in various obvious respects, all without departing from the spirit and scope of the present invention.

Claims (3)

1.一种基于高斯混合模型聚类的波分多址超宽带多用户检测方法，其特征在于：所述基于高斯混合模型聚类的波分多址超宽带多用户检测方法包括以下步骤：1. a wavelength division multiple access ultra-wideband multi-user detection method based on Gaussian mixture model clustering, is characterized in that: the described wavelength division multiple access ultra-wideband multi-user detection method based on Gaussian mixture model clustering comprises the following steps: 步骤一：获取高斯信道下波分多址超宽带信号，将超宽带信号输入至K个匹配滤波器中进行初步检测，得到匹配滤波结果y＝[y1,y2,...,yK]T，其中y1,y2,...,yK为第1个用户到第K个用户的匹配滤波结果；Step 1: Obtain the WDM UWB signal under the Gaussian channel, input the UWB signal into K matched filters for preliminary detection, and obtain the matched filtering result y=[y 1 , y 2 ,...,y K ] T , where y 1 , y 2 ,...,y K are the matched filtering results from the 1st user to the Kth user; 步骤二：将K个用户的匹配滤波结果y进行符号判决和码元映射，将匹配滤波结果y映射为符合高斯混合模型的码元映射结果
Figure FDA0002692772360000011
的具体过程为：Step 2: Perform symbol decision and symbol mapping on the matched filtering results y of the K users, and map the matched filtering result y to a symbol mapping result conforming to the Gaussian mixture model
Figure FDA0002692772360000011
The specific process is: 步骤二一：令初始映射函数
Figure FDA0002692772360000012
其中
Figure FDA0002692772360000013
是匹配滤波结果y进行符号判决的结果；其中A为对角矩阵，
Figure FDA0002692772360000014
为第一个用户到第K个用户匹配滤波结果y进行符号判决的结果；Step 21: Let the initial mapping function
Figure FDA0002692772360000012
in
Figure FDA0002692772360000013
is the result of the symbol decision of the matched filtering result y; where A is a diagonal matrix,
Figure FDA0002692772360000014
The result of the symbol decision for the first user to the Kth user matched filtering result y; 步骤二二：对F
Figure FDA0002692772360000015
求偏导，得到：Step 22: For F
Figure FDA0002692772360000015
Taking the partial derivative, we get:
Figure FDA0002692772360000016
Figure FDA0002692772360000016
 步骤二三：令映射方程为：Step 23: Let the mapping equation be:
Figure FDA0002692772360000017
Figure FDA0002692772360000017
 其中Aj为接受到的第j个超宽带信号的幅度值；Wherein A j is the amplitude value of the received j-th ultra-wideband signal; 根据
Figure FDA0002692772360000018
映射方程改写为：according to
Figure FDA0002692772360000018
The mapping equation is rewritten as:
Figure FDA0002692772360000019
Figure FDA0002692772360000019
 其中bi为发送码元，nj为高斯信道下的高斯白噪声；where b i is the transmitted symbol, and n j is the Gaussian white noise under the Gaussian channel; 步骤二四：将所有用户信号幅度大小统一并设定为1，即A1＝A2＝...＝AK＝1，映射方程简化为：Step 24: Unify and set the amplitudes of all user signals to 1, that is, A 1 =A 2 =...= AK =1, and the mapping equation is simplified as:
Figure FDA00026927723600000110
Figure FDA00026927723600000110
 将bj分为以下两种情况：Divide b j into the following two cases:
Figure FDA0002692772360000021
Figure FDA0002692772360000021
 其中H0为
Figure FDA0002692772360000022
是正确码元的情况，H1为
Figure FDA0002692772360000023
是错误码元的情况；where H0 is
Figure FDA0002692772360000022
is the case of the correct symbol, H 1 is
Figure FDA0002692772360000023
is the case of an error symbol; 如果
Figure FDA0002692772360000024
为正确码元则
Figure FDA0002692772360000025
若
Figure FDA0002692772360000026
为错误码元则
Figure FDA0002692772360000027
得到：if
Figure FDA0002692772360000024
is the correct code element
Figure FDA0002692772360000025
like
Figure FDA0002692772360000026
is an error code element
Figure FDA0002692772360000027
get:
Figure FDA0002692772360000028
Figure FDA0002692772360000028
 其中N为高斯分布，σ2为高斯分布的方差；where N is the Gaussian distribution, and σ 2 is the variance of the Gaussian distribution; 步骤二五：由于K个匹配滤波器的互相关系数矩阵R＝(rij)K×K，满足且rii＞＞|rij|，rii≈1，i,j＝1,2,…,K,i≠j，得到：Step 25: Since the cross-correlation coefficient matrix R=(r ij ) K×K of the K matched filters satisfies and r ii >>|r ij |, r ii ≈1, i,j=1,2,… ,K,i≠j, get:
Figure FDA0002692772360000029
Figure FDA0002692772360000029
Figure FDA00026927723600000210
为第j个用户匹配滤波结果y进行符号判决的结果；
Figure FDA00026927723600000210
The result of the symbol decision for the jth user matched filtering result y; 步骤三：将码元映射结果
Figure FDA00026927723600000211
进行高斯混合模型聚类，将错误码元纠错后，输出波分多址超宽带多用户检测结果。Step 3: Map the symbols to the result
Figure FDA00026927723600000211
Gaussian mixture model clustering is performed to correct the error symbols, and output the UWB multi-user detection results of WDM multiple access. 2.根据权利要求1所述一种基于高斯混合模型聚类的波分多址超宽带多用户检测方法，其特征在于：所述步骤一中所述K个匹配滤波器的互相关系数矩阵R＝(rij)K×K，满足且rii＞＞|rij|，rii≈1，i,j＝1,2,…,K，i≠j；其中rii为互相关系数矩阵R中第i行第i列元素，rij为互相关系数矩阵R中第i行第j列元素。2. a kind of wavelength division multiple access ultra-wideband multi-user detection method based on Gaussian mixture model clustering according to claim 1, is characterized in that: the cross-correlation coefficient matrix R of K matched filters described in described step 1 =(r ij ) K×K , satisfying and r ii >>|r ij |, r ii ≈1, i,j=1,2,…,K, i≠j; where r ii is the cross-correlation coefficient matrix R In the i-th row and the i-th column element, r ij is the i-th row and the j-th column element in the cross-correlation coefficient matrix R. 3.根据权利要求2所述一种基于高斯混合模型聚类的波分多址超宽带多用户检测方法，其特征在于：所述步骤三中将码元映射结果
Figure FDA00026927723600000212
进行高斯混合模型聚类，将错误码元纠错的具体过程为：3. a kind of wavelength-division multiple access ultra-wideband multi-user detection method based on Gaussian mixture model clustering according to claim 2, is characterized in that: in described step 3, by symbol mapping result
Figure FDA00026927723600000212
The Gaussian mixture model clustering is carried out, and the specific process of correcting the error symbols is as follows: 步骤三一：令正确码元映射后的分布均值初值为0，错误码元映射后的分布均值初值设为2，进行高斯混合模型聚类；Step 31: Set the initial value of the distribution mean after correct symbol mapping to 0, and set the initial value of the distribution mean after wrong symbol mapping to 2, and perform Gaussian mixture model clustering; 步骤三二：高斯混合模型聚类结果分为两类：错误码元和正确码元；将错误码元取反之后和正确码元一起输出，得到波分多址超宽带多用户检测结果。Step 32: Gaussian mixture model clustering results are divided into two categories: wrong symbols and correct symbols; after inverting the wrong symbols and outputting together with the correct symbols, the WDM-UWB multi-user detection result is obtained.
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