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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mixture model
multiple access
division multiple
mapping
wide band
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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
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L1/00—Arrangements for detecting or preventing errors in the information received
    • H04L1/004—Arrangements for detecting or preventing errors in the information received by using forward error control
    • H04L1/0045—Arrangements at the receiver end
    • H04L1/0047—Decoding adapted to other signal detection operation
    • H04L1/0048—Decoding adapted to other signal detection operation in conjunction with detection of multiuser or interfering signals, e.g. iteration between CDMA or MIMO detector and FEC decoder
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    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
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Abstract

The invention discloses a wavelength division multiple access ultra wide band multi-user detection method based on Gaussian mixture model clustering, and relates to a wavelength division multiple access ultra wide band 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 wide band multi-user detection method. The invention comprises the following steps: firstly, the method comprises the following steps: obtaining 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; II, secondly: carrying out symbol judgment 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; thirdly, the method comprises the following steps: mapping the symbols to results
Figure DDA0001665551540000011
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 is used in the field of ultra-wideband communication.

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. A wavelength division multiple access ultra wide band multi-user detection method based on Gaussian mixture model clustering is characterized in that: the 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 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 symbol mapping on the matched filtering results y of the K users, and matchingMapping the matched filtering result y into a code element mapping result conforming to a Gaussian mixture model
Figure FDA0002692772360000011
The specific process comprises the following steps:
step two, firstly: let the initial mapping function
Figure FDA0002692772360000012
Wherein
Figure FDA0002692772360000013
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 FDA0002692772360000014
a result of performing symbol decision for the matched filtering result y from the first user to the Kth user;
step two: to F
Figure FDA0002692772360000015
And (3) calculating a partial derivative to obtain:
Figure FDA0002692772360000016
step two and step three: let the mapping equation be:
Figure FDA0002692772360000017
wherein A isjThe amplitude value of the received jth ultra-wideband signal is obtained;
according to
Figure FDA0002692772360000018
The mapping equation is rewritten as:
Figure FDA0002692772360000019
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＝...＝AK1, the mapping equation is simplified as:
Figure FDA00026927723600000110
b is tojThe following two cases are distinguished:
Figure FDA0002692772360000021
wherein H0Is composed of
Figure FDA0002692772360000022
Case of correct symbol, H1Is composed of
Figure FDA0002692772360000023
Is the case of an erroneous symbol;
if it is not
Figure FDA0002692772360000024
Is a correct code element
Figure FDA0002692772360000025
If it is
Figure FDA0002692772360000026
Is an error code element
Figure FDA0002692772360000027
Obtaining:
Figure FDA0002692772360000028
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|，rii1, i, j ≠ 1,2, …, K, i ≠ j, yielding:
Figure FDA0002692772360000029
Figure FDA00026927723600000210
a result of performing symbol decision for the jth user matched filtering result y;
step three: mapping the symbols to results
Figure FDA00026927723600000211
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.
2. The WDM-UWB multi-user detection method based on Gaussian mixture model clustering according to claim 1, wherein the WDM-UWB multi-user detection method comprises: the cross-correlation coefficient matrix R of the K matched filters in the first step is (R ═ R)ij)K×KIs satisfied with and rii＞＞|rij|，rii1, 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.
3. The WDM-UWB multi-user detection method based on Gaussian mixture model clustering according to claim 2, wherein the WDM-UWB multi-user detection method comprises: mapping the code element in the third step
Figure FDA00026927723600000212
Performing Gaussian mixture model clustering, wherein the specific process of correcting the error code element comprises the following steps:
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.
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