CN102185632B - Genetic algorithm-based differential multi-user detection method in ultra wide band system - Google Patents

Genetic algorithm-based differential multi-user detection method in ultra wide band system Download PDF

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CN102185632B
CN102185632B CN 201110066585 CN201110066585A CN102185632B CN 102185632 B CN102185632 B CN 102185632B CN 201110066585 CN201110066585 CN 201110066585 CN 201110066585 A CN201110066585 A CN 201110066585A CN 102185632 B CN102185632 B CN 102185632B
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朱光喜
吴伟民
孔政敏
钟梁
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Huazhong University of Science and Technology
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Abstract

The invention discloses a genetic algorithm-based differential multi-user detection method in an ultra wide band (UWB) system. A complementary error function mutation-based genetic algorithm (CEFM-GA) is combined with a differential algorithm (DA). In the indoor UWB environment, the method overcomes the defect of consistent mutation adopted in the traditional GA, effectively improves the bit error rate (BER) performance of a multi-user detection system, and greatly reduces the calculation complexity of the system, so that the system is simpler and more practical.

Description

In the radio ultra wide band system based on the difference multi-user test method of genetic algorithm
Technical field
The present invention relates to signal detection technique in ultra broadband (UWB) system, be specifically related to a kind of multi-user test method with lower complexity based on genetic algorithm (GA).
Background technology
Along with the high speed development of wireless communication technology, and the promotion of data service, short-distance wireless communication presents huge development potentiality.In the last few years, short-distance wireless communication technology obtains fast development, the concepts such as wireless sensor network, Wireless Personal Network (WPAN) and wireless body territory net (WBAN) are suggested and are paid close attention to widely, have started the upsurge of short-distance wireless communication.Can say the logical important development and application direction that has become future wireless system of short-distance wireless.
The UWB technology has the characteristics such as high transfer rate, low-power consumption, antijamming capability are strong, and it has become one of focus of present wireless communication field research and development, and is regarded as the key technology of next generation wireless communication.
Multi-user's UWB system is an important topic of recent UWB technical research.In the multi-user UWB system, introduced multiple access technology.It can make the shared frequency spectrum of transmitting of a plurality of users, and (TH) spread spectrum sends a plurality of subscriber signals during by direct sequence (DS) or jumping.For multi-user UWB system, Multiuser Detection (MUD) algorithm is divided three classes usually: Optimum Detection, linearity test algorithm and non-linear detection algorithm.Optimum Detection is maximum likelihood (ML) algorithm, its best results, but complexity is the highest, is unfavorable for real-time processing; Linear algorithm mainly comprises decorrelation and least mean-square error (MMSE) method, and decorrelation and MMSE algorithm all need cross-correlation matrix is inverted, and when number of users is a lot, uses the complexity of decorrelation and MMSE algorithm still too large; Therefore tool realistic meaning is the non-linear detection algorithm, and it mainly comprises disturbing eliminates (IC) and intelligent algorithm, and they calculate by simplifying, and realize that suboptimum detects effect, obtain better balance between performance and complexity.
Yet traditional IC algorithm is BER (error rate) poor-performing in the UWB system, so adopt the Multiuser Detection of intelligent algorithm to be widely studied.In intelligent algorithm, it is the most extensive that GA uses in engineering optimization.But, limited based on the multi-user test method elevator system BER performance of traditional GA, thereby and its complexity can increase exponentially along with the increase of GA iterations.
Therefore, be necessary to propose a kind of can the BER of Effective Raise system performance in the UWB system, can reduce again the multi-user test method of system complexity.
Summary of the invention
The object of the present invention is to provide in a kind of radio ultra wide band system the difference multi-user test method based on genetic algorithm, solve the problem of traditional IC and GA multiuser detection algorithm BER performance deficiency, solved simultaneously the higher problem of iteration detection algorithm computation complexity.
Based on the difference multi-user test method of genetic algorithm, carry out in accordance with the following steps in the radio ultra wide band system:
(1) with K user's signal y=[y to be detected 1, y 2... y K] TAdopt respectively the first main genetic algorithm Multiuser Detection and the first Aided Genetic Algorithm Multiuser Detection to do the first round and detect, correspondence obtains first round master testing result
Figure BDA0000051024280000021
With the auxiliary detection result
Figure BDA0000051024280000022
T represents transposition;
(2) to first round testing result With
Figure BDA0000051024280000024
Carry out calculus of differences and obtain difference matrix
(3) in the inquiry difference matrix Nonzero element, with its corresponding signal y to be detected iIn element combinations generate in order to the second signal to be detected of taking turns detection
Figure BDA0000051024280000027
(4) to the second signal to be detected of taking turns detection
Figure BDA0000051024280000028
Adopt the second genetic algorithm Multiuser Detection to do second and take turns detection, obtain second and take turns testing result
Figure BDA0000051024280000029
(5) take turns testing result with second
Figure BDA00000510242800000210
In element substitute successively in order Middle nonzero element obtains final detection result
Figure BDA00000510242800000212
Wherein, the iterations of the iterations of the main genetic algorithm Multiuser Detection of the iterations of the second genetic algorithm Multiuser Detection 〉=first＞first Aided Genetic Algorithm Multiuser Detection.
As optimization, described the first master, first assists the second genetic algorithm Multiuser Detection mode of reaching specific as follows:
(P1) initialization iterations g treats detection signal and generates g for chromosome as binary coding, and the genic value in the chromosome is 1 or-1;
(P2) calculate g for each chromosomal adaptive value;
(P3) select chromosome dyad according to the larger principle of the larger selected probability of adaptive value at g in for chromosome, not selected chromosome directly as g+1 for chromosome; The chromosome of choosing then in twos pairing carries out crossing operation, and the new chromosome that crossing operation is obtained carries out genetic mutation and generates g+1 for chromosome, and the genetic mutation probability is
Figure BDA00000510242800000213
σ is noise variance;
(P4) calculate g+1 for each chromosomal adaptive value according to the mode identical with step (P2);
(P5) all g+1 are carried out adaptive value relatively for chromosome and g for chromosome, if some g+1 generation chromosomal adaptive value less than chromosomal adaptive value of some g generation, then these g+1 are updated to these g for chromosome for chromosome;
(P6) g=g+1, if g less than iterations threshold value G, then returns step (P2), otherwise, enter step (P7);
(P7) calculate chromosomal adaptive value of G generation according to the mode identical with step (P2), the chromosome of adaptive value maximum is final detection result.
The invention has the beneficial effects as follows: the scheme that the present invention solves above-mentioned first technical problem is the multi-user test method (being CEFM-GA) that proposes a kind of GA based on complementary error function variation (CEFM); The scheme that solves second technical problem is to propose again a kind of difference algorithm (DA) on original CEFM-GA multi-user test method.Not only overcome the problem of traditional IC and the higher BER of GA multiuser detection algorithm, and greatly reduced computation complexity.
Description of drawings
Fig. 1 is based on CEFM-GA difference Multiuser Detection (being CEFM-GADA) structured flowchart.
Fig. 2 is CEFM-GA Multiuser Detection flow chart.
Fig. 3 is the variation probability schematic diagram based on CEFM.
Fig. 4 is under the CM1 channel of IEEE 802.15.3a, the BER performance chart of CEFM-GADA and other multi-user test method.
Fig. 5 is under the CM2 channel of IEEE 802.15.3a, the BER performance chart of CEFM-GA DA and other multi-user test method.
Fig. 6 is under the CM3 channel of IEEE 802.15.3a, the BER performance chart of CEFM-GADA and other multi-user test method.
Fig. 7 is under the CM4 channel of IEEE 802.15.3a, the BER performance chart of CEFM-GA DA and other multi-user test method.
Fig. 8 is under the CM1-4 channel, neutral element chart of percentage comparison in the difference matrix of CEFM-GADA.
Fig. 9 is GA, CEFM-GA under different genetic iteration number of times and the computation complexity figure of CEFM-GA DA.
Figure 10 is the computation complexity figure of the various multi-user test methods of different user when counting.
Embodiment
Now reach by reference to the accompanying drawings embodiment to realizing that the present invention specifically describes.
With reference to figure 1, Fig. 1 shows the Multiuser Detection with DA structure, i.e. the difference Multiuser Detection.Specifically, the structured flowchart that is based on the multiplex detection of CEFM-GA difference (being CEFM-GA DA) shown in Figure 1.Be described in further detail the implementation step of CEFM-GADA proposed by the invention below in conjunction with Fig. 1.
1. when the first round is detected with signal y (y=[y to be detected 1, y 2... y K] T) carry out first round detection by a main CEFM-GA and an auxiliary CEFM-GA multi-user detector respectively.When wherein signal y to be detected is by main CEFM-GA detector, detect used through its signal value of a buffer memory in order to next round first.
In this step, the genetic iteration number of times is G in the auxiliary CEFM-GA detector 0=1, the genetic iteration number of times is G in the main CEFM-GA detector 1＞G 0
2. again with main CEFM-GA detector output
Figure BDA0000051024280000041
With auxiliary CEFM-GA detector output
Figure BDA0000051024280000042
Carry out calculus of differences by the DA module in the lump and obtain difference matrix
Figure BDA0000051024280000043
Figure BDA0000051024280000044
And will preserve its result.Wherein, concrete calculus of differences can be expressed as
Figure BDA0000051024280000045
Subscript (1) expression first round CEFM-GA Multiuser Detection.
3. the nonzero element in the inquiry difference matrix, and in multiple user signals to be detected, find out the element combinations corresponding with this nonzero element position and generate in order to the second signal to be detected of taking turns.Specific implementation adopts the DA trigger, and the DA module arithmetic is obtained
Figure BDA0000051024280000046
Feed back to the DA trigger, judge the signal that obtains needs and need not again to detect.In other words, the DA trigger can be according to i user's difference sequence
Figure BDA0000051024280000047
Trigger from the y in the buffer i, when
Figure BDA0000051024280000048
When middle nonzero element reached, the DA trigger was just carried out once and is triggered, and namely closed trigger switch is judged as and need to again detects; Otherwise, then be judged as and need not again to detect.Here the initial condition of trigger is closed.Behind the DA trigger, generate a new sequence
Figure BDA0000051024280000049
Be second and take turns the input of CEFM-GA Multiuser Detection.Wherein, the CEFM-GA Multiuser Detection is taken turns in subscript (2) expression second; I ∈ 1,2 ..., K}.
4. the signal that the needs that at last step 3 fed back to detect again
Figure BDA00000510242800000410
Be sent to the CEFM-GA detector and carry out again second and take turns detection (at this moment the genetic iteration number of times is G '), again with the output of CEFM-GA detector
Figure BDA00000510242800000411
Alternative operation through the DA module (is about to
Figure BDA00000510242800000412
Middle element substitutes successively by former order
Figure BDA00000510242800000413
Middle nonzero element) after, obtains
Figure BDA00000510242800000414
After taking turns detection through second, the final detection result of DA module output CEFM-GA DA multi-user detector
Figure BDA00000510242800000415
Figure BDA00000510242800000416
It specifically can be expressed as
Figure BDA00000510242800000417
It should be noted that the CEFM-GA multi-user detector that adopts in the described step 1 all is based on the CEFM-GA algorithm.But the present invention is not limited to the CEFM-GA algorithm, but generally is applicable to genetic algorithm (GA).It is that the BER performance of CEFM-GA algorithm is better than traditional GA that the present invention adopts the cause of CEFM-GA algorithm.The below specifies this CEFM-GA algorithm.Such as Fig. 2, the flow process of CEFM-GA algorithm comprises following calculation step.
1. encode and the initialization population
When the CEFM-GA algorithm begins, the test data of K * 1 (K is number of users) vector
Figure BDA0000051024280000051
Must be converted into binary serial data by encoding mechanism.This binary serial data is called as chromosome, and its serial data element is called as gene.Here the CEFM-GA Multiuser Detection is a kind of multiobject optimized algorithm, searches the most qualified a plurality of users' binary bits combination, namely seeks the test data vector close to the multi-user data signal d of UWB transmitting terminal.In order to be without loss of generality, we adopt binary phase shift keying (BPSK) modulation system in the UWB system model.Therefore, chromosomal binary coding is the modulating data string of BPSK.In this case, the gene number in the chromosome also is the test data vector of BPSK modulation
Figure BDA0000051024280000052
In bit number, be number of users.
After having encoded, CEFM-GA will generate an initial population that is comprised of P member (chromosome or individuality).In population, each chromosome or individuality are a K * 1 vectors.Test data in this vector
Figure BDA0000051024280000053
Corresponding i user's data.Here our initial genetic algebra g=1.
2. adaptive value is estimated
Each individuality in the population behind the coding is carried out adaptive value to be estimated.Here adopt target function to go to estimate individual adaptive value.Adaptive value represents degree of closeness individual and optimal solution, and namely optimum individuality can make the target function value maximization.We choose As target function.Wherein, R=SS TExpression auto-correlation and cross-correlation matrix
Figure BDA0000051024280000055
Represent respectively every bit signal energy of i user, UWB channel impulse response and the spread spectrum code sequence of lognormal decline,
Figure BDA0000051024280000056
The expression linear convolution.), n=∫ TsS i(t) n (t) dt, (n (t) is the additive white Gaussian noise of zero-mean, T SBe symbol period), y=Rd+n (d is the data-signal vector that the employing BPSK mode of K * 1 is modulated), () TThe transposition of representing matrix.
Upper target function value is defined as adaptive value.Therefore, optimal detection or namely be to make target function near the solution vector of optimal detection
Figure BDA0000051024280000057
Obtain peaked individuality
Figure BDA0000051024280000058
Obtain thus accurate survey of judgement of ideal adaptation degree in the population: the individuality with larger target function value (being adaptive value)
Figure BDA0000051024280000059
Its fitness is also higher.
3. select
After calculating individual fitness, go breeding of future generation according to this adaptive value according to excellent individual in the predetermined selection probability selection population, p individual selected probability can be expressed as
Figure BDA0000051024280000061
Wherein
Figure BDA0000051024280000062
Represent that g is for p of heredity individual target function value or adaptive value;
Figure BDA0000051024280000063
Represent that g is for target function value or the adaptive value (being the minimum target functional value) of poor individuality in the heredity.Specifically, produce a random number P from 0 to 1 cIf, P c≤ P S, p, then select p individuality to go breeding of future generation; Otherwise it is not selected.
4. intersect
Above-mentioned selected individuality is matched in twos, intercourse portion gene separately.Specifically, crossing operation is to choose at random one or more crosspoints, then exchanges the partial binary serial data of two individualities of front, crosspoint or back, produces two new individualities.(for example, two individualities are respectively-1-111 and 1-11-1 that first that chooses its binary string intersects as the crosspoint, produces a pair of new individuality after intersecting to be respectively-1-11-1 and 1-111.)
5. based on complementary error function variation (CEFM)
The variation computing is the key of GA algorithm success or not.Individuality after the above-mentioned intersection is made a variation according to the CEFM criterion that proposes.Definition variation probability
Figure BDA0000051024280000064
Expression from data j be converted to data l probability (j, l ∈ 1,1}).
Fig. 3 shows under the BPSK modulation system, the variation probability based on CEFM that proposes among the present invention
Figure BDA0000051024280000065
By shown in Figure 3 ,-1 is separated by vertical dotted line with 1, and the N of Gaussian Profile (0, σ) concentrate on the left-half of dotted line, namely-1 in the zone at place.Here σ is illustrated in the noise variance under the specific signal to noise ratio (SNR).The variation probability from-1 to 1 (namely
Figure BDA0000051024280000066
) being proportional to the area that the shade among Fig. 3 distributes, can be expressed as
p m ( - 1 → 1 ) = ( 1 / 2 ) · erfc [ ( 1 / σ ) · ( - 1 - 1 ) / 2 ] = ( 1 / π ) · ∫ 1 / σ ∞ e - t 2 dt ;
Similar to following formula, can obtain equally
p m ( 1 → - 1 ) = ( 1 / 2 ) · erfc [ ( 1 / σ ) · ( 1 + 1 / 2 ) ] = ( 1 / π ) · ∫ 1 / σ ∞ e - t 2 dt
Erfc[wherein] be expressed as complementary error function (CEF).
6. elitism strategy
With elitism strategy classic a part of individuality in the parent is removed to replace that the poorest part individuality of gene in the filial generation at last, avoid losing the excellent individual that some have higher adaptive value with this.In other words, the larger a part of individuality of adaptive value in g generation is gone replace g+1 that part that adaptive value is less in generation individual.
Above six flow processs that calculation step is the CEFM-GA multiuser detection algorithm.As shown in Figure 2, CEFM-GA is a cyclic process, turns back to again the 2nd calculation step after namely executing the 6th calculation step, until genetic algebra g reaches maximum G at every turn.When genetic algebra maximum G or Population Size P were enough large, the final result of GA algorithm Multiuser Detection will infinite approach optimal detection solution.
The present invention through the CEFM-GA of the above-mentioned differential configuration of implementation in the UWB system, obtains a kind of difference multi-user test method---CEFM-GADA based on GA of novelty according to indoor UWB system channel feature.The multi-user test method that adopts the present invention to propose has not only overcome the problem of traditional IC and the higher BER of GA multi-user test method, and greatly reduces computation complexity.Relatively be elaborated below by several common methods in the present invention and the prior art.
Several common methods in the CEFM-GA DA method that the present invention proposes and the prior art: matched filter (MF), counteracting serial interference (SIC), Parallel Interference Cancellation (PIC) and traditional GA multi-user test method, the BER performance under UWB channel (IEEE 802.15.3a CM1-4) is shown in Fig. 4-7.By finding out among Fig. 4-7, no matter CEFM-GA DA multi-user test method BER performance of the present invention obviously is better than traditional SIC, PIC and traditional GA multi-user test method under which kind of channel.CEFM-GA DA compares the performance gain that also has in various degree without the CEFM-GA of differential configuration.Can find out in addition, take turns the increase of the genetic iteration number of times G ' that CEFM-GA detects along with second, the BER performance of CEFM-GA DA obviously improves.Special when G '=20 or 30, the performance of CEFM-GADA obviously is better than genetic iteration number of times G and is 10 traditional GA (G=10) and CEFM-GA (G=10).This will be attributed to the born advantage of genetic algorithm---and increase genetic algebra and can obtain more excellent individuality.
Fig. 8 shows under the CM1-4 channel, the difference matrix of CEFM-GADA
Figure BDA0000051024280000071
The percentage of middle nonzero element (be second of CEFM-GA DA take turns detect the ratio that input data volume and the first via detect the input data volume).As shown in Figure 8, under the CM1-4 channel in the difference matrix average percent of nonzero element be respectively 9.80%, 10.07%, 10.36% and 10.52%, their mean value is near 10%.That is to say only have 10% data to be sent to second of CEFM-GA DA and take turns detection; Take turns in the detection second in other words, can avoid 90% calculating operation nearly.This also means, can improve the BER performance by increasing genetic algebra G value.
Fig. 9 shows under different genetic algebras, the computation complexity of GA, CEFM-GA and CEFM-GA DA.Here, can utilize the method for floating-point operation to obtain computation complexity.As seen from Figure 9, along with the genetic iteration number of times increases, the computation complexity of the CEFM-GA DA that the present invention proposes increasess slowly, and is starkly lower than the computation complexity of traditional GA and CEFM-GA.Although G ' is not quite similar, the computation complexity of three CEFM-GA DA is very approaching.One of reason: the genetic iteration number of times G of the CEFM-GA DA first round main CEFM-GA detector when detecting 1Genetic iteration number of times G (G less than traditional GA and CEFM-GA 1=0.3G); Former therefore two: second of CEFM-GADA takes turns and detects input data volume (difference matrix
Figure BDA0000051024280000072
In nonzero element) only have the first round to detect 10% of input data volume.
Figure 10 shows the computation complexity of the various multi-user test methods with different user number.As shown in Figure 10, the computation complexity of the CEFM-GA DA of the present invention's proposition increases the slowest.When number of users K＞16, the computation complexity of CEFM-GA DA (G '=10) is minimum.That is to say, when power system capacity became large, the computation complexity of CEFM-GADA increased the slowest.Also can be found out by Fig. 9 and Figure 10 in addition, when adopting traditional SIC, PIC and GA multi-user test method, will cause the waste of a lot of computational resources.
The present invention utilizes CEFM-GA and DA structure, thereby has formed the difference multi-user test method in a kind of new UWB system.Use the present invention can not only overcome traditional SIC, PIC and the lower problem of GA multi-user test method BER performance, and can also greatly reduce the computation complexity of multi-user detection system, so that system is simpler, practical.Therefore, the present invention has good actual application prospect.
The above; be better embodiment of the present invention only, but protection scope of the present invention is not limited to this, any those skilled in the art are in the disclosed technical scope of the present invention; the conversion that can expect easily and replacement all should be included in the protection category of the present invention.Therefore, protection scope of the present invention should be as the criterion with the protection range of claim.

Claims (2)

  1. In the radio ultra wide band system based on the difference multi-user test method of genetic algorithm, be specially:
    (1) with K user's signal y=[y to be detected l, y 2... y K] TAdopt respectively the first main genetic algorithm Multiuser Detection and the first Aided Genetic Algorithm Multiuser Detection to do the first round and detect, correspondence obtains first round master testing result b → ( 1 ) = [ b → 1 ( 1 ) , b → 2 ( 1 ) , · · · b → K 1 ) ] T With the auxiliary detection result b ~ ( 1 ) = [ b ~ 1 ( 1 ) , b ~ 2 ( 1 ) , · · · b ~ K ( 1 ) ] T , T represents transposition;
    (2) to first round testing result b → ( 1 ) = [ b → 1 ( 1 ) , b → 2 ( 1 ) , · · · b → K 1 ) ] T With b ~ ( 1 ) = [ b ~ 1 ( 1 ) , b ~ 2 ( 1 ) , · · · b ~ K ( 1 ) ] T Carry out calculus of differences and obtain difference matrix
    Figure FDA00003423043300015
    (3) in the inquiry difference matrix Nonzero element, with its corresponding signal y to be detected iIn element combinations generate in order to the second signal to be detected of taking turns detection
    Figure FDA00003423043300017
    I=1 ..., K;
    (4) to the second signal to be detected of taking turns detection
    Figure FDA00003423043300018
    Adopt the second genetic algorithm Multiuser Detection to do second and take turns detection, obtain second and take turns testing result
    Figure FDA00003423043300019
    (5) take turns testing result with second
    Figure FDA000034230433000110
    In element substitute successively in order
    Figure FDA000034230433000111
    Middle nonzero element obtains final detection result
    Wherein, the iterations of the iterations of the main genetic algorithm Multiuser Detection of the iterations of the second genetic algorithm Multiuser Detection 〉=first＞first Aided Genetic Algorithm Multiuser Detection.
  2. In the radio ultra wide band system according to claim 1 based on the difference multi-user test method of genetic algorithm, it is characterized in that, described first main, first auxiliary and the second genetic algorithm Multiuser Detection mode is specific as follows:
    P1 initialization iterations g treats detection signal and generates g for chromosome as binary coding, and the genic value in the chromosome is 1 or-1;
    P2 calculates g for each chromosomal adaptive value;
    P3 selects chromosome dyad according to the larger principle of the larger selected probability of adaptive value at g in for chromosome, not selected chromosome directly as g+1 for chromosome; The chromosome of choosing then in twos pairing carries out crossing operation, and the new chromosome that crossing operation is obtained carries out genetic mutation and generates g+1 for chromosome, and the genetic mutation probability is p m ( 1 - ↔ 1 ) = ( 1 / π ) · ∫ 1 / σ ∞ e - t 2 dt , σ is noise variance, and t is integration variable;
    P4 calculates g+1 for each chromosomal adaptive value according to the mode identical with step P2;
    P5 carries out adaptive value relatively for chromosome and g for chromosome with all g+1, if some g+1 generation chromosomal adaptive value less than chromosomal adaptive value of some g generation, then these g+1 are updated to these g for chromosome for chromosome;
    P6g=g+1, if g then returns step P2 less than iterations threshold value G, otherwise, enter step P7;
    P7 calculates chromosomal adaptive value of G generation according to the mode identical with step P2, and the chromosome of adaptive value maximum is final detection result.
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KR20090004022A (en) * 2007-07-06 2009-01-12 인하대학교 산학협력단 Multiuser detect device of ds-cdma system and method thereof
US20090186658A1 (en) * 2007-12-21 2009-07-23 University Of New Brunswick Joint communication and electromagnetic optimization of a multiple-input multiple-output ultra wideband base station antenna

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KR20090004022A (en) * 2007-07-06 2009-01-12 인하대학교 산학협력단 Multiuser detect device of ds-cdma system and method thereof
US20090186658A1 (en) * 2007-12-21 2009-07-23 University Of New Brunswick Joint communication and electromagnetic optimization of a multiple-input multiple-output ultra wideband base station antenna

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