CN102142859B - Direct sequence ultra wide band multi-user detection method based on minimum mean squared error and artificial fish-swarm joint - Google Patents

Abstract

The invention provides a direct sequence ultra wide band multi-user detection method based on minimum mean squared error and artificial fish-swarm joint, relating to the field of signal detection. The invention solves the problems of poor system real-time and low detection performance caused by high complexity of the traditional direct sequence ultra wide band multi-user detection method. The multi-user detection method provided by the invention comprises the steps of: firstly, primarily detecting a direct sequence ultra wide band signal through a matched filter; secondly, detecting by using a suboptimal minimum mean squared error to obtain a suboptimal solution of multi-user detection; and finally, by using the suboptimal solution as an initial value, optimizing by using an artificial fish-swarm algorithm to obtain a performance close to the optimal multi-user detection. The invention is suitable for a multi-user detection process with higher requirements on error rate performance and real-time.

Description

Direct sequence UWB multi-user detection method based on least mean-square error and artificial fish-swarm associating

Technical field

The present invention relates to the input field, be specifically related to a kind of multi-user test method based on least mean-square error and artificial fish-swarm associating.

Background technology

The direct sequence ultra broadband (Direct Sequence Ultra-wideband is one of the modulation system of super broad band radio communication DS-UWB), and its signal waveform is:

$S^{\left(k\right)}\left(t\right)=\underset{j=-\∞}{\overset{\∞}{\mathrm{\Σ}}}\underset{n=0}{\overset{N_s-1}{\mathrm{\Σ}}}{d_j}^{\left(k\right)}{g_n}^{\left(k\right)}p(t-j{T}_f-n{T}_c)$

Wherein, d _j ^(k){ 1 ,+1} is k user's binary message symbol to ∈, g _n ^(k){ 1 ,+1} is k user's pseudo random sequence to ∈, T _cBe the chip period of pseudo random sequence, T _fThe expression symbol period, and N is arranged _s=T _f/ T _c, i.e. each information symbol N _sIndividual pulse represents that p (t) is the ultra broadband single pulse signal, adopts Gaussian pulse usually.

Utilize the orthogonality between each user's pseudo random sequence, can distinguish each user's information at receiving terminal by matched filter.Yet in actual use because pseudo noise code is not strict orthogonal, can produce when using correlation reception multiple access disturb (Multiple Access Interference, MAI).Particularly in asynchronous transmission channel and multipath propagation environment, it is more serious that MAI will become, and has a strong impact on detection performance and the capacity of system.

(Multiuser Detection, MUD) technology is a kind of receiving terminal technology that can eliminate or weaken MAI to Multiuser Detection.1986, Verdu has proposed optimum multiuser detection algorithm, the situation of using this algorithm can make the detection performance of system approach single user system, but its computation complexity and number of users exponent function relation, when number of users is very big, the amount of calculation of this algorithm is very big, causes the real-time of system very poor, generally seldom uses on the engineering.And the MMSE detection algorithm of suboptimum, though its computation complexity is very low, it detects performance and optimal situation differs greatly, and is not suitable for high-quality communication situation.

Summary of the invention

The present invention is in order to solve existing direct sequence UWB multi-user detection method because the system real time that the complexity height causes is poor, and detect the low problem of performance, thereby provide a kind of direct sequence UWB multi-user detection method based on least mean-square error and artificial fish-swarm associating.

Based on the direct sequence UWB multi-user detection method of least mean-square error and artificial fish-swarm associating, it is realized by following steps:

Step 1, obtain the direct sequence ultra-broadband signal, described direct sequence ultra-broadband signal is input in K the matched filter that is in the collaborative work state simultaneously, the matched filtering that acquires K user is y as a result _iA described K user and K matched filter are corresponding one by one;

The cross correlation matrix number of K matched filter is: R=(r _Ij) _{K * K}, in the formula, r _Ij＞＞| r _Ij|, i, j=1,2 ... K, i ≠ j, K are positive integer;

K the user's that step 2, the least mean-square error multiuser detection algorithm that utilizes suboptimum obtain step 1 matched filtering is y as a result _iDetect processing, obtain K user's suboptimal solution b=[b ₁, b ₂..., b _i..., b _K];

Step 3, K user's that step 1 is obtained matched filtering is y as a result _iCarry out optimum Multiuser Detection, its detection method is described optimum Multiuser Detection to be equivalent to find the solution discrete function Ω (b)=2b ^TAy-b ^TThe maximum of ARAb is in the formula: A=diag (A ₁, A ₂..., A _i..., A _K), y=[y ₁, y ₂..., y _i..., y _K] ^T, b=[b ₁, b ₂..., b _i..., b _K] ^TAnd b _i∈ { 1,1};

Step 4, utilize artificial fish-swarm algorithm that the discrete function described in the step 3 is carried out optimizing, obtain K approximate optimal solution

The initial value of every artificial fish in the described artificial fish-swarm algorithm is K user's of step 2 acquisition suboptimal solution b=[b ₁, b ₂..., b _i..., b _K] or its variation operation result;

Step 5, K the approximate optimal solution that step 4 is obtained

As K user's testing result of direct sequence ultra-broadband signal to be detected, finish the Multiuser Detection of direct sequence ultra-broadband signal.

K the user's that the least mean-square error multiuser detection algorithm that utilizes suboptimum described in the step 2 obtains step 1 matched filtering is y as a result _iThe concrete grammar that detects processing is:

With K user's obtaining in the step 1 matched filtering y as a result _iBe expressed in matrix as: y=RAb+n, wherein n is a zero-mean Gaussian random vector, and its covariance matrix is: E[nn ^T]=σ ²R, σ ²Energy for noise; Matrix y is carried out linear transformation M=A ^-1[R+ σ ²A ^-2] ^-1, make function Ω (M)=E[||b-My|| ²] reach minimum value, thus the matched filtering that realizes K user y as a result _iDetect processing.

K the user's that step 1 is obtained matched filtering is y as a result _iCarry out optimum Multiuser Detection and adopt the Maximum likelihood sequence detection method.

The initial value of artificial fish-swarm described in the step 4 is K user's of step 2 acquisition least mean-square error suboptimal solution b=[b ₁, b ₂..., b _i..., b _K] the concrete grammar of variation operation result be:

Steps A 1, K user's obtaining from step 2 least mean-square error suboptimal solution b=[b ₁, b ₂..., b _i..., b _K] in select a bit element at random, make it and-1 carry out XOR, that is:

Obtain after the computing b '=(b as a result ₁, b ₂..., b _i' ..., b _K), and with its initial value as the artificial fish of N+1 bar;

Steps A 2, the value of N is added 1, and return execution in step A1, till the initial value of every fish in obtaining artificial fish-swarm;

The initial value of described N is 1.

Utilize artificial fish-swarm algorithm that the discrete function described in the step 3 is carried out optimizing described in the step 4, obtain K approximate optimal solution Method realized by following steps:

Step B1, preliminary bulletins plate: in the optimizing function with the initial value substitution step 3 of every artificial fish, the size of the corresponding functional value of initial value of more every artificial fish will be composed to bulletin board corresponding to the artificial fish state of functional value maximum;

The selection of step B2, artificial fish behavior: artificial fish has four kinds of behaviors, be respectively: foraging behavior, the behavior of bunching, behavior and random behavior knock into the back, every artificial fish is according to self wherein a kind of behavior of optimum of residing environmental selection, the global optimizing of the optimizing function in the performing step three obtains to approach the result of optimum Multiuser Detection.

The renewal of step B3, bulletin board: after each iteration is finished in this algorithm, the artificial fish of each bar all will obtain new state value; Optimizing function in the state value substitution step 3 that these are new is selected maximal function value wherein, if this value greater than the value of preserving on the bulletin board, is then upgraded bulletin board; Otherwise bulletin board remains unchanged;

Step B4, judge that whether end condition satisfies: when the value in the bulletin board has reached the optimal value of function in the step 3, or the iterations of algorithm reached set point, and then algorithm stops to carry out, and execution in step B5; Otherwise, return execution in step B2;

Step B5, after this algorithm stops carrying out, with the value output of preserving in the bulletin board, as the Multiuser Detection result of this direct sequence ultra-broadband signal to be detected.

The Mathematical Modeling of the foraging behavior of artificial fish is described in the step B2:

The Mathematical Modeling of behavior of bunching is:

The Mathematical Modeling of behavior of knocking into the back is:

Wherein, X _iCurrent state for artificial fish; Within sweep of the eye, X _jThe state value of expression picked at random, X _cBe the center of the shoal of fish, X _MaxOptimum individual in the expression shoal of fish; Y _i, Y _j, Y _cAnd Y _MaxThe optimizing function is at X in the corresponding step 3 of difference _i, X _j, X _cAnd X _MaxThe value at place; The arbitrary width of the artificial fish of Random (step) expression; X _InextNext step mobile status of representing artificial fish; n _fBe artificial fish number within the vision; δ is the crowding factor.

Distance between the artificial fish among the step B2 under each behavior state of artificial fish adopts the XOR operator to calculate.

Beneficial effect: the invention provides a kind of associating multi-user test method based on least mean-square error (MMSE) and artificial fish-swarm (AFSA), the MMSE that this method is at first carried out suboptimum detects, the suboptimal solution of certain error rate is satisfied in acquisition, and with its initial value as artificial fish-swarm algorithm, carry out global optimizing again, thereby obtain to approach the result of optimum Multiuser Detection, the computation complexity of method of the present invention is low, system real time is strong, and it is higher to detect performance.

Description of drawings

Fig. 1 is schematic flow sheet of the present invention.

Embodiment

Embodiment one, in conjunction with Fig. 1 this embodiment is described, based on the direct sequence UWB multi-user detection method of least mean-square error and artificial fish-swarm associating, it is realized by following steps:

Step 1, obtain the direct sequence ultra-broadband signal, described direct sequence ultra-broadband signal is input in K the matched filter that is in the collaborative work state simultaneously, the matched filtering that acquires K user is y as a result _iA described K user and K matched filter are corresponding one by one;

The cross correlation matrix number of K matched filter is: R=(r _Ij) _{K * K}, in the formula, r _Ij＞＞| r _Ij|, i, j=1,2 ... K, i ≠ j, K are positive integer;

K the user's that step 2, the least mean-square error multiuser detection algorithm that utilizes suboptimum obtain step 1 matched filtering is y as a result _iDetect processing, obtain K user's suboptimal solution b=[b ₁, b ₂..., b _i..., b _K]; Each user's suboptimal solution is b _i, detect as if the information to K user this moment, and its error rate is less than 0.1;

Step 3, K user's that step 1 is obtained matched filtering is y as a result _iCarry out optimum Multiuser Detection, its detection method is described optimum Multiuser Detection to be equivalent to find the solution discrete function Ω (b)=2b ^TAy-b ^TThe maximum of ARAb is in the formula: A=diag (A ₁, A ₂..., A _i..., A _K), y=[y ₁, y ₂..., y _i..., y _K] ^T, b=[b ₁, b ₂..., b _i..., b _K] ^TAnd b _i∈ { 1,1};

Step 4, utilize artificial fish-swarm algorithm that the discrete function described in the step 3 is carried out optimizing, obtain K approximate optimal solution

The initial value of every artificial fish in the described artificial fish-swarm algorithm is K user's of step 2 acquisition suboptimal solution b=[b ₁, b ₂..., b _i..., b _K] or its variation operation result;

Step 5, K the approximate optimal solution that step 4 is obtained

As K user's testing result of direct sequence ultra-broadband signal to be detected, finish the Multiuser Detection of direct sequence ultra-broadband signal.

K the user's that the least mean-square error multiuser detection algorithm that utilizes suboptimum described in the step obtains step 1 matched filtering is y as a result _iThe concrete grammar that detects processing is:

With K user's obtaining in the step 1 matched filtering y as a result _iBe expressed in matrix as: y=RAb+n, wherein n is a zero-mean Gaussian random vector, and its covariance matrix is: E[nn ^T]=σ ²R; Matrix y is carried out linear transformation, make function Ω (M)=E[||b-My|| ²] reach minimum value, thus the matched filtering that realizes K user y as a result _iDetect processing.

The K that in the step 3 step 1 an is obtained user's matched filtering is y as a result _iCarry out optimum Multiuser Detection and adopt the Maximum likelihood sequence detection method.

With the least mean-square error suboptimum testing result b=(b that draws in the step 2 ₁, b ₂..., b _i..., b _K) as the initial value of the artificial fish of N bar;

The initial value of artificial fish-swarm described in the step 4 is K user's of step 2 acquisition least mean-square error suboptimal solution b=[b ₁, b ₂..., b _i..., b _K] the concrete grammar of variation operation result be:

Steps A 1, K user's obtaining from step 2 least mean-square error suboptimal solution b=[b ₁, b ₂..., b _i..., b _K] in select a bit element at random, make it and-1 carry out XOR, that is:

Obtain after the computing b '=(b as a result ₁, b ₂..., b _i' ..., b _K), and with its initial value as the artificial fish of N+1 bar;

Steps A 2, the value of N is added 1, and return execution in step A1, till the initial value of every fish in obtaining artificial fish-swarm;

The initial value of described N is 1.Utilize artificial fish-swarm algorithm that the discrete function described in the step 3 is carried out optimizing described in the step 4, obtain K approximate optimal solution

Method realized by following steps:

Step B1, preliminary bulletins plate: in the optimizing function with the initial value substitution step 3 of every artificial fish, the size of the corresponding functional value of initial value of more every artificial fish will be composed to bulletin board corresponding to the artificial fish state of functional value maximum;

The selection of step B2, artificial fish behavior: artificial fish has four kinds of behaviors, be respectively: foraging behavior, the behavior of bunching, behavior and random behavior knock into the back, every artificial fish is according to self wherein a kind of behavior of optimum of residing environmental selection, thereby the global optimizing of the optimizing function in the performing step three obtains to approach the result of optimum Multiuser Detection.

The renewal of step B3, bulletin board: after each iteration is finished in this algorithm, the artificial fish of each bar all will obtain new state value; Optimizing function in the state value substitution step 3 that these are new is selected maximal function value wherein, if this value greater than the value of preserving on the bulletin board, is then upgraded bulletin board; Otherwise bulletin board remains unchanged;

Step B4, judge that whether end condition satisfies: when the value in the bulletin board has reached the optimal value of function in the step 3, or the iterations of algorithm reached set point, and then algorithm stops to carry out, and execution in step B5; Otherwise, return execution in step B2;

Step B5, after this algorithm stops carrying out, with the value output of preserving in the bulletin board, as the Multiuser Detection result of this direct sequence ultra-broadband signal to be detected.

The Mathematical Modeling of the foraging behavior of artificial fish is described in the step B2:

The Mathematical Modeling of behavior of bunching is:

The Mathematical Modeling of behavior of knocking into the back is:

Wherein, X _iCurrent state for artificial fish; Within sweep of the eye, X _jThe state value of expression picked at random, X _cBe the center of the shoal of fish, X _MaxOptimum individual in the expression shoal of fish; Y _i, Y _j, Y _cAnd Y _MaxThe optimizing function is at X in the corresponding step 3 of difference _i, X _j, X _cAnd X _MaxThe value at place; The arbitrary width of the artificial fish of Random (step) expression; X _InextNext step mobile status of representing artificial fish; n _fBe artificial fish number within the vision; δ is the crowding factor.

Distance between the artificial fish among the step B2 under each behavior state of artificial fish adopts the XOR operator to calculate.

For example: the state of two artificial fishes is respectively X ₁=(1 ,-1,1 ,-1) and X ₂=(1,1 ,-1 ,-1), then the distance between them is at this moment

And for the calculating at shoal of fish center, the state value with all artificial fishes within the vision adds up here, obtains a vectorial X _c, if its element is greater than 0, then this element get+1; Otherwise, get-1.In each iteration of this algorithm, every artificial fish need select its optimum behavior to come actual the execution.

Can setting of concrete parameter is as shown in table 1:

Table 1:

Parameter name	Value
		Artificial fish number	3
Field range	2
		Iterations	5
The crowding factor	1
		The dimension (system user number) of artificial fish state vector	10

Claims (5)

1. based on the direct sequence UWB multi-user detection method of least mean-square error and artificial fish-swarm associating, it is realized by following steps:

Step 1, obtain the direct sequence ultra-broadband signal, described direct sequence ultra-broadband signal is input in K the matched filter that is in the collaborative work state simultaneously, the matched filtering that acquires K user is y as a result _iA described K user and K matched filter are corresponding one by one;

The cross correlation matrix number of K matched filter is: R=(r _Ij) K * K, in the formula, r _Ij| r _Ij|, i, j=1,2 ... K, i ≠ j, K are positive integer;

K the user's that step 2, the least mean-square error multiuser detection algorithm that utilizes suboptimum obtain step 1 matched filtering is y as a result _iDetect processing, obtain K user's suboptimal solution b=[b ₁, b ₂..., b _i..., b _K];

Step 3, K user's that step 1 is obtained matched filtering is y as a result _iCarry out optimum Multiuser Detection, its detection method is described optimum Multiuser Detection to be equivalent to find the solution discrete function Ω (b)=2b ^TAy-b ^TThe maximum of ARAb is in the formula: A=diag (A ₁, A ₂..., A _i..., A _K), y=[y ₁, y ₂..., y _i..., y _K] ^T, b=[b ₁, b ₂..., b _i..., b _K] ^TAnd b _i∈ { 1,1};

Step 4, utilize artificial fish-swarm algorithm that the discrete function described in the step 3 is carried out optimizing, obtain K approximate optimal solution The initial value of every artificial fish in the described artificial fish-swarm algorithm is K user's of step 2 acquisition suboptimal solution b=[b ₁, b ₂..., b _i..., b _K] or its variation operation result;

Step 5, K the approximate optimal solution that step 4 is obtained As K user's testing result of direct sequence ultra-broadband signal to be detected, finish the Multiuser Detection of direct sequence ultra-broadband signal;

It is characterized in that: the initial value of artificial fish-swarm described in the step 4 is K user's of step 2 acquisition least mean-square error suboptimal solution b=[b ₁, b ₂..., b _i..., b _K] the concrete grammar of variation operation result be:

Steps A 1, K user's obtaining from step 2 least mean-square error suboptimal solution b=[b ₁, b ₂..., b _i..., b _K] in select a bit element at random, make it and-1 carry out XOR, that is:

Result after the acquisition computing

And with its initial value as the artificial fish of N+1 bar;

Steps A 2, the value of N is added 1, and return execution in step A1, till the initial value of every fish in obtaining artificial fish-swarm;

The initial value of described N is 1.

2. K the user's that the direct sequence UWB multi-user detection method based on the associating of least mean-square error and artificial fish-swarm according to claim 1, the least mean-square error multiuser detection algorithm that it is characterized in that utilizing described in the step 2 suboptimum obtain step 1 the matched filtering yi concrete grammar that detects processing as a result is:

With K user's obtaining in the step 1 matched filtering y as a result _iBe expressed in matrix as: y=RAb+n, wherein n is a zero-mean Gaussian random vector, and its covariance matrix is: E[nn ^T]=σ ²R, σ ²Energy for noise; Matrix y is carried out linear transformation M=A ^-1[R+ σ ²A ^-2] ^-1, make function Ω (M)=E[||b-My|| ²] reach minimum value, thus the matched filtering that realizes K user y as a result _iDetect processing.

3. the direct sequence UWB multi-user detection method based on the associating of least mean-square error and artificial fish-swarm according to claim 1, optimum Multiuser Detection employing Maximum likelihood sequence detection method is carried out in the matched filtering that it is characterized in that K user in the step 3 step 1 being obtained yi as a result.

4. the direct sequence UWB multi-user detection method based on the associating of least mean-square error and artificial fish-swarm according to claim 1, it is characterized in that utilizing described in the step 4 artificial fish-swarm algorithm that the discrete function described in the step 3 is carried out optimizing, obtain K approximate optimal solution Method realized by following steps:

Step B1, preliminary bulletins plate: in the optimizing function with the initial value substitution step 3 of every artificial fish, the size of the corresponding functional value of initial value of more every artificial fish will be composed to bulletin board corresponding to the artificial fish state of functional value maximum;

The selection of step B2, artificial fish behavior: artificial fish has four kinds of behaviors, be respectively: foraging behavior, the behavior of bunching, behavior and random behavior knock into the back, every artificial fish is according to self wherein a kind of behavior of optimum of residing environmental selection, the global optimizing of the optimizing function in the performing step three obtains to approach the result of optimum Multiuser Detection.

The renewal of step B3, bulletin board: after each iteration is finished in this algorithm, the artificial fish of each bar all will obtain new state value; Optimizing function in the state value substitution step 3 that these are new is selected maximal function value wherein, if this value greater than the value of preserving on the bulletin board, is then upgraded bulletin board; Otherwise bulletin board remains unchanged;

Step B4, judge that whether end condition satisfies: when the value in the bulletin board has reached the optimal value of function in the step 3, or the iterations of algorithm reached set point, and then algorithm stops to carry out, and execution in step B5; Otherwise, return execution in step B2;

Step B5, after this algorithm stops carrying out, with the value output of preserving in the bulletin board, as the Multiuser Detection result of this direct sequence ultra-broadband signal to be detected.

5. the direct sequence UWB multi-user detection method based on the associating of least mean-square error and artificial fish-swarm according to claim 1 is characterized in that the distance between the artificial fish under each behavior state of artificial fish among the step B2 adopts the XOR operator to calculate.

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