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CN109672487B - Interference adjusting method of robustness self-adaptive variable load filter - Google Patents

Interference adjusting method of robustness self-adaptive variable load filter Download PDF

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CN109672487B
CN109672487B CN201811521927.4A CN201811521927A CN109672487B CN 109672487 B CN109672487 B CN 109672487B CN 201811521927 A CN201811521927 A CN 201811521927A CN 109672487 B CN109672487 B CN 109672487B
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interference
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CN109672487A (en
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连振宇
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Ningbo Qise Jia Metal Products Co ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/309—Measuring or estimating channel quality parameters
    • H04B17/336—Signal-to-interference ratio [SIR] or carrier-to-interference ratio [CIR]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
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    • H04B1/00—Details of transmission systems, not covered by a single one of groups H04B3/00 - H04B13/00; Details of transmission systems not characterised by the medium used for transmission
    • H04B1/06—Receivers
    • H04B1/16—Circuits
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
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    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/03—Shaping networks in transmitter or receiver, e.g. adaptive shaping networks
    • H04L25/03006—Arrangements for removing intersymbol interference
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/03—Shaping networks in transmitter or receiver, e.g. adaptive shaping networks
    • H04L25/03006—Arrangements for removing intersymbol interference
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Abstract

The invention discloses an interference adjusting method of a robustness self-adaptive variable load filter, which comprises the following steps of acquiring a characteristic value of each noise component by using a sampler of the filter under the condition that a system detects that spreading codes of transmission signals are not matched to generate interference; then loading different load levels behind each characteristic value; to increase variable load item in the autocorrelation matrix eigenvalue of the desired signal, and finally obtain a correlation interference adjustment formula of variable load technology to deal with the deficiency of performance improvement under the original mismatch of spreading codes, the interference adjustment formula is as follows:
Figure RE-DDA0001977498550000011
and adding a diagonal matrix to the obtained self-phase correlation matrix to finally obtain a weight vector, controlling the weight vector between the optimal deviation and norm control, selecting a proper gamma value, and substituting the gamma value into a formula to ensure that the interference regulation formula meets the requirement. The invention realizes self-regulation when the spread spectrum codes are not matched to generate interference.

Description

Interference adjusting method of robustness self-adaptive variable load filter
Technical Field
The invention relates to the technical field of wireless communication anti-interference, in particular to an interference adjusting method of a robustness self-adaptive variable load filter.
Background
The wireless communication system under high speed access is prone to cause mismatching of the desired spreading codes, resulting in serious degradation of output performance and MAI problem. In the conventional technique, when there is no mismatch between desired spreading codes, a very good output signal-to-interference plus noise ratio (SINR) is achieved by using a multi-constrained minimum variance (MCMV) detector, but once there is mismatch between desired spreading codes, especially when the input signal-to-noise ratio (SNR) increases, the degree of cancellation of the desired spreading codes as interference signals increases, and the output signal-to-interference plus noise ratio (SINR) is severely degraded, so that the mismatch between the spreading codes is one of the important factors affecting the performance of the wireless communication system, and since it is impossible to be completely correct in the actual transmission channel environment, the receiving end receives the desired spreading codes, which are affected by other paths and channel fading, and the receiving end is affected by signal fading and signal fading The generation circuit generates a mismatch phenomenon of spreading codes to generate an non-orthogonal situation, so that once the spreading codes of a received intended user are not orthogonal, the correlation between other users and the intended user is increased, and the problem of interference (MAI) becomes relatively serious, so that how to solve the technical problem of performance degradation caused by the deviation of transmission signals (especially the mismatch of the spreading codes) in wireless communication (such as MC-CDMA), and finally, the technical effects of increasing the strength of the intended signal, suppressing noise, eliminating the interference in the intended signal and enabling the detector to be more adaptive are particularly important.
Thus, many scholars have improved this aspect, such as those shown in fig. 6 and 7, that is, the detector corresponding to the prior art blind adaptive multiuser detection technique is called Minimum Output Energy (MOE) detector. Wherein b iskIs a detection signal, CkIs a spreading code, v (t) is noise, and thus the output signal can represent x (n), which has the following disadvantages:
for convenience, assuming the desired user is the first user and the detector weight vector is W1, the estimated bits are written as follows:
Figure GDA0002914790080000021
wherein the sign of the inner product of the vectors is definedIs composed of<x,y>＝xTy; first, the cost function of the MOE detector is defined as follows:
Figure GDA0002914790080000022
in fact, the weight vector of the MOE detector can be regarded as the sum of two mutually orthogonal vectors, which is expressed as follows:
Figure GDA0002914790080000023
one of the vectors C1 is the spreading code of the first user, and the other vector is
Figure GDA0002914790080000024
Is an adaptation vector representing the magnitude of the weight adapted to the environment,
Figure GDA0002914790080000025
representing two vectors orthogonal to each other, and thus an adaptive vector
Figure GDA0002914790080000026
Can also be expressed as follows:
Figure GDA0002914790080000027
the adaptive MOE detector output is:
Figure GDA0002914790080000028
where ρ is1k＝W1 Hxk[n]K is 2, K and v1[n]＝W1 Hv[n]If a weight is available such that ρ1kApproximately equal to 0, K equal to 2, · · K, the bits can be accurately estimated, and the output signal power to interference plus noise ratio (SINR) is as follows:
Figure GDA0002914790080000031
when the above formula is analyzed
Figure GDA0002914790080000032
In this case, representing complete cancellation of interference from other users, the performance of the MOE detector approaches that of the decorrelating detector, where the SINR is rewritten as follows:
Figure GDA0002914790080000033
from the above formula, it can be known that the noise power is amplified to (1+ | W | | non-calculation)2)σ2Considering next how the detector finds a MOE-based weight vector, the MOE cost function defined by equation (2.15) is shown below:
Figure GDA0002914790080000034
the weight vector W1 of the detector being sought minimizes the above equation, and the weight vector also minimizes the mean square error of the output at this time, as follows:
Figure GDA0002914790080000041
therefore, if a handle is
Figure GDA0002914790080000042
Is limited to
Figure GDA0002914790080000043
In time, minimizing the MOE cost function is also minimizing the output Mean Square Error (MSE), and an abrupt gradient descent algorithm is used for finding out the adaptive vector
Figure GDA0002914790080000044
Minimization
Figure GDA0002914790080000045
And
Figure GDA0002914790080000046
first on the MOE merit function
Figure GDA0002914790080000047
Finding a pair vector
Figure GDA0002914790080000048
A gradient vector is obtained as follows:
Figure GDA0002914790080000049
Figure GDA00029147900800000410
at any time, equation (2.16) is satisfied, and thus the vector component of x that is orthogonal to the spreading code of the first user is calculated as C1＝X-<C1,X>C1I.e., all interference components, replace the last vector X of equation (2.23), thus producing a projection vector orthogonal to C1, and finally derive the LMS-based stochastic gradient adaptation algorithm as follows:
Figure GDA00029147900800000411
wherein
Figure GDA00029147900800000412
And Y is1[n]＝C1 TX[n]To receive the projected vector at C1, the adaptive vector only eliminates other users and does not affect the desired user, and when the weight vectors are% iteratively converged, a weight vector Wi that eliminates interference of other users is obtained, as shown in fig. 6.
Finally, a system block diagram of the MOE detector is shown in FIG. 7, which is a schematic representation of the sameIt can be seen that there may be less occurrences in the weight update iteration process
Figure GDA00029147900800000413
In the meantime, the estimated bit in equation (2.14) is zero, and a phenomenon of making a decision incorrectly occurs, which is called fuzzy region (ambiguity region) or desired signal cancellation (desired signal cancellation), which is a big disadvantage of the detector, so although the detector has previously proved to be close to the performance of the decorrelation detector, because of the above problem, it is not ideal that the researchers have also begun to research and design more, better and more practical adaptive methods, and the present invention is proposed.
Disclosure of Invention
The present invention provides an interference adjustment method for a robust adaptive variable load filter, so as to solve the technical problem of "the output performance has serious fading phenomenon and interference problem due to mismatching of desired spreading codes easily caused by a wireless communication system under high speed access" proposed in the background art. The invention can be applied to the condition that the spread spectrum codes are not matched on a multi-user detection system of sound, image, wireless communication and the like, and adds a variable load item in the autocorrelation matrix characteristic value of a desired signal so as to overcome the defect of performance improvement under the condition of original mismatch of the spread spectrum codes, thereby not only increasing the output signal and interference plus noise ratio, but also effectively reducing the bit error rate so as to obtain better efficiency.
In order to achieve the purpose, the invention provides the following technical scheme: a robust adaptive variable load filter interference adjusting method specifically comprises the following steps:
A) when the detection system of the adaptive variable load filter detects that the spread spectrum codes of the transmission signals are not matched to generate interference, the sampler of the filter is used for acquiring the characteristic value lambda of each noise componenti(ii) a Then at each eigenvalue lambdaiWith different load levels (gamma/lambda) applied afterwardsi)2Gamma; to realize the addition of variable load terms in the autocorrelation matrix eigenvalues of the desired signal,finally, a related interference regulation formula of a variable load technology is obtained to overcome the defect of performance improvement under the condition of mismatch of original spreading codes, wherein the interference regulation formula is as follows:
Figure GDA0002914790080000051
B) then obtaining gamma value, firstly adding a diagonal matrix on the obtained self-phase correlation matrix, and finally limiting the target function sum to be
Figure GDA0002914790080000061
Instead, it is changed into
Figure GDA0002914790080000062
And use (R + gamma I)-1Replacing the original autocorrelation inverse matrix R-1Finally, a weight vector is obtained
Figure GDA0002914790080000063
Comprises the following steps:
Figure GDA0002914790080000064
and then, controlling the weight vector between the optimal deviation and norm control, selecting a proper gamma value, substituting the gamma value into a formula (1-1) to ensure that an interference regulation formula meets the requirement, and applying the interference regulation formula to a filter to realize self-adaptive matching regulation.
Further, in step B, how to specifically optimize the weight vector and finally the γ value when K users use the same transmission signal is specifically as follows:
at this time, the baseband signal received by the receiving end is defined as:
Figure GDA0002914790080000065
wherein a isk、bkAnd ck(t) respectively represent the k-th usersAmplitude, data signal and user spreading code; bk∈{±1}，TsIs the symbol period, K is the number of users; v (t) is additive white high-term noise;
however, after passing through the matched filter sampler, the received signal x (t) may become a Lx 1 data vector:
Figure GDA0002914790080000071
where n is the nth bit interval, the spreading code matrix is C ═ C1 c2 … ck]Wherein the k-th row vector of c is the k-th user spread, noise v [ n ] respectively]An L x 1 additive white Gaussian noise vector with zero mean and variance of
Figure GDA0002914790080000072
A＝diag(c1 c2···ck)，ckIs the spreading code vector for the kth user, in other words,
Figure GDA0002914790080000073
and the superscript T is defined as the transpose,
let w be assumed at this timekSet as the weight vector of the kth user detector, then
Figure GDA0002914790080000074
Wherein, yk[n]＝Wk Hx[n]Defined as the output signal, is scaled [. cndot. ]]HRepresenting as a conjugate complex transpose;
fourthly, it is assumed that the receiving end receives the complete virtual noise code, which is cdC, each group of PN codes is allocated to each user for use; however, considering that the receiving end receives the spreading code of the desired user and is affected by the attenuation of multiple paths and other channels, and further the situation of generating a mismatch with the PN code generated by the receiving end code circuit occurs, the user actually receives at the receiving end
Figure GDA0002914790080000075
And c, which are usually mismatched at the transmitter, so that the mismatch of PN codes received by the intended user is represented by:
Figure GDA0002914790080000076
wherein the spreading code offset Δ C is a zero mean and the variance is
Figure GDA0002914790080000081
A gaussian random variable matrix;
fifthly, obtaining a weight vector according to the obtained formula (1-3), and finally selecting a proper gamma value between the optimal deviation and norm control according to the weight vector control.
Compared with the prior art, the invention has the beneficial effects that: the invention can be applied to the condition that the spread spectrum codes are not matched on a multi-user detection system of sound, image, wireless communication and the like, and adds a variable load item in the autocorrelation matrix characteristic value of a desired signal so as to overcome the defect of performance improvement under the condition of original mismatch of the spread spectrum codes, thereby not only increasing the output signal and interference plus noise ratio, but also effectively reducing the bit error rate so as to obtain better efficiency.
Drawings
Fig. 1 shows the reciprocal of the eigenvalue when γ is 0.1 in this example
Figure GDA0002914790080000082
Comparative graph of curves.
FIG. 2 is a diagram showing the comparison of the bit error rate observed by the spread spectrum code offset obtained between the detector of the present invention and the detector of the prior art (in the figure, value is the spread spectrum code offset, and BER is the observed bit error rate);
FIG. 3 is a diagram showing the comparison of the observed bit error rate obtained from the present invention and the prior art structure detector (where SNR is the varying input SNR and BER is the observed bit error rate);
FIG. 4 is a diagram showing the comparison between the present invention and the prior art detector to obtain the change input SNR and observe the output SINR (in the figure, SNR is the change input SNR, and SINR is the output signal to interference plus noise ratio);
FIG. 5 is a diagram illustrating the comparison of the variance element observed output SINR between the present invention and the prior art structure detector (SINR is the output signal to interference plus noise ratio);
FIG. 6 is a diagram illustrating weight vectors for canceling other interfering users in the prior art;
FIG. 7 is a system block diagram of a prior art minimum output energy detector.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the figure: the MCMV, MDL-MCMV and DL-MCMV detectors are all traditional detectors; the RMDL-MCMV is a detector corresponding to the method of the present invention, and is defined as a diagonalized load multi-limiting minimum variance detector of the adaptive variable load filter. The embodiment provided by the invention comprises the following steps: a robust adaptive variable load filter interference adjusting method specifically comprises the following steps:
A) when the detection system of the adaptive variable load filter detects that the spread spectrum codes of the transmission signals are not matched to generate interference, the sampler of the filter is used for acquiring the characteristic value lambda of each noise componenti(ii) a Then at each eigenvalue lambdaiWith different load levels (gamma/lambda) applied afterwardsi)2Gamma; to increase variable load terms in the autocorrelation matrix eigenvalue of the desired signal and finally obtain a related interference adjustment formula of variable load technique to deal with the performance improvement deficiency under the mismatch of original spreading codes, the interference adjustment formula is as followsThe following:
Figure GDA0002914790080000091
B) then obtaining gamma value, firstly adding a diagonal matrix on the obtained self-phase correlation matrix, and finally limiting the target function sum to be
Figure GDA0002914790080000092
Instead, it is changed into
Figure GDA0002914790080000093
And use (R + gamma I)-1Replacing the original autocorrelation inverse matrix R-1Finally, a weight vector is obtained
Figure GDA0002914790080000101
Comprises the following steps:
Figure GDA0002914790080000102
and then, controlling the weight vector between the optimal deviation and norm control, selecting a proper gamma value, substituting the gamma value into a formula (1-1) to ensure that an interference regulation formula meets the requirement, and applying the interference regulation formula to a filter to realize self-adaptive matching regulation.
Further, in step B, how to specifically optimize the weight vector and finally the γ value when K users use the same transmission signal is specifically as follows:
at this time, the baseband signal received by the receiving end is defined as:
Figure GDA0002914790080000103
wherein a isk、bkAnd ck(t) respectively representing the amplitude of the kth user, the data signal and the user spreading code; bk∈{±1}，TsIs the symbol period, K is the number of users; v (t) is additive white plateauNoise;
however, after passing through the matched filter sampler, the received signal x (t) may become a Lx 1 data vector:
Figure GDA0002914790080000104
where n is the nth bit interval, the spreading code matrix is C ═ C1 c2 … ck]Wherein the k-th row vector of c is the k-th user spread, noise v [ n ] respectively]An L x 1 additive white Gaussian noise vector with zero mean and variance of
Figure GDA0002914790080000111
A＝diag(c1 c2···ck)，ckIs the spreading code vector for the kth user, in other words,
Figure GDA0002914790080000112
and the superscript T is defined as the transpose,
let w be assumed at this timekSet as the weight vector of the kth user detector, then
Figure GDA0002914790080000113
Wherein, yk[n]＝Wk Hx[n]Defined as the output signal, is scaled [. cndot. ]]HRepresenting as a conjugate complex transpose;
fourthly, it is assumed that the receiving end receives the complete virtual noise code, which is cdC, each group of PN codes is allocated to each user for use; however, considering that the receiving end receives the spreading code of the desired user and is affected by the attenuation of multiple paths and other channels, and further the situation of generating a mismatch with the PN code generated by the receiving end code circuit occurs, the user actually receives at the receiving end
Figure GDA0002914790080000114
C is usually not matched with c at the transmitting end, so that the intended user can use the deviceThe received PN code mismatch is represented by:
Figure GDA0002914790080000115
wherein the spreading code offset Δ C is a zero mean and the variance is
Figure GDA0002914790080000116
A gaussian random variable matrix;
fifthly, obtaining the detector adopted by the method according to the obtained formula (1-3), and finally selecting a proper gamma value between the optimal deviation and norm control according to the weight vector control.
Specifically analyzing the differences between the method of the present embodiment and the prior art: the present technique directly proposes the robustness of the variable load technique, combines multiple-constraint minimum variation rules, proposes the detector adopted by the method, and uses G ═ G (G ═ G)3+γ3I)-3R2R replacing traditional Multiple Constrained Minimum Variance (MCMV) detectors-1Constructing a weight vector for the multiuser detector, as follows:
wk，RMDL＝G-1C(CTG-1C)-1fk
the above-mentioned R can be found-1、(R+γI)-1The relationship between the characteristic values of M and G can be expressed as:
Figure GDA0002914790080000121
when lambda isi＜γ
Figure GDA0002914790080000122
When lambda isi＞γ
For smaller extreme values of lambdaiRepresenting a noise component and its inverse singular value of 1/lambdaiWill become large and thus, in the case of spreading code mismatchUnder the condition of the water will be WkThe norm of (1/lambda) is shifted to a very large value, resulting in higher side waves and enhancing the interference of noise to the system, called noise enhancement (noise enhancement), in this case 1/lambdaiAn accurate value cannot be estimated. Conversely, to combat the mismatch situation, the smaller eigenvalues must be limited;
assuming that the gamma value of the detector used in the method is considered as a starting point to separate the reciprocal of the eigenvalue as an estimate or limit, for the DL technique, the reciprocal of the eigenvalue is 1/(λ:)i+ gamma) is smaller than 1/gamma, then
Figure GDA0002914790080000134
The norm of (a) will not tend to become large; however, when using a larger detector for this method, the gamma value will be from 1/λiA significant deviation is caused, which will result from the view of the optimized detector, so in other words, for the detector used in the method, a specific value of γ is substituted between the deviation and norm control, and the weight vector control is in the range of γ equal to 0.1 for the specific deviation and norm between the optimum deviation and norm control, for the reason: as shown in fig. 1, 1/λ of a conventional multi-constrained minimum variation (MCMV) detectoriIn comparison, this method is to determine the characteristic value λiFollowed by different load levels (gamma/lambda)i)2γ, achieving that smaller γ can be loaded to higher levels; thus, it can be seen from fig. 1 that the proposed approach not only reduces drift but is also more robust than the conventional MCMV detector.
Because when λ isi＝0，
Figure GDA0002914790080000131
When lambda isiWhen the value is equal to gamma, the second phase is,
Figure GDA0002914790080000132
and is at 1/lambdaiAnd
Figure GDA0002914790080000133
a gap of about 3dB is created between them;
however, when λi> gamma, is provided by the signal portion,
Figure GDA0002914790080000141
is ratio of
Figure GDA0002914790080000142
Is closer to 1/lambdai；
In summary, when λ isiWhen the signal is more than gamma, the method provided by the invention has better value close to 1/lambda than the traditional MCMV detectoriTherefore, the method provided by the invention has less deviation and is more robust.
In summary, it is found that the method in the above embodiments can indeed improve the performance of the detector under the condition of mismatched spreading codes, however, the weight vector using this technique must be between the deviation of the best solution and the norm control, so that the proper value of γ of DL can be selected to obtain good performance; therefore, in order to solve the above-mentioned phenomenon in the prior art, the prior art uses the diagonalization loading technique as the capability of improving the beam former against pointing error, and is finally applied to the multi-user detection, specifically, M ═ G (G) is adopted2+γ2I)-1R substitution is derived from the inverse matrix R-1Obtaining a multi-user detector weight vector
Figure GDA0002914790080000143
Figure GDA0002914790080000144
At this moment, the weight vector is loaded on the eigenvalue of the autocorrelation matrix by using the diagonalized load technique, and finally, the disadvantage that the detector of the invention has to select the gamma value can be solved, but the invention can not select the more suitable gamma value against the situation that the larger spreading codes are not matched, and can not select the best gamma value more adaptively to increase the intensity of the desired signal, suppress the noise and eliminate the interference in the desired signal, so that the detector can more adaptively and effectively solve the problem, in conclusion, the method of the invention can be applied to the detection system of multiple users such as sound, image and wireless communication under the situation that the spreading codes are not matched, the variable load item is added in the autocorrelation matrix eigenvalue of the desired signal to deal with the deficiency of performance improvement under the original mismatching of the spreading codes, not only the output signal and interference plus noise ratio can be increased, and can effectively reduce the bit error rate to obtain better performance.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a non-volatile computer-readable storage medium, and can include the processes of the embodiments of the methods described above when the program is executed. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), or the like.
Any reference to memory, storage, database, or other medium as used herein may include non-volatile and/or volatile memory. Suitable non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM), which acts as external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), Enhanced SDRAM (ESDRAM), synchronous Link (Synchlink) DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and bus dynamic RAM (RDRAM).
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
This section is not claimed and is not referred to in the present disclosure, so it is deleted and not patented if not inventive in its entirety.

Claims (2)

1. A robust adaptive variable load filter interference regulation method is characterized by specifically comprising the following steps:
A) when the detection system of the adaptive variable load filter detects that the spread spectrum codes of the transmission signals are not matched to generate interference, the sampler of the filter is used for acquiring the characteristic value lambda of each noise componenti(ii) a Then at each eigenvalue lambdaiWith different load levels (gamma/lambda) applied afterwardsi)2Gamma; to realize the addition of variable load terms in the autocorrelation matrix eigenvalues of the desired signal, and finallyObtaining a correlation interference adjustment formula of a variable load technology to deal with the deficiency of performance improvement under the condition of mismatch of original spreading codes, wherein the interference adjustment formula is as follows:
Figure FDA0002914790070000011
B) then obtaining gamma value, firstly adding a diagonal matrix on the obtained autocorrelation matrix, finally changing the objective function and limitation into
Figure FDA0002914790070000012
And
Figure FDA0002914790070000013
and use (R + gamma I)-1Replacing the original autocorrelation inverse matrix R-1Finally, a weight vector is obtained
Figure FDA0002914790070000014
Comprises the following steps:
Figure FDA0002914790070000015
and then, controlling the weight vector between the optimal deviation and norm control, selecting a proper gamma value, substituting the gamma value into a formula (1-1) to ensure that an interference regulation formula meets the requirement, and applying the interference regulation formula to a filter to realize self-adaptive matching regulation.
2. The method of claim 1, wherein the step of adjusting the interference comprises: in step B, how to optimize the weight vector and finally the γ value specifically when K users use the same transmission signal is as follows:
at this time, the baseband signal received by the receiving end is defined as:
Figure FDA0002914790070000021
wherein a isk、bkAnd ck(t) respectively representing the amplitude of the kth user, the data signal and the user spreading code; bk∈{±1}，TsIs the symbol period, K is the number of users; v (t) is additive white Gaussian noise;
however, after passing through the matched filter sampler, the received signal x (t) may become a Lx 1 data vector:
Figure FDA0002914790070000022
wherein n is the nth bit interval, the spreading code matrix is C ═ C1 c2…ck]Wherein the k-th row vector of c is the k-th user spread, noise v [ n ] respectively]An L x 1 additive white Gaussian noise vector with zero mean and variance of
Figure FDA0002914790070000023
A＝diag(c1 c2…ck)，ckIs the spreading code vector for the kth user, in other words,
Figure FDA0002914790070000024
and superscript T is defined as transpose;
let w be assumed at this timekSet as the weight vector of the kth user detector, then
Figure FDA0002914790070000025
Wherein,
Figure FDA0002914790070000026
defined as the output signal, is scaled [. cndot. ]]HRepresenting as a conjugate complex transpose;
fourthly, the receiving end is supposed to receive complete virtual impuritiesThe signal code is cdC, each group of PN codes is allocated to each user for use; however, considering that the receiving end receives the spreading code of the desired user and is affected by the attenuation of multiple paths and other channels, and further the situation of generating a mismatch with the PN code generated by the receiving end code circuit occurs, the user actually receives at the receiving end
Figure FDA0002914790070000031
And c, which are usually mismatched at the transmitter, so that the mismatch of PN codes received by the intended user is represented by:
⑤
Figure FDA0002914790070000032
wherein the spreading code offset Δ C is a zero mean and the variance is
Figure FDA0002914790070000033
A gaussian random variable matrix;
sixthly, obtaining a weight vector according to the obtained formula (1-3), and finally selecting a proper gamma value between the optimal deviation and norm control according to the weight vector control.
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