CN103841583A - Wireless network optimized mass signaling data collecting method based on compressed sensing - Google Patents

Abstract

The invention discloses a wireless network optimized mass signaling data collecting method based on compressed sensing. The method comprises the steps of building a signaling collecting system, building an original data preprocessing model, building a signaling data classification selector, conducting classification selection to conduct rarefaction on indexes, building a measurement matrix, using a compressed sensing method for compressing the data after rarefaction, building a data recovery algorithm, and utilizing parallel computing for speeding up to complete the recovery task. According to the method, the large-ratio compression of the signaling data can be achieved well under the condition of the mass signaling data, the signaling data information can be restored in an appropriately-precise or appropriately-accurate mode, particularly for the binary situation, the signaling data can be collected in a divisible and discardable transmission mode in the wireless network transmission environment, transmission loads are reduced, the method can be successfully applied to the follow-up same or similar tasks, and energy is saved.

Description

A kind of radio network optimization magnanimity signaling data acquisition method based on compressed sensing

Technical field

The present invention relates to the technical field that radio network optimization magnanimity signaling data gathers, refer in particular to a kind of radio network optimization magnanimity signaling data acquisition method based on compressed sensing.

Background technology

Follow granting and the domestic mobile Internet develop rapidly of 3G license, the exponential growth of terminal data amount, how on the 2G of existing maturation network, developing rapidly and healthily mobile Internet, development Various types of data business becomes the focus of domestic three large operators concern at present.Take China Mobile as example, as one of wireless carriers of Largest In China, its user's basic business data and value-added service data rapidly increase in recent years, and the arrival of large data age, concerning operator, is challenge and opportunity.Operator thinks the status of firm basic communication service, wins the admission ticket of the Internet, can, in conjunction with the advantage of self, miscellaneous service and user's behavioural habits be connected, better service-user.And how what is more important, under the epoch of data service great development, is stored efficiently wireless user's mass data and is carried out the network optimization and become the urgent problem of confronting.

The research of data compression is always very active, but substantially requires all comparatively strict to the integrality of data.Occur damaging as long as data go out the data of active or reception in the process sending, data are just difficult to recover.In recent years, " compressed sensing " algorithm of studying awfully hot door has brought new approaches for addressing this problem.The original intention of compressed sensing is in collection signal, to reduce sample rate, accurately initial data is recovered and approach by restoration algorithm afterwards, and single pixel camera of rice university realizes based on this principle.But the advantage of compressed sensing aspect compression also taken charge of a department, particularly undesirable when network speed, while being difficult to receive a complete data, common compression algorithm is difficult to stable work, and compressed sensing can be worked preferably.In the time that radio network optimization mass data is gathered, use compressed sensing algorithm, yet there are no bibliographical information.It is the compression to data effectively not only, saves transmission cost, and can also allow to transmit the loss of data after time compression, and a small amount of loss of data after compression can not impact the recovery of former data in the ordinary course of things.

In recent years, compressed sensing has obtained gratifying achievements at the numerous areas such as medical examination, Internet of Things.Compressed sensing also becomes the one innovation of collection signal method, and existing strict mathematical theory proves its correctness and validity.Due to the peculiar effect of compressed sensing, and the fusion faculty of full-fledged a series of method for solving in compressed sensing and non-linear optimal solution theory, the data acquisition algorithm based on compressed sensing is widely studied and applied in fields such as image, sound, mass datas.

Existing radio network optimization data acquisition is mostly to adopt simply signal collecting card to carry out copying and saving, usage data compression algorithm is not compressed it, all sparse or can be sparse and every data have a lot of fields, caused the waste of storage data space, and the load of transmission is larger.If use compressed sensing to compress it, not only can reduce the storage space of data and the load of transmission, can also strengthen the fail safe of data.Particularly weak at network signal and often there is bust this in the situation that, can simply take the strategy that abandons, and not affect the reception of data.

Two most crucial technology of compressed sensing build exactly to be measured matrix and selects restoration algorithm, measures matrix and need to meet RIP character, and restoration algorithm is related to efficiency and the accuracy rate while recovering data; Comprehensive the demand, the measurement matrix that the present invention adopts is got by the graceful matrix of hada, and restoration algorithm adopts ripe orthogonal matching pursuit algorithm.

The graceful matrix of hada, has very strong incoherence, and vector is all pairwise orthogonal, and each number is all 1 or-1, has greatly reduced amount of calculation.The major defect of current this method is to be difficult to adapt to the data compression of random length, and it requires initial data length to be necessary for 2 n power (n is positive integer), and often may be undesirable in reality.The radio network optimization data length that will gather due to the present invention is controllable, so as long as data length is made as to 2 n power.

Data recovery algorithm is used to the reduction to packed data, asks a globally optimal solution, is mainly divided into greedy algorithm, L1 convex optimized algorithm, TVL3 restoration algorithm etc.Orthogonal matching pursuit algorithm, is called for short OMP algorithm, and circulation each time is all found out and a column vector with residual error inner product minimum, but than MP algorithm many a step quadrature operation, accelerated the process and the accuracy rate that has improved selection atom of iteration.Performance aspect efficiency is also outstanding than base tracing algorithm, and can adopt parallel computation to accelerate to solve speed.Therefore,, in the time that former data length is larger, adopt the simple and good performance of OMP algorithm.

Many Practical Project systems, because the data of system itself have sparse property or for can be sparse, can adopt a kind of method based on compressed sensing that it is gathered and is stored, this is for the small-sized machine of terminal, and performance meets the demands.And because compressed sensing amount of calculation in the process of restoring is larger, the later stage adopts computing capability stronger computer to restore data and can address this problem, packed data effectively can approached in accurate situation like this.

Summary of the invention

The object of the invention is to the deficiencies in the prior art and defect, a kind of radio network optimization magnanimity signaling data acquisition method based on compressed sensing is provided, the method can realize well the vast scale compression to signaling data in the situation that of signaling data magnanimity, and can approach the signaling data information that accurately or exactly recovers, particularly for two-value situation; Can, in the transmission environment of wireless network, realize the collection of signaling data with a kind of divisible, discardable transmission, reduce the load of transmission, it successfully can be applied in the middle of follow-up same or analogous task to conserve energy.

For achieving the above object, technical scheme provided by the present invention is: a kind of radio network optimization magnanimity signaling data acquisition method based on compressed sensing, comprises the following steps:

1) set up the acquisition system take mobile communication signaling as target, data while being used for mobile phone terminal call and online gather, can in each base station or upper strata terminal, set up, particular location is such as the Gb mouth between BSC and SGSN, or IuPS mouth between RNC and SGSN etc.;

2) set up initial data pretreated model, useless or incomplete original signaling data is deleted, and core data is extracted and preliminary treatment;

3) set up signaling data categorizing selection device, according to step 1) set up signaling acquisition system and step 2) set up initial data pretreated model, data after preliminary treatment are classified, the sparse property of classification based on data, for the sparse property of signal, if wherein only having minority element is non-zero, this signal is sparse.Conventionally the natural sign in time domain, as voice, image etc. all right and wrong are sparse, but may be sparse at some transform domain, such as wavelet field; Wherein, classifying rules is: if data itself meet rarefaction feature, without carrying out rarefaction operation; If do not meet, according to the rarefaction method of wavelet transformation or Fourier transform, data that can rarefaction are converted, for the data after conversion, if not enough sparse property still, can further process, such as, for image, can be by setting a threshold values (carrying out statistical analysis according to image pixel value obtains), and the data below this threshold values are all removed is 0, make the sparse property of data fit;

4) set up and there is non coherent measurement matrix, data by the method for compressed sensing after to rarefaction are compressed, the method of setting up measurement matrix is many, the conventional random matrix, the graceful matrix of hada etc. that have employing Gaussian Profile, and these can meet linear incoherent character; Wherein orthogonal each other between each measurement vector of the graceful matrix of hada, each measurement vector is made up of 1 or-1 value, the m power that the length requirement of each vector is 2, and m is positive integer;

5) set up data recovery algorithm and complete data recovery task, according to the measurement matrix described in step 4), step 3) is measured to obtained measurement result, data recovery algorithm based on orthogonal matching pursuit OMP algorithm completes the recovery to sparse data, this be actually one ask optimum solution owe to determine process, finally convert initial data to by inverse transformation again.

In step 1), described acquisition system, all signaling datas that mobile phone terminal is sent are preserved, and described signaling data includes user identity, session time started, conversation end time, surfing flow, network data.

In step 2) in, described initial data pretreated model, for each signaling data, if signaling data be incomplete, this abandons, and directly removes this data entry; For each signaling data, may exist multiple indexs, need separately to analyze or combined analysis, if user's network data is a pictures or one section of voice, video, need to combine, according to demand, signaling data is carried out to preliminary treatment.

In step 3), described signaling data categorizing selection device is as follows:

x _i＝f(s _i)

Wherein, for step 2) data that obtain change into polytype tables of data s _i, may be word, image, voice; Take image data as example, x _ifor image data s _ito carrying out the value of the sparse transform-based of small echo after multiplying each other, by rarefaction methods such as wavelet transformations, make data x _imeet sparse characteristic; After wavelet transformation, carry out compression sampling and restore the x obtaining by restoration algorithm _i, also should pass through wavelet inverse transformation, to obtain s simultaneously _i; If this index s _iitself has met sparse property, x _i=s _i, restore afterwards also without carrying out an inverse transformation.

In step 4), described measurement matrix, the sparse data obtaining with step 3) has incoherence; Each of the graceful matrix of hada measured between vector orthogonal each other, and each measurement vector is made up of 1 or-1 value, the m power that the length requirement of each vector is 2, and m is positive integer, adopts the graceful matrix of hada as measuring matrix, its perfect matrix is as follows:

H ₀＝1

$H_1=\frac{1}{\sqrt{2}}\left[\begin{array}{cc}1& 1\\ 1& -1\end{array}\right]$

$H_k=\frac{1}{\sqrt{2}}\left[\begin{array}{cc}{H}_{k-1}& H_{k-1}\\ H_{k-1}& -H_{k-1}\end{array}\right]$

Wherein, the m power that the length that requires each vector is 2, m is positive integer; In the time that length n is taken as 65536, k is 16, H ₁₆be 65536 to be multiplied by 65536 matrix, the numerical value of this matrix is unified, meets compression and the equity requirement of restoring; If compression ratio is 0.1, choose front 65536 row of measuring matrix, form the measurement matrix A of a 65536*65536; Measuring matrix and the sparse initial data y=Ax that multiplies each other, obtain measured value y, the length of y is x 1/10;

In step 5), described data recovery algorithm, is in the time that needs restore data, the measured value y obtaining according to step 4), and the measurement matrix that obtains arranging according to the length of y solves the process of x; And adopt orthogonal matching pursuit OMP algorithm, and can recover exactly initial data x, orthogonal matching pursuit OMP algorithm is a repeatedly iterative process, residual error is initialized as initial data y, Increment Matrix Φ _ibe initialized as sky, subscript i represents the variable in the i time iteration, and the process of each interior circulation is as follows:

1) from measuring the column vector atom of finding out matrix with residual error inner product maximum, then this column vector is added in Increment Matrix, and from measuring matrix, this column vector is deleted;

2) solve an optimal problem

${\hat{x}}_i=\arg \min \left|\right|y-{\widetilde{\mathrm{\Φ}}}_{i}x\left|\right|$

$\hat{x}={\left({\widetilde{\mathrm{\Φ}}}^T\widetilde{\mathrm{\Φ}}\right)}^{-1}{\widetilde{\mathrm{\Φ}}}^{T}y$

In this solution procedure, can use matrix decomposition to make the calling program can parallel processing.? be defined as C, because C is Nonsingular symmetrical matt, while inverting, can adopt LDL ^tdecompose, and upgrade L and D according to up-to-date atom;

$L_{i,j}=\frac{1}{D_{j,i}}(C_{i,j}-\underset{k=1}{\overset{j-1}{\mathrm{\&Sigma;}}}{L}_{i,k}{L}_{j,k}{D}_{k,k})(i<j)$

$D_{j,j}=C_{j,j}-\underset{k=1}{\overset{j-1}{\mathrm{\&Sigma;}}}{L}_{j,k}{L}_{j,k}{D}_{k,k}$

The contrary of C is:

$C^{-1}=L^{{-1}^T}{D}^{-1}{L}^{-1}$

3) upgrade residual error

$y=y-{\mathrm{\&Phi;}}_i{\hat{x}}_i$

Through after iteration repeatedly, above-mentioned algorithm will obtain an optimal solution

if primary signal has through rarefaction conversion, right

carry out once corresponding inverse transformation and just can obtain initial data x; If not through rarefaction conversion,

Compared with prior art, tool has the following advantages and beneficial effect in the present invention:

1, magnanimity signaling data acquisition method of the present invention is taked novel compressed sensing algorithm to the collection of wireless network optimization data, can be in the transmission environment of wireless network, realize the collection of signaling data with a kind of divisible, discardable transmission, reduce the load of transmission, it successfully can be applied in the middle of follow-up same or analogous task, save transmission bandwidth, reduced the memory space of data;

2, magnanimity signaling data acquisition method of the present invention not only can be realized the integrality that has guaranteed data in the situation that of unstable networks, has avoided the consumption of data re-transmission, and can carry out encryption to a certain degree to data, has guaranteed the fail safe of data.

3, magnanimity signaling data acquisition method of the present invention can be realized well the vast scale compression to signaling data in the situation that of signaling data magnanimity, and can approach the signaling data information that accurately or exactly recovers, particularly for two-value situation.

Accompanying drawing explanation

Fig. 1 is signaling acquisition system overall framework figure.

Fig. 2 is the packet that cellphone subscriber produces while surfing the Net.

Fig. 3 is the part field of the user of China Mobile Internet data bag.

Fig. 4 is the picture example of a 256*256.

Fig. 5 is the data of image data after rarefaction in Fig. 4.

Fig. 6 is the schematic diagram after data recovery.

Embodiment

Below in conjunction with specific embodiment, the invention will be further described.

Shown in Figure 1, the radio network optimization magnanimity signaling data acquisition method based on compressed sensing described in the present embodiment, its concrete condition is as follows:

1) set up acquisition system take mobile communication signaling as target, the data when to mobile phone terminal call and online gather, and can in each base station or upper strata terminal, set up.

2) set up initial data pretreated model, useless or incomplete original signaling data is deleted, and core data is extracted and preliminary treatment.

3) set up signaling data categorizing selection device, signaling acquisition system and the step 2 set up according to step 1)) the initial data pretreated model set up, the data after preliminary treatment are classified; If data itself meet rarefaction feature, without carrying out rarefaction operation; Otherwise, according to the rarefaction method of wavelet transformation or Fourier transform, data that can rarefaction are converted, set a threshold values, it is 0 that the data below threshold values are all removed, and makes the sparse degree of data fit.

4) set up and have non coherent measurement matrix, according to compressive sensing theory, the measurement data requiring except step 3) need to have the feature of rarefaction, and another requirement is to measure matrix to have very strong incoherence; Set up that to measure the method for matrix many, as long as meet linear incoherent feature, just release signal more accurately; The conventional random matrix, the graceful matrix of hada etc. that have employing Gaussian Profile; What the present invention adopted is the graceful matrix of hada, and each of the graceful matrix of hada measured between vector has very strong incoherence, and calculates comparatively simply, but requires the m power (m is positive integer) that the length of each vector is 2.

5) set up data recovery algorithm and complete data recovery task, according to the measurement matrix described in step 4), step 3) is measured to obtained measurement result, data recovery algorithm based on OMP completes the recovery to sparse data, this be actually one ask optimum solution owe to determine process, finally convert initial data to by inverse transformation again.

In step 1), described acquisition system, all signaling datas that mobile phone terminal is sent are preserved, its acquisition system can realize by self-designed or the existing signal collecting card in market, each signaling data all has a hundreds of field, topmost user identity, session time started, conversation end time, surfing flow, the network data etc. of comprising.

In step 2) in, described initial data pretreated model, for each data signaling, if data signaling be incomplete, this abandons, and directly removes this data entry; Be that we are unconcerned for data, as control command, also can directly delete; For each data, may exist multiple indexs, need separately to analyze or combined analysis, such as user's network data is a pictures or one section of voice, video, need to combine, according to demand, signaling data is carried out to preliminary treatment.

In step 3), described signaling data categorizing selection device is as follows:

x _i＝f(s _i)

Wherein, for step 2) data that obtain change into polytype tables of data s _i, may be word, image, voice; Take image data as example, x _ifor image data s _ito carrying out the value of the sparse transform-based of small echo after multiplying each other, by rarefaction methods such as wavelet transformations, make data x _imeet sparse characteristic; After wavelet transformation, carry out compression sampling and restore the x obtaining by restoration algorithm _i, also should pass through wavelet inverse transformation, to obtain s simultaneously _i; If this index s _iitself has met sparse property, x _i=s _i, restore afterwards also without carrying out an inverse transformation.

In step 4), described measurement matrix, the sparse data obtaining with step 3) has very strong incoherence; Each of the graceful matrix of hada measured between vector orthogonal each other, and each measurement vector is made up of 1 or-1 value, and the m power (m is positive integer) that the length requirement of each vector is 2 adopts the graceful matrix of hada as measuring matrix, and its perfect matrix is as follows:

H ₀＝1

$H_1=\frac{1}{\sqrt{2}}\left[\begin{array}{cc}1& 1\\ 1& -1\end{array}\right]$

$H_k=\frac{1}{\sqrt{2}}\left[\begin{array}{cc}{H}_{k-1}& H_{k-1}\\ H_{k-1}& -H_{k-1}\end{array}\right]$

Wherein, the m power that the length that requires each vector is 2, m is positive integer; In the time that length n is taken as 65536, k is 16, H ₁₆be 65536 to be multiplied by 65536 matrix, the numerical value of this matrix is unified, meets compression and the equity requirement of restoring; If compression ratio is 0.1, choose front 65536 row of measuring matrix, form the measurement matrix A of a 65536*65536; Measuring matrix and the sparse initial data y=Ax that multiplies each other, obtain measured value y, the length of y is x 1/10; Adopt the graceful matrix of hada as measuring matrix, except thering is orthogonal property, also have the benefit that can improve computational speed in the time measuring compression, avoided the inefficiency of gaussian random matrix floating number multiplication, and only carried out plus and minus calculation.

In step 5), described data recovery algorithm, is in the time that needs restore data, the measured value y obtaining according to step 4), and the measurement matrix that obtains arranging according to the length of y solves the process of x; And adopt orthogonal matching pursuit OMP algorithm, and can recover exactly initial data x, orthogonal matching pursuit OMP algorithm is a repeatedly iterative process, residual error is initialized as initial data y, Increment Matrix Φ _ibe initialized as sky, subscript i represents the variable in the i time iteration, and the process of each interior circulation is as follows:

1) from measuring the column vector atom of finding out matrix with residual error inner product maximum, then this column vector is added in Increment Matrix, and from measuring matrix, this column vector is deleted;

2) solve an optimal problem

${\hat{x}}_i=\arg \min \left|\right|y-{\widetilde{\mathrm{\&Phi;}}}_{i}x\left|\right|$

$\hat{x}={\left({\widetilde{\mathrm{\&Phi;}}}^T\widetilde{\mathrm{\&Phi;}}\right)}^{-1}{\widetilde{\mathrm{\&Phi;}}}^{T}y$

In this solution procedure, can use matrix decomposition to make the calling program can parallel processing.? be defined as C, because C is Nonsingular symmetrical matt, while inverting, can adopt LDL ^tdecompose, and upgrade L and D according to up-to-date atom;

$L_{i,j}=\frac{1}{D_{j,i}}(C_{i,j}-\underset{k=1}{\overset{j-1}{\mathrm{\&Sigma;}}}{L}_{i,k}{L}_{j,k}{D}_{k,k})(i<j)$

$D_{j,j}=C_{j,j}-\underset{k=1}{\overset{j-1}{\mathrm{\&Sigma;}}}{L}_{j,k}{L}_{j,k}{D}_{k,k}$

The contrary of C is:

$C^{-1}=L^{{-1}^T}{D}^{-1}{L}^{-1}$

4) upgrade residual error

$y=y-{\mathrm{\&Phi;}}_i{\hat{x}}_i$

Through after iteration repeatedly, above-mentioned algorithm will obtain an optimal solution

if primary signal has through rarefaction conversion, right

carry out once corresponding inverse transformation and just can obtain initial data x; If not through rarefaction conversion,

Below in conjunction with Fig. 2 to Fig. 6, with the compression acquisition problems of the user of China Mobile surfing flow data, the inventive method is specifically described, its situation is as follows:

1) user of China Mobile Internet data model

The user of China Mobile Internet data bag is if Fig. 2 is as shown, when the packet of user's online is dealt into base station from terminal, be transformed into one or more signaling, comprise personally identifiable information and miscellaneous service information, each data entry has up to a hundred fields (Fig. 3 has only shown the exemplary field of small part), due to the difference of business, it is empty may existing a lot of fields.Take an image data as example, in order to gather quickly this pictures, can separately use compressed sensing algorithm to compress storage each column data of picture:

From packet, extract the Internet data that belongs to same user, and identify a pictures, because the pixel value transition in picture is milder, can pass through the sparse conversion of small echo, make it there is sparse characteristic.Cromogram is divided into the figure that closes of three RGB, and also can regard three different gray-scale map stacks of primary colours as, and in order more simply and intuitively to represent employing gray-scale map to be showed its sparse characteristic, original image data as shown in Figure 4.

2) utilize wavelet transform matrix to carry out rarefaction

Generate wavelet transform matrix w=DWT (n), wherein n is the length of the every row of initial data X, supposes that X is the matrix of 256*256, and n is 256.

The data after rarefaction are: XX=w*sparse (X) * w', and through the data on flows after wavelet transformation as shown in Figure 5, ater represents that value is 0; If there is the wavelet transformation of this step, need to carry out inverse transformation once when restored data.

3) compression acquisition phase

For image data faster, the data length of each row is only taken as 256, generates the graceful measurement matrix H of hada of 256*256 size ₇:

H ₀＝1

$H_1=\frac{1}{\sqrt{2}}\left[\begin{array}{cc}1& 1\\ 1& -1\end{array}\right]$

$H_k=\frac{1}{\sqrt{2}}\left[\begin{array}{cc}{H}_{k-1}& H_{k-1}\\ H_{k-1}& -H_{k-1}\end{array}\right]$

Compression ratio r (0≤r≤1) is set, supposes that r is 0.5, get measurement matrix M of the capable measurement vector of front 256*0.5=128 formation of measuring matrix, utilize M to compress measurement to data.

y＝M*XX

Wherein, M is for measuring matrix, and XX is the matrix of initial data rarefaction.

4) the data recovery stage

According to the measured value y that obtained above, and according to solving the process of x with consistent measurement matrix above, what adopt here is orthogonal matching pursuit OMP algorithm, can recover comparatively accurately initial data x, because recovery is a process very consuming time, so OMP algorithm has been taked to the thought of parallel computation, has been improved and restore efficiency.

$\hat{x}={\left({\widetilde{\mathrm{\&Phi;}}}^T\widetilde{\mathrm{\&Phi;}}\right)}^{-1}{\widetilde{\mathrm{\&Phi;}}}^{T}y$

Wherein, Φ is constantly the atom set of expansion, and element is to measuring the column vector of the maximum inner product that matrix chooses in each circulation.

Obtain an optimal solution by OMP algorithm afterwards, because primary signal has through sparse conversion, so right

carry out once corresponding inverse transformation

obtain initial data x, as shown in Figure 6; Can find out from the image recovering, can be with larger compression ratio storage user data under very high precision.

The examples of implementation of the above are only the present invention's preferred embodiment, not limit practical range of the present invention with this, therefore the variation that all shapes according to the present invention, principle are done all should be encompassed in protection scope of the present invention.

Claims (5)

1. the radio network optimization magnanimity signaling data acquisition method based on compressed sensing, is characterized in that, comprises the following steps:

1) set up acquisition system take mobile communication signaling as target, the data when to mobile phone terminal call and online gather, and can in each base station or upper strata terminal, set up;

2) set up initial data pretreated model, useless or incomplete original signaling data is deleted, and core data is extracted and preliminary treatment;

3) set up signaling data categorizing selection device, according to step 1) set up signaling acquisition system and step 2) set up initial data pretreated model, data after preliminary treatment are classified, and classifying rules is: if data itself meet rarefaction feature, without carrying out rarefaction operation; If do not meet, according to the rarefaction method of wavelet transformation or Fourier transform, data that can rarefaction are converted, for the data after conversion, if not enough sparse property still, can further process, such as, for image, can be by setting a threshold values, and the data below this threshold values are all removed is 0, make the sparse property of data fit;

4) set up and have a non coherent measurement matrix, the data by the method for compressed sensing after to rarefaction are compressed;

5) set up data recovery algorithm and complete data recovery task, according to the measurement matrix described in step 4), step 3) is measured to obtained measurement result, data recovery algorithm based on orthogonal matching pursuit OMP algorithm completes the recovery to sparse data, this be actually one ask optimum solution owe to determine process, finally convert initial data to by inverse transformation again.

2. a kind of radio network optimization magnanimity signaling data acquisition method based on compressed sensing according to claim 1, it is characterized in that: in step 1), described acquisition system, all signaling datas that mobile phone terminal is sent are preserved, and described signaling data includes user identity, session time started, conversation end time, surfing flow, network data.

3. a kind of radio network optimization magnanimity signaling data acquisition method based on compressed sensing according to claim 1, it is characterized in that: in step 2) in, described initial data pretreated model, for each signaling data, if signaling data be incomplete, this abandons, and directly removes this data entry; For each signaling data, may exist multiple indexs, need separately to analyze or combined analysis, if user's network data is a pictures or one section of voice, video, need to combine, according to demand, signaling data is carried out to preliminary treatment.

4. a kind of radio network optimization magnanimity signaling data acquisition method based on compressed sensing according to claim 1, is characterized in that: in step 3), described signaling data categorizing selection device is as follows:

x _i＝f(s _i)

Wherein, for step 2) data that obtain change into polytype tables of data s _i, as word, image, voice; x _ifor s _ivalue after multiplying each other with the sparse transform-based of small echo, by the rarefaction method of wavelet transformation, makes data x _imeet sparse characteristic; After wavelet transformation, carry out compression sampling and restore the x obtaining by restoration algorithm _i, also simultaneously by wavelet inverse transformation, to obtain s _iif, this index s _iitself has met sparse property, x _i=s _i, restore afterwards also without carrying out an inverse transformation.

5. a kind of radio network optimization magnanimity signaling data acquisition method based on compressed sensing according to claim 1, is characterized in that:

In step 4), described measurement matrix, the sparse data obtaining with step 3) has incoherence; Each of the graceful matrix of hada measured between vector orthogonal each other, and each measurement vector is made up of 1 or-1 value, the m power that the length requirement of each vector is 2, and m is positive integer, adopts the graceful matrix of hada as measuring matrix, its perfect matrix is as follows:

H ₀＝1

$H_1=\frac{1}{\sqrt{2}}\left[\begin{array}{cc}1& 1\\ 1& -1\end{array}\right]$

$H_k=\frac{1}{\sqrt{2}}\left[\begin{array}{cc}{H}_{k-1}& H_{k-1}\\ H_{k-1}& -H_{k-1}\end{array}\right]$

Wherein, the m power that the length that requires each vector is 2, m is positive integer; In the time that length n is taken as 65536, k is 16, H ₁₆be 65536 to be multiplied by 65536 matrix, the numerical value of this matrix is unified, meets compression and the equity requirement of restoring; If compression ratio is 0.1, choose front 65536 row of measuring matrix, form the measurement matrix A of a 65536*65536; Measuring matrix and the sparse initial data y=Ax that multiplies each other, obtain measured value y, the length of y is x 1/10;

In step 5), described data recovery algorithm, is in the time that needs restore data, the measured value y obtaining according to step 4), and the measurement matrix that obtains arranging according to the length of y solves the process of x; And adopt orthogonal matching pursuit OMP algorithm, and can recover exactly initial data x, orthogonal matching pursuit OMP algorithm is a repeatedly iterative process, residual error is initialized as initial data y, Increment Matrix Φ _ibe initialized as sky, subscript i represents the variable in the i time iteration, and the process of each interior circulation is as follows:

1) from measuring the column vector atom of finding out matrix with residual error inner product maximum, then this column vector is added in Increment Matrix, and from measuring matrix, this column vector is deleted;

2) solve an optimal problem

${\hat{x}}_i=\arg \min \left|\right|y-{\widetilde{\mathrm{\&Phi;}}}_{i}x\left|\right|$

$\hat{x}={\left({\widetilde{\mathrm{\&Phi;}}}^T\widetilde{\mathrm{\&Phi;}}\right)}^{-1}{\widetilde{\mathrm{\&Phi;}}}^{T}y$

? be defined as C, because C is Nonsingular symmetrical matt, while inverting, can adopt LDL ^tdecompose, and upgrade L and D according to up-to-date atom;

$L_{i,j}=\frac{1}{D_{j,i}}(C_{i,j}-\underset{k=1}{\overset{j-1}{\mathrm{\&Sigma;}}}{L}_{i,k}{L}_{j,k}{D}_{k,k})(i<j)$

$D_{j,j}=C_{j,j}-\underset{k=1}{\overset{j-1}{\mathrm{\&Sigma;}}}{L}_{j,k}{L}_{j,k}{D}_{k,k}$

The contrary of C is:

$C^{-1}=L^{{-1}^T}{D}^{-1}{L}^{-1}$

3) upgrade residual error

$y=y-{\mathrm{\&Phi;}}_i{\hat{x}}_i$

Through after iteration repeatedly, above-mentioned algorithm will obtain an optimal solution if primary signal has through rarefaction conversion, right

carry out once corresponding inverse transformation and just can obtain initial data x; If not through rarefaction conversion,

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