CN103745489B - A kind of method of setting up base station signal field intensity map based on compressed sensing - Google Patents

Abstract

The present invention relates to compressed sensing technical field and wireless communication technology field, in mobile communication, radio communication, specially refer to the method for setting up base station signal field intensity map in the mobile communications networks such as Wi-Fi WLAN, cellular communication based on compressed sensing. The present invention is based on Its Sparse Decomposition and the compressed sensing technology of redundant dictionary, by constructing special observing matrix, on the single width actual measurement field intensity map basis of low resolution, construct high-resolution field strength distribution map. By method provided by the invention, can effectively carry out restoration and reconstruction to wireless signal field map, conscientiously reduce the workload in off-line phase measuring-signal field intensity value in the application such as wireless network planning, wireless field density location, improve efficiency and the accuracy of signal strength signal intensity map structuring.

Description

A kind of method of setting up base station signal field intensity map based on compressed sensing

Technical field

The present invention relates to compressed sensing technical field and wireless communication technology field, as mobile communication, channel radioIn letter, specially refer in the mobile communications networks such as Wi-Fi WLAN, cellular communication based on compressionThe method of base station signal field intensity map is set up in perception.

Background technology

In mobile communications network, the field intensity of base station signal distribution situation is geographically schemed on the spot doughtily, is nothingVery valuable data in line communication operation, at the planning cloth of the service based on geographical position, wireless networkIn the business such as office, there is important application. But wireless signal field map is but difficult to obtain in simple modeGet. This is mainly to be caused by the communication environments of wireless signal complexity. In mobile communications network, base station is to connecingPropagation path between receipts machine is very complicated, the reflection of barrier from simple line-of-sight propagation to various complexity,The processes such as refraction, diffraction and multipath transmisstion all may run into. So the signal strength in wireless transmission dividesCloth can not accurately be predicted, the difficulty that the randomness of wireless transmission has caused wireless field density to obtain.

Common wireless signal field map is to obtain by surveying or survey the method that adds interpolation. Directly with realSurvey method time, obtain high-resolution map and can only obtain by a large amount of field survey work, thereforeWorkload is huge, can only be used in the region that area is less; First the field intensity map in larger region needs by realityThe field intensity data of some are obtained in survey, then reach higher resolution ratio by the method for interpolation. According to interpolationThe quality of method, the actual measurement workload needing and the final accuracy of map obtaining have very large difference, existingMethod still needs larger actual measurement workload. Therefore, be necessary to further investigate the constructing technology of field intensity map,Reduce the resolution ratio of surveying workload and improving map. Advanced field intensity map structuring technology can reduce at doubleActual measurement workload, saves time and manpower and materials. According to the mathematical method using in field intensity map structuring process,Field intensity Map building method can be divided into classical interpolation method, propagation model computing method, super-resolution rebuilding methodDeng.

Classical interpolation method is the image interpolation algorithm of using for reference in image processing field, utilizes close position pointKnow or survey field intensity value, producing the field intensity value of unknown position point with certain interpolating function. These algorithm bagsDraw together proximal point algorithm, bilinear interpolation etc. Although classical interpolation method is easily gone fast, rebuilds effect alsoUndesirable, high-frequency information is lost serious, and resolution ratio is difficult for improving.

Propagation model computing method is propagation (decay) model that makes full use of wireless signal, comprises certainty mouldType and empirical model, calculate the field intensity data of unknown point. But, due to answering of actual propagation environmentPolygamy is that certainty propagation model or experience propagation model are all difficult to simulate exactly actual signalPropagation condition, the signal strength map therefore calculating according to propagation model still has larger error, suitableBe limited in scope.

Super-resolution rebuilding method is different with interpolation method. Super-resolution rebuilding can be according to a width or several identical fieldsScape but the field intensity map of different angles, reconstruct according to certain mathematical programming that a width is more clear (to be differentiatedRate is higher) field intensity map. The key of Super-resolution Reconstruction be to obtain image itself low resolution copy andCorresponding relation between high-resolution copy, utilizes the reconstruction rule of this pass series structure than common interpolation letterNumber is more accurate. At present super-resolution research can be divided into three main category: based on interpolation, based on reconstruction andBased on the method for study. Wherein the method based on study is the focus side of super-resolution algorithms research in recent yearsTo, its basic ideas are by given training atlas, calculate image block and the training plan of test sample book and concentrateNeighborhood relationships between image block set, and construct optimum weights constraint, obtain priori and approach surveySample High Resolution Ground Map originally. When the information providing when high-resolution data does not meet high-resolution demand,Method based on study can obtain more map high-frequency information, thereby tool has great advantage.

The existing Super-resolution Reconstruction method based on study is mainly used in image processing field, in open source information, goes backBe not applied to the report of wireless signal field map structuring. The surpassing based on study that image processing field is usedDifferentiate algorithm for reconstructing and mainly contain document " ImageSuper-ResolutionasSparseRepresentationofRawImagePatches”(JianchaoYang,IEEEConferenceOnComputerVisionandPatternRecognition, 2008) report in, the method masterWanting principle is the Its Sparse Decomposition model in redundant dictionary based on picture signal, utilizes the high-resolution pair of imageThis has the identical feature of sparse coefficient under same redundant dictionary with low resolution copy, observation is obtainedLow-resolution image carry out Its Sparse Decomposition, recycling is decomposed the sparse coefficient and the high-resolution dictionary that obtain and is enteredRow Super-resolution Reconstruction.

Said method is also not exclusively applicable to the structure of wireless signal field map. In wireless signal field,Overdamp process is relevant with factors such as propagation distance, propagation paths, processes completely not and press image modeConsider these factors. On the other hand, the compressed sensing technology based on Its Sparse Decomposition technology, needs to introduce observation squareBattle array can be considered the signal attenuation constituent element that some are actual in signal recovery process, therefore has certainRoom for improvement.

Summary of the invention

The object of the invention is to overcome the above-mentioned deficiency of prior art, provide a kind of and set up based on compressed sensingThe method of base station signal field intensity map is constructed the High Resolution Ground Map that base station radio signal strength distributes.

For achieving the above object, the invention provides following technical scheme:

A method of setting up base station signal field intensity map based on compressed sensing, comprises the following steps:

(1), according to the resolution ratio of needed final map, determine the number of the location point in this field intensity mapAmount, the quantity of establishing location point is n, has been wherein m through the location point number of actual measurement, n should meetm<n<100m；

(2), according to the field intensity numerical value of an existing m eyeball in this map, utilize Krieger (kriging)Common interpolation algorithm, sets up the field intensity value of all unmeasured location points, obtains preliminary field strength distribution interpolationMap, establishing the matrix form that this preliminary field intensity map is corresponding is C, corresponding by column vector form is

(3) the field intensity value of, establishing all m eyeball in map Matrix C is P₁，P₂，…，P_i,…,P_m。Define a radius of influence R, and think that an eyeball i is subject to interior other positions of border circular areas that radius is RPut the impact of field intensity value a little, claim that these points are " affecting a little " of position i, calculate each eyeball" affect a little " quantity, use variable K_iRepresent the quantity of the impact point of each eyeball i;

(4), constructed the sampling matrix A of corresponding eyeball field intensity value, the element in A by preliminary field intensity Matrix CBe the weighing factor coefficient of the field intensity value of the impact point of corresponding each eyeball i, these coefficients are calculated by following formula:

${\mathrm{\&alpha;}}_{ik}=\frac{1}{K_i}\frac{P_i}{P_{ik}},k=1,2,......,K_i$

Wherein α_ikK that is eyeball i affects the field intensity of point with respect to the weight of the field intensity of eyeball i,P_iThe field intensity of eyeball i position, P_ikIt is the field intensity of k the impact point position of eyeball i.The field intensity value P of eyeball i_iCan be written as K_iThe weighted sum of the field intensity value of individual impact point:

$P_i={\mathrm{\&alpha;}}_{i1}{P}_{i1}+{\mathrm{\&alpha;}}_{i2}{P}_{i2}+...{\mathrm{\&alpha;}}_{\mathrm{ik}}{P}_{\mathrm{ik}}+...+{\mathrm{\&alpha;}}_{{\mathrm{iK}}_i}{P}_{{\mathrm{iK}}_i}$

To whole investigation region, the field intensity value of all eyeballs can be write as matrix product form:

$\stackrel{\&RightArrow;}{M}=A\stackrel{\&RightArrow;}{C}$

Wherein:

For the vector of all eyeball field intensity values formations, $\stackrel{\&RightArrow;}{M}={\left[\begin{array}{cccc}{P}_1& P_2& ......& P_m\end{array}\right]}^T;$

(5), using the sampling matrix A that sets up in step (4) as observing matrix, set up compressed sensing model:

$\stackrel{\&RightArrow;}{M}=AB\stackrel{\&RightArrow;}{S},$

Wherein,For eyeball field intensity vector, A is sampling matrix, and B is the field by KSVD algorithm constructionThe former word bank of figure Its Sparse Decomposition doughtily,For field intensity map Its Sparse Decomposition coefficient to be solved;

(6), follow the trail of BP restructing algorithm by base, the sparse coefficient in solution procedure (5)

(7), according to the sparse coefficient obtainingReconstruct high-resolution field intensity map vector

$\stackrel{\&RightArrow;}{X}=B\stackrel{\&RightArrow;}{S}$

According to this vectorWrite again as matrix form X, obtained final field intensity high-resolution map.

The present invention is based on Its Sparse Decomposition and the compressed sensing technology of redundant dictionary, by constructing special observation squareBattle array, on the single width actual measurement field intensity map basis of low resolution, constructs high-resolution field strength distribution map.By method provided by the invention, can effectively carry out restoration and reconstruction to wireless signal field map, conscientiouslyReduce the work in off-line phase measuring-signal field intensity value in the application such as wireless network planning, wireless field density locationMeasure, improve efficiency and the accuracy of signal strength signal intensity map structuring.

Compared with prior art, beneficial effect of the present invention:

1. the present invention is applied to compressed sensing technology the building process of the field intensity map of wireless base station signalIn, can realize and use the sampled point structure high-resolution of low resolution (more sparse) (higherDensity) signal strength map, thereby can reduce actual measurement workload, improve map structuring efficiency.

2. the present invention has utilized the compressed sensing principle of interpolation technique and image simultaneously, by first use gramLeague (unit of length) Interpolation Process has been utilized the distance factor of signal attenuation process, simultaneously again by compressed sensing process of reconstructionUtilize the former word bank by similar wireless signal Map building, thereby in process of reconstruction, used moreActual signal information, instead of simply wireless signal map is regarded as to picture signal, its result is than generalProcess of image interpolation more approaches true field intensity signal, has higher accuracy.

Brief description of the drawings

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

Fig. 2 is an example of actual measurement field intensity map.

Fig. 3 is the preliminary field intensity map that Fig. 2 is carried out to Kriging interpolation.

Fig. 4 is that the final high-resolution field recovering is schemed doughtily.

Detailed description of the invention

Below in conjunction with the drawings and specific embodiments, the present invention is further illustrated.

In order to make object, technical scheme and the advantage of invention clearer, below in conjunction with accompanying drawing and enforcementExample, is further elaborated to the present invention. Should be appreciated that specific embodiment described herein only usesTo explain the present invention, be not intended to limit the present invention.

Embodiment 1

The present embodiment has been introduced a kind of method of setting up base station signal field intensity map based on compressed sensing, as accompanying drawingShown in 1, comprise the following steps:

(1), first collect some existing base station signal high-resolution fields and scheme doughtily, utilize KSVD algorithmCarry out sparse redundant dictionary decomposition, obtain the redundant dictionary storehouse of corresponding wireless signal field map.

(2), taking the field intensity map example in Fig. 2 as example, set final recovery field intensity map resolution ratio and be50 × 50. Fig. 2 is carried out to Ordinary Kriging Interpolation (Kriging) algorithm interpolation, obtain 50 × 50 preliminary insertingValue map, as shown in Figure 3, its image array is expressed as C, and what map matrix was corresponding presses column vector formBe expressed as

(3) the field intensity value of, establishing all 625 eyeballs in map Matrix C is P₁，P₂，…，P_i,…,P₆₂₅。Definition radius of influence R=1.5, the unit of this radius is the row or column in Matrix C, and thinks an eyeballI is subject to the impact of the field intensity value of other location points within the scope of radius R, claims " the impact that these points are position iPoint ". Calculate " affecting a little " quantity of each eyeball. Use variable K_iRepresent the shadow of each eyeball iThe quantity of ringing point, in this example, each eyeball has 8 to affect a little.

(4), constructed the sampling matrix A of corresponding eyeball field intensity value, the element in A by preliminary field intensity Matrix CBe the weighing factor coefficient of the field intensity value of the impact point of corresponding each eyeball i, these coefficients are calculated by following formula:

${\mathrm{\&alpha;}}_{ik}=\frac{1}{8}\frac{P_i}{P_{ik}},k=1,2,......,8$

Wherein α_ikK that is eyeball i affects the field intensity of point with respect to the weight of the field intensity of eyeball i,P_iThe field intensity of eyeball i position, P_ikIt is the field intensity of k the impact point position of eyeball i.The field intensity value P of eyeball i_iCan be written as K_iThe weighted sum of the field intensity value of individual impact point:

P_i＝α_i1P_i1+α_i2P_i2+...+α_ikP_ik+...+α_i8P_i8

To whole investigation region, the field intensity value of all eyeballs can be write as matrix product form:

$\stackrel{\&RightArrow;}{M}=A\stackrel{\&RightArrow;}{C}$

Wherein:

For the vector of all eyeball field intensity values formations, $\stackrel{\&RightArrow;}{M}={\left[\begin{array}{cccc}{P}_1& P_2& ......& P_{625}\end{array}\right]}^T;$

A is the sampling weight coefficient α by all impact points of the each eyeball of aforesaid correspondence_ikThe sampling formingMatrix, matrix A has following form:

$\begin{array}{c}A=\\ \left[\begin{array}{cccccccccccccccccccc}{\mathrm{\&alpha;}}_{11}& {\mathrm{\&alpha;}}_{12}& {\mathrm{\&alpha;}}_{13}& 0& \mathrm{...}& 0& {\mathrm{\&alpha;}}_{14}& 0& {\mathrm{\&alpha;}}_{15}& 0& \mathrm{...}& 0& {\mathrm{\&alpha;}}_{16}& {\mathrm{\&alpha;}}_{17}& {\mathrm{\&alpha;}}_{18}& 0& \mathrm{...}& \mathrm{...}& \mathrm{...}& 0\\ 0& 0& {\mathrm{\&alpha;}}_{21}& {\mathrm{\&alpha;}}_{22}& {\mathrm{\&alpha;}}_{23}& 0& \mathrm{...}& 0& {\mathrm{\&alpha;}}_{24}& 0& {\mathrm{\&alpha;}}_{25}& 0& \mathrm{...}& 0& {\mathrm{\&alpha;}}_{26}& {\mathrm{\&alpha;}}_{27}& {\mathrm{\&alpha;}}_{28}& \mathrm{...}& \mathrm{...}& 0\\ \mathrm{..}& \mathrm{..}& \mathrm{..}& \mathrm{..}& \mathrm{..}& \mathrm{..}& \mathrm{..}& \mathrm{..}& \mathrm{..}& \mathrm{..}& \mathrm{..}& \mathrm{..}& \mathrm{..}& \mathrm{..}& \mathrm{..}& \mathrm{..}& \mathrm{..}& \mathrm{..}& \mathrm{..}& 0\\ \mathrm{..}& \mathrm{..}& \mathrm{..}& \mathrm{..}& \mathrm{..}& \mathrm{..}& \mathrm{..}& \mathrm{..}& \mathrm{..}& \mathrm{..}& \mathrm{..}& \mathrm{..}& \mathrm{..}& \mathrm{..}& \mathrm{..}& \mathrm{..}& \mathrm{..}& \mathrm{..}& \mathrm{..}& 0\\ 0& \mathrm{..}& \mathrm{..}& 0& 0& {\mathrm{\&alpha;}}_{6251}& {\mathrm{\&alpha;}}_{6252}& {\mathrm{\&alpha;}}_{6253}& 0& \mathrm{...}& 0& {\mathrm{\&alpha;}}_{6254}& 0& {\mathrm{\&alpha;}}_{6255}& 0& \mathrm{...}& 0& {\mathrm{\&alpha;}}_{6256}& {\mathrm{\&alpha;}}_{6257}& {\mathrm{\&alpha;}}_{6258}\end{array}\right]\end{array}$

(5), using the sampling matrix A that sets up in step (4) as observing matrix, set up compressed sensing mouldType:

$\stackrel{\&RightArrow;}{M}=AB\stackrel{\&RightArrow;}{S},$

Wherein,For eyeball field intensity vector, A is sampling matrix, and B is the field by KSVD algorithm constructionThe former word bank of figure Its Sparse Decomposition doughtily,For field intensity map Its Sparse Decomposition coefficient vector to be solved;

(6), follow the trail of BP restructing algorithm by base, the sparse coefficient in solution procedure (5)Pass throughFollowing sparse constraint item is solved and obtains sparse coefficientVector:

$\underset{\stackrel{\&RightArrow;}{S}}{min}\left|\right|\stackrel{\&RightArrow;}{M}-AB\stackrel{\&RightArrow;}{S}|{|}_2^2+\mathrm{\&lambda;}|\left|\stackrel{\&RightArrow;}{S}\right|{|}_0$

In above formulaA，B，The same step of definition (5), λ is regularization parameter,Represent L2Norm, || ||₀Represent L0 norm.

(7), according to the sparse coefficient obtainingReconstruct high-resolution field intensity map vector

$\stackrel{\&RightArrow;}{X}=B\stackrel{\&RightArrow;}{S}$

According to this vectorWrite again as matrix form X, obtained final field intensity high-resolution map as accompanying drawing 4.

The present embodiment is applied to compressed sensing technology on the super-resolution rebuilding of wireless signal field map,By constructing a kind of weight sampling matrix as the observing matrix in compressive sensing theory, make to measure chess matrix analogueDeamplification forming process and signal actual attenuation process more approaching, thereby realize according to a small amount of actual measurementIn basis of signals, improve the resolution ratio of reconstruction signal map, realized use low resolution (rarerDredge) sampled point build the signal strength map of high-resolution (being higher density), thereby can reduceActual measurement workload, improves map structuring efficiency. The present embodiment adopts the Its Sparse Decomposition principle of image in addition, logicalCross the distance factor that first uses Krieger Interpolation Process to utilize signal attenuation process, simultaneously again by compressionPerception process of reconstruction has been utilized the former word bank by similar wireless signal Map building, thereby sharp in process of reconstructionUse more actual signal information, instead of simply wireless signal map has been regarded as to picture signal, itsResult more approaches true field intensity signal than general process of image interpolation, has higher accuracy, ensures extensiveSignal strength map after multiple reconstruction is compared with traditional approach better effects if.

The foregoing is only preferred embodiment of the present invention, in order to limit the present invention, not all at thisAny amendment of doing within bright spirit and principle, be equal to and replace and improvement etc., all should be included in the present inventionProtection domain within.

Claims (1)

1. a method of setting up base station signal field intensity map based on compressed sensing, is characterized in that: compriseFollowing steps:

(1), according to the resolution ratio of needed final map, determine the number of the location point in this field intensity mapAmount, the quantity of establishing location point is n, has been wherein m through the location point number of actual measurement, n should meetm<n<100m；

(2), according to the field intensity numerical value of an existing m eyeball in this map, utilize Krieger (kriging)Common interpolation algorithm, sets up the field intensity value of all unmeasured location points, obtains preliminary field strength distribution interpolationMap, establishing the matrix form that this preliminary field intensity map is corresponding is C, corresponding by column vector form is

(3) the field intensity value of, establishing all m eyeball in map Matrix C is P₁，P₂，…，P_i,…,P_m，Define a radius of influence R, and think that an eyeball i is subject to interior other positions of border circular areas that radius is RPut the impact of field intensity value a little, claim that these points are " affecting a little " of position i, calculate each eyeball" affect a little " quantity, use variable K_iRepresent the quantity of the impact point of each eyeball i;

(4), constructed the sampling matrix A of corresponding eyeball field intensity value, the element in A by preliminary field intensity Matrix CBe the weighing factor coefficient of the field intensity value of the impact point of corresponding each eyeball i, these coefficients are calculated by following formula:

${\mathrm{\&alpha;}}_{ik}=\frac{1}{K_i}\frac{P_i}{P_{ik}},k=1,2,......,K_i$

Wherein α_ikK that is eyeball i affects the field intensity of point with respect to the weight of the field intensity of eyeball i,P_iThe field intensity of eyeball i position, P_ikThe field intensity of k the impact point position of eyeball i,The field intensity value P of eyeball i_iCan be written as K_iThe weighted sum of the field intensity value of individual impact point:

$P_i={\mathrm{\&alpha;}}_{i1}{P}_{i1}+{\mathrm{\&alpha;}}_{i2}{P}_{i2}+...{\mathrm{\&alpha;}}_{\mathrm{ik}}{P}_{\mathrm{ik}}+...+{\mathrm{\&alpha;}}_{{\mathrm{iK}}_i}{P}_{{\mathrm{iK}}_i}$

To whole investigation region, the field intensity value of all eyeballs can be write as matrix product form:

$\stackrel{\&RightArrow;}{M}=A\stackrel{\&RightArrow;}{C}$

Wherein:

For the vector of all eyeball field intensity values formations, $\stackrel{\&RightArrow;}{M}={\left[\begin{array}{cccc}{P}_1& P_2& \mathrm{......}& P_m\end{array}\right]}^T;$

(5), using the sampling matrix A that sets up in step (4) as observing matrix, set up compressed sensing model:

$\stackrel{\&RightArrow;}{M}=AB\stackrel{\&RightArrow;}{S},$

Wherein,For eyeball field intensity vector, A is sampling matrix, and B is the field by KSVD algorithm constructionThe former word bank of figure Its Sparse Decomposition doughtily,For field intensity map Its Sparse Decomposition coefficient to be solved;

(6), follow the trail of BP restructing algorithm by base, the sparse coefficient in solution procedure (5)

(7), according to the sparse coefficient obtainingReconstruct high-resolution field intensity map vector

$\stackrel{\&RightArrow;}{X}=B\stackrel{\&RightArrow;}{S}$

According to this vectorWrite again as matrix form X, obtained final field intensity high-resolution map.