CN103260240A - Scattering information source locating method based on distribution matching in large-scale MIMO system - Google Patents
Scattering information source locating method based on distribution matching in large-scale MIMO system Download PDFInfo
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Abstract
In a large-scale MIMO system, a scattering information source locating method based on probability distribution matching comprises the following operation steps: (1) a signal preprocessing stage, namely an average autocorrelation matrix of a received user signal is computed, an antenna array response vector is multiplied by the average autocorrelation matrix, the vector representation of a probability density function of overlapped user direction of arrival (DOA) is obtained; (2) an iteration estimation stage, namely the obtained vector is normalized, the position information of users is obtained by matching of the probability density function, and then estimated signals are deleted from the normalized vector. After multi-time loop iteration in the process, the position information of all the users can be obtained. The approximate orthogonality of the antenna response vector of the large-scale MIMO system is used for converting the average autocorrelation matrix of the received signal to the vector, and a probability distribution matching method is used for obtaining the position information of the users. Under the premise that estimation accuracy of the user position information is improved, complexity of estimation can be lowered.
Description
Technical field
The present invention relates to a kind of scattering signal source locating method for extensive MIMO, exactly, is a kind of estimate the center DOA of scattering information source and method of angle spread, belongs to wireless communication technology field.
Background technology
Along with the continuous growth of data service, existing antenna amount has been difficult to satisfy system to the demand of spectrum efficiency.By increasing the antenna amount of base station, can greatly improve diversity gain and the spatial multiplexing gain of system, this has just formed extensive mimo system.In the large-scale and multiple users mimo system, the base station has disposed hundreds and thousands of antennas, and each user only disposes an antenna, adopt time division duplex, all signals are handled, and comprise that channel estimating, input and signal precoding all in the base station processing, reduce the computation burden of user terminal.Yet because the aerial array area is limited, the antenna distance of extensive antenna array is less, and in rural area and suburb, because the angle spread of signal is less, there is correlation in the reception signal of antenna for base station.Therefore, need to adopt beam forming technique to obtain the space diversity gain (referring to shown in Figure 1) of directive antenna gain rather than wide-angle expansion.And beam forming technique needs user position information, and namely the DOA of subscriber signal estimates.Different with the localization method in the radar system, at wireless communication system, signal has passed through multipath reflection and has arrived the base station, so the DOA of signal is a plurality of values rather than a value in certain scope, and information source then shows as the scattering information source.Because the general Gaussian distributed of DOA of multipath signal, so in wireless communication system, the localization method of scattering information source is exactly average and the variance of estimating that DOA distributes, corresponding is exactly center DOA and the angle spread of scattering information source.Yet in extensive mimo system, traditional DOA method of estimation complexity at the scattering information source is high, is the key point that can scattering source DOA method of estimation through engineering approaches so reduce complexity.
In extensive MIMO, computation complexity is a vital problem of implementation.Existing method of estimation all is based on the calculating of matrix, and wherein the hunting zone of part method is very big.The best method of estimation of performance is maximum likelihood (ML) and approximate maximum likelihood (AML) algorithm, yet, search has each time all comprised matrix computations, and the variable of search comprises each subscriber signal power, noise power and each user's angle information, therefore complexity is the highest, is difficult in extensive mimo system practical.The covariance coupling is estimated (COMET) method by coupling covariance matrix and average cross correlation matrix, can be near the ML algorithm performance, though Sou Suo amount of calculation reduces to some extent each time, the scope of search does not become, so complexity is still very high.Has similar estimation expression formula based on the method for subspace and the method that is shaped based on wave beam, wherein utilize the orthogonality of pseudo noise space and noiseless covariance matrix to estimate center DOA and angle spread based on the method for subspace, and obtained the estimated value of positional information based on the method that wave beam is shaped by minimise interference power.The scope of the search of these two kinds of methods has included only center DOA and the angle spread of unique user, and complexity greatly reduces than ML and COMET, but each search has still comprised matrix computations, so complexity is still than higher.
In addition, existing method of estimation all is that each user's of hypothesis angle spread is less, utilize the approximate expression of Taylor expansion picked up signal covariance matrix to carry out the estimation of positional information then, yet this being similar under the bigger condition of angle spread has than mistake, can cause the performance of existing method of estimation to reduce along with the increase of angle spread.
So the problem at performance and this two aspect of complexity in the existing method the present invention proposes a kind of scattering information source center DOA of low complex degree and the method for estimation of angle spread.
Summary of the invention
In view of this, the purpose of this invention is to provide a kind of probability distribution coupling of in extensive mimo system, utilizing and carry out the method for low complex degree location, be a kind of in extensive mimo system, utilize probability distribution to mate to estimate the low-complexity method of center DOA and angle spread.The inventive method has been utilized the nearly orthogonal of antenna response vector in the extensive mimo system, the average cross correlation matrix based on response vector is converted into one observes vector.The calculating of vector is only carried out in each search, and center DOA and the angle spread that only need search for unique user, has fundamentally reduced the complexity of calculating.And the inventive method is to be similar to covariance matrix by extensive antenna amount, rather than is similar to covariance matrix as existing method by supposing little angle spread, thereby has reduced the evaluated error under the wide-angle expansion condition, has improved estimated performance.
In order to achieve the above object, the invention provides the scattering signal source locating method that mates based on probability distribution in a kind of extensive mimo system, be used for following scene: comprise a base station and a plurality of user's communications system, all users are to base station transmit signals in a period of time; The extensive aerial array in base station is even linear array; All users and antenna for base station battle array are in same plane; Under the effect of base station power control, all users' signal to noise ratio is approximate identical.It is characterized in that: described method comprises following two operating procedures:
(1) the Signal Pretreatment stage: calculate the average autocorrelation matrix of the subscriber signal vector that receives as the estimation of signal covariance matrix, and with response vector and the average autocorrelation matrix multiple of aerial array to different angles, obtain the vector representation of probability density function of the user DOA of stack.The concrete steps of the Signal Pre-Processing Method of carrying are as follows:
(11) signal vector that receives and the conjugate transpose of self multiply each other and obtain a matrix, and a plurality of such matrix phase adductions of different time-gap are on average just obtained the average autocorrelation matrix, and this average autocorrelation matrix is used as the estimation of signal covariance matrix.
(12) be divided into the M section to-90 ° to 90 °, M is antenna for base station quantity, and the corresponding antenna response vector of the end points angle of all sections has nearly orthogonal.Take advantage of the average autocorrelation matrix with its conjugate transpose while premultiplication of these vector sums and the right side, observe vector thereby obtain one.
(13) minimum value of this observation vector is used as the estimation that receives noise, and this each value of observing vector is deducted this minimum value, the vector representation that all users' of acquisition stack DOA distributes.
Obviously, if antenna for base station quantity M is more big, the orthogonality of antenna response vector is just more strong so, so these response vectors can be tieed up one group of orthogonal basis of complex space as M, and the element of the observation vector that obtains is exactly M the characteristic value that receives signal covariance matrix.Under each element of observing vector condition inequality, this observes vector is one to one with covariance matrix.Therefore, estimate that by observing vector angle parameter can not bring the loss on the precision.By each element of observing in the vector is deducted this vectorial minimum value, removed the part of noise power in observing vector, thus the vector representation that all users' of acquisition stack DOA distributes.Because the method for estimation carried of the present invention is by the very big covariance matrix that be similar to of extensive antenna amount, rather than the hypothesis low-angle expands to be similar to covariance matrix, thereby fundamentally reduced the source of error of estimation, improved estimated accuracy.And the vector of acquisition can be expressed as the stack that all user DOA distribute, and has become vectorial multiplication so search for when calculating at every turn from multiplication of matrices.Because noise power is eliminated, search does not comprise signal power and noise power.Therefore, fundamentally reduced the complexity of calculating.
(2) iteration estimation stages: the vector that obtains by first of normalization, and mate this vector with probability density function and obtain user position information, the then user's that deletion estimates from normalized vector signal in stage.This process just can obtain all user position information through behind the loop iteration repeatedly.The concrete steps of the iteration method of estimation of carrying are as follows:
(21) with this vector of maximum normalization of the vector that obtains, the vector with the normalization probability density function deducts normalized vector then, and calculates the mould of this difference vector.Then, by changing average and the variance of normalization probability density function, search for the minimum value of this mould value.The normalization probability density function of minimum value correspondence is exactly one of them user's DOA normalization probability density function so, thereby obtains this user's center DOA and the estimation of angle spread.
(22) from normalized vector, deduct the vector of the normalization probability density function of first estimating user, and repeat above-mentioned steps and obtain second user's center DOA and the estimation of angle spread.Move in circles, up to estimating all user position information.
When each user's DOA distribution function nearly orthogonal, namely the absolute value of the difference of two users' center DOA much larger than angle spread and the time, mould square can approximate representation subtracts each other the quadratic sum of delivery afterwards with normalized vector for the vector of all user's normalization probability density functions in the step (21).Because square being inversely proportional to the variance of probability-distribution function of mould, so along with antenna for base station quantity increases and each user's probability-distribution function is tending towards mutually orthogonal, estimate that finally asymptotic convergence arrives real center DOA and angle spread.And, because the vector that obtains in the step (21) is the stack of probability distribution, by the search local minimum, can obtain the approximate evaluation of all users' center DOA, therefore the scope of search greatly reduces, and complexity further reduces.
The localization method that the present invention is based on the probability distribution coupling is a kind of customer center DOA for extensive mimo system and the method for estimation of angle spread.Its advantage is: under the prerequisite that improves estimated performance, reduced computation complexity widely.The innovation key of the inventive method is: utilize extensive aerial array to the nearly orthogonal of the response vector of different angles, the average autocorrelation matrix of signal is converted into the observation vector, this element of observing vector is approximately the characteristic value of average autocorrelation matrix, observes vector by this and obtain all user position information under the condition of loss of accuracy hardly.The present invention is a kind of method of estimation of low complex degree, can be shaped for the wave beam of extensive mimo system to realize providing accurate angle information.
Description of drawings
Fig. 1 is application scenarios of the present invention: scattering information source DOA schematic diagram.
Fig. 2 is the flow chart of localization method among the extensive MIMO of the present invention.
Fig. 3 is in the embodiment of the invention, and center DOA estimates the analogous diagram of root-mean-square error (RMSE) and antenna amount relation.
Fig. 4 is in the embodiment of the invention, and angle spread is estimated the analogous diagram of RMSE and antenna amount relation.
Embodiment
For making the purpose, technical solutions and advantages of the present invention clearer, the present invention is described in further detail below in conjunction with accompanying drawing.
Referring to Fig. 1, introduce the application scenarios of the inventive method earlier: comprise a base station and a plurality of user's communications system, all users are to base station transmit signals in a period of time; The extensive aerial array in base station is even linear array; All users and antenna for base station battle array are in same plane; Under the effect of base station power control, all users' signal to noise ratio is approximate identical;
Average cross correlation matrix for base station received signal; z
j, j=1,2 ..., M, be with
Relevant value;
Be the k time value behind the preceding normalized vector of iteration;
For
With the value after the maximum normalization.
Referring to Fig. 2, introduce following two operating procedures of the inventive method:
(1) the Signal Pretreatment stage: calculate the average autocorrelation matrix of the subscriber signal vector that receives as the estimation of signal covariance matrix, and with response vector and the average autocorrelation matrix multiple of aerial array to different angles, obtain the vector representation of probability density function of the user DOA of stack.The content of operation of this step is:
(11) calculate the average cross correlation matrix that receives signal
(12)-90 ° to 90 ° scope evenly is divided into the M five equilibrium, M is antenna for base station quantity, with the response vector of every section end points angle calculation antenna array, and with its conjugate transpose of this vector sum take advantage of on premultiplication and the right side respectively
Obtain z
j, j=1,2 ..., M.
(13) each z
j, j=1,2 ..., M, each element deduct its minimum value, obtain
K=0 is set.
(2) iteration estimation stages: the vector that obtains by first of normalization, and mate this vector with probability density function and obtain user position information, the then user's that deletion estimates from normalized vector signal in stage.This process just can obtain all user position information through behind the loop iteration repeatedly.Concrete steps are as follows:
(21) k=k+1 judges whether k equals K+1.If be lower than desired value, then carry out following steps, otherwise finish algorithm.
(22) use
Greatest member normalization
Obtain
(23) by search, searching makes
Subtract each other afterwards center DOA and the angle spread of square minimum of delivery with the vector representation of normalization probability density function, as the estimated value of this user perspective parameter.
(24) calculate the normalization probability density function with the angle parameter that estimates, then from
In deduct the vector representation of this normalization probability density, obtain
(25) turn back to step (21).
In order to show the low complex degree of the inventive method, at the computation complexity of a special case of this simple analysis.When antenna for base station quantity is 100, number of users is 2, and maximum angle expands to 4 °, and step-size in search is 0.1 °, and the order of magnitude of the complex multiplication that conventional method needs is at least 7.2 * 10
8, and the order of magnitude of the complex multiplication that the inventive method needs is 1.28 * 10
6, as seen adopt the inventive method can reduce computation complexity significantly.
In order to show the Practical Performance of the inventive method, the applicant has carried out repeatedly emulation and has implemented test.In the pilot system the network configuration model be application scenarios shown in Figure 1.The result of l-G simulation test has carried out emulation from the estimation of center DOA and two aspects of estimation of angle spread respectively as shown in Figure 3 and Figure 4.In order to embody the superiority of the inventive method intuitively, the simulation result of this method and the localization method of existing scattering information source are contrasted.
As seen from Figure 3, the inventive method has reduced the RMSE that center DOA estimates significantly, that is to say that the method for proposition can improve the estimated accuracy of center DOA.Fig. 4 shows that the angle spread that the inventive method obtains estimates to approach the method based on the subspace, and performance is better than other method simultaneously.Because the precision of estimating at first is based upon on the precision basis of center DOA estimation, and then compares the estimated accuracy of angle spread, the inventive method is higher than other method for the estimated accuracy of center DOA and angle spread.
The above only is preferred embodiments of the present invention, and is in order to limit the present invention, within the spirit and principles in the present invention not all, any modification of making, is equal to replacement, improvement etc., all should be included within the scope of protection of the invention.
Claims (3)
- Among the extensive MIMO based on the scattering signal source locating method of probability distribution coupling, be used for following scene: comprise a base station and a plurality of user's communications system, all users are to base station transmit signals in a period of time; The extensive aerial array in base station is even linear array; All users and antenna for base station battle array are in same plane; Under the effect of base station power control, all users' signal to noise ratio is approximate identical.It is characterized in that: described method comprises following two operating procedures:(1) the Signal Pretreatment stage: calculate the average autocorrelation matrix of the subscriber signal vector that receives as the estimation of signal covariance matrix, and with response vector and the average autocorrelation matrix multiple of aerial array to different angles, obtain the vector representation of probability density function of the user DOA of stack;(2) iteration estimation stages: the vector that obtains by first of normalization, and mate this vector with probability density function and obtain user position information, then this part signal that estimates of deletion in normalized vector in stage.This process just can obtain all user position information through behind the loop iteration repeatedly.
- 2. method according to claim 1, it is characterized in that: in the described step (1), Signal Pretreatment further comprises following content of operation:(11) signal vector that receives and the conjugate transpose of self multiply each other and obtain a matrix, and a plurality of such matrix phase adductions of different time-gap are on average just obtained the average autocorrelation matrix, and this average autocorrelation matrix is used as the estimation of signal covariance matrix,(12) be divided into the M section to-90 ° to 90 °, M is antenna for base station quantity, and the corresponding antenna response vector of the end points angle of all sections has nearly orthogonal.Take advantage of the average autocorrelation matrix with its conjugate transpose while premultiplication of these vector sums and the right side, observe vector thereby obtain one,(13) minimum value of this observation vector deducts this minimum value to this each value of observing vector as the estimation that receives noise, the vector representation that all users' of acquisition stack DOA distributes.
- 3. method according to claim 1, it is characterized in that: described step (2) further comprises following content of operation:(21) this vector of maximum normalization of the vector of usefulness acquisition, vector with normalized probability density function deducts normalized vector then, and calculate the mould of this difference vector, then, by changing average and the variance of normalization probability density function, search for the minimum value of this mould value, the normalization probability density function of minimum value correspondence is exactly one of them user's DOA normalization probability density function so, thereby obtain this user's center DOA and the estimation of angle spread(22) from normalized vector, deduct the vector of the normalization probability density function of first estimating user, and repeat above-mentioned steps and obtain second user's center DOA and the estimation of angle spread, move in circles, up to estimating all user position information.
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| CN104023395A (en) * | 2014-06-20 | 2014-09-03 | 北京邮电大学 | Scattering information source positioning method based on beam-space transformation in large-scale MIMO system |
| CN104023395B (en) * | 2014-06-20 | 2017-09-01 | 北京邮电大学 | The scattering signal source locating method changed in extensive MIMO based on beam space |
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| CN110418974B (en) * | 2017-03-15 | 2021-03-12 | 赛普拉斯半导体公司 | Estimating angle measurements for source tracking using a phased array system |
| CN108769937A (en) * | 2018-05-02 | 2018-11-06 | 西京学院 | A kind of indoor locating system and method based on virtual subdistrict |
| CN108769937B (en) * | 2018-05-02 | 2019-04-23 | 西京学院 | A kind of indoor locating system and method based on virtual subdistrict |
| US11283502B2 (en) | 2018-10-17 | 2022-03-22 | Nokia Solutions And Networks Oy | Position likelihood based beamformer optimization |
| CN110636018A (en) * | 2019-09-29 | 2019-12-31 | 哈尔滨工程大学 | Grid compensation large-scale MIMO channel estimation method |
| CN110636018B (en) * | 2019-09-29 | 2021-12-24 | 哈尔滨工程大学 | Grid compensation large-scale MIMO channel estimation method |
| CN111931121A (en) * | 2020-09-21 | 2020-11-13 | 南昌大学 | Distributed positioning and speed measuring method based on large-scale antenna array |
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