CN113938360A - Distributed MIMO system covariance matrix estimation method based on fingerprint positioning - Google Patents
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Abstract
The invention discloses a distributed MIMO system covariance matrix estimation method based on fingerprint positioning, which aims at the situation that a covariance matrix changes along with time due to rapid environmental change, eliminates interference and noise by introducing random phase shift, and obtains more accurate covariance matrix estimation by changing a pilot frequency distribution mode by utilizing fingerprint positioning so as to solve the problems that the prior art mostly does not consider unknown covariance matrix, the covariance matrix estimation is inaccurate and the calculation complexity is high. Compared with the prior art, the method does not additionally increase pilot frequency, has low calculation complexity on the basis of high estimation accuracy, can further effectively improve the accuracy of covariance matrix estimation by the proposed pilot frequency distribution updating algorithm based on fingerprint positioning, is suitable for various wireless communication systems, and has very important significance for researching non-ideal factors of mobile scenes, so the method has certain practical value.
Description
Technical Field
The invention relates to the technical field of distributed MIMO systems, in particular to a distributed MIMO system covariance matrix estimation method based on fingerprint positioning.
Background
Distributed MIMO systems are a practical, scalable network MIMO scenario, where a large number of access points are geographically distributed and connected to one central processing unit, serving all users with the same time-frequency resources. In addition to the advantages of good propagation and channel reinforcement, MIMO has the characteristics of a distributed architecture, such as the benefits of macro diversity, no handover, and higher coverage. Therefore, the research of channel estimation techniques based on the case where the covariance matrix is unknown becomes more and more important.
However, all of these desirable advantages rely heavily on accurate channel state information. The covariance matrix of the channel vector characterizes the spatial correlation and is generally considered to be completely known in most techniques. But due to the mobility of the user and the linear relationship between the covariance matrix dimension and the number of antennas, the covariance matrix is not known in practical situations. Especially in the scene of fast channel change such as high-speed movement, the estimation of the covariance matrix and thus the accurate estimation of the channel are very beneficial to the analysis and optimization of the system performance. Therefore, the research of high-precision low-complexity covariance matrix estimation technology becomes more and more important.
Currently, wireless positioning is receiving increasing attention, especially in next generation communication systems, because location information can provide context-aware communication services. Fingerprint positioning is a fingerprint map based on channel state statistics, and is widely developed due to higher positioning accuracy in a rich scattering environment. Due to the limitation of the channel coherence interval length and the access of a large number of users, it cannot be guaranteed that the users are allocated to mutually orthogonal pilots, which will cause pilot pollution and seriously degrade the system performance. Pilot pollution is related to the user location of the shared pilots, and thus pilot pollution can be mitigated by optimizing pilot allocation based on location information. Therefore, the estimation accuracy of the covariance matrix can be improved by using the position information, which has an important role in dealing with non-ideal factors in the communication system, however, no solution for fusing the covariance matrix estimation with the positioning information has appeared in the prior art.
Disclosure of Invention
In view of the above, an object of the present invention is to provide a method for estimating a covariance matrix of a distributed MIMO system based on fingerprint positioning, so as to solve the problem in the prior art that the accuracy of channel estimation is not high in a real situation due to the fact that the covariance matrix is not considered to be unknown. The method and the device take the estimated covariance matrix as the fingerprint for positioning, perform clustering according to the positioning information, further change the pilot frequency distribution to obtain more accurate covariance matrix estimation, and improve the precision of covariance matrix estimation.
In order to achieve the purpose, the invention adopts the following technical scheme:
a distributed MIMO system covariance matrix estimation method based on fingerprint positioning, in the distributed MIMO system, a total of M access points are connected to a central processing unit, each access point has N antennas, jointly serves K single-antenna users in an area, and reference points of Z single antennas are uniformly distributed in a coverage area, the method specifically comprises the following steps:
step S1, establishing a channel model of distributed MIMO and an uplink signal transmission model to obtain a channel estimation expression of the covariance matrix under an unknown condition;
step S2, firstly, introducing random phase shift by using a channel block fading model, alternately using the distributed pilot frequency and the random phase shift of the pilot frequency in adjacent coherent blocks to obtain a received signal expression of the adjacent coherent blocks, then estimating sample covariance matrix estimation by using the spaced pilot frequency, and finally obtaining individual covariance matrix estimation by using the alternate received signal and considering Hermite symmetry;
step S3, firstly, the estimated covariance matrix is used as a fingerprint, the covariance matrix of a reference point is estimated as a fingerprint database, the user fingerprint is compared with the fingerprint database by using Euclidean distance, the reference point corresponding to the minimum Euclidean distance is used as the initial position of the user, then the user is clustered by using a K-means algorithm according to the position of the user, finally, orthogonal pilot frequency is adopted in the cluster according to the clustering result, then the pilot frequency is redistributed, and the covariance matrix is estimated again.
Further, the step S1 specifically includes:
step S101, considering a block fading channel, modeling a channel vector from user k to all access points as:
in the formula (1), the first and second groups,λk,mrepresenting the large scale fading, I, from the m-th access point to the k-th userNRepresenting an N-dimensional identity matrix, hkRepresenting small-scale fading whose elements obey a standard Rayleigh distribution
Step S102, the number P of orthogonal pilot frequencies with length tau in the estimation of the assumed uplink channel<K, use ofRepresenting the user subset using pilot p, after associating the received training signal with the conjugate of the pilot sequence, the base station estimates the channel:
in the formula (2), ypIndicating the received signal of all users transmitting pilot p, r is the transmit power,representing the noise, σ, of the transmitted pilot p2Representing the noise power;
due to gkSatisfy the requirement ofThen toChannel gkThe expression for the minimum mean square error, MMSE, estimate of (c) is:
in the formula (3), IMNRepresents MN dimension unit matrix, let sigmapFor the covariance matrix of the received signal, ΛiChannel g representing user iiThe covariance matrix of (2).
Step S103, because ΛkSum ΣpAre all unknowns, so equation (3) is converted to:
Further, the step S2 specifically includes:
step S201, firstly, assuming that the channel is a block fading model, specifically, the channel is at tauc=BcTcIs unchanged within a symbol, wherein BcExpressed as coherence Bandwidth, TcExpressed as time, then assume that the covariance matrix is at τsThe coherent blocks are not changed, finally, the irrelevance of random phase shift is utilized to eliminate interference and noise, therefore, each user alternately transmits the distributed pilot frequency X in the adjacent coherent blockspAnd phase shifted pilot ΦkComprises the following steps:
in the formula (5), phik[2n]Denotes the pilot, X, sent by the 2n coherent block user kp[2n-1]The pilot representing the 2n-1 th coherent block is the pilot p allocated to user k, where n is 1,2k,2nRandom phase shift of 2n coherent block of k users, independent of channel vector and noise, and satisfying
Step S202, selecting N∑≤τsPer 2-interval coherent block estimation of covariance matrix sigma of received signalpThe expression is:
in the formula (6), yp[2n-1]Representing the received signal of the 2N-1 coherent block, N∑Representation for estimating ΣpNumber of coherent blocks, superscript (x)HWhich represents the transpose of the conjugate,expressed as the covariance matrix sigma of the received signalp(ii) an estimate of (d);
step S203, in order to estimate the individual covariance matrix LambdakAn adjacent coherent block is required for decorrelation, and therefore, the pilot is sent to the adjacent coherent block, and the received signal observation is:
in the formula (7) and the formula (8),representing the received signal observations when user k transmits the 2n-1 coherent block pilot signal,represents the received signal observation, θ, of user k when it transmits the 2 n-th coherent block pilot signali,2nRepresenting the random phase generated by the pilot sequence generator when user i is in the 2n th coherence block.
Considering the signal is free from noise and random phase thetak,2nBy observing the neighboring coherent blocks, forThe covariance matrix of the channel obtained from the correlation of the adjacent staggered pilot received signal observations is:
in the formula (10), NΛRepresentation for estimating ΛkThe required number of coherent block logarithms.
Further, the step S3 specifically includes:
step S301, calculating covariance matrix estimation of the reference point and the user respectively, wherein the covariance matrix estimation of the reference point approximately adopts orthogonal pilot frequency as a fingerprint databaseWherein the content of the first and second substances,……,representing a covariance matrix of reference point 1 to reference point Z estimates as fingerprints in a fingerprint database; estimating covariance matrix for user kComparing the fingerprints with a fingerprint database as fingerprints, and measuring the correlation degree between two different fingerprints by using Euclidean distance, wherein the expression is as follows:
in the formula (11), the reaction mixture,denoted as the ith row and jth column of the covariance matrix for user k,row i and column j of a covariance matrix representing an estimate of a reference point z in the fingerprint database;
selecting a reference point with the minimum Euclidean distance as an initial position of a user k;
step S302, dividing K users into K users by adopting a K-means clustering algorithm according to the user positioning resultA disjoint cluster, where P represents the number of orthogonal pilots, and the set of users in cluster t is denoted by Γt;
Step S303, for | Gammat|>P clusters, sorting the distance from each user to the corresponding gravity center, determining the P users with the minimum distance to directly form a cluster, and keeping the rest | gammatI-P usesThe user is saved in the set D; for | Γt|<P, and in addition, randomly selecting P- | Γ in the set Dt| users make up a cluster;
step S304, distributing orthogonal pilot frequency in the cluster, and estimating the covariance matrix of user k again
The invention has the beneficial effects that:
the invention considers the problem that the covariance matrix in the actual channel is unknown, carries out the estimation of the covariance matrix, the proposed method does not additionally increase pilot frequency, but only utilizes the special structure of sending the pilot frequency, effectively eliminates the interference and noise, further relieves the pilot frequency pollution by utilizing the fingerprint positioning mode, improves the estimation precision of the covariance matrix, has low calculation complexity, and is suitable for various wireless communication systems.
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Fig. 1 is a schematic diagram of a cellular-free distributed massive MIMO system provided in embodiment 1;
fig. 2 is a schematic flowchart of a method for estimating covariance matrix of a distributed MIMO system based on fingerprint positioning according to embodiment 1;
FIG. 3 is a simulation diagram of the relationship between the coherent block logarithm of individual covariance matrices and the normalized mean square error for channel estimation NMSE using different covariance matrix estimation methods, where random pilot represents the initial allocation of a random pilot estimation covariance matrix, fingerprint positioning represents the reallocation of a pilot estimation covariance matrix after fingerprint positioning, and via-Q is the estimation of the covariance matrix using a classical via-Q method;
fig. 4 is a simulation diagram of the relationship between the logarithm of coherent blocks used to compute the individual covariance matrices and the total spectral efficiency in MRC and ZF receivers using different covariance matrix estimation methods.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, 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 some, but not all, embodiments of the present invention. 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.
Example 1
Referring to fig. 1 to fig. 4, the present embodiment provides a method for estimating a covariance matrix of a distributed MIMO system based on fingerprint positioning, where the method is implemented based on the following large-scale MIMO system and an uplink received signal model of the system thereof, and specifically includes:
as shown in fig. 1, assume that a large-scale MIMO system without cellular distribution adopts a Time Division Duplex (TDD) mode, where M equals to 5 APs and each AP has N equals to 50 antennas. The reference points are uniformly distributed in the area at an interval of r-2 km, the area has K-12 users, the users are assumed to be equipped with single antennas and are independently distributed, and P-4 pilots are shared, so as to improve the utilization rate of the pilot resources.
The channel model consists of three parts: path loss, shadow fading, and small-scale fading, which can be expressed asWherein the content of the first and second substances,let path fading factor α be 3.7 and reference distance be 1, sm,kTo satisfy the exponentially normally distributed shadow fading variables, hm,kRepresenting small-scale fading whose elements obey a standard Rayleigh distributionThus the uplink channel g of user k to all access pointskSatisfy the requirement ofNamely, it iscov(gk,gk)=Λk. The transmission power per user is ρ 1W, and the noise correlation σ is2-63 dBm. K users transmit information simultaneously, so the uplink receiving signal model of the system is
In equation (1), y indicates that the AP receives information from all users, and xkIs the information that is to be transmitted,representing noise, let g bekIndependently of n.
Based on the non-cellular distributed large-scale MIMO system and the constructed uplink received signal model, the covariance matrix estimation method provided by the embodiment specifically includes the following steps:
step S1, establishing an uplink signal transmission model of the distributed MIMO system to obtain a channel estimation expression under the condition that a covariance matrix is unknown:
in this embodiment, step S1 specifically includes:
step S11, assuming the channel is a block fading model, in a certain phase bandwidth BcAnd a certain coherence time TcThe channel remains unchanged, i.e. the channel is at τc=BcTcRemaining unchanged for 200 symbols. The covariance matrix is constant over the transmission bandwidth and changes slowly in time compared to the fast change of the channel vector, so it can be reasonably assumed that it is at τsRemaining unchanged in 20000 coherent blocks.
Step S12, useIndicating that, after the received training signal is associated with the conjugate of the pilot sequence using the subset of users of pilot p, the observed value of the base station is:
in the formula (2), ypIndicating the received signals of all users transmitting pilot p,noise representing the transmitted pilot p, due to gkSatisfy the requirement ofThen toChannel gkThe minimum mean square error MMSE estimate of (c) should be:
in the formula (3), IMNRepresents MN dimension unit matrix, let sigmapFor the covariance matrix of the received signal, ΛiChannel g representing user iiThe covariance matrix of (2).
Step S13, in practical applications, the covariance matrix is usually imperfect, which means ΛkSum ΣpAre all unknown numbers, and therefore the channel estimation expression is converted into
Step S2, adopting channel block fading model, introducing random phase shift, and using distributed pilot frequency and the random phase shift of the pilot frequency alternately in adjacent coherent blocks to obtain the receiving signal expression of the adjacent coherent blocks. Estimating a sample covariance matrix estimate using the spaced pilots; individual covariance matrix estimates are obtained using the alternating received signals and taking into account hermitian symmetry.
In this embodiment, step S2 specifically includes:
step S21, interference and noise are eliminated by using the irrelevancy of random phase shift, so each user alternately sends the allocated pilot X in adjacent coherent blockspAnd phase shifted pilot ΦkThe expression is:
in the formula (5), phik[2n]Pilot, X, representing the 2n coherent block user kp[2n-1]The pilot representing the 2n-1 th coherent block is the pilot p assigned to user k, n is 1,2k,2nRandom phase shift of 2n coherent block of k users, independent of channel vector and noise, and satisfying
Step S22, selecting N∑=5000<τsPer 2 coherent block interval coherent block estimation sigmapThe expression is:
in the formula (6), yp[2n-1]Indicating the received signal of the 2n-1 th coherent block, superscript (-) toHDenotes conjugate transpose when N∑In the case of the larger size,the estimation of (c) will be more accurate.
Step S23, in order to estimate the individual covariance matrix LambdakRequires adjacent coherent blocks to decorrelateAlternatively, the pilot is sent to adjacent coherent blocks, with a received signal observation of
In the formula (7) and the formula (8),representing the received signal observations when user k transmits the 2n-1 coherent block pilot signal,represents the received signal observation, θ, of user k when it transmits the 2 n-th coherent block pilot signali,2nRepresenting the random phase generated by the pilot sequence generator when user i is in the 2n th coherence block.
Considering the signal is free from noise and random phase thetak,2nThe present embodiment eliminates interference by observing neighboring coherent blocks, forThe covariance matrix of the channel estimated from the adjacent staggered pilot received signal observations is:
In the formula (10), NΛRepresentation for estimating ΛkThe required number of coherent block logarithms.
The general flowchart of the covariance matrix estimation method based on fingerprint positioning in step S3 is shown in fig. 2. In the off-line phase, the uplink pilot signals may be sent by the single antenna test equipment at each different access point in turn. Therefore, there is no mutual interference between the access points. The present embodiment employs the estimated reference point covariance matrices as the fingerprint database because they can reflect the location information. In the online phase, each user estimates the covariance matrix as a fingerprint and compares it with a fingerprint database. And obtaining an initial position according to the matching result, and clustering the users by using a K-means algorithm. And reallocating pilot frequency according to the clustering result, and re-estimating the covariance matrix to obtain more accurate channel estimation. The following are specific implementation steps:
step S31, respectively calculating covariance matrix estimation of the reference point and the user, and using the covariance matrix estimation of the reference point as a fingerprint databaseWherein the content of the first and second substances,……,representing the covariance matrix of reference point 1 to reference point Z estimates as fingerprints in the fingerprint database. Estimating covariance matrix for user kAs fingerprints, the fingerprints are compared with a fingerprint database, and Euclidean distances are used for measuring the correlation degree between two different fingerprints
In the formula (11), the reaction mixture,denoted as the ith row and jth column of the covariance matrix for user k,row i and column j of the covariance matrix representing the estimate of reference point z in the fingerprint database.
The reference point with the smallest euclidean distance is selected as the initial position of the user k.
Step S32, dividing K users into K users by adopting a K-means clustering algorithm according to the user positioning resultA plurality of disjoint clusters, each cluster having a center of gravity, and the number of iterations T being 20. Where P represents the number of orthogonal pilots, and the set of users in cluster t is denoted by Γt。
Step S33 for | Γt|>And P clusters, sequencing the distance from each user to the corresponding gravity center, and determining that the P users with the minimum distance directly form one cluster. The rest | Gammat-P users are kept in a set D; for | Γt|<P, and in addition, randomly selecting P- | Γ in the set DtThe | users together make up a cluster.
Step S34, distributing orthogonal pilot frequency in the cluster, estimating again the covariance matrix of user k
The whole process of estimating the covariance matrix based on fingerprint positioning by using the method provided by the embodiment is shown above.
Fig. 3 is a simulation diagram showing the relationship between the coherent block logarithm of the individual covariance matrix and the normalized mean square error of the channel estimate NMSE for different covariance matrix estimation methods, where the error represents the channel estimate error for known and unknown covariance matrices. The random pilot frequency represents the initial distribution of the covariance matrix of the random pilot frequency estimation, the fingerprint positioning represents the redistribution of the covariance matrix of the pilot frequency estimation after fingerprint positioning, and via-Q is a classic covariance matrix estimation method, which eliminates interference by sending the pilot frequency of other users sharing the same pilot frequency. Random pilot frequency distribution is adopted in the initial stage, and the positioning and pilot frequency redistribution algorithm based on the fingerprint is obtained by carrying out covariance matrix re-estimation after fingerprint positioning and pilot frequency redistribution. It can be seen that under the same random pilot allocation, the covariance matrix estimation performance of the method is better than via-Q and is close to the case that the covariance matrix is known. On the basis of fingerprint-based positioning, the covariance matrix estimation becomes more accurate due to pilot reallocation.
Fig. 4 is a simulation diagram showing the relationship between the logarithm of coherent blocks used to compute the individual covariance matrices and the total spectral efficiency in different covariance matrix estimation methods under MRC and ZF receivers. It can be seen that the covariance matrix estimation method provided by the embodiment has better total spectrum efficiency than the via-Q method, and the system performance is further improved after fingerprint positioning and pilot frequency reallocation. At the same time, the overall spectral efficiency is increased with the number of coherent block logarithms N used to compute the individual covariance matrix estimatesΛIs increased because the calculated covariance matrix estimate is more accurate. In addition, the system performance of the ZF receiver is superior to the MRC receiver.
In summary, the present invention provides a distributed antenna system covariance matrix estimation method for eliminating interference and noise by introducing random phase shift and using cross correlation of distributed pilot frequency and random phase shift pilot frequency, aiming at the covariance matrix estimation problem of a distributed MIMO system, so as to solve the problem that the channel estimation accuracy is not high in the real situation because the covariance matrix is not known in the prior art. In addition, the estimated covariance matrix is used as a fingerprint for positioning, clustering is carried out according to positioning information, and then pilot frequency distribution is changed to obtain more accurate covariance matrix estimation, so that the accuracy of covariance matrix estimation is improved, and the method has practical significance.
The invention is not described in detail, but is well known to those skilled in the art.
The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations could be devised by those skilled in the art in light of the present teachings without departing from the inventive concepts. Therefore, the technical solutions available to those skilled in the art through logic analysis, reasoning and limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.
Claims (4)
1. A distributed MIMO system covariance matrix estimation method based on fingerprint positioning, in the distributed MIMO system, a total of M access points are connected to a central processing unit, each access point has N antennas, jointly serves K single-antenna users in an area, and reference points of Z single antennas are uniformly distributed in a coverage area, the method is characterized by specifically comprising the following steps:
step S1, establishing a channel model of distributed MIMO and an uplink signal transmission model to obtain a channel estimation expression of the covariance matrix under an unknown condition;
step S2, firstly, introducing random phase shift by using a channel block fading model, alternately using the distributed pilot frequency and the random phase shift of the pilot frequency in adjacent coherent blocks to obtain a received signal expression of the adjacent coherent blocks, then estimating sample covariance matrix estimation by using the spaced pilot frequency, and finally obtaining individual covariance matrix estimation by using the alternate received signal and considering Hermite symmetry;
step S3, firstly, the estimated covariance matrix is used as a fingerprint, the covariance matrix of a reference point is estimated as a fingerprint database, the user fingerprint is compared with the fingerprint database by using Euclidean distance, the reference point corresponding to the minimum Euclidean distance is used as the initial position of the user, then the user is clustered by using a K-means algorithm according to the position of the user, finally, orthogonal pilot frequency is adopted in the cluster according to the clustering result, then the pilot frequency is redistributed, and the covariance matrix is estimated again.
2. The method of claim 1, wherein the step S1 specifically includes:
step S101, considering a block fading channel, modeling a channel vector from user k to all access points as:
in the formula (1), the first and second groups,lk,mrepresenting the large scale fading, I, from the m-th access point to the k-th userNRepresenting an N-dimensional identity matrix, hkRepresenting small-scale fading whose elements obey a standard Rayleigh distribution
Step S102, the number P of orthogonal pilot frequencies with length tau in the estimation of the assumed uplink channel<K, use ofRepresenting the user subset using pilot p, after associating the received training signal with the conjugate of the pilot sequence, the base station estimates the channel:
in the formula (2), ypRepresenting the received signals of all users transmitting pilot p, p is the transmit power,representing the noise, σ, of the transmitted pilot p2Representing the noise power;
due to gkSatisfy the requirement ofThen toChannel gkThe expression for the minimum mean square error, MMSE, estimate of (c) is:
in the formula (3), IMNRepresents MN dimension unit matrix, let sigmapFor the covariance matrix of the received signal, ΛiChannel g representing user iiThe covariance matrix of (a);
step S103, because ΛkSum ΣpAre all unknowns, so equation (3) is converted to:
3. The method of claim 2, wherein the step S2 specifically includes:
step S201, firstly, assuming that the channel is a block fading model, specifically, the channel is at tauc=BcTcIs unchanged within a symbol, wherein BcExpressed as coherence Bandwidth, TcExpressed as time, then assume that the covariance matrix is at τsAnd is unchanged in coherent block, finally utilizes irrelevance of random phase shift to eliminate interference and noise,so that each user alternately transmits the allocated pilot X in adjacent coherent blockspAnd phase shifted pilot ΦkComprises the following steps:
in the formula (5), phik[2n]Denotes the pilot, X, sent by the 2n coherent block user kp[2n-1]The pilot representing the 2n-1 th coherent block is the pilot p allocated to user k, where n is 1,2k,2nRandom phase shift of 2n coherent block of k users, independent of channel vector and noise, and satisfying
Step S202, selecting N∑≤τsPer 2-interval coherent block estimation of covariance matrix sigma of received signalpThe expression is:
in the formula (6), yp[2n-1]Representing the received signal of the 2N-1 coherent block, N∑Representation for estimating ΣpNumber of coherent blocks, superscript (x)HWhich represents the transpose of the conjugate,expressed as the covariance matrix sigma of the received signalp(ii) an estimate of (d);
step S203, in order to estimate the individual covariance matrix LambdakAn adjacent coherent block is required for decorrelation, and therefore, the pilot is sent to the adjacent coherent block, and the received signal observation is:
in the formula (7) and the formula (8),representing the received signal observations when user k transmits the 2n-1 coherent block pilot signal,represents the received signal observation, θ, of user k when it transmits the 2 n-th coherent block pilot signali,2nRepresenting the random phase generated by the pilot sequence generator when the user i is in the 2n th coherent block;
considering the signal is free from noise and random phase thetak,2nBy observing the neighboring coherent blocks, forThe covariance matrix of the channel obtained according to the correlation of the observed values of the adjacent staggered pilot frequency receiving signals is:
in the formula (10), NΛRepresentation for estimating ΛkThe required number of coherent block logarithms.
4. The method of claim 3, wherein the step S3 specifically includes:
step S301, calculating covariance matrix estimation of the reference point and the user respectively, wherein the covariance matrix estimation of the reference point approximately adopts orthogonal pilot frequency as a fingerprint databaseWherein the content of the first and second substances,representing a covariance matrix of reference point 1 to reference point Z estimates as fingerprints in a fingerprint database; estimating covariance matrix for user kComparing the fingerprints with a fingerprint database as fingerprints, and measuring the correlation degree between two different fingerprints by using Euclidean distance, wherein the expression is as follows:
in the formula (11), the reaction mixture,denoted as the ith row and jth column of the covariance matrix for user k,row i and column j of a covariance matrix representing an estimate of a reference point z in the fingerprint database;
selecting a reference point with the minimum Euclidean distance as an initial position of a user k;
step S302, dividing K users into K users by adopting a K-means clustering algorithm according to the user positioning resultA disjoint cluster, where P represents the number of orthogonal pilots, and the set of users in cluster t is denoted by Γt;
Step S303, for | Gammat|>P clusters, sorting the distance from each user to the corresponding gravity center, determining the P users with the minimum distance to directly form a cluster, and keeping the rest | gammat-P users are kept in a set D; for | Γt|<P, and in addition, randomly selecting P- | Γ in the set Dt| users make up a cluster;
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US20210204241A1 (en) * | 2017-10-12 | 2021-07-01 | Southeast University | 5g-signal-based doa fingerprint-based positioning method |
CN113067783A (en) * | 2021-03-18 | 2021-07-02 | 东南大学 | Covariance matrix estimation method for distributed antenna system |
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