CN112910518A - Method for estimating number of transmitting antennas of MIMO system under non-Gaussian noise in unmanned aerial vehicle communication - Google Patents
Method for estimating number of transmitting antennas of MIMO system under non-Gaussian noise in unmanned aerial vehicle communication Download PDFInfo
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Abstract
The invention belongs to the technical field of MIMO system parameter estimation in unmanned aerial vehicle communication, and discloses a method for estimating the number of transmitting antennas of an MIMO system under non-Gaussian noise in unmanned aerial vehicle communication, wherein an observation signal matrix is processed by using fractional low-order statistics to construct a correlation matrix based on the fractional low-order statistics; performing eigenvalue decomposition on the constructed correlation matrix, and constructing detection statistics based on eigenvalue weighting; and calculating a detection threshold based on the central limit theorem, and estimating the number of transmitting antennas of the MIMO system by adopting a serial hypothesis testing method. The MIMO system transmitting antenna number estimation method under non-Gaussian noise in unmanned aerial vehicle communication has a good effect of estimating the number of transmitting antennas of the MIMO system under the condition of alpha stable distributed noise, and can effectively realize the transmitting antenna number estimation of the MIMO system under the condition of alpha stable distributed noise. When the mixed signal-to-noise ratio is higher than 5dB, the correct detection probability reaches over 90 percent, and the method also has better performance for the impulse noise with different characteristic indexes.
Description
Technical Field
The invention belongs to the technical field of parameter estimation of an MIMO (multiple input multiple output) system in unmanned aerial vehicle communication, and particularly relates to a method for estimating the number of transmitting antennas of the MIMO system under non-Gaussian noise in unmanned aerial vehicle communication.
Background
Currently, a Multiple-Input Multiple-Output (MIMO) technology is widely used in intelligent wireless communication systems such as electronic reconnaissance and cognitive radio systems, with its efficient spectrum utilization and high-speed information transfer. For intelligent wireless systems, the application of MIMO technology necessarily introduces new problems and challenges to its key technologies (such as communication parameter estimation, signal identification, channel estimation, etc.). Wherein, the estimation of the number of antennas at the transmitting end is an important issue introduced by the MIMO technology. The accurate estimation of the number of the transmitting antennas of the MIMO system is a necessary precondition for subsequent signal identification, and can provide necessary prior information for blind identification of other parameters, so that intensive research is carried out on the important problem of blind estimation of the number of the MIMO transmitting antennas.
With regard to the problem of estimating the number of transmit antennas of the MIMO system, various methods have been proposed, and the existing methods are roughly classified into two types: information theory methods and feature-based methods. Somekh O et al propose a transmit antenna estimation method based on Minimum Description Length (MDL) and Akaike Information Criterion (AIC). The MDL/AIC algorithm can implement a method for estimating the number of transmit antennas under low signal-to-noise ratio conditions, but the two methods are less robust to timing offset and frequency offset. Shi M et al propose adaptive transmit antenna number estimation based on Schur's complementary code test, which does not require computation of eigenvalues of covariance matrix and has no specific requirements for the MIMO system receive antennas. Hassan K et al propose two identification methods based on objective information theoretical criterion for the identification problem of the number of space-related MIMO transmitting antennas. The two proposed methods are robust to spatial correlation of MIMO channels, but their performance is susceptible to timing and frequency offsets.
Oularbi M R et al propose a new algorithm that utilizes the orthogonality of the pilot signals to identify the number of base station antennas. This algorithm exhibits good performance, but it requires a priori knowledge of the pilot pattern. Mohammadkarimi et al propose a new feature-based transmit antenna number estimation method that uses second-order moments and fourth-order statistics as features to achieve transmit antenna estimation. Li T et al propose two hypothesis test-based transmit antenna number identification algorithms, namely a Wishart matrix maximum eigenvalue (WME) -based algorithm and a second moment-based one-step predictive eigenvalue ceiling (SM-PET) algorithm. Li T et al propose a hypothesis testing algorithm based on characteristic values of higher order moments for detecting the number of transmit antennas in a MIMO system.
The above MIMO system transmit antenna number estimation method assumes that the environmental noise is additive white gaussian noise. However, in many practical applications there are various non-gaussian noises such as artificial impulse noise, co-channel interference and low frequency atmospheric noise, which are usually modeled by an alpha-stationary distribution. Because the alpha stable distribution noise has no finite second moment, the performance of the existing MIMO system transmitting antenna number estimation method under the Gaussian background is seriously degraded. Therefore, a new method and system for estimating the number of transmit antennas of MIMO system under alpha stable distributed noise is needed.
Through the above analysis, the problems and defects of the prior art are as follows:
(1) the existing method for estimating the number of transmitting antennas of the MIMO system has poor robustness to timing offset and frequency offset, and the performance is easily influenced by the timing offset and the frequency offset.
(2) In the existing method for estimating the number of the transmitting antennas of the MIMO system, because the alpha stable distribution noise does not have a limited second moment, the performance of the existing method for estimating the number of the transmitting antennas of the MIMO system under the Gaussian background is seriously degraded.
(3) The literature rarely mentions the method for estimating the number of transmitting antennas of the MIMO system under alpha stable distributed noise.
The difficulty in solving the above problems and defects is: the non-Gaussian noise has thick tailing, and finite variance does not exist under the normal condition, so the construction of the statistical characteristic adapting to the alpha stable distribution noise, the construction of the characteristic statistics and the calculation of the detection threshold are the technical difficulties for realizing the estimation of the number of the transmitting antennas of the MIMO system under the alpha stable distribution noise.
The significance of solving the problems and the defects is as follows: the method has the advantages that technical support can be provided for the intelligent wireless system by estimating the number of the transmitting antennas of the MIMO system under the condition of stably distributed noise of alpha, the scene range of the intelligent wireless system can be expanded, and the anti-interference capability of the intelligent wireless system can be effectively improved.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a method and a system for estimating the number of transmitting antennas of an MIMO system under non-Gaussian noise in unmanned aerial vehicle communication, in particular to a method for estimating the number of transmitting antennas of the MIMO system under alpha stable distributed noise.
The invention is realized in this way, a method for estimating the number of transmitting antennas of a MIMO system under non-Gaussian noise in unmanned aerial vehicle communication, the method for estimating the number of transmitting antennas of the MIMO system under non-Gaussian noise in unmanned aerial vehicle communication comprises the following steps:
processing the observation signal matrix by using the fraction low-order statistic to construct a correlation matrix based on the fraction low-order statistic; performing eigenvalue decomposition on the constructed correlation matrix, and constructing detection statistics based on eigenvalue weighting; and calculating a detection threshold based on the central limit theorem, and estimating the number of transmitting antennas of the MIMO system by adopting a serial hypothesis testing method.
Further, the method for estimating the number of the transmitting antennas of the MIMO system under the non-Gaussian noise in the unmanned aerial vehicle communication comprises the following steps:
step one, processing observation signals r (n) by utilizing the fraction low order statistic to construct a correlation matrix G based on the fraction low order statisticr；
Step two, the matrix G is alignedrCarrying out characteristic value decomposition and constructing a detection statistic T based on characteristic weightingl；
Step three, calculating a detection threshold eta based on the central limit theoremlAnd estimating the number of transmitting antennas of the MIMO system by adopting a hypothesis testing method.
Further, in the first step, based on an MIMO system in the non-cooperative unmanned aerial vehicle cognitive network, the number of antennas configured by the master user and the cognitive user is set to be Q and K, respectively, and a signal received at the kth antenna may be represented as:
where N represents the number of observation signal samples, wk(n) represents additive non-Gaussian noise, hk,qRepresenting a fading channel between a kth antenna of a secondary user and a q-th antenna of a primary user, wherein an observation signal is shown in the form of a matrix as follows:
r(n)＝Hs(n)+w(n)；
where H denotes a K × Q fading channel matrix, and s (n) ═ s1(n),...,sK(n)]TRepresenting a Q × N transmission signal matrix, w (N) ═ w1(n),...,wK(n)]TRepresenting a K x 1 additive non-gaussian noise matrix.
Characterizing alpha stable distribution by a characteristic function, wherein the expression is as follows:
in the formula (I), the compound is shown in the specification,
wherein, alpha is called a characteristic index and is used for measuring the thickness of the tail of the distribution function; gamma is called the dispersion coefficient; beta is called symmetrical parameter, a is called position parameter, beta is 0 to represent that the distribution is symmetrical alpha stable distribution S alpha S; if a is 0 and γ is 1, the stable distribution is referred to as the standard α stable distribution.
The hybrid signal-to-noise ratio is defined as:
wherein r (n) ═ r1(n),…,rM(n)]T，w(n)＝[w1(n),...,wK(n)]T。
Further, in the step one, the observation signal r (n) is processed by using the fractional low order statistic to construct a correlation matrix G based on the fractional low order statisticrThe method comprises the following steps:
wherein r isi(n) is the element in the ith row and nth column of the observation matrix r, p is the fractional lower order index, and α is the noise figure index.
Further, in step two, the pair matrix GrCarrying out characteristic value decomposition and constructing a detection statistic T based on characteristic weightingl(k) The method comprises the following steps:
correlation matrix GrThe characteristic value is characterized by lkAnd, and:
constructing statistics T based on eigenvalueslComprises the following steps:
where K is the number of receiving antennas, ljDenotes the jth characteristic value, hmiIs a channel coefficient, si(n) is a transmission signal, wm(n) is alpha stationary distribution noise.
Further, in step three, the detection threshold expression is:
where N is the number of signal samples and t is the threshold.
The estimating the number of the transmitting antennas of the MIMO system by adopting the hypothesis testing method comprises the following steps:
setting initial value k to 1, and calculating Tl(k) When T isl(k)＞ηlIf so, let k equal k +1 and update Tl(k) And with a detection threshold ηlThe comparison is repeated if Tl(k)≤ηlThen, the estimated value of the number of transmitting antennas is output
Another objective of the present invention is to provide an estimation system for the number of transmit antennas of a MIMO system using the estimation method for the number of transmit antennas of a MIMO system under non-gaussian noise in unmanned aerial vehicle communication, where the estimation system for the number of transmit antennas of a MIMO system under non-gaussian noise in unmanned aerial vehicle communication comprises:
the matrix construction module is used for processing the observation signal matrix by utilizing the fraction low-order statistic to construct a correlation matrix based on the fraction low-order statistic;
the detection statistic construction module is used for carrying out eigenvalue distribution on the constructed correlation matrix and constructing detection statistic based on eigenvalue weighting;
and the transmitting antenna number estimation module is used for calculating a detection threshold based on the central limit theorem and estimating the number of transmitting antennas of the MIMO system by adopting a serial hypothesis testing method.
The invention also aims to provide a wireless communication parameter system applying the method for estimating the number of the transmitting antennas of the MIMO system under the non-Gaussian noise in the unmanned aerial vehicle communication.
Another object of the present invention is to provide a computer program product stored on a computer readable medium, including a computer readable program, which when executed on an electronic device, provides a user input interface to implement the method for estimating the number of transmit antennas of a MIMO system under non-gaussian noise in drone communication.
Another object of the present invention is to provide a computer-readable storage medium, which stores instructions that, when executed on a computer, cause the computer to execute the method for estimating the number of transmit antennas of a MIMO system under non-gaussian noise in drone communication.
By combining all the technical schemes, the invention has the advantages and positive effects that: the method for estimating the number of the transmitting antennas of the MIMO system under the alpha stable distributed noise can effectively realize the estimation of the number of the transmitting antennas of the MIMO system under the non-Gaussian noise in the unmanned aerial vehicle communication.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments of the present invention will be briefly described below, and it is obvious that the drawings described below are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of a method for estimating the number of transmit antennas of a MIMO system under non-gaussian noise in unmanned aerial vehicle communication according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of performance of estimating the number of transmit antennas of the MIMO system under non-gaussian noise in unmanned aerial vehicle communication according to an embodiment of the present invention.
Fig. 3 is a schematic diagram of performance of estimating the number of transmit antennas of the MIMO system configured for different antennas under non-gaussian noise in unmanned aerial vehicle communication according to an embodiment of the present invention.
Fig. 4 is a block diagram of a system for estimating the number of transmit antennas of a MIMO system under non-gaussian noise in unmanned aerial vehicle communication according to an embodiment of the present invention;
in the figure: 1. a matrix construction module; 2. a detection statistic construction module; 3. and a transmission antenna number estimation module.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Aiming at the problems in the prior art, the invention provides a method for estimating the number of transmitting antennas of a MIMO system under non-Gaussian noise in unmanned aerial vehicle communication, and the invention is described in detail below with reference to the accompanying drawings.
As shown in fig. 1, the method for estimating the number of transmit antennas of the MIMO system under non-gaussian noise in unmanned aerial vehicle communication according to the embodiment of the present invention includes the following steps:
s101, processing an observation signal matrix by using the fractional low order statistic to construct a correlation matrix based on the fractional low order statistic;
s102, performing eigenvalue decomposition on the matrix constructed in the S101, and constructing detection statistics based on feature weighting;
s103, calculating a detection threshold based on the central limit theorem, and estimating the number of transmitting antennas of the MIMO system by adopting a serial hypothesis testing method.
As shown in fig. 4, the system for estimating the number of transmit antennas of the MIMO system under non-gaussian noise in unmanned aerial vehicle communication according to the embodiment of the present invention includes:
the matrix construction module 1 is used for processing the observation signal matrix by utilizing the fractional low-order statistic to construct a correlation matrix based on the fractional low-order statistic;
the detection statistic construction module 2 is used for carrying out eigenvalue distribution on the constructed correlation matrix and constructing detection statistic based on eigenvalue weighting;
and the transmitting antenna number estimation module 3 is used for calculating a detection threshold based on the central limit theorem and estimating the number of transmitting antennas of the MIMO system by adopting a serial hypothesis testing method.
The present invention will be further described with reference to the following examples.
Example 1
The method for estimating the number of the transmitting antennas of the MIMO system under the non-Gaussian noise in the unmanned aerial vehicle communication comprises the following steps:
firstly, processing observation signals r (n) by utilizing the fractional low-order statistic to construct a correlation matrix G based on the fractional low-order statisticr；
Considering a non-cooperative MIMO system, assuming that the number of antennas configured by the primary user and the cognitive user is Q and K, respectively, the signal received at the kth antenna can be represented as:
where N represents the number of observation signal samples, wk(n) represents additive non-Gaussian noise, hk,qRepresenting the fading channel between the kth antenna of the secondary user and the q-th antenna of the primary user. The observed signals may be represented in matrix form:
r(n)＝Hs(n)+w(n)；
where H denotes a K × Q fading channel matrix, and s (n) ═ s1(n),...,sK(n)]TRepresenting a Q × N transmission signal matrix, w (N) ═ w1(n),...,wK(n)]TRepresenting a K x 1 additive non-gaussian noise matrix.
Considering that the alpha stable distribution has no uniform probability density function expression, the method is characterized by adopting a characteristic function, and the expression is as follows:
in the formula (I), the compound is shown in the specification,
wherein, alpha is called a characteristic index and is used for measuring the thickness of the tail of the distribution function; gamma is called the dispersion coefficient; beta is called symmetrical parameter, a is called position parameter, beta is 0 to represent that the distribution is symmetrical alpha stable distribution S alpha S; if a is 0 and γ is 1, the stable distribution is referred to as the standard α stable distribution.
In the present invention, the hybrid signal-to-noise ratio is defined as:
wherein r (n) ═ r1(n),…,rM(n)]T，w(n)＝[w1(n),...,wK(n)]T。
Processing observation signals r (n) by utilizing the fractional low order statistic to construct a correlation matrix G based on the fractional low order statisticr：
Wherein r isi(n) is the element in the ith row and nth column of the observation matrix r, p is the fractional order index, and α is the characteristic index.
Second, for the matrix GrCarrying out characteristic value decomposition and constructing a detection statistic T based on characteristic weightingl；
To correlation matrix GrPerforming eigenvalue decomposition with eigenvalue characteristic of lkAnd, and:
feature weighting based detection statistic TlComprises the following steps:
where K is the number of receiving antennas, ljDenotes the jth characteristic value, hk,qRepresenting the fading channel, s, between the kth antenna of the secondary user and the q-th antenna of the primary useri(n) is a transmission signal, wm(n) is alpha stationary distribution noise.
Thirdly, calculating a detection threshold eta based on the central limit theoremlAnd estimating the number of transmitting antennas of the MIMO system by adopting a hypothesis testing method.
The detection threshold expression is:
where N is the number of signal samples and t is the threshold.
And estimating the number of transmitting antennas of the MIMO system by adopting a hypothesis test method. Setting initial value k to 1, and calculating Tl(k) When T isl(k)＞ηlIf so, let k equal k +1 and update Tl(k) And with a detection threshold ηlThe comparison is repeated if Tl(k)≤ηlThen, the estimated value of the number of transmitting antennas is output
The technical effects of the present invention will be described in detail with reference to simulations.
In order to evaluate the present inventionAnd (4) performing simulation verification. The received signal sample is N-800, the secondary user antenna is K-8, the number Q of primary user antennas is 4, and the alpha stable distribution noise characteristic index is 1.9. The invention adopts correct estimation probability
As an evaluation index. The simulation experiment adopts 5000 iterations of statistical simulation to verify the performance. The estimation method (FLOS-HT) based on the fraction low order statistic and the existing hypothesis test method (HOM-HT) based on the high order moment statistic are subjected to pair analysis, and the simulation result is shown in FIG. 2. As can be seen from FIG. 2, the method of the present invention has a strong adaptive capacity to alpha stable distribution noise, and compared with the existing algorithm, the method of the present invention has obvious performance advantages. In addition, as can be seen from fig. 2, the method provided by the present invention still has good estimation performance under different characteristic indexes. Fig. 3 shows the effect of the number of antennas received by the MIMO system on the method of the present invention, and it can be seen from fig. 3 that the estimated performance of the method of the present invention increases with the increase of the number of antennas, that is, the performance of the method can be improved by increasing the number of antennas.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When used in whole or in part, can be implemented in a computer program product that includes one or more computer instructions. When loaded or executed on a computer, cause the flow or functions according to embodiments of the invention to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center via wire (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL), or wireless (e.g., infrared, wireless, microwave, etc.)). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that includes one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
The above description is only for the purpose of illustrating the present invention and the appended claims are not to be construed as limiting the scope of the invention, which is intended to cover all modifications, equivalents and improvements that are within the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. A method for estimating the number of transmitting antennas of a MIMO system under non-Gaussian noise in unmanned aerial vehicle communication is characterized by comprising the following steps:
processing the observation signal matrix by using the fraction low-order statistic to construct a correlation matrix based on the fraction low-order statistic; performing eigenvalue decomposition on the constructed correlation matrix, and constructing detection statistics based on eigenvalue weighting; and calculating a detection threshold based on the central limit theorem, and estimating the number of transmitting antennas of the MIMO system by adopting a serial hypothesis testing method.
2. The method for estimating the number of transmit antennas of the MIMO system in non-gaussian noise in drone communication according to claim 1, wherein the method for estimating the number of transmit antennas of the MIMO system comprises the steps of:
step one, processing observation signals r (n) by utilizing the fraction low order statistic to construct a correlation matrix G based on the fraction low order statisticr；
Step two, the matrix G is alignedrCarrying out characteristic value decomposition and constructing a detection statistic T based on characteristic weightingl；
Step three, based on the central limit theoremCalculating the detection threshold etalAnd estimating the number of transmitting antennas of the MIMO system by adopting a hypothesis testing method.
3. The method according to claim 2, wherein in the first step, based on the non-cooperative MIMO system, the number of antennas configured by the primary user and the cognitive user is set to Q and K, respectively, and the signal received at the kth antenna can be represented as:
where N represents the number of observation signal samples, wk(n) represents additive non-Gaussian noise, hk,qRepresenting a fading channel between a kth antenna of a secondary user and a q-th antenna of a primary user, wherein an observation signal is shown in the form of a matrix as follows:
r(n)＝Hs(n)+w(n)；
where H denotes a K × Q fading channel matrix, and s (n) ═ s1(n),...,sK(n)]TRepresenting a Q × N transmission signal matrix, w (N) ═ w1(n),...,wK(n)]TRepresenting a K × 1 additive non-gaussian noise matrix;
characterizing alpha stable distribution by a characteristic function, wherein the expression is as follows:
in the formula (I), the compound is shown in the specification,
wherein, alpha is called a characteristic index and is used for measuring the thickness of the tail of the distribution function; gamma is called the dispersion coefficient; beta is called symmetrical parameter, a is called position parameter, beta is 0 to represent that the distribution is symmetrical alpha stable distribution S alpha S; if a is 0 and γ is 1, the stable distribution is called a standard α stable distribution;
the hybrid signal-to-noise ratio is defined as:
wherein r (n) ═ r1(n),…,rM(n)]T，w(n)＝[w1(n),...,wK(n)]T。
4. The method of claim 2, wherein in step one, the observation signal r (n) is processed by using the fractional low order statistic to construct a correlation matrix G based on the fractional low order statisticrThe method comprises the following steps:
wherein r isi(n) is the element in the ith row and nth column of the observation matrix r, p is the fractional lower order index, and α is the noise figure index.
5. The method according to claim 2, wherein in step two, the pair matrix G is used to estimate the number of the antennas transmitted by the MIMO system under non-gaussian noise in the drone communicationrCarrying out characteristic value decomposition and constructing a detection statistic T based on characteristic weightinglThe method comprises the following steps:
correlation matrix GrThe characteristic value is characterized by lkAnd, and:
constructing statistics T based on eigenvaluesl(k) Comprises the following steps:
where K is the number of receiving antennas, ljDenotes the jth characteristic value, hmiIs a channel coefficient, si(n) is a transmission signal, wm(n) is alpha stationary distribution noise.
6. The method according to claim 2, wherein in step three, the detection threshold expression is as follows:
wherein, N is the number of signal samples, and t is a threshold;
the estimating the number of the transmitting antennas of the MIMO system by adopting the hypothesis testing method comprises the following steps:
setting initial value k to 1, and calculating Tl(k) When T isl(k)＞ηlIf so, let k equal k +1 and update Tl(k) And with a detection threshold ηlThe comparison is repeated if Tl(k)≤ηlThen output the dataEstimated value of number of transmitting antennas
7. An MIMO system transmitting antenna number estimation system applying the method for estimating the number of the MIMO system transmitting antennas under the non-Gaussian noise in the unmanned aerial vehicle communication according to any one of claims 1 to 6, wherein the system for estimating the number of the MIMO system transmitting antennas under the non-Gaussian noise in the unmanned aerial vehicle communication comprises:
the matrix construction module is used for processing the observation signal matrix by utilizing the fraction low-order statistic to construct a correlation matrix based on the fraction low-order statistic;
the detection statistic construction module is used for carrying out eigenvalue distribution on the constructed correlation matrix and constructing detection statistic based on eigenvalue weighting;
and the transmitting antenna number estimation module is used for calculating a detection threshold based on the central limit theorem and estimating the number of transmitting antennas of the MIMO system by adopting a serial hypothesis testing method.
8. A wireless communication parameter system applying the method for estimating the number of the transmitting antennas of the MIMO system under the non-Gaussian noise in the unmanned aerial vehicle communication according to any one of claims 1 to 6.
9. A computer program product stored on a computer readable medium, comprising a computer readable program for providing a user input interface to implement the method for estimating the number of transmit antennas of the MIMO system under non-gaussian noise in drone communication according to any one of claims 1 to 6 when executed on an electronic device.
10. A computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the method for estimating the number of transmit antennas of the MIMO system under non-gaussian noise in drone communication according to any one of claims 1 to 6.
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