Cellular-free large-scale MIMO unmanned aerial vehicle RIS deployment method and system

Abstract

The invention relates to the technical field of wireless communication, and discloses an unmanned aerial vehicle RIS deployment method and system for cellular-free large-scale MIMO, and the method comprises the steps: constructing a cellular-free large-scale MIMO system which comprises an access point, a user and an eavesdropper; a link is formed among the unmanned aerial vehicle RIS, the access point and the user, so that full coverage of the access point and the user is realized; calculating a downlink reachable safety rate of the target user under the assistance of the unmanned aerial vehicle RIS, and maximizing the safety rate of the target user to construct a target function; and solving the target function through a gradient descent alternating iteration algorithm to obtain the optimal phase shift and deployment position of the unmanned aerial vehicle RIS. According to the invention, on the premise of not introducing extra system energy consumption, the security rate of an eavesdropped user is greatly improved, and full coverage of a communication system is realized; the phase shift and spatial position deployment of the unmanned aerial vehicle RIS are jointly optimized, the full coverage of ground access points and users is realized, the security of a physical layer is enhanced, and the connectivity of Internet of Things equipment is improved.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a method and system for deploying RIS of a non-cellular large-scale MIMO unmanned aerial vehicle. Background Art

[0002] Cell-free massive MIMO technology can use densely deployed access points to eliminate cell boundary restrictions and effectively alleviate the serious inter-cell interference in traditional massive MIMO systems. RIS integrates a large number of low-cost passive reflective elements with phase shift controllers to reflect the incident signal in the desired direction, and by adjusting the phase of the reflective element, it can effectively increase the signal strength of the target user and weaken the signal strength of the eavesdropper.

[0003] However, due to the inherent broadcast nature of wireless signals, any user in a cell-free massive MIMO system can theoretically receive wireless signals and initiate eavesdropping. The working mode of APs collaborating and trusting each other brings higher communication risks to eavesdroppers in cell-free massive MIMO.

[0004] Compared with ground-based RIS, existing RIS-assisted cell-free massive MIMO research has not yet delved into the impact of the location deployment of UAV RIS on secure communication issues; at the same time, a method needs to be found to make full use of the limited backhaul link capacity. Summary of the invention

[0005] In view of the above existing problems, the present invention is proposed.

[0006] Therefore, the present invention provides a method and system for deploying RIS of UAV without cellular massive MIMO to solve the problem that the existing RIS-assisted cellular massive MIMO does not deeply involve the impact of the location deployment of UAV RIS on the security communication problem, and the working mode of mutual cooperation and mutual trust of APs brings higher communication risks to eavesdroppers and cannot fully utilize the limited backhaul link capacity.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] In a first aspect, the present invention provides a method for deploying RIS of a non-cellular massive MIMO UAV, comprising: constructing a non-cellular massive MIMO system, the system comprising an access point, a user, and an eavesdropper;

[0009] By forming a link between the drone RIS and the access points and users, full coverage of the access points and users is achieved;

[0010] Calculate the target user's achievable downlink safe rate with the assistance of the UAV RIS, and construct an objective function by maximizing the target user's safe rate;

[0011] The objective function is solved by the gradient descent alternating iterative algorithm to obtain the optimal phase shift and deployment position of the UAV RIS.

[0012] As a preferred solution of the RIS deployment method of the non-cellular massive MIMO UAV of the present invention, wherein: the construction of the non-cellular massive MIMO system includes:

[0013] M access points and K users are evenly distributed in the plane;

[0014] and, an eavesdropper;

[0015] Among them, all access points, users and eavesdroppers are single-antenna devices.

[0016] As a preferred solution of the non-cellular massive MIMO UAV RIS deployment method of the present invention, wherein: forming a link between the UAV RIS and the access point and the user includes:

[0017] The wireless channel between the mth access point and the kth user includes a direct link and a reflected link;

[0018] Among them, the direct link is a Rayleigh channel expressed as:

[0019]

[0020] In the formula, β mk represents the large-scale fading coefficient of the direct link between the mth access point and the kth user, h mk represents the small-scale fading coefficient of the direct link between the mth access point and the kth user;

[0021] As a preferred solution of the non-cellular massive MIMO UAV RIS deployment method of the present invention, wherein: forming a link between the UAV RIS and the access point and the user includes:

[0022] The wireless channel between the mth access point and the kth user includes a direct link and a reflected link;

[0023] Among them, the reflection link includes two links: the access point to the drone RIS and the drone RIS to the user;

[0024] Among them, the link from the mth access point to the UAV RIS is expressed as:

[0025]

[0026] In the formula, subscript m represents the mth access point, subscript r represents the UAV RIS, and Respectively represent g mr LoS component and NLoS component, K mr represents the Ricean k factor of the mth access point to UAV RIS link, β mr represents the large-scale fading coefficient of the mth access point to UAV RIS link,

[0027] Among them, the link from UAV RIS to the kth user is expressed as:

[0028]

[0029] Where, subscript k represents the kth user, β rk represents the large-scale fading coefficient of the link from the UAV RIS to the kth user, K rk represents the corresponding Rice k factor of the link from the UAV RIS to the kth user, and Respectively represent g rk LoS component and NLoS component.

[0030] As a preferred solution of the RIS deployment method of the non-cellular massive MIMO UAV of the present invention, the link further includes:

[0031] The total wireless channel from the mth access point to the kth user is obtained by replacing the direct link g mk Reflection link with gain factor Add together to get;

[0032] The total wireless channel from the mth access point to the eavesdropper e is obtained by adding the direct link between the mth access point and the eavesdropper e and the reflection link with a gain coefficient between the mth access point and the eavesdropper e.

[0033] As a preferred solution of the RIS deployment method of the non-cellular massive MIMO UAV of the present invention, wherein: constructing an objective function by maximizing the safety rate of the target user includes:

[0034] With the goal of maximizing the traversal security rate of the eavesdropped user, under the constraints of the UAV RIS phase shift and spatial position, the optimal UAV RIS phase shift and optimal spatial position deployment are found, and the objective function is established, which is expressed as:

[0035]

[0036] st(x r ,y r )∈A,

[0037] h r ∈(0,60],

[0038]

[0039] Among them, R sec It indicates the target user’s downlink safe rate achievable under the assistance of UAV RIS, (x r ,y r ,h r ) represents the three-dimensional position coordinate vector of the UAV RIS including the horizontal coordinate, the vertical coordinate and the flight altitude, and A represents the area range of the system.

[0040] As a preferred solution of the RIS deployment method for non-cellular massive MIMO UAVs of the present invention, the objective function is solved by the gradient descent alternating iterative algorithm to obtain the optimal phase shift and deployment position of the UAV RIS, including:

[0041] Set the initial position and initial phase of the UAV RIS to obtain the set of safe downlink rates that the target user can reach with the assistance of the UAV RIS, initialize the iteration step size s1,s2>0, the iteration termination error σ, and the iteration variables i,j=0;

[0042] Perform the first iterative calculation to obtain the first descending direction of the phase shift matrix of the safe speed to the UAV RIS, and iterate the UAV RIS phase shift to obtain the first safe speed after iteration, which is recorded as R sec (φ i+1 );

[0043] The first iteration of the loop is calculated until |R sec (φ i+1 )-R sec (φ i )|<σ, record the optimal value of phase shift φ opt =φ i+1 ;

[0044] Perform the second iterative calculation to obtain the second descent direction of the safety rate for the spatial deployment of the UAV RIS, and iterate the spatial deployment position of the UAV RIS to obtain the second safety rate after iteration, which is recorded as

[0045] The second iteration of the loop is calculated until we get So far, the best spatial position of deployment is

[0046] The first and second iterations of the loop are repeated until until;

[0047] Get the optimal phase shift φ of the UAV RIS opt and optimal deployment space location

[0048] In a second aspect, the present invention provides a non-cellular massive MIMO UAV RIS deployment system, comprising: a system construction module for constructing a non-cellular massive MIMO system, the system comprising an access point, a user, and an eavesdropper;

[0049] The connection coverage module is used to form a link between the drone RIS and the access points and users to achieve full coverage of the access points and users;

[0050] A function construction module is used to calculate the target user's achievable downlink safe rate with the assistance of the UAV RIS, and to construct an objective function by maximizing the target user's safe rate;

[0051] The solution module is used to solve the objective function through the gradient descent alternating iterative algorithm to obtain the optimal phase shift and deployment position of the UAV RIS.

[0052] In a third aspect, the present invention provides a computing device, comprising:

[0053] Memory and processor;

[0054] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the RIS deployment method of the unmanned aerial vehicle without cellular large-scale MIMO are implemented.

[0055] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the non-cellular massive MIMO UAV RIS deployment method.

[0056] Compared with the prior art, the present invention has the following beneficial effects: the present invention constructs a cellular-free massive MIMO point, the points are evenly distributed in a plane, and there is an eavesdropper in the system; the UAV RIS is used to achieve full coverage of ground access points (APs) and users and enhance the physical layer security of the system, and calculates the downlink safe rate of the target user assisted by the UAV RIS; the optimal phase shift and spatial deployment position of the UAV RIS are obtained by taking the maximization of the safe rate of the target user as the objective function; the safe rate of the eavesdropped user can be greatly improved and the full coverage of the communication system can be achieved without introducing additional system energy consumption; in a cellular-free massive MIMO system with an eavesdropper, the UAV RIS phase shift and spatial position deployment are jointly optimized for the first time to achieve full coverage of ground access points (APs) and users and enhance the physical layer security of the system, thereby improving the connectivity and coverage of IoT devices. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0058] Figure 1 A schematic diagram of the overall process logic of the method for deploying RIS of a drone without cellular massive MIMO according to an embodiment of the present invention;

[0059] Figure 2 A curve diagram showing the change of the safety rate with the number of APs M in the RIS deployment method for unmanned aerial vehicles without cellular massive MIMO according to an embodiment of the present invention;

[0060] Figure 3 This is a comparison diagram of the effects of three UAV RIS deployment schemes in the UAV RIS deployment method without cellular massive MIMO according to an embodiment of the present invention;

[0061] Figure 4 A schematic diagram of a RIS deployment system device for a non-cellular massive MIMO UAV according to an embodiment of the present invention. DETAILED DESCRIPTION

[0062] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0063] Example 1

[0064] Reference Figure 1 and Figure 4 , as an embodiment of the present invention, provides a method for deploying RIS of a UAV without cellular massive MIMO, including: Figure 1 As shown:

[0065] S100: Building a cell-free massive MIMO system, which includes access points, users, and eavesdroppers;

[0066] In an embodiment of the present application, constructing a non-cellular massive MIMO system includes:

[0067] M access points and K users are evenly distributed in the plane;

[0068] and, an eavesdropper;

[0069] Among them, all access points, users and eavesdroppers are single-antenna devices.

[0070] For example, a non-cellular massive MIMO system is provided, where there are M = 20 access points, K = 10 users evenly distributed in a square plane area with a side length of 400 meters, and there is an eavesdropper in the system; wherein all access points, users and eavesdroppers are single-antenna devices; wherein the transmission power p of the access point (AP) is d =30dBm, all Ricean k factors are set to 5dB.

[0071] S200: A link is formed between the drone RIS and the access points and users to achieve full coverage of the access points and users;

[0072] In the embodiment of the present application, a link is formed between the drone RIS and the access point and the user, including:

[0073] The wireless channel between the mth access point and the kth user includes: a direct link and a reflection link;

[0074] Preferably, the direct link is a Rayleigh channel expressed as:

[0075]

[0076] In the formula, β mk represents the large-scale fading coefficient of the direct link between the mth access point and the kth user, h mk represents the small-scale fading coefficient of the direct link between the mth access point and the kth user;

[0077] In another embodiment, the direct link may also have a Ricean channel mode, expressed as:

[0078]

[0079] In the formula, K mk This is the Ricean K factor in the direct link.

[0080] In the embodiment of the present application, a link is formed between the drone RIS and the access point and the user, including:

[0081] The wireless channel between the mth access point and the kth user includes a direct link and a reflected link;

[0082] Among them, the reflection link includes two links: the access point to the drone RIS and the drone RIS to the user;

[0083] Among them, the link from the mth access point to the UAV RIS is expressed as:

[0084]

[0085] Where, subscript m represents the mth access point, subscript r represents the UAV RIS, β mr represents the large-scale fading coefficient of the mth access point to UAV RIS link, and Respectively represent g mr LoS component and NLoS component, K mr represents the Ricean k factor of the mth access point to UAV RIS link;

[0086] It should be noted that the drone high-altitude model is preferably used here, namely:

[0087]

[0088] Among them, d mr represents the Euclidean distance from the mth AP to the UAV RIS, P LoS (d mr ) represents the LoS probability of the corresponding link.

[0089] Among them, the link from UAV RIS to the kth user is expressed as:

[0090]

[0091] Where, subscript k represents the kth user, β rk represents the large-scale fading coefficient of the link from the UAV RIS to the kth user, K rk represents the corresponding Rice k factor of the link from the UAV RIS to the kth user, and Respectively represent g rk LoS component and NLoS component.

[0092] In the embodiment of the present application, the link further includes:

[0093] The total wireless channel from the mth access point to the kth user is obtained by replacing the direct link g mk Reflection link with gain factor Add together to get;

[0094] Specifically, it can be expressed as:

[0095]

[0096] Among them, the phase shift matrix of the UAV RIS is expressed as:

[0097]

[0098] In the formula, It represents the phase shift of the incident signal by the lth reflective element of the UAV RIS, and L represents the number of reflective elements of the UAV RIS.

[0099] The total wireless channel from the mth access point to the eavesdropper e is obtained by adding the direct link between the mth access point and the eavesdropper e and the reflected link with a gain coefficient between the mth access point and the eavesdropper e;

[0100] Specifically, it can be expressed as:

[0101]

[0102] In the formula, the superscript H represents the conjugate transpose operator, g me represents the direct link between the mth access point and the eavesdropper e, represents the reflection link with gain coefficient between the mth access point and the eavesdropper e.

[0103] It should be noted that all large-scale fading coefficients adopt the model β = d -α ; d represents the Euclidean distance of the corresponding path, α represents the attenuation coefficient on the corresponding path; considering the difference between the direct link and the reflected link environment, different power attenuation factors are used for them, specifically, they can be:

[0104] It should also be noted that compared with ground RIS, UAV RIS is particularly suitable for handling emergency or communication tasks of limited duration due to its fast deployment and flexible configuration. It can provide users with economical and efficient wireless connections, and UAV RIS can effectively enhance the existing network coverage and reduce construction costs.

[0105] S300: Calculate the target user's achievable downlink safe rate with the assistance of the UAV RIS, and construct an objective function to maximize the target user's safe rate;

[0106] Specifically, the downlink safe rate that can be achieved by the target user with the assistance of the UAV RIS can be expressed as:

[0107] R sec =log2(1+SNR k )-log2(1+SNR e )

[0108] Among them, the signal-to-noise ratio (SNR) of the eavesdropped user is k It is expressed as:

[0109]

[0110] The eavesdropper's signal-to-noise ratio (SNR) e Expressed as:

[0111]

[0112] In the formula, the superscript * represents the conjugate operator, and is the noise power of the eavesdropped user and the eavesdropper, p d is the transmission power of each access point; k′ represents any one of the k-1 users except the kth user; M can represent the total number of access points or the mark of the last access point; Indicates the amplitude of the reflection link from the mth AP to the kth user; represents the product of the LoS components of the reflection link from the mth AP to the kth user; represents the power gain of the reflection link from the mth AP to the kth user;

[0113] It represents the calculation factor of the target user's useful signal power.

[0114] In the embodiment of the present application, the objective function is constructed by maximizing the safety rate of the target user, including:

[0115] With the goal of maximizing the traversal security rate of the eavesdropped user, under the constraints of the UAV RIS phase shift and spatial position, the optimal UAV RIS phase shift and optimal spatial position deployment are found, and the objective function is established, which is expressed as:

[0116]

[0117] st(xr ,y r )∈A,

[0118] h r ∈(0,60],

[0119]

[0120] Among them, R sec It indicates the target user’s downlink safe rate achievable under the assistance of UAV RIS, (x r ,y r ,h r ) represents the three-dimensional position coordinate vector of the UAV RIS including the horizontal coordinate, the vertical coordinate and the flight altitude, and A represents the area range of the system.

[0121] S400: The objective function is solved by the gradient descent alternating iterative algorithm to obtain the optimal phase shift and deployment position of the UAV RIS.

[0122] In the embodiment of the present application, the objective function is solved by the gradient descent alternating iterative algorithm to obtain the optimal phase shift and deployment position of the UAV RIS, including:

[0123] Set the initial position of the drone RIS, specifically expressed as (unit: meter); and, the initial phase is φ 0 , in order to obtain the set of safe downlink rates that can be achieved by the target user under the assistance of UAV RIS, which is specifically expressed as Initialize the iteration step size s1, s2>0, the iteration termination error σ, and the iteration variables i, j=0;

[0124] Specifically, the iteration termination error σ can be 10 -5 Or customize according to actual needs;

[0125] Perform the first iteration calculation to obtain the first descending direction of the phase shift matrix of the safety rate to the UAV RIS, which is specifically expressed as The UAV RIS phase shift is iterated, which is specifically expressed as To get the first safe rate after iteration, denoted as R sec (φ i+1 );

[0126] The first iteration of the loop is: let i = i + 1, repeat R sec (φ i+1 ) calculation steps until |R sec (φ i+1 )-R sec (φ i )|<σ, record the optimal value of phase shift φ opt =φ i+1;

[0127] Perform the second iteration calculation to obtain the second descent direction of the safety rate for the UAV RIS spatial deployment, which is specifically expressed as The spatial deployment position of the UAV RIS is iterated, which is specifically expressed as To obtain the second safe rate after iteration, recorded as

[0128] The second iteration of the loop is: j = j + 1, and the above is repeated The calculation steps are as follows, until we get So far, the best spatial position of deployment is

[0129] The first and second iterations of the loop are repeated until until;

[0130] Get the optimal phase shift φ of the UAV RIS opt and optimal deployment space location

[0131] It should be noted that the optimized deployment position and phase offset of the UAV RIS can effectively improve the safe rate of the target user; and through optimization, the UAV RIS system without cellular massive MIMO can improve the physical layer security as much as possible while ensuring a certain service quality, thereby maximizing the improvement effect of the UAV RIS on the system security.

[0132] The above is a schematic scheme of a method for deploying RIS of a non-cellular massive MIMO drone in this embodiment. It should be noted that the technical solution of the system for deploying RIS of a non-cellular massive MIMO drone and the technical solution of the above-mentioned method for deploying RIS of a non-cellular massive MIMO drone belong to the same concept. For details not described in detail in the technical solution of the system for deploying RIS of a non-cellular massive MIMO drone in this embodiment, please refer to the description of the technical solution of the above-mentioned method for deploying RIS of a non-cellular massive MIMO drone.

[0133] like Figure 4 As shown, the drone RIS deployment system without cellular massive MIMO in this embodiment includes:

[0134] A system building module 100 is used to build a non-cellular massive MIMO system, the system including an access point, a user and an eavesdropper;

[0135] The connection coverage module 200 is used to form a link between the UAV RIS and the access point and the user to achieve full coverage of the access point and the user;

[0136] The function construction module 300 is used to calculate the target user's downlink safe rate achievable under the assistance of the UAV RIS, and to construct an objective function by maximizing the target user's safe rate;

[0137] The solution module 400 is used to solve the objective function through a gradient descent alternating iterative algorithm to obtain the optimal phase shift and deployment position of the UAV RIS.

[0138] This embodiment also provides a computing device, which is applicable to the case of RIS deployment of unmanned aerial vehicles without cellular massive MIMO, including:

[0139] A memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the UAV RIS deployment method for realizing non-cellular large-scale MIMO as proposed in the above embodiment.

[0140] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for deploying RIS of a drone for realizing non-cellular massive MIMO is implemented as proposed in the above embodiment.

[0141] The storage medium proposed in this embodiment and the UAV RIS deployment method for realizing non-cellular massive MIMO proposed in the above embodiment belong to the same inventive concept. The technical details not described in detail in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0142] Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course can also be implemented by hardware. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ReadOnly, Memory, ROM), random access memory (Random Access Memory, RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform the methods of various embodiments of the present invention.

[0143] Example 2

[0144] Reference Figure 2 and Figure 3 Based on the previous embodiment, this embodiment provides an application comparison case of a non-cellular massive MIMO UAV RIS security deployment method to illustrate the feasibility and beneficial effects of our solution.

[0145] Figure 2 and Figure 3 This is a curve chart showing the change of the security rate with the number of APs M under the UAV RIS security deployment method of the non-cellular massive MIMO system, and a comparison of the effects of three UAV RIS deployment schemes.

[0146] exist Figure 2 The accuracy of the analysis results of the present invention is verified. The simulated safety rate is compared with the average safety rate under different user numbers k1, k2, k3 and AP antenna number M in the present invention. It can be seen from the figure that the simulation and theory are highly consistent and follow a monotonically increasing trend, which proves the correctness of the average safety rate in the present invention. In addition, it can be found that as the number of APs in the area increases, the received signal strength of the target user also increases, and its safety rate increases accordingly; when the number of APs is constant, the safety rate decreases as the total number of users increases. This is because when the number of users increases, the interference between users in the system area becomes more serious.

[0147] exist Figure 3 In order to more clearly show the effectiveness of the proposed safety deployment scheme, the safety rates under three deployment schemes are compared: no RIS, UAV RIS with random phase offset and position, and UAV RIS with optimal phase offset and position. The results show that even if non-optimized random values ​​are used for the phase offset and position of the UAV RIS, the safety rate is significantly improved compared to the scheme without RIS assistance. When the optimal UAV RIS phase offset and position of the optimized scheme is used, the safety rate can be further significantly improved. It can be seen that UAV RIS has a significant improvement effect on the physical layer security of the system, and the proposed UAV RIS deployment method has further explored the potential of UAV RIS in improving physical layer security.

[0148] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for deploying RIS of unmanned aerial vehicles without cellular massive MIMO, characterized in that: include: Building a cell-free massive MIMO system, the system comprising access points, users, and eavesdroppers; By forming a link between the drone RIS and the access points and users, full coverage of the access points and users is achieved; Calculate the target user's achievable downlink safe rate with the assistance of the UAV RIS, and construct an objective function by maximizing the target user's safe rate; The objective function is solved by the gradient descent alternating iterative algorithm to obtain the optimal phase shift and deployment position of the UAV RIS.

2. The method for deploying RIS of a drone without cellular massive MIMO as claimed in claim 1, characterized in that: The construction of a cell-free massive MIMO system includes: M access points and K users are evenly distributed in the plane; and, an eavesdropper; Among them, all access points, users and eavesdroppers are single-antenna devices.

3. The method for deploying RIS of a drone without cellular massive MIMO as claimed in claim 2, characterized in that: The drone RIS forms a link with the access point and the user, including: The wireless channel between the mth access point and the kth user includes a direct link and a reflected link; Among them, the direct link is a Rayleigh channel expressed as: In the formula, β mk represents the large-scale fading coefficient of the direct link between the mth access point and the kth user, h mk represents the small-scale fading coefficient of the direct link between the mth access point and the kth user.

4. The method for deploying RIS of a drone without cellular massive MIMO as claimed in claim 2 or 3, characterized in that: The drone RIS forms a link with the access point and the user, including: The wireless channel between the mth access point and the kth user includes a direct link and a reflected link; Among them, the reflection link includes two links: the access point to the drone RIS and the drone RIS to the user; Among them, the link from the mth access point to the UAV RIS is expressed as: In the formula, subscript m represents the mth access point, subscript r represents the UAV RIS, and Respectively represent g mr LoS component and NLoS component, K mr represents the Ricean k factor of the mth access point to UAV RIS link, β mr represents the large-scale fading coefficient of the mth access point to the UAV RIS link, Among them, the link from UAV RIS to the kth user is expressed as: Where, subscript k represents the kth user, β rk represents the large-scale fading coefficient of the link from the UAV RIS to the kth user, K rk represents the corresponding Rice k factor of the link from the UAV RIS to the kth user, and Respectively represent g rk LoS component and NLoS component.

5. The method for deploying RIS of a drone without cellular massive MIMO as claimed in claim 4, characterized in that: The link also includes: The total wireless channel from the mth access point to the kth user is obtained by replacing the direct link g mk Reflection link with gain factor Φg rk Add together to get; The total wireless channel from the mth access point to the eavesdropper e is obtained by adding the direct link between the mth access point and the eavesdropper e and the reflection link with a gain coefficient between the mth access point and the eavesdropper e.

6. The method for deploying RIS of a drone without cellular massive MIMO as claimed in claim 4, characterized in that: The objective function is constructed by maximizing the safety rate of the target user, including: With the goal of maximizing the traversal security rate of the eavesdropped user, under the constraints of the UAV RIS phase shift and spatial position, the optimal UAV RIS phase shift and optimal spatial position deployment are found, and the objective function is established, which is expressed as: max R sec (φ,x r ,y r ,h r ) φ,(x r ,y r ,h r ) s.t.(x r ,y r )∈A, h r ∈(0,60], Among them, R sec It indicates the target user’s downlink safe rate achievable under the assistance of UAV RIS, (x r ,y r ,h r ) represents the three-dimensional position coordinate vector of the UAV RIS including the horizontal coordinate, the vertical coordinate and the flight altitude, and A represents the area range of the system.

7. The method for deploying RIS of a drone without cellular massive MIMO as claimed in claim 1 or 6, characterized in that: The objective function is solved by the gradient descent alternating iterative algorithm to obtain the optimal phase shift and deployment position of the UAV RIS, including: Set the initial position and initial phase of the UAV RIS to obtain the set of safe downlink rates that the target user can reach with the assistance of the UAV RIS, initialize the iteration step size s1,s2>0, the iteration termination error σ, and the iteration variables i,j=0; Perform the first iterative calculation to obtain the first descending direction of the phase shift matrix of the safe speed to the UAV RIS, and iterate the UAV RIS phase shift to obtain the first safe speed after iteration, which is recorded as R sec (φ i+1 ); The first iteration of the loop is calculated until |R sec (φ i+1 )-R sec (φ i )|<σ, record the optimal value of phase shift φ opt =φ i+1 ; Perform the second iterative calculation to obtain the second descent direction of the safety rate for the spatial deployment of the UAV RIS, and iterate the spatial deployment position of the UAV RIS to obtain the second safety rate after iteration, which is recorded as The second iteration of the loop is calculated until we get σ, the spatial position of the best deployment is recorded as The first and second iterations of the loop are repeated until until; Get the optimal phase shift φ of the UAV RIS opt and optimal deployment space location 8. A system using the RIS deployment method for unmanned aerial vehicle without cellular massive MIMO as described in any one of claims 1 to 7, characterized in that: include: A system building module for building a cell-free massive MIMO system, the system comprising an access point, a user, and an eavesdropper; The connection coverage module is used to form a link between the drone RIS and the access points and users to achieve full coverage of the access points and users; A function construction module is used to calculate the target user's achievable downlink safe rate with the assistance of the UAV RIS, and to construct an objective function by maximizing the target user's safe rate; The solution module is used to solve the objective function through the gradient descent alternating iterative algorithm to obtain the optimal phase shift and deployment position of the UAV RIS.

9. An electronic device, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the non-cellular massive MIMO drone RIS deployment method described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the method for deploying RIS of a drone without cellular massive MIMO as described in any one of claims 1 to 7.

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