Unmanned aerial vehicle base station deployment method and apparatus, electronic device, and storage medium

Abstract

The application provides a method and device for deploying a UAV base station, electronic equipment and a storage medium. The method comprises: obtaining position information of users and three-dimensional geographic information of building bodies in a region to be deployed, and determining occlusion information of the users according to the position information and the three-dimensional geographic information; the occlusion information is an invisible region of the users in the region to be deployed after the line of sight of the users is blocked by the building bodies; determining a channel gain model related to the position of the UAV base station based on the occlusion information; determining a communication rate model based on the channel gain model; performing iterative processing on a specified variable in the communication rate model, and obtaining a communication rate output by the communication rate model after each iteration until the communication rate meets a preset condition; determining the specified variable obtained when the communication rate meets the preset condition as a target variable, and deploying the UAV base station based on the target variable. The method of the application ensures the communication performance of the users served by the UAV base station in the presence of obstacles.

Description

TECHNICAL FIELD

[0001] The present application relates to unmanned aerial vehicle communication technology, and in particular to an unmanned aerial vehicle base station deployment method and device, an electronic device, and a storage medium. BACKGROUND

[0002] With the increasing number of wireless communication users and demand, existing ground cellular networks are difficult to meet the communication needs in complex scenarios, such as remote areas, post-disaster environments, and traffic hotspot areas. Therefore, there is an urgent need for new network access platforms to improve the service capability of wireless communication systems.

[0003] Due to the high mobility and low cost characteristics of unmanned aerial vehicles, they have been considered as air base stations or relay-assisted ground communication facilities to enhance service capability. Compared with traditional ground communication systems with fixed infrastructure, unmanned aerial vehicle-assisted communication systems provide new degrees of freedom in the spatial dimension, which can further improve service quality by adjusting deployment positions according to traffic and task requirements. In particular, as the number of users increases, the user distribution becomes more and more widespread, and the communication demand also increases. Application of a coordinated communication network composed of multiple unmanned aerial vehicles can further improve access capability, expand coverage, and improve communication reliability, and has attracted widespread attention.

[0004] However, in actual applications, obstacles such as buildings can cause serious blockage of air-to-ground communication links between unmanned aerial vehicles and ground users, hinder the establishment of line-of-sight paths, and cause deterioration of communication performance. The blocking effect of buildings has not been reasonably considered in current research on unmanned aerial vehicle position deployment, so as to guarantee the communication performance of unmanned aerial vehicle base stations in the presence of obstacles. SUMMARY

[0005] The present application provides an unmanned aerial vehicle base station deployment method, device, electronic device, and storage medium to solve the problem that the current unmanned aerial vehicle base station cannot guarantee the communication performance in the presence of obstacles.

[0006] In a first aspect, the present application provides an unmanned aerial vehicle base station deployment method, comprising:

[0007] Obtaining position information of users in a to-be-deployed area and three-dimensional geographic information of building bodies, and determining blocking information of the users according to the position information and the three-dimensional geographic information; wherein the three-dimensional geographic information comprises positions and sizes of the building bodies, and the blocking information is an invisible area of the users in the to-be-deployed area after a line of sight of the users is blocked by the building bodies;

[0008] determine a channel gain model related to a location of a UAV base station based on the blocking information; wherein the UAV base station is located in the to-be-deployed area; the channel gain model is used to determine a channel gain between a user and the UAV base station according to the location of the UAV base station and the blocking information;

[0009] determine a communication rate model based on the channel gain model, the communication rate model being used to output a communication rate of a user according to specified variables; the specified variables include location information of a UAV base station, transmit power of the UAV base station, and an association variable: the association variable representing an association relationship between a user, a UAV base station, and a subcarrier transmitted by the UAV base station to the user;

[0010] perform iterative processing on the specified variables in the communication rate model, and obtain a communication rate output by the communication rate model after each iteration until the communication rate meets a preset condition; the preset condition is used to determine whether the communication rate meets a maximum communication rate of a target user in the to-be-deployed area, the target user being a user with the minimum communication rate in the to-be-deployed area;

[0011] determine a target variable based on the specified variables when the communication rate meets the preset condition, and deploy the UAV base station based on the target variable.

[0012] In a second aspect, the present application provides a UAV base station deployment device, comprising:

[0013] a blocking information determination module, configured to acquire location information of a user in a to-be-deployed area and three-dimensional geographic information of a building body, and determine blocking information of the user according to the location information and the three-dimensional geographic information; wherein the three-dimensional geographic information includes a location and a size of the building body, and the blocking information is an invisible area of the user in the to-be-deployed area after a line of sight of the user is blocked by the building body;

[0014] a channel gain model determination module, configured to determine a channel gain model related to a location of a UAV base station based on the blocking information; wherein the UAV base station is located in the to-be-deployed area; the channel gain model is used to determine a channel gain between a user and the UAV base station according to the location of the UAV base station and the blocking information;

[0015] a communication rate model determination module, configured to determine a communication rate model based on the channel gain model, the communication rate model being used to output a communication rate of a user according to specified variables; the specified variables include location information of a UAV base station, transmit power of the UAV base station, and an association variable: the association variable representing an association relationship between a user, a UAV base station, and a subcarrier transmitted by the UAV base station to the user;

[0016] an iteration module configured to perform an iteration process on a designated variable in the communication rate model, and obtain a communication rate output by the communication rate model after each iteration until the communication rate meets a preset condition; the preset condition is used to determine whether the communication rate meets a condition that a communication rate of a target user in the to-be-deployed area is maximized, the target user being a user with the minimum communication rate in the to-be-deployed area;

[0017] a deployment module configured to determine the designated variable obtained when the communication rate meets the preset condition as a target variable, and deploy the UAV base station based on the target variable.

[0018] In a third aspect, the present application provides an electronic device, comprising: a processor, and a memory connected with the processor in communication; the memory stores computer-executed instructions; and the processor executes the computer-executed instructions stored in the memory to implement the method in the first aspect.

[0019] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executed instructions, and the computer-executed instructions are executed by a processor to implement the method for deploying a UAV base station in the first aspect.

[0020] In a fifth aspect, the present application provides a computer program product, comprising a computer program, and the computer program is executed by a processor to implement the method in the first aspect.

[0021] The unmanned aerial vehicle base station deployment method provided in the application can accurately determine the visible area and the invisible area of the user in the to-be-deployed area according to the position information of the user and the three-dimensional geographic information of the building body. Then, the channel gain model related to the position of the unmanned aerial vehicle base station is determined based on the shielding information; the unmanned aerial vehicle base station is located in the to-be-deployed area, and the channel gain model can be used to determine the channel gain between the user and the unmanned aerial vehicle base station according to the position of the unmanned aerial vehicle base station and the shielding information, thereby establishing an air-to-ground channel model considering shielding. Next, the communication rate model is determined based on the channel gain model, and the communication rate model is used to output the communication rate of the user according to the specified variable; the specified variable includes the position information of the unmanned aerial vehicle base station, the transmission power of the unmanned aerial vehicle base station, and the associated variable: the associated variable represents the association relationship between the user, the unmanned aerial vehicle base station and the subcarrier sent by the unmanned aerial vehicle base station to the user. The specified variable in the communication rate model is iteratively processed, and the communication rate output by the communication rate model after each iteration is obtained until the communication rate meets the preset condition; the preset condition is used to determine whether the communication rate meets the maximum communication rate of the target user in the to-be-deployed area, and the target user is the user with the minimum communication rate in the to-be-deployed area. Finally, the specified variable obtained when the communication rate meets the preset condition is determined as the target variable, and the unmanned aerial vehicle base station is deployed based on the target variable. That is, the communication rate model can consider the shielding effect of the building on the unmanned aerial vehicle base station to calculate the communication rate between the unmanned aerial vehicle base station and the user, and then combine the preset condition to obtain the target variable under the condition of considering the shielding effect by iteratively processing the above-mentioned specified variable. Finally, the unmanned aerial vehicle base station is deployed through the target variable, so that the communication rate of the user with the minimum communication rate in the to-be-deployed area can be maximized, and the communication performance of the ground user served by the unmanned aerial vehicle base station in the case of obstacles can be improved. BRIEF DESCRIPTION OF DRAWINGS

[0022] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and serve to explain the principles of the application.

[0023] Figure 1 is an application scenario schematic diagram of an unmanned aerial vehicle base station deployment method according to an example embodiment;

[0024] Figure 2 is a method flowchart of an unmanned aerial vehicle base station deployment method according to an example embodiment;

[0025] Figure 3 is a method flow chart of a UAV base station deployment method according to another exemplary embodiment;

[0026] Figure 4 is a building occlusion schematic diagram in a to-be-deployed area according to an embodiment; Figure 3

[0027] Figure 5 is a schematic diagram of the minimum communication rate of system users varying with the number of ground users K under different methods when M=4 and N=4 according to an embodiment; Figure 3

[0028] Figure 6 is a schematic diagram of the minimum communication rate of system users varying with the number of UAVs M under different methods when K=8 and N=4 according to an embodiment; Figure 3

[0029] Figure 7 is a schematic diagram of the minimum communication rate of system users varying with the number of subcarriers N under different methods when K=8 and M=4 according to an embodiment; Figure 3

[0030] Figure 8 is a block diagram of a UAV base station deployment apparatus according to an exemplary embodiment;

[0031] Figure 9 is a structural schematic diagram of an electronic device according to an exemplary embodiment.

[0032] The above-described accompanying drawings have shown the explicit embodiments of the present application, which will be described in more detail hereinafter. These accompanying drawings and textual descriptions are not intended to limit the scope of the concept of the present application by any means, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0033] The exemplary embodiments will be described in detail herein with reference to the attached drawings. The same or similar components are denoted by the same or similar reference numerals throughout the drawings and the textual description, unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all the embodiments consistent with the present application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of the present application, as detailed in the appended claims.

[0034] ​​​​In recent years, unmanned aerial vehicle (UAV) assisted communication systems have attracted extensive attention due to their support for seamless coverage of the 5th Generation Mobile Communication Technology (5G). Benefiting from their controllable three-dimensional (3-D) mobility and low cost, UAVs can serve as aerial base stations (BSs), relays, or aerial access points for coverage enhancement, communication relaying, and data collection and distribution. Compared with traditional ground communication with fixed infrastructure, UAV assisted communication systems provide a new degree of freedom in the spatial dimension, which can further improve the communication performance by exploiting the flexible 3-D mobility of UAVs.

[0035] However, the above works rely on simplified / statistical channel models of the air-to-ground (A2G) communication link between the UAV and the ground users. For example, it is assumed that the A2G channel is dominated by a Line-of-Sight (LoS) path, or the existence probability of the LoS path is modeled as a function of the elevation angle with respect to the A2G link (which can be referred to as a probabilistic LoS channel). The simplified / statistical channel models make the optimization of the deployment of the UAVs more tractable and are suitable for the average performance analysis of UAV communication. However, in practical applications, the topography conditions such as buildings and other obstacles can cause severe blockage of the air-ground link and dramatically degrade the received signal strength, especially in densely populated urban areas. Therefore, the communication design based on the simplified LoS channel and / or the statistical channel cannot guarantee the performance in a specific environment and can not be suitable for the practical application of UAV assisted communication.

[0036] The UAV base station deployment method provided in the present application aims to solve the above technical problems of the prior art.

[0037] The technical solutions of the present application and how the technical solutions of the present application solve the above technical problems will be described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes can not be described in detail in some embodiments. The embodiments of the present application will be described below with reference to the drawings.

[0038] Exemplarily, the specific application scenarios of the present application can be as follows Figure 1The illustrated unmanned aerial vehicle deployment system includes unmanned aerial vehicle base stations, users, and buildings. The area where the unmanned aerial vehicles, users, and buildings are located can be considered as a to-be-deployed area. In the to-be-deployed area, the unmanned aerial vehicle base stations can provide communication services to users through different subcarriers. For example, the unmanned aerial vehicle base stations can respectively serve different users through subcarrier 1, subcarrier 2, and subcarrier 3. The unmanned aerial vehicle deployment system can further include an unmanned aerial vehicle server that is communicatively connected to each unmanned aerial vehicle base station in the to-be-deployed area and controls the position, transmission power, and subcarrier used for service of each unmanned aerial vehicle base station. Optionally, the unmanned aerial vehicle base stations can also be communicatively connected to user terminals of the users.

[0039] As an example, the unmanned aerial vehicle deployment system can be a multi-unmanned aerial vehicle orthogonal frequency division multiple access (OFDMA) downlink communication network located in an outdoor environment with obstacles such as buildings.

[0040] It can be understood that the user can refer to a user terminal, which can include but is not limited to a tablet computer, a notebook computer, a smart phone, a smart wearable device, and the like.

[0041] Figure 2 A method for deploying unmanned aerial vehicle base stations according to an example embodiment can be applied to the application scenario in Figure 1 As illustrated in Figure 2 The method for deploying unmanned aerial vehicle base stations can include the following steps.

[0042] 110. Obtain position information of users in a to-be-deployed area and three-dimensional geographic information of buildings, and determine occlusion information of the users according to the position information and the three-dimensional geographic information. The three-dimensional geographic information includes the position and size of the buildings, and the occlusion information is an invisible area in which a line of sight of the user in the to-be-deployed area is occluded by the buildings.

[0043] Exemplarily, the UAV deployment method can be applied to the UAV server described above, which can obtain three-dimensional geographic information of a building body in a geographic database corresponding to a region to be deployed. In addition, the UAV server can receive positioning information of the UAV base station and the user, and then establish a spatial position model of the UAV base station (hereinafter also referred to as a UAV), the user, and the ground building body in the region to be deployed based on the three-dimensional geographic information and the positioning information. The spatial position model can be established in a three-dimensional Cartesian coordinate system. The UAV server can convert the three-dimensional geographic information and the positioning information into coordinates in the three-dimensional Cartesian coordinate system, for example, convert the positioning information of the user into coordinates in the three-dimensional Cartesian coordinate system to obtain the position information of the user. Similarly, the position information of the UAV base station can also be obtained in the above manner. The positioning information can be global positioning system (GPS) positioning information or Beidou positioning information, which is not limited here.

[0044] The three-dimensional geographic information of the building body converted into the three-dimensional Cartesian coordinate system can be contour coordinates of the building body. When the position of the UAV base station or the user changes, the UAV server can synchronize the change to the spatial position model.

[0045] Since the relative positions of the UAV base station, the user, and the building body in the three-dimensional space can be known from the spatial position model, the occlusion information of the user can be calculated according to the relative positions. The occlusion information is a set of three-dimensional coordinates corresponding to an invisible region of the user in the region to be deployed after the line of sight of the user is blocked by the building body, i.e., the invisible region is a spatial region.

[0046] 120. Determine a channel gain model related to the position of the UAV base station based on the occlusion information; wherein the UAV base station is located in the region to be deployed; the channel gain model is used to determine the channel gain between the user and the UAV base station according to the position of the UAV base station and the occlusion information.

[0047] In some embodiments, for each user, the occlusion information of the user and the position information of all UAV base stations can be used to determine which UAV base stations are in the invisible region and which UAV base stations are in the visible region. For the UAV base stations in the invisible region, since the communication between the UAV base station and the user is blocked by the building body, a first channel gain function that meets the current condition can be established. For the UAV base stations in the visible region, since the communication between the UAV base station and the user is not blocked by the building body, a second channel gain function that meets the current condition can be established. Then, a channel gain model related to the position of the UAV base station can be constructed by combining the first channel gain function and the second channel gain function.

[0048] 130. determining a communication rate model based on the channel gain model, the communication rate model being configured to output a communication rate of the user according to specified variables, the specified variables including the location information of the UAV base station, the transmission power of the UAV base station, and an association variable representing an association relationship between the user, the UAV base station, and a subcarrier transmitted by the UAV base station to the user.

[0049] 140. iteratively processing the specified variables in the communication rate model and obtaining a communication rate output by the communication rate model after each iteration until the communication rate meets a preset condition, the preset condition being configured to determine whether the communication rate meets a maximum communication rate of a target user in the to-be-deployed area, the target user being a user with the minimum communication rate in the to-be-deployed area.

[0050] 150. determining the specified variables obtained when the communication rate meets the preset condition as target variables, and deploying the UAV base station based on the target variables.

[0051] Exemplarily, the target variables include a target association variable, target location information of the UAV base station, and a target transmission power of the UAV base station. After determining the target variables, the UAV server can update the current association variable in the to-be-deployed area to the target association variable, control the corresponding UAV base station to update the current location information to the target location information, and control the corresponding UAV base station to update the current transmission power to the target transmission power, thereby achieving the deployment of the UAV base station.

[0052] It can be seen that in the embodiment, the position information of the user in the to-be-deployed area and the three-dimensional geographic information of the building body are acquired, and the shielding information of the user is determined according to the position information and the three-dimensional geographic information. Then, the channel gain model related to the position of the unmanned aerial base station is determined based on the shielding information. Next, the communication rate model is determined based on the channel gain model, the communication rate model is used to output the communication rate of the user according to the specified variable, and the specified variable includes the position information of the unmanned aerial base station, the transmission power of the unmanned aerial base station and the associated variable. The specified variable in the communication rate model is iteratively processed, and the communication rate output by the communication rate model after each iteration is obtained until the communication rate meets the preset condition; the preset condition is used to determine whether the communication rate meets the maximum communication rate of the target user in the to-be-deployed area, and the target user is the user with the minimum communication rate in the to-be-deployed area. Finally, the specified variable obtained when the communication rate meets the preset condition is determined as the target variable, and the unmanned aerial base station is deployed based on the target variable. That is, the communication rate model can calculate the communication rate between the unmanned aerial base station and the user by considering the shielding effect of the building on the unmanned aerial base station, and by combining the preset condition, the target variable under the shielding effect can be obtained by iteratively processing the above-mentioned specified variable. Finally, the unmanned aerial base station is deployed through the target variable, so that the communication rate of the user with the minimum communication rate in the to-be-deployed area can be maximized, and the communication rate of the user in the to-be-deployed area can be improved as a whole, thereby ensuring the communication performance of the ground user served by the unmanned aerial base station in the presence of obstacles.

[0053] Figure 3 A method for deploying an unmanned aerial base station according to an example embodiment, which can be applied to an unmanned aerial base station in Figure 1 as shown in Figure 3 , the method for deploying an unmanned aerial base station can include:

[0054] 210, acquiring the position information of the user in the to-be-deployed area and the three-dimensional geographic information of the building body, and determining the shielding information of the user according to the position information and the three-dimensional geographic information. The three-dimensional geographic information includes the position and size of the building body, and the shielding information is the invisible area of the user in the to-be-deployed area whose line of sight is blocked by the building body.

[0055] As an example, in the to-be-deployed area, M unmanned aerial base stations and K ground users can be included, and the M unmanned aerial base stations can provide services for the K ground users through N orthogonal subcarriers. Wherein x m represents the three-dimensional coordinates of the mth unmanned aerial base station (also referred to as unmanned aerial base station m) in the M unmanned aerial base stations, represents a set of three-dimensional coordinates corresponding to the to-be-deployed area, the corresponding area can be represented as The three-dimensional coordinates of the k-th user (also called user k) among the K ground users are represented as u. k . The area to be deployed may include A building body, where q represents The q-th building (also called building q) among a group of buildings (hereinafter also referred to as buildings). The occlusion information (also called the occluded spatial domain) of user k relative to building q can be denoted as... The expression that can be modeled as a polyhedron is as follows:

[0056]

[0057] in, It is to form a shielded area The index of the boundary surface. and These are the external normal vector and offset of user k relative to the i-th boundary surface of building q, respectively, determined by the visible side of user k relative to building q. Here, the "visible side" for user k refers to the side whose inner product of its external normal vector and the line-of-sight vector from the user to any point on that side is negative.

[0058] As a more specific example, such as Figure 4 As shown, taking building 1 and building 2 in the area to be deployed as an example, according to Figure 4 It can be seen that the side A1B1B2A2 of building 1 is visible to user k. Therefore, planes SA1A2, SA2B2, and SB2B1 form the occlusion zone. The boundary surface. Where, d k,1 (x m d represents the distance from building 1 to the drone base station m. k,2 (x m ) represents the distance from building 2 to the drone base station m.

[0059] 220. Determine the channel gain model related to the location of the UAV base station based on occlusion information. The UAV base station is located in the area to be deployed; the channel gain model is used to determine the channel gain between the user and the UAV base station based on the location and occlusion information of the UAV base station.

[0060] In some implementations, step 220 may include the following specific implementations:

[0061] 221. Based on the obstruction information and the location of the drone base station, determine the line-of-sight information between the user and the drone base station. The line-of-sight information indicates whether there is a line-of-sight link between the user and the drone base station.

[0062] 222. Channel parameters are defined according to the LOS information, including path loss exponent and channel gain at 1-meter reference distance.

[0063] 223. Channel gain model is determined based on the channel parameters.

[0064] Exemplarily, the channel parameters are defined as:

[0065]

[0066] wherein x m is the position information of the UAV base station m, a k (x m ) is the path loss exponent, b k (x m ) is the channel gain at 1-meter reference distance; (a1, b1) represents that the LOS information is that there is a LOS link between the user and the UAV base station; (a2, b2) represents that the LOS information is that there is no LOS link between the user and the UAV base station; a1 is a first preset constant, b1 is a second preset constant, a2 is a third preset constant, and b2 is a fourth preset constant.

[0067] wherein the channel gain model is:

[0068]

[0069] wherein g k (x m ) is the channel gain model, and u k is the position information of the user k.

[0070] Exemplarily, after obtaining the blocked space of the user k relative to all buildings , whether there is a LOS path between the user k and the UAV m can be determined by judging whether the UAV is located in the blocked space . If the UAV m is deployed in any blocked space of the user k, i.e. , the air-to-ground link is blocked, and there is no LOS path, i.e. the LOS information is that there is no LOS link between the user and the UAV base station.

[0071] Otherwise, when , the air-to-ground link is not blocked by any obstruction, and there is a LOS path, i.e. the LOS information is that there is no LOS link between the user and the UAV base station. An example of the scenario of the blocked space of the user relative to the building is shown in Figure 4 . It can be defined that: then is equivalent to d k,q (x m )≤0. Therefore, according to the blocking information, the communication conditions between the UAV base station and the user can be divided into the following two kinds:

[0072] Non-line-of-sight condition: Equivalent to

[0073] Line-of-sight condition: Equivalent to

[0074] It can be seen that the line-of-sight / non-line-of-sight condition can be described as a step function with respect to min q∈Q {d k,q (x m )}.

[0075] In some embodiments, the channel parameters (α k (x m ), β k (x m )) can be modeled as continuous differentiable functions; specifically, a Sigmoid function can be employed to approximate the step function:

[0076]

[0077] where the smoothing coefficient η can control the scale of approximation, representing the rate of change of the degree of obstruction between the line-of-sight and non-line-of-sight channels. The distance ‖x m -u k ‖ is used for normalization, ensuring the same channel obstruction condition in the same direction relative to the user. Thus, the channel parameters α k (x m ) and β k (x m ) can be approximated as continuous differentiable functions as follows:

[0078]

[0079] Substituting the channel parameters α k (x m ) and β k (x m ) obtained at this time into , the final channel gain model can be obtained.

[0080] 230. Determine a communication rate model based on the channel gain model, the communication rate model being configured to output a communication rate of the user according to specified variables. The specified variables include position information of the UAV base station, transmit power of the UAV base station, and an association variable representing an association relationship between the user, the UAV base station, and subcarriers transmitted by the UAV base station to the user.

[0081] Following the above example, the communication rate model is:

[0082]

[0083] Where X represents the set of location information of UAV base stations, P represents the set of transmission power of UAV base stations, C represents the set of related variables, and R... k，m，n (X, P, C) represents the communication rate of user k when served by UAV base station m via subcarrier n, and j∈M\{m} indicates that UAV base station j is a UAV base station other than UAV base station m in the set M of UAV base stations; σ 2 For the user's additive white Gaussian noise power, p m，n p represents the transmit power of the UAV base station m on subcarrier n. j，n This represents the transmit power of the UAV base station j on subcarrier n.

[0084] As a more concrete example, consider a scenario where each drone base station has N orthogonal subcarriers, where n represents the nth subcarrier (or subcarrier n) among the N subcarriers. Assuming user k is served by drone m via carrier n, the received signal-to-interference-plus-noise ratio (SINNR) for user k can be expressed as:

[0085]

[0086] Where σ 2 The power of additive white Gaussian noise at the user end.

[0087] If user k is served by drone m via carrier n, c k,m,n =1; otherwise c k,m,n = 0. Therefore, the achievable communication rate (bps / Hz) for user k served by drone m via carrier n can be expressed as a function of X, P, and C:

[0088]

[0089] The log function is base 2. Therefore, the achievable speed for user k is:

[0090]

[0091] 240. Input the currently specified variable into the communication rate model and obtain the communication rate output by the communication rate model.

[0092] 250. If it is determined that the communication rate does not meet the preset conditions, the currently specified variable is updated based on the preset constraints to obtain the updated specified variable; wherein, the constraints are used to restrict the specified variable from being updated within a specified range.

[0093] 260、based on the updated designated variable, return to performing the step of inputting the current designated variable into the communication rate model, and obtaining the communication rate output by the communication rate model, until the communication rate meets the preset condition. That is, based on the updated designated variable, re-perform steps 240 to 260.

[0094] Exemplarily, after obtaining the communication rate model, a mathematical optimization problem (hereinafter referred to as an optimization problem for short) can be constructed according to the communication rate model, which is to design the deployment of the unmanned aerial vehicle positions and the allocation of resources (including the allocation of power, the association of users / subcarriers) to maximize the minimum communication rate of the system. That is, an optimization problem model is constructed, and the expression of the objective function of the optimization problem model can be as follows:

[0095]

[0096] wherein, represents the minimum achievable rate between users. After determining the objective function, the constraint conditions in the optimization problem model also need to be set.

[0097] In some embodiments, the constraint conditions can include a first constraint condition, a second constraint condition and a third constraint condition for the association variable; wherein:

[0098] The first constraint condition (hereinafter also referred to as constraint 1) is:

[0099] c k,m,n ∈{0,1},k∈K,m∈M,n∈N;

[0100] wherein, k represents a user k, m represents an unmanned aerial vehicle base station m, n represents a subcarrier n, K represents a set of users, M represents a set of unmanned aerial vehicle base stations, N represents a set of subcarriers, and c k,m,n represents the association variable, wherein if the user k is served by the unmanned aerial vehicle base station m through the subcarrier n, c k,m,n =1 is determined; otherwise, c k,m,n =0 is determined.

[0101] The second constraint condition (hereinafter also referred to as constraint 2) is:

[0102]

[0103] The third constraint condition (hereinafter also referred to as constraint 3) is:

[0104]

[0105] wherein, constraint 1 represents c k,m,nThe optimization variables are combined. Constraint 2 guarantees that each carrier of each UAV can only serve at most one user. Constraint 3 guarantees that each user can be served by one carrier of one UAV.

[0106] In some embodiments, the constraint conditions can further include a constraint condition for the transmission power, for example:

[0107] Constraint 4: p m,n ≥ 0, m e M, n e N

[0108] Constraint 5:

[0109] wherein constraint 4 and constraint 5 guarantee that the transmission power of each UAV base station on each subcarrier is non-negative, and the total transmission power does not exceed the maximum value P max .

[0110] In some embodiments, the constraint conditions can further include a constraint condition for the UAV base station position, for example:

[0111] Constraint 6:

[0112] Constraint 7:

[0113] wherein constraint 6 limits the area where each UAV base station can be deployed to the to-be-deployed area. Constraint 7 guarantees that the position of the UAV base station is subject to the collision avoidance constraint, wherein d min represents the minimum safety distance for collision avoidance between UAVs.

[0114] In some embodiments, the method for deploying UAVs can further include:

[0115] 261. determining a target value according to the communication rate and a preset penalty factor, the preset penalty factor being used to determine the degree of violation of the constraint condition by the specified variable.

[0116] 262. determining that the communication rate meets the preset condition if a difference between the target value and a target value obtained by last iteration of the specified variable is less than a first threshold value.

[0117] 270. determining the specified variable obtained when the communication rate meets the preset condition as a target variable, and deploying the UAV base station based on the target variable.

[0118] In the above example, since the above established optimization problem involves combined planning variables (i.e., associated variables) and the variables are highly coupled, the problem is difficult to solve optimally, so in the present embodiment, the penalty function method and the block successive convex approximation (BSCA) technique can be used to solve the above optimization problem. First, by relaxing the combined optimization variable C to a continuous variable and introducing a penalty factor to add a penalty term in the objective function, the above optimization problem is converted into a penalty problem. Then, a double-loop optimization framework is used: the inner loop iteration solves the penalty problem given the penalty factor, and alternately optimizes the UAV position X and the resource allocation {P, C}; the outer loop iteration updates the penalty factor to reduce the violation of the relaxed constraints.

[0119] As an example, constraint 1 in the above optimization problem model can be equivalently converted into the following continuous constraint:

[0120] Constraint 8: 0 ≤ c k,m,n ≤ 1, k ∈ K, m ∈ M, n ∈ N

[0121] Constraint 9: c k,m,n (1 - c k,m,n ) ≤ 0, k ∈ K, m ∈ M, n ∈ N

[0122] That is, constraint 1 is replaced by constraint 8 and constraint 9, and then a penalty factor Λ is introduced in the above objective function,

[0123] where Λ = {λ k,m,n , k ∈ K, m ∈ M, n ∈ N} and constraint 9 is dualized, so that the above optimization problem can be converted into the following penalty problem model:

[0124]

[0125] where

[0126] In some embodiments, the specific implementation of 270 can include:

[0127] The associated variables in the current specified variables and the transmit power of the UAV base station are determined as the first update item, and the position information of the UAV base station in the current specified variables is determined as the second update item; the first update item or the second update item in the current specified variables is updated based on the preset constraint condition to obtain the updated specified variables, and the un-updated item in the current specified variables in this update is taken as the update item in the next update.

[0128] For example, when performing the first update on the current designated variable, only the position information of the UAV base station in the current designated variable can be updated, and the associated variable and the transmission power of the UAV base station in the current designated variable remain unchanged. When performing the second update on the first updated designated variable, only the associated variable and the transmission power of the UAV base station in the first updated designated variable can be updated, and the position information of the UAV base station in the first updated designated variable remains unchanged. Similarly, in subsequent updates, the first update item and the second update item can be alternately updated.

[0129] In some embodiments, step 270 can include:

[0130] 271. determining a constraint violation value of the target variable according to the preset penalty factor, the constraint condition, and the target variable, the constraint violation value representing a degree of violation of the constraint condition by the designated variable.

[0131] 272. if it is determined that the constraint violation value is less than the second threshold value, deploying the UAV base station based on the target variable.

[0132] In some embodiments, step 270 can further include:

[0133] 273. if it is determined that the constraint violation value is greater than or equal to the second threshold value, updating the penalty factor to obtain an updated penalty factor;

[0134] 274. returning to perform the step of determining the target value according to the communication rate and the preset penalty factor based on the updated penalty factor until the constraint violation value is less than the second threshold value, determining the designated variable obtained when the constraint violation value is less than the second threshold value as the target variable, and deploying the UAV base station based on the target variable.

[0135] Using the above example, steps 271 to 274 can be regarded as solving the above penalty problem model, wherein:

[0136] The overall solution process can be composed of two layers of iterations, i.e. inner iteration and outer iteration, the inner iteration alternately optimizes the position deployment of the UAV base station (i.e. the position information of the UAV base station) and the resource allocation (i.e. the transmission power of the UAV base station and the associated variable), and the outer iteration optimizes the penalty factor. Wherein, l represents the lth inner iteration, and L represents the Lth outer iteration.

[0137] As an example, when performing the inner iteration operation (hereinafter referred to as step one), for a given penalty factor Λ L The (L+1)th outer iteration of the above penalty problem is decomposed into a position deployment sub-problem and a resource allocation sub-problem, and the two sub-problems are iteratively solved by alternating optimization, and a set of sub-optimal solutions can be finally obtained. For simplicity of notation, define:

[0138]

[0139]

[0140] then Definition:

[0141]

[0142] where Z(X, P, C) represents the given Λ L The objective function of the lower penalty problem model.

[0143] In solving the above penalty problem model, first, in the (l+1)th inner iteration, fix the power allocation and the associated Then the penalty problem is transformed into the following location deployment subproblem:

[0144]

[0145] s.t. constraint 6, constraint 7

[0146] The above location deployment subproblem is a non-convex optimization problem. For a given local point X l , the following approximation problem of the location deployment subproblem can be solved

[0147]

[0148] where:

[0149]

[0150]

[0151]

[0152] where,

[0153]

[0154]

[0155]

[0156] Since the above approximation problem is a convex problem, it can be solved by a solver. Let its optimal solution be then X l＊ -X l constitute Z(X, P l , C l) in the direction of X = X l , in which direction the target value can continue to rise compared to the value currently at the local point X l . Given the direction of rise X l＊ - X l , the position of the drone is updated as:

[0157]

[0158] where the step size determined by the backtracking line search: given a constant ζ, τ ∈ (0, 1), is set to where t l is the smallest integer that makes the constraint 10 and the following inequality satisfied:

[0159]

[0160] where

[0161]

[0162]

[0163] After updating the position of the drone X l+1 , the fixed drone position, the penalized problem is converted into a resource allocation subproblem as follows:

[0164]

[0165] The above resource allocation subproblem is a non-convex optimization problem. For a given local point (P l , C l ), an approximate problem of the following resource allocation problem can be solved:

[0166]

[0167] where:

[0168]

[0169]

[0170]

[0171]

[0172] where the above approximate problem is a convex problem and can be solved by a solver. Let its optimal solution be: which is the direction of rise of Z(X l+1 , P, C) at (P l , C l ).

[0173] Similarly, the resource allocation variables can be updated by backtracking line search as follows:

[0174]

[0175] where the step size is subject to:

[0176]

[0177] where:

[0178]

[0179]

[0180]

[0181] By iteratively optimizing the above position deployment subproblem and resource allocation subproblem, the inner iteration can be stopped when the growth of the objective function of the penalized problem, i.e., Z(X l+1 , P l+1 , C l+1 ) - Z(X l , P l , C l ) is less than a first threshold, or the number of iterations l exceeds a maximum number.

[0182] In addition, when performing the outer iteration operation (hereinafter referred to as Step Two), for the (L+1)th outer iteration, the value of the fixed penalty factor Λ L is obtained by performing the above inner iteration operation, a set of suboptimal solutions of the penalized problem is recorded as:

[0183] and In order to reduce the degree of violation of constraint 9, the penalty factor Λ is updated according to the following strategy:

[0184]

[0185] where the step size γ L is:

[0186]

[0187] Finally, in Step Three, the above inner iteration operation (Step One) and outer iteration operation (Step Two) are repeatedly performed until the degree of violation of constraint 9 less than the second threshold value. Finally, the position information, the transmission power, the association variable and the value of the penalty factor of each UAV base station in the region to be deployed can be obtained. And the position information, the transmission power, the association variable and the value of the penalty factor of each UAV base station are used as the above-mentioned target variables to deploy the UAV base stations in the region to be deployed.

[0188] As an example, the present example provides three deployment methods as a comparison, respectively, a "fixed association" method, a "K-means position" method, and a "no geographic information" method. Among them, the "fixed association" method fixes the association variable C as the initial value Then, the inner iteration step corresponding method is used to optimize the UAV deployment position and power allocation. Since the scheme satisfies the constraint 9, steps two and three do not need to be performed. The "K-means position" method uses the K-means clustering algorithm to divide the users into M groups according to the horizontal coordinates of the users, and then uses the above-mentioned joint optimization of the deployment position of the UAV, resource allocation, and the penalty factor to maximize the minimum communication rate of the users to perform resource allocation. The "no geographic information" method assumes that the system has no available geographic information. Under the assumption that the air-to-ground channel is a line-of-sight channel, the above-mentioned joint optimization of the deployment position of the UAV, resource allocation, and the penalty factor to maximize the minimum communication rate of the users is performed. Finally, the actual line-of-sight / non-line-of-sight channel state of the UAV base station position information is calculated to calculate the actual communication rate.

[0189] Figure 5 It is shown that when M = 4, N = 4, the minimum communication rate of the system users varies with the number of ground users K under several different methods. From Figure 5 It can be seen from the figure that the method of the present embodiment (i.e., the algorithm in the figure) is superior to the "fixed association" method, the "K-means position" method, and the "no geographic information" method in terms of the minimum communication rate of the users under any number of users, which embodies the advantage of the method of the present embodiment in considering the influence of building shielding on the air-to-ground channel while simultaneously optimizing the three-dimensional position deployment, power allocation, and user / subcarrier association.

[0190] Figure 6 It is shown that when K = 8, N = 4, the minimum communication rate of the system users varies with the number of UAVs M under several different methods. From Figure 6 It can be seen from the figure that the method of the present embodiment is superior to the other three comparison methods in terms of the minimum communication rate of the users under any number of UAVs, which embodies the advantage of the method of the present embodiment.

[0191] Figure 7 It is shown that when K = 8, M = 4, the minimum communication rate of the system users varies with the number of subcarriers N under several different methods. From Figure 7As can be seen, the method in this embodiment outperforms the other three comparative methods in terms of minimum user communication rate under any number of subcarriers, demonstrating the advantages of the method in this embodiment.

[0192] Depend on Figures 5-7 Simulation results for different numbers of users, drones, and subcarriers show that the method in this embodiment can guarantee actual communication performance compared with the drone location design based on the assumed line-of-sight channel in related technologies, and can achieve a higher minimum reachability compared with fixed location and fixed association cases, demonstrating the advantages of the method in this embodiment.

[0193] Figure 8 This is an exemplary embodiment illustrating a drone base station deployment device, such as... Figure 8 As shown, the device 300 may include:

[0194] The occlusion information determination module 310 is used to obtain the location information of the user and the three-dimensional geographic information of the building in the area to be deployed, and to determine the occlusion information of the user based on the location information and the three-dimensional geographic information; wherein, the three-dimensional geographic information includes the location and size of the building, and the occlusion information is the invisible area in the area to be deployed after the user's line of sight is blocked by the building.

[0195] The channel gain model determination module 320 is used to determine the channel gain model related to the location of the UAV base station based on the occlusion information; wherein the UAV base station is located in the area to be deployed; the channel gain model is used to determine the channel gain between the user and the UAV base station according to the location and occlusion information of the UAV base station.

[0196] The communication rate model determination module 330 is used to determine the communication rate model based on the channel gain model. The communication rate model is used to output the user's communication rate according to specified variables. The specified variables include the location information of the UAV base station, the transmission power of the UAV base station, and related variables: the related variables represent the relationship between the user, the UAV base station, and the subcarriers transmitted by the UAV base station to the user.

[0197] The iteration module 340 is used to iterate over the specified variables in the communication rate model and obtain the communication rate output by the communication rate model after each iteration until the communication rate meets the preset conditions. The preset conditions are used to determine whether the communication rate meets the requirement of maximizing the communication rate of the target user in the area to be deployed. The target user is the user with the lowest communication rate in the area to be deployed.

[0198] The deployment module 350 is used to determine the specified variable obtained when the communication rate meets the preset conditions as the target variable, and to deploy the UAV base station based on the target variable.

[0199] In some embodiments, the iteration module 340 comprises:

[0200] The communication rate obtaining submodule is configured to input the current designated variable into the communication rate model, and obtain the communication rate output by the communication rate model.

[0201] The updating submodule is configured to, if it is determined that the communication rate does not satisfy the preset condition, update the current designated variable based on the preset constraint condition to obtain an updated designated variable; wherein the constraint condition is used to limit the update of the designated variable within a designated range.

[0202] The repeating submodule is configured to return to execute the step of inputting the current designated variable into the communication rate model and obtaining the communication rate output by the communication rate model based on the updated designated variable until the communication rate satisfies the preset condition.

[0203] In some embodiments, the constraint condition comprises a first constraint condition, a second constraint condition and a third constraint condition for the associated variable; wherein:

[0204] The first constraint condition is:

[0205] c k,m,n ∈{0, 1}, k ∈ K, m ∈ M, n ∈ N;

[0206] wherein k represents a user k, m represents a UAV base station m, n represents a subcarrier n, K represents a set of users, M represents a set of UAV base stations, N represents a set of subcarriers, and c k,m,n represents the associated variable, wherein if the user k is served by the UAV base station m through the subcarrier n, it is determined that c k,m,n = 1; otherwise, it is determined that c k,m,n = 0.

[0207] The second constraint condition is:

[0208]

[0209] The third constraint condition is:

[0210]

[0211] In some embodiments, the apparatus 300 further comprises:

[0212] The violation degree determining module is configured to determine a target value according to the communication rate and a preset penalty factor, and the preset penalty factor is used to determine the violation degree of the designated variable to the constraint condition.

[0213] The preset condition determining module is configured to, if it is determined that the difference between the target value and the target value obtained by last iteration of the designated variable is less than a first threshold value, determine that the communication rate satisfies the preset condition.

[0214] In some embodiments, the deployment module 350 is specifically configured to determine a constraint violation value of the target variable according to the preset penalty factor, the constraint condition and the target variable, the constraint violation value representing a degree of violation of the constraint condition by the specified variable; and if it is determined that the constraint violation value is less than a second threshold value, deploying the UAV base station based on the target variable.

[0215] In some embodiments, the deployment module 350 is specifically further configured to, if it is determined that the constraint violation value is greater than or equal to the second threshold value, update the penalty factor to obtain an updated penalty factor; return to execute the step of determining the target value according to the communication rate and the preset penalty factor based on the updated penalty factor until the constraint violation value is less than the second threshold value, and determine the specified variable obtained when the constraint violation value is less than the second threshold value as the target variable, and deploy the UAV base station based on the target variable.

[0216] In some embodiments, the update sub-module is specifically configured to determine an associated variable in the current specified variable and a transmission power of the UAV base station as a first update item, and determine position information of the UAV base station in the current specified variable as a second update item; update the first update item or the second update item in the current specified variable based on the preset constraint condition to obtain an updated specified variable, and take an un-updated item in the current specified variable in this update as an update item in the next update.

[0217] In some embodiments, the channel gain model determination module 320 is specifically configured to determine visibility information between the user and the UAV base station according to the occlusion information and the position of the UAV base station, the visibility information representing whether there is a visibility link between the user and the UAV base station; define a channel parameter according to the visibility information, the channel parameter including a path loss index and a channel gain at a reference distance of 1 meter; and determine a channel gain model based on the channel parameter.

[0218] In some embodiments, the channel parameter is defined as:

[0219]

[0220] wherein, x m is the position information of the UAV base station m, a k (x m ) is the path loss index, b k (x m ) is the channel gain at a reference distance of 1 meter; (a1, b1) represents that the visibility information is that there is a visibility link between the user and the UAV base station; (a2, b2) represents that the visibility information is that there is no visibility link between the user and the UAV base station; a1 is a first preset constant, b is a second preset constant, a2 is a third preset constant, and b2 is a fourth preset constant.

[0221] The channel gain model is:

[0222]

[0223] where g k (x m ) is the channel gain model, u k is the location information of user k.

[0224] In some embodiments, the communication rate model is:

[0225]

[0226] where X represents a set of location information of the UAV base stations, P represents a set of transmission power of the UAV base stations, C represents a set of correlation variables, R k，m，n (X, P, C) represents the communication rate of user k served by UAV base station m through subcarrier n, j e M \ {m} represents that the UAV base station j is a UAV base station other than the UAV base station m in the set M of UAV base stations; σ 2 is the power of the additive white Gaussian noise of the user, p m，n represents the transmission power of the UAV base station m on the subcarrier n, p j，n represents the transmission power of the UAV base station j on the subcarrier n.

[0227] Figure 9 is a structural schematic diagram of an electronic device according to an example embodiment, which can be a computer, a server, etc. Wherein the electronic device can correspond to the UAV server in the above embodiments. As Figure 9 shown, the electronic device can specifically include a receiver 40, a transmitter 41, a processor 42, and a memory 43. Wherein the above receiver 40 and transmitter 41 are used to realize data transmission between the electronic device and the user, the UAV base station, respectively, the above memory stores computer execution instructions; the above processor executes the computer execution instructions stored in the above memory to realize the UAV base station deployment method in the above embodiments.

[0228] In example embodiments, a non-transitory computer readable storage medium is also provided, when the instructions in the storage medium are executed by the processor of the terminal device, the terminal device can execute the UAV base station deployment method of the above electronic device.

[0229] In example embodiments, a computer program product is also provided, which includes a computer program, when the computer program is executed by the processor, the UAV base station deployment method in the above embodiments.

[0230] Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. It is intended that the specification and examples be considered as exemplary only, with the true scope and spirit of the application being indicated by the following claims.

[0231] It is to be understood that the application is not limited to the precise construction herein disclosed and shown in the drawings, and that various modifications and changes can be made by those skilled in the art without departing from the scope of the application. The scope of the application is limited only by the claims that follow.

Claims

1. A method for deploying a drone base station, characterized in that, include: The system acquires the location information of users and the three-dimensional geographic information of buildings in the area to be deployed, and determines the occlusion information of users based on the location information and the three-dimensional geographic information; wherein, the three-dimensional geographic information includes the location and size of the buildings, and the occlusion information is the invisible area in the area to be deployed after the user's line of sight is blocked by the buildings; Based on the occlusion information, a channel gain model related to the location of the drone base station is determined; wherein, the drone base station is located in the area to be deployed; the channel gain model is used to determine the channel gain between the user and the drone base station according to the location of the drone base station and the occlusion information. The communication rate model is determined based on the channel gain model. The communication rate model is used to output the user's communication rate according to specified variables. The specified variables include the location information of the UAV base station, the transmit power of the UAV base station, and correlation variables. The correlation variables represent the correlation between the user, the UAV base station, and the subcarriers transmitted by the UAV base station to the user. The specified variables in the communication rate model are iteratively processed to obtain the communication rate output by the communication rate model after each iteration, until the communication rate meets a preset condition; the preset condition is used to determine whether the communication rate satisfies the condition that the communication rate of the target user in the area to be deployed is maximized, wherein the target user is the user with the lowest communication rate in the area to be deployed; wherein the preset condition includes: the rate of the target user with the lowest communication rate is maximized, and all constraint violation values ​​are less than a second threshold; The specified variable obtained when the communication rate meets the preset conditions is determined as the target variable, and the UAV base station is deployed based on the target variable; wherein, the target variable includes target-related variables, target location information of the UAV base station, and transmission power of the UAV base station; The iterative processing of specified variables in the communication rate model, and obtaining the communication rate output by the communication rate model after each iteration, until the communication rate meets a preset condition, includes: Input the currently specified variable into the communication rate model and obtain the communication rate output by the communication rate model; If it is determined that the communication rate does not meet the preset conditions, the currently specified variable is updated based on the preset constraints to obtain the updated specified variable; wherein, the constraints are used to restrict the specified variable from being updated within a specified range. Based on the updated specified variable, return to the step of inputting the current specified variable into the communication rate model and obtaining the communication rate output by the communication rate model, until the communication rate meets the preset condition; The deployment of the UAV base station based on the target variable includes: Based on the preset penalty factor, the constraint conditions, and the target variable, the constraint violation value of the target variable is determined, and the constraint violation value represents the degree to which the specified variable violates the constraint conditions; If the constraint violation value is determined to be less than the second threshold, then the drone base station is deployed based on the target variable; If it is determined that the constraint violation value is greater than or equal to the second threshold, then the penalty factor is updated to obtain the updated penalty factor; Based on the updated penalty factor, the step of determining the target value according to the communication rate and the preset penalty factor is performed until the constraint violation value is less than the second threshold. The specified variable obtained when the constraint violation value is less than the second threshold is determined as the target variable, and the UAV base station is deployed based on the target variable.

2. The method according to claim 1, characterized in that, The constraints include a first constraint, a second constraint, and a third constraint on the associated variables; wherein: The first constraint is: c k,m，n ∈{0,1},k∈K,m∈M,n∈N; Where k represents user k, m represents UAV base station m, n represents subcarrier n, K represents the set of users, M represents the set of UAV base stations, N represents the set of subcarriers, and c k，m，n The associated variable is defined as follows: if user k is served by UAV base station m via subcarrier n, then c is determined. k，m，n =1; otherwise, determine c. k，m，n =0; The second constraint is: The third constraint is:

3. The method according to claim 1, characterized in that, The method further includes: The target value is determined based on the communication rate and a preset penalty factor, wherein the preset penalty factor is used to determine the degree to which the specified variable violates the constraint. If the difference between the target value and the target value obtained from the previous iteration on the specified variable is less than a first threshold, then the communication rate is determined to meet the preset condition.

4. The method according to any one of claims 1 to 3, characterized in that, Updating the currently specified variable based on preset constraints includes: The associated variables in the currently specified variables and the transmit power of the UAV base station are determined as the first update item, and the location information of the UAV base station in the currently specified variables is determined as the second update item; The first or second update item in the currently specified variable is updated based on preset constraints to obtain the updated specified variable, and the unupdated items in the currently specified variable in this update are used as update items in the next update.

5. The method according to any one of claims 1 to 3, characterized in that, The step of establishing a channel gain model related to the location of the UAV base station based on the occlusion information includes: Based on the obstruction information and the location of the drone base station, the line-of-sight information between the user and the drone base station is determined, and the line-of-sight information indicates whether there is a line-of-sight link between the user and the drone base station; Channel parameters are defined based on the line-of-sight information, including the path loss exponent and the channel gain at a reference distance of 1 meter. The channel gain model is determined based on the channel parameters.

6. The method according to claim 5, characterized in that, The channel parameters are defined as follows: Where, x m For the location information of the drone base station m, α k (x m ) represents the path loss exponent, β k (x m ) represents the channel gain at a reference distance of 1 meter; (α1, β1) indicates that the line-of-sight information indicates that there is a line-of-sight link between the user and the drone base station; (α2, β2) indicates that there is no line-of-sight link between the user and the drone base station; α1 is a first preset constant, β1 is a second preset constant, α2 is a third preset constant, and β2 is a fourth preset constant; The channel gain model is as follows: Among them, g k (x m ) represents the channel gain model, u k This provides the location information for user k.

7. The method according to claim 6, characterized in that, The communication rate model is as follows: Where X represents the set of location information of the UAV base station, P represents the set of transmission power of the UAV base station, C represents the set of related variables, and R k，m，n (X, P, C) represents the communication rate of user k when served by UAV base station m via subcarrier n, and j∈M\{m} indicates that UAV base station j is a UAV base station other than UAV base station m in the set M of UAV base stations; σ 2 For the user's additive white Gaussian noise power, p m,n p represents the transmit power of the UAV base station m on subcarrier n. j，n This represents the transmit power of the UAV base station j on subcarrier n.

8. A drone base station deployment device, comprising: The occlusion information determination module is used to acquire the location information of the user and the three-dimensional geographic information of the building in the area to be deployed, and to determine the occlusion information of the user based on the location information and the three-dimensional geographic information; wherein, the three-dimensional geographic information includes the location and size of the building, and the occlusion information is the invisible area in the area to be deployed after the user's line of sight is blocked by the building; A channel gain model determination module is used to determine a channel gain model related to the location of the UAV base station based on the occlusion information; wherein the UAV base station is located in the area to be deployed; the channel gain model is used to determine the channel gain between the user and the UAV base station according to the location of the UAV base station and the occlusion information; The communication rate model determination module is used to determine the communication rate model based on the channel gain model. The communication rate model is used to output the user's communication rate according to specified variables. The specified variables include the location information of the UAV base station, the transmit power of the UAV base station, and correlation variables. The correlation variables represent the correlation between the user, the UAV base station, and the subcarriers transmitted by the UAV base station to the user. An iteration module is used to iterate over specified variables in the communication rate model and obtain the communication rate output by the communication rate model after each iteration until the communication rate meets a preset condition. The preset condition is used to determine whether the communication rate satisfies the condition that the communication rate of the target user in the area to be deployed is maximized, where the target user is the user with the lowest communication rate in the area to be deployed. The preset condition includes: the rate of the target user with the lowest communication rate is maximized, and all constraint violation values ​​are less than a second threshold. The deployment module is used to determine the specified variable obtained when the communication rate meets the preset conditions as the target variable, and to deploy the UAV base station based on the target variable; wherein, the target variable includes target-related variables, target location information of the UAV base station, and transmission power of the UAV base station; The iterative module includes: The communication rate acquisition submodule is used to input the currently specified variable into the communication rate model and acquire the communication rate output by the communication rate model; An update submodule is used to update the currently specified variable based on preset constraints if it is determined that the communication rate does not meet preset conditions, thereby obtaining the updated specified variable; wherein, the constraints are used to restrict the specified variable from being updated within a specified range. The repeating submodule is used to return to the step of inputting the current specified variable into the communication rate model and obtaining the communication rate output by the communication rate model based on the updated specified variable, until the communication rate meets the preset condition; The deployment module is specifically used to determine the constraint violation value of the target variable based on the preset penalty factor, the constraint condition, and the target variable. The constraint violation value represents the degree to which the specified variable violates the constraint condition. If the constraint violation value is determined to be less than the second threshold, then the drone base station is deployed based on the target variable; If it is determined that the constraint violation value is greater than or equal to the second threshold, then the penalty factor is updated to obtain the updated penalty factor; Based on the updated penalty factor, the step of determining the target value according to the communication rate and the preset penalty factor is performed until the constraint violation value is less than the second threshold. The specified variable obtained when the constraint violation value is less than the second threshold is determined as the target variable, and the UAV base station is deployed based on the target variable.

9. An electronic device, comprising: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the UAV base station deployment method as described in any one of claims 1 to 7.

11. A computer program product comprising a computer program that, when executed by a processor, implements the method of any one of claims 1 to 7.

Images