A Drone Base Station Handoff Method and System Based on Maximum Average Signal-to-Noise Ratio

Abstract

The present invention relates to a method and system for switching of an unmanned aerial vehicle (UAV) base station based on the maximum average signal-to-noise ratio, belonging to the technical field of UAV communication. The method includes the following steps: obtaining the position information and flight direction vectors of each base station and the UAV, calculating the Euclidean distance between the UAV and the base station, and calculating the large-scale fading based on this. The power expectation of the small-scale fading is obtained through the Rice distribution combined with the expectation approximation algorithm, and then the instantaneous signal-to-noise ratio and the average signal-to-noise ratio are calculated. The handover problem is modeled as an objective function with the maximum average signal-to-noise ratio as the objective, and the base station with the largest average signal-to-noise ratio is selected as the target base station for handover. By introducing the average signal-to-noise ratio as the handover decision basis, the present invention effectively alleviates the problem of frequent base station handovers caused by instantaneous signal fluctuations, and reduces the handover delay and the risk of communication interruption.

Description

Technical Field

[0001] The present invention relates to a method and system for switching UAV base stations based on the maximum average signal-to-noise ratio, belonging to the technical field of UAV communication. Background Art

[0002] With the rapid development of UAV technology, its application fields have expanded from traditional power base station inspections and agricultural monitoring to emerging scenarios such as environmental monitoring and aerial logistics. In these applications, a stable and reliable wireless network connection is not only the basis for ensuring UAV flight control but also the key to realizing real-time data transmission. However, in low-density areas with sparse base station coverage (such as suburbs), the problem of switching UAV wireless network access points has become increasingly prominent, and this technical bottleneck directly affects the execution efficiency and communication quality of UAV tasks.

[0003] Currently, the mainstream wireless network switching mechanism mainly makes decisions based on static indicators such as instantaneous signal strength (such as instantaneous signal-to-noise ratio) and bandwidth. When the detected signal strength is lower than the preset threshold, the system will automatically switch to a base station with a stronger signal. However, this switching strategy based on instantaneous indicators has obvious deficiencies when dealing with high-speed UAV flight or dynamically changing flight paths: the drastic fluctuation of signal strength easily leads to system judgment lag or misjudgment, thus triggering frequent switching, which not only increases the switching delay but may even cause communication interruption. More critically, this traditional method fails to fully consider the dynamic characteristics of UAV flight, cannot accurately predict the change trend of signal quality between the base station and the UAV, and lacks predictability and adaptability to flight paths and environmental changes. Especially in complex application scenarios such as aerial logistics, high-speed cruising, and suburban inspections, the existing mechanism is difficult to meet the stringent requirements of UAVs for high-stability network connections.

[0004] The patent application document with the publication number "CN117395669A" discloses a channel clustering and modeling method for 6G UAV air-to-ground communication scenarios. The problems of this method are as follows: It uses VB-GMM clustering and MCD evolution tracking, involves statistical modeling and iterative optimization of multi-dimensional data (time delay, angle, power, etc.), and requires a large amount of computational overhead. And it requires multi-dimensional channel data in the full frequency band (sub-6GHz and millimeter waves), and relies on high-precision measurement equipment (such as USRP) or complex ray tracing simulations, with high hardware and software costs. Summary of the Invention

[0005] In order to solve the problems existing in the above-mentioned prior art, the present invention proposes a method and system for switching UAV base stations based on the maximum average signal-to-noise ratio.

[0006] The technical solution of the present invention is as follows:

[0007] On the one hand, the present invention provides a UAV base station handover method based on the maximum average signal-to-noise ratio, comprising the following steps:

[0008] Obtain the location information of each base station, the location information of the UAV, and the flight direction vector of the UAV;

[0009] Calculate the Euclidean distance between the UAV and the base station;

[0010] Based on the Euclidean distance, calculate the large-scale fading between the UAV and the base station, and obtain the power expectation of the small-scale fading between the UAV and the base station through the Rice distribution combined with the expectation approximation algorithm;

[0011] Calculate the instantaneous signal-to-noise ratio and the average signal-to-noise ratio between the UAV and the base station according to the large-scale fading and the power expectation;

[0012] Model the handover problem between the UAV and the base station as an objective function with the maximum average signal-to-noise ratio as the objective, and obtain the base station with the maximum average signal-to-noise ratio of the UAV as the target base station for handover through the objective function.

[0013] As a preferred embodiment, let the number of base stations be , and the location information of the th base station is expressed as:

[0014] ;

[0015] ;

[0016] where represents the location information of the th base station, represents the longitude of the th base station, represents the latitude of the th base station, represents the height difference between the th base station and the ground;

[0017] Let a time interval be , and the starting moment of a time interval is , The location information of the UAV at the moment

[0018] ;

[0019] where represents the location information of the UAV at the moment , represents the longitude of the UAV at the moment , represents the latitude of the UAV at the moment The latitude of the UAV at a moment, It means that at the height difference between the UAV in flight and the ground;

[0020] The flight direction vector of the UAV at a moment is expressed as:

[0021] ;

[0022] Among them, It means the flight direction vector of the UAV at a moment, It means the axis of the flight direction vector of the UAV at a moment in the three-dimensional coordinate, It means the axis of the flight direction vector of the UAV at a moment in the three-dimensional coordinate, It means the axis of the flight direction vector of the UAV at a moment in the three-dimensional coordinate.

[0023] As a preferred embodiment, let any moment of a time interval be The position information of the UAV at any moment is expressed as:

[0024] ;

[0025] Among them, It means the longitude of the UAV at any moment , It means the latitude of the UAV at any moment , It means the height difference between the UAV in flight and the ground at any moment ;

[0026] Calculate the Euclidean distance between the UAV at any moment and the th base station:

[0027] ;

[0028] Among them, It means the Euclidean distance between the UAV at any moment and the th base station.

[0029] As a preferred embodiment, the calculation method of the large-scale fading is:

[0030] ;

[0031] Among them, represents the carrier frequency, represents the maximum value function, represents at any moment the large-scale fading between the UAV and the th base station;

[0032] Let the Rice factor of the Rice distribution be , and the small-scale fading amplitude between the UAV and the th base station is characterized by the Rice distribution:

[0033] ;

[0034] Among them, represents the modified Bessel function of the first kind of order zero, represents the probability density function, represents the average power of the non-line-of-sight multipath channel between the UAV and the th base station;

[0035] Use the expectation approximation algorithm to obtain the power expectation of the small-scale fading between the UAV and the th base station at any moment :

[0036] ;

[0037] Among them, represents the expectation approximation function.

[0038] As a preferred embodiment, the calculation method of the instantaneous signal-to-noise ratio is:

[0039] Calculate the wireless channel gain between the UAV and the base station:

[0040] ;

[0041] Among them, represents the wireless channel gain between the UAV and the th base station at any moment ;

[0042] Calculate the instantaneous signal-to-noise ratio between the UAV and the th base station according to the wireless channel gain:

[0043] ;

[0044] Substitute the power expectation of the small-scale fading in the instantaneous signal-to-noise ratio calculation method:

[0045] ;

[0046] Wherein, represents the instantaneous signal-to-noise ratio between the UAV and the th base station at any moment, represents the transmit power of the UAV, represents the noise power; Based on the instantaneous signal-to-noise ratio, calculate the average signal-to-noise ratio between the UAV and the

[0047] th base station within a time interval: ; th base station:

[0048] ;

[0049] Wherein, represents the average signal-to-noise ratio between the UAV and the th base station within a time interval. As a preferred embodiment, the objective function is expressed as:

[0050] ;

[0051] ;

[0052] ;

[0053] Wherein, represents the constraint condition, represents the base station with the maximum average signal-to-noise ratio of the UAV, represents obtaining the th base station with the maximum average signal-to-noise ratio.

[0054] On the other hand, the present invention also provides a UAV base station handover system based on the maximum average signal-to-noise ratio, including:

[0055] Information acquisition module: Obtain the position information of each base station, the position information of the UAV, and the flight direction vector of the UAV, and calculate the Euclidean distance between the UAV and the base station;

[0056] Large-scale fading module: Calculate the large-scale fading between the UAV and the base station based on the Euclidean distance, and obtain the power expectation of the small-scale fading between the UAV and the base station through the Rice distribution combined with the expectation approximation algorithm;

[0057] Signal-to-noise ratio module: Calculate the instantaneous signal-to-noise ratio and the average signal-to-noise ratio between the UAV and the base station according to the large-scale fading and the power expectation;

[0058] Modeling module: The handover problem between the UAV and the base station is modeled as an objective function with the maximum average signal-to-noise ratio as the objective, and the base station with the maximum average signal-to-noise ratio of the UAV is obtained through the objective function as the target base station for handover.

[0059] As a preferred embodiment, let the number of base stations be , and the location information of the th base station is expressed as:

[0060] ;

[0061] ;

[0062] Among them, represents the location information of the th base station, represents the longitude of the th base station, represents the latitude of the th base station, represents the height difference between the th base station and the ground;

[0063] Let a time interval be , and the start time of a time interval is , The location information of the UAV at time

[0064] ;

[0065] Among them, represents the location information of the UAV at time, represents the longitude of the UAV at time, represents the latitude of the UAV at time, represents the height difference between the UAV and the ground during flight at time;

[0066] The flight direction vector of the UAV at time

[0067] ;

[0068] Among them, represents the flight direction vector of the UAV at time, represents the axis of the flight direction vector of the UAV at time in the three-dimensional coordinate, represents the The flight direction vector of the drone at a moment in the three-dimensional coordinate axis, represents the flight direction vector of the drone at a moment in the three-dimensional coordinate axis.

[0069] As a preferred embodiment, let any moment in a time interval be , and the position information of the drone at any moment is expressed as:

[0070] ;

[0071] wherein, represents the longitude of the drone at any moment , represents the latitude of the drone at any moment , represents the height difference between the drone and the ground during flight at any moment ;

[0072] Calculate the Euclidean distance between the drone and the th base station at any moment :

[0073] ;

[0074] wherein, represents the Euclidean distance between the drone and the th base station at any moment .

[0075] As a preferred embodiment, the calculation method of the large-scale fading is:

[0076] ;

[0077] wherein, represents the carrier frequency, represents the maximum value function, represents the large-scale fading between the drone and the th base station at any moment ;

[0078] Let the Rice factor of the Rice distribution be , and the small-scale fading amplitude of the drone and the th base station is characterized by the Rice distribution :

[0079] ;

[0080] wherein, denotes the modified Bessel function of the zeroeth order, denotes the probability density function, denotes the average power of the non-line-of-sight multipath channel between the UAV and the th base station;

[0081] The expected approximation algorithm is used to obtain the power expectation of the small-scale fading between the UAV and the th base station at any moment: where :

[0082] ;

[0083] wherein, denotes the expected approximation function.

[0084] The present invention has the following beneficial effects:

[0085] By introducing the average signal-to-noise ratio (instead of the instantaneous signal-to-noise ratio) as the handover decision basis, the present invention effectively alleviates the problem of frequent base station handovers caused by instantaneous signal fluctuations, reduces the handover delay and the risk of communication interruption. Through the comprehensive analysis of large-scale fading (path loss) and small-scale fading (modeled by the Rice distribution), the present invention dynamically predicts the change trend of the channel quality in the future period, enhancing the communication stability of the UAV in high-speed movement or complex environments. By obtaining the flight direction vector and position information of the UAV in real time, and combining the trajectory prediction within the time interval, the present invention optimizes the handover timing and target selection of the base station, improving the adaptability to dynamic flight scenarios. With the maximum average signal-to-noise ratio as the optimization target, the present invention preferentially selects the base station with the best signal quality, ensuring that the UAV always maintains a highly reliable and low-latency network connection during the mission execution. The present invention uses the expected approximation algorithm to handle the randomness of small-scale fading, achieving a fast estimation of the channel power expectation with reasonable computational overhead, enhancing the real-time performance of the algorithm and being applicable to resource-constrained UAV platforms. BRIEF DESCRIPTION OF THE DRAWINGS

[0086] Figure 1 is a flowchart of the implementation of the method of the present invention.

[0087] Figure 2 is a schematic diagram of the application scenario of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0088] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0089] It should be understood that the step numbers used herein are only for convenience of description and do not limit the order of execution of the steps.

[0090] It should be understood that the terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include the plural forms.

[0091] The terms "comprising" and "including" indicate the presence of the described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or their combinations.

[0092] The term " / and" refers to any combination and all possible combinations of one or more of the associated listed items and includes these combinations.

[0093] Embodiment 1:

[0094] See Figure 1 , the invention provides a method for switching an unmanned aerial vehicle base station based on the maximum average signal-to-noise ratio, including the following steps:

[0095] Obtain the location information of each base station, the location information of the unmanned aerial vehicle, and the flight direction vector of the unmanned aerial vehicle;

[0096] Calculate the Euclidean distance between the unmanned aerial vehicle and the base station;

[0097] Based on the Euclidean distance, calculate the large-scale fading between the unmanned aerial vehicle and the base station, and obtain the power expectation of the small-scale fading between the unmanned aerial vehicle and the base station through the Rice distribution combined with the expectation approximation algorithm;

[0098] Calculate the instantaneous signal-to-noise ratio and the average signal-to-noise ratio between the unmanned aerial vehicle and the base station according to the large-scale fading and the power expectation;

[0099] Model the handover problem between the unmanned aerial vehicle and the base station as an objective function with the maximum average signal-to-noise ratio as the target, and obtain the base station with the maximum average signal-to-noise ratio of the unmanned aerial vehicle as the target base station through the objective function for handover.

[0100] See Figure 2 , as a preferred embodiment, let the number of base stations be , the position information of the th base station is expressed as:

[0101] ;

[0102] ;

[0103] Among them, represents the location information of the th base station, represents the longitude of the th base station, represents the latitude of the th base station, represents the height difference between the th base station and the ground;

[0104] The drone obtains its own real-time location information through a positioning system (such as GPS positioning or other navigation means);

[0105] Let a time interval be , and the moment when a time interval starts is , The location information of the drone at time

[0106] ;

[0107] Among them, represents the location information of the drone at time, represents the longitude of the drone at time, represents the latitude of the drone at time, represents the height difference between the drone's flight and the ground at time;

[0108] The flight direction vector of the drone at time

[0109] ;

[0110] Among them, represents the flight direction vector of the drone at represents the axis of the flight direction vector of the drone at in the three-dimensional coordinate, represents the axis of the flight direction vector of the drone at in the three-dimensional coordinate, axis.

[0111] As a preferred implementation manner, let any moment of a time interval be , the drone at any moment The position information is expressed as:

[0112] ;

[0113] wherein, represents the longitude of the UAV at any moment and represents the latitude of the UAV at any moment and represents the height difference between the UAV and the ground during flight;

[0114] Calculate the Euclidean distance between the UAV and the th base station at any moment:

[0115] ;

[0116] wherein, represents the Euclidean distance between the UAV and the th base station at any moment.

[0117] Since the flight trajectory of the UAV is known within a time interval , the distances between the UAV and each base station can be accurately calculated, and thus the average value of the large-scale fading can be accurately obtained. However, the small-scale fading is random and difficult to accurately predict, so we use an ideal approximation to approximate it.

[0118] As a preferred embodiment, the calculation method of the large-scale fading is:

[0119] ;

[0120] wherein, represents the carrier frequency, represents the maximum value function, represents the large-scale fading between the UAV and the th base station at any moment;

[0121] Let the Rice factor of the Rice distribution be , and the value of the Rice factor is related to the flight environment of the UAV, the flight altitude of the UAV, and the distance from the base station. The value range is 5 - 20 dB in an open environment and 0 - 10 dB in an urban environment.

[0122] Characterize the small-scale fading amplitude between the UAV and the th

[0123] ;

[0124] Among them, represents the modified Bessel function of the 0th order, represents the probability density function, represents the average power of the non-line-of-sight multipath channel between the UAV and the th base station, estimated empirically;

[0125] Use the expected approximation algorithm to obtain the small-scale fading between the UAV and the th base station at any time power expectation , the power expectation is an ideal approximation value of the square of the small-scale fading :

[0126] ;

[0127] Among them, represents the expected approximation function.

[0128] The wireless channel gain between the UAV and the th base station is mainly composed of large-scale fading and small-scale fading . The large-scale fading is the power loss caused by path loss and shadow fading during the signal propagation process, and the path loss increases with the increase of the distance between the terminal and the base station. The small-scale fading reflects the serious fading of the signal at the receiving end due to the randomness of the phase caused by the transmission through multiple paths in a short distance or a short time.

[0129] As a preferred embodiment, the calculation method of the instantaneous signal-to-noise ratio is:

[0130] Calculate the wireless channel gain between the UAV and the base station:

[0131] ;

[0132] Among them, represents the wireless channel gain between the UAV and the th base station at any time ;

[0133] Calculate the instantaneous signal-to-noise ratio between the UAV and the th base station according to the wireless channel gain:

[0134] ;

[0135] Use the power expectation of small-scale fading in the instantaneous signal-to-noise ratio calculation method Substitute into:

[0136] ;

[0137] Among them, Represents the instantaneous signal-to-noise ratio between the UAV and the th base station at any moment ; Represents the transmission power of the UAV, Represents the noise power, and the value range according to the environment is 10 -15 -10 -13 W;

[0138] Based on the instantaneous signal-to-noise ratio, calculate the average signal-to-noise ratio between the UAV and the th base station in a time interval: ;

[0139] ;

[0140] Substitute the instantaneous signal-to-noise ratio into the original formula:

[0141] ;

[0142] Use the power expectation of small-scale fading Substitute into:

[0143] ;

[0144] Among them, Represents the average signal-to-noise ratio between the UAV and the th base station in a time interval ;

[0145] As a preferred embodiment, the objective function is expressed as:

[0146] ;

[0147] ;

[0148] Among them, Represents the constraint condition, Represents the base station with the maximum average signal-to-noise ratio of the UAV, Represents obtaining the th base station with the maximum average signal-to-noise ratio

[0149] Example 2:

[0150] The present invention also provides a UAV base station handover system based on the maximum average signal-to-noise ratio, including:

[0151] Information acquisition module: Obtain the location information of each base station, the location information of the UAV, and the flight direction vector of the UAV, and calculate the Euclidean distance between the UAV and the base station;

[0152] Scale fading module: Calculate the large-scale fading between the UAV and the base station based on the Euclidean distance, and obtain the power expectation of the small-scale fading between the UAV and the base station through the Rice distribution combined with the expectation approximation algorithm;

[0153] Signal-to-noise ratio module: Calculate the instantaneous signal-to-noise ratio and the average signal-to-noise ratio between the UAV and the base station according to the large-scale fading and the power expectation;

[0154] Modeling module: Model the handover problem between the UAV and the base station as an objective function with the maximum average signal-to-noise ratio as the target, and obtain the base station with the maximum average signal-to-noise ratio of the UAV as the target base station for handover through the objective function.

[0155] As a preferred embodiment, let the number of base stations be , and the location information of the th base station is expressed as:

[0156] ;

[0157] ;

[0158] Among them, represents the location information of the th base station, represents the longitude of the th base station, represents the latitude of the th base station, represents the height difference between the th base station and the ground;

[0159] Let a time interval be , and the start time of a time interval is . The location information of the UAV at time is expressed as:

[0160] ;

[0161] Among them, represents the location information of the UAV at time , represents the longitude of the UAV at time , represents the latitude of the UAV at time , represents the height of the UAV at time The height difference between the UAV and the ground during flight at a certain moment;

[0162] The flight direction vector of the UAV at a certain moment is expressed as:

[0163] ;

[0164] Among them, represents the flight direction vector of the UAV at a certain moment, represents the axis of the flight direction vector of the UAV at a certain moment in the three-dimensional coordinate, represents the axis of the flight direction vector of the UAV at a certain moment in the three-dimensional coordinate, represents the axis of the flight direction vector of the UAV at a certain moment in the three-dimensional coordinate.

[0165] As a preferred embodiment, let any moment within a time interval be , and the position information of the UAV at any moment is expressed as:

[0166] ;

[0167] Among them, represents the longitude of the UAV at any moment , represents the latitude of the UAV at any moment , represents the height difference between the UAV and the ground during flight at any moment ;

[0168] Calculate the Euclidean distance between the UAV and the th base station at any moment:

[0169] ;

[0170] Among them, represents the Euclidean distance between the UAV and the th base station at any moment

[0171] As a preferred embodiment, the calculation method of the large-scale fading is:

[0172] ;

[0173] Among them, represents the carrier frequency, represents the maximum value function, represents at any moment The large-scale fading between the UAV and the th base station;

[0174] Let the Rice factor of the Rice distribution be , and characterize the small-scale fading amplitude between the UAV and the th base station through the Rice distribution :

[0175] ;

[0176] Among them, represents the modified Bessel function of the first kind of order zero, represents the probability density function, represents the average power of the non-line-of-sight multipath channel between the UAV and the th base station, represents the exponential function;

[0177] Use the expectation approximation algorithm to obtain the power expectation of the small-scale fading between the UAV and the th base station at any moment :

[0178] ;

[0179] Among them, represents the expectation approximation function.

[0180] In the embodiments of the present application, "at least one" means one or more, and "a plurality" means two or more. "And / or" describes the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent the situation where A exists alone, A and B exist simultaneously, and B exists alone. Among them, A and B can be singular or plural. The character " / " generally represents an "or" relationship between the front and back associated objects. "At least one of the following" and its similar expressions refer to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, and c can represent: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, and c can be single or multiple.

[0181] Those of ordinary skill in the art can realize that the various units and algorithm steps described in the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. A professional technician can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0182] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0183] In several embodiments provided by this application, if any function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (hereinafter referred to as ROM), random access memories (hereinafter referred to as RAM), magnetic disks, or optical discs that can store program codes.

[0184] The above are only the embodiments of the present invention, and do not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be similarly included in the patent protection scope of the present invention.

Claims

1. A method for switching of an unmanned aerial vehicle base station based on the maximum average signal-to-noise ratio, characterized in that It includes the following steps: Obtain the location information of each base station, the location information of the drone, and the flight direction vector of the drone; Calculate the Euclidean distance between the drone and the base station; Calculate the large-scale fading between the drone and the base station based on the Euclidean distance, which is expressed by the formula: ； Among them, represents the carrier frequency, represents the maximum value function, represents at any moment the large-scale fading between the UAV and the th base station, represents at any moment the Euclidean distance between the UAV and the th base station, represents at any moment the height difference between the UAV and the ground during flight; Obtain the power expectation of the small-scale fading between the drone and the base station through the Rice distribution combined with the expectation approximation algorithm. The specific steps are as follows: Let the Rice factor of the Rice distribution be , and the small-scale fading amplitude between the UAV and the -th base station is characterized by the Rice distribution : ； Among them, represents the modified zero-order Bessel function, represents the probability density function, represents the average power of the non-line-of-sight multipath channel between the UAV and the th base station; Obtain at any time using the expected approximation algorithm The small-scale fading of the drone and the th base station power expectation : ； Among them, represents the expected approximation function; Calculate the instantaneous signal-to-noise ratio and the average signal-to-noise ratio between the drone and the base station according to the large-scale fading and the power expectation. The specific steps are as follows: Calculate the wireless channel gain between the drone and the base station: ； Among them, represents the wireless channel gain between the UAV and the th base station at any moment; Calculate the instantaneous signal-to-noise ratio between the UAV and the th base station according to the wireless channel gain: ； Substitute the power expectation of small-scale fading in the instantaneous signal-to-noise ratio calculation method into: ； Among them, represents the instantaneous signal-to-noise ratio between the UAV and the th base station at any moment, where represents the transmission power of the UAV, represents the noise power; Based on the instantaneous signal-to-noise ratio calculation over a time interval The average signal-to-noise ratio between the drone and the th base station: ； Among them, represents the average signal-to-noise ratio of the UAV and the th base station within a time interval; Model the handover problem between the drone and the base station as an objective function with the maximum average signal-to-noise ratio as the target, and obtain the base station with the maximum average signal-to-noise ratio with the drone as the target base station for handover through the objective function.

2. The method for switching an unmanned aerial vehicle base station based on the maximum average signal-to-noise ratio according to claim 1, wherein Let the number of base stations be , and the location information of the -th base station is represented as: ； ； Among them, represents the location information of the th base station, represents the longitude of the th base station, represents the latitude of the th base station, represents the height difference between the th base station and the ground; Let a time interval be , and the moment when a time interval starts be , The position information of the drone at the moment is expressed as: ； Among them, represents the position information of the drone at moment, represents the longitude of the drone at moment, represents the latitude of the drone at moment, represents the height difference between the drone in flight and the ground at moment; The flight direction vector of the UAV at a certain moment is expressed as: ； Among them, represents the flight direction vector of the drone at time represents the axis of the flight direction vector of the drone at time in the three-dimensional coordinate system, represents the axis of the flight direction vector of the drone at time in the three-dimensional coordinate system, axis.

3. The method for switching an unmanned aerial vehicle base station based on the maximum average signal-to-noise ratio according to claim 2, wherein Let any moment within a time interval be , and the position information of the drone at any moment is represented as: ​ ； Among them, represents the longitude of the UAV at any moment and represents the latitude of the UAV at any moment ; Calculate at any moment The Euclidean distance between the UAV and the th base station: 。 4. The method for switching an unmanned aerial vehicle base station based on the maximum average signal-to-noise ratio according to claim 1, wherein The objective function is expressed as: ； ； Among them, represents a constraint condition, represents the base station with the maximum average signal-to-noise ratio of the UAV, represents obtaining the th base station with the maximum average signal-to-noise ratio.

5. A drone base station handover system based on the maximum average signal-to-noise ratio, characterized in that, It includes: Information acquisition module: Obtain the location information of each base station, the location information of the drone, and the flight direction vector of the drone, and calculate the Euclidean distance between the drone and the base station; Scale fading module: Calculate the large-scale fading between the drone and the base station based on the Euclidean distance, which is expressed by the formula: ； Among them, represents the carrier frequency, represents the maximum value function, represents at any moment the large-scale fading between the UAV and the th base station, represents at any moment the Euclidean distance between the UAV and the th base station, represents at any moment the height difference between the UAV in flight and the ground; Obtain the power expectation of the small-scale fading between the drone and the base station through the Rice distribution combined with the expectation approximation algorithm. The specific steps are as follows: Let the Rice factor of the Rice distribution be , and the small-scale fading amplitude of the UAV and the -th base station is characterized by the Rice distribution : ； Among them, represents the modified zero-order Bessel function, represents the probability density function, represents the average power of the non-line-of-sight multipath channel between the drone and the th base station; Obtain at any time using the expectation approximation algorithm The small-scale fading of the UAV and the th base station Power expectation of : ； Among them, represents the expected approximation function; Signal-to-noise ratio module: Calculate the instantaneous signal-to-noise ratio and the average signal-to-noise ratio between the drone and the base station according to the large-scale fading and the power expectation. The specific steps are as follows: Calculate the wireless channel gain between the drone and the base station: ； Among them, represents the wireless channel gain between the UAV and the th base station at any moment; Calculate the instantaneous signal-to-noise ratio of the UAV and the th base station according to the wireless channel gain: ； Use the power expectation of small-scale fading in the instantaneous signal-to-noise ratio calculation method Substitute into: ； Among them, represents the instantaneous signal-to-noise ratio between the UAV and the nth base station at any moment, represents the transmission power of the UAV, and represents the noise power; Based on the instantaneous signal-to-noise ratio calculation over a time interval The average signal-to-noise ratio between the drone and the th base station: ； Among them, represents the average signal-to-noise ratio of the UAV and the th base station within a time interval; Modeling module: Model the handover problem between the drone and the base station as an objective function with the maximum average signal-to-noise ratio as the target, and obtain the base station with the maximum average signal-to-noise ratio with the drone as the target base station for handover through the objective function.

6. The drone base station handover system based on the maximum average signal-to-noise ratio according to claim 5, wherein, Let the number of base stations be , and the location information of the -th base station is represented as: ； ； Among them, represents the location information of the th base station, represents the longitude of the th base station, represents the latitude of the th base station, represents the height difference between the th base station and the ground; Let a time interval be , and the moment when a time interval starts be , The position information of the drone at the moment is expressed as: ； Among them, represents the position information of the drone at moment, represents the longitude of the drone at moment, represents the latitude of the drone at moment, represents the height difference between the drone in flight and the ground at moment; The flight direction vector of the drone at a moment is expressed as: ； Among them, represents the flight direction vector of the drone at time represents the axis of the flight direction vector of the drone at time in the three-dimensional coordinate system, represents the axis of the flight direction vector of the drone at time in the three-dimensional coordinate system, axis.

7. The drone base station handover system based on the maximum average signal-to-noise ratio according to claim 6, wherein Let any moment within a time interval be , and the position information of the drone at any moment is represented as: ​ ； Among them, represents the longitude of the UAV at any moment and represents the latitude of the UAV at any moment ; Calculate at any moment The Euclidean distance between the UAV and the th base station: 。

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