Distributed unmanned aerial vehicle base station wireless backhaul resource allocation method, device, equipment and medium

Abstract

The invention discloses a distributed unmanned aerial vehicle base station wireless backhaul resource allocation method and device, equipment and a medium, and relates to the technical field of wireless communication, and the method comprises the steps: determining a corresponding access transmission rate and a backhaul transmission rate based on the deployment positions and flight periods of a macro base station and an unmanned aerial vehicle base station, and a preset frequency resource allocation proportion; constructing a wireless backhaul resource allocation problem based on a plurality of constraint conditions, the access transmission rate and the backhaul transmission rate; converting the wireless backhaul resource allocation problem based on a preset slack variable and a preset data reconstruction method to obtain a post-conversion optimization problem; performing distributed disassembly on the converted optimization problem based on signal interference among the unmanned aerial vehicle base stations to obtain a target optimization problem; and iteratively updating the target optimization problem based on a preset variable updating method until a target data transmission rate is obtained, so as to determine a resource allocation strategy during wireless backhaul. In this way, the network parallel processing capacity can be improved by optimizing communication network resource allocation.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technologies, and particularly to a method, device, equipment, and medium for wireless backhaul resource allocation of distributed unmanned aerial vehicle (UAV) base stations. Background Art

[0002] As a key element of 5G (5th Generation Mobile Communication Technology), UAV base stations can be flexibly and quickly deployed to areas such as disaster-stricken areas, remote mountainous areas, and temporary event venues that cannot be covered by ground base stations, providing emergency and temporary communication. They also have the advantages of relatively low construction costs, a wide coverage area that is not restricted by the geographical environment, the ability to flexibly adjust positions to enhance capacity to relieve network congestion, the ability to quickly respond in emergency situations to ensure communication, relatively simple maintenance, and the ability to adapt to temporary communication requirements such as large gatherings.

[0003] However, the link transmission for wireless backhaul to UAV base stations brings more challenging problems. First, there is inevitably limited link capacity when using limited spectrum resources for wireless backhaul, which restricts the transmission capacity through wireless backhaul. Second, existing research or methods on wireless backhaul of UAV base stations mostly adopt a centralized processing method, where the calculations and decisions in the communication network are concentrated in the central node, which may lead to an overloaded transmission load on this node, easily resulting in communication delays and congestion, and becoming a bottleneck in system performance. How to increase the network parallel processing ability while considering the limited capacity of wireless backhaul of UAV base stations, and improve the overall processing efficiency and response speed of the system is a problem that needs to be solved. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide a method, device, equipment, and medium for wireless backhaul resource allocation of distributed UAV base stations, which can improve the overall processing efficiency and response speed of the communication system. The specific solutions are as follows:

[0005] In a first aspect, the present application discloses a method for wireless backhaul resource allocation of a distributed UAV base station, including:

[0006] Based on the first deployment position of the macro base station, the second deployment position and flight period of the UAV base station, and a preset frequency resource allocation ratio, determine the corresponding access transmission rate and backhaul transmission rate when performing wireless backhaul between the macro base station and the UAV base station;

[0007] Construct a number of constraint conditions, and construct a wireless backhaul resource allocation problem based on the number of constraint conditions, the access transmission rate, and the backhaul transmission rate;

[0008] Transform the wireless backhaul resource allocation problem based on a preset slack variable and a preset data reconstruction method to obtain a transformed optimization problem;

[0009] Perform distributed decomposition on the transformed optimization problem based on the signal interference between the UAV base stations to obtain an objective optimization problem composed of global space resource variables and local space resource variables corresponding to each UAV base station;

[0010] Perform iterative update on the objective optimization problem based on a preset variable update method until the local space resource variables are equal to the global space resource variables to obtain an objective data transmission rate, so as to determine a resource allocation strategy for wireless backhaul between the macro base station and the UAV base station based on the objective data transmission rate.

[0011] Optionally, determining the corresponding access transmission rate and backhaul transmission rate for wireless backhaul between the macro base station and the UAV base station based on the first deployment position of the macro base station, the second deployment position of the UAV base station and the flight period, and a preset frequency resource allocation ratio includes:

[0012] Determine the first network frequency resource of the UAV base station in the access transmission stage and the second network frequency resource in the backhaul transmission stage based on a preset network resource allocation ratio, and determine the base station frequency resource provided by the macro base station in the access transmission stage;

[0013] Determine the first channel gain of the macro base station based on the first deployment position of the macro base station, and determine the first transmission signal-to-interference-plus-noise ratio of the macro base station based on the first channel gain and a first preset signal-to-interference-plus-noise ratio model;

[0014] Input the first transmission signal-to-interference-plus-noise ratio and the base station frequency resource into a preset access transmission rate model to obtain a first access transmission rate, and input the second network frequency resource and the first channel gain into a preset backhaul transmission rate model to obtain a backhaul transmission rate;

[0015] Determine the second channel gain of the UAV base station based on the second deployment position and the flight period of the UAV base station, and input the second channel gain into a second preset signal-to-interference-plus-noise ratio model to obtain the second transmission signal-to-interference-plus-noise ratio of the UAV base station;

[0016] Input the first network frequency resource and the second transmission signal-to-interference-plus-noise ratio into the preset access transmission rate model to obtain a second access transmission rate.

[0017] Optionally, constructing a number of constraint conditions and constructing a wireless backhaul resource allocation problem based on the number of constraint conditions, the access transmission rate and the backhaul transmission rate includes:

[0018] Construct a number of constraint conditions based on the first channel gain and the second network frequency resource;

[0019] Construct a wireless backhaul resource allocation problem based on the number of constraint conditions, the first access transmission rate, the second access transmission rate, and the backhaul transmission rate.

[0020] Optionally, the constructing a number of constraint conditions based on the first channel gain and the second network frequency resource includes:

[0021] Define the transmission capacity during wireless backhaul of the drone base station based on the first access transmission rate, the backhaul transmission rate, and the first channel gain to obtain a first constraint condition;

[0022] Define the moving distance and flight speed of the drone base station during the flight cycle to obtain a second constraint condition, and define the value range of the second network frequency resource to obtain a third constraint condition.

[0023] Optionally, the transforming the wireless backhaul resource allocation problem based on a preset slack variable and a preset data reconstruction method to obtain a transformed optimization problem includes:

[0024] Introduce a preset slack variable to reconstruct the wireless backhaul resource allocation problem to obtain a reconstructed optimization problem;

[0025] Perform convex optimization transformation on the objective constraint condition in the reconstructed optimization problem by using the first-order Taylor approximation method to obtain a transformed optimization problem; the transformed optimization problem is an approximate convex optimization problem.

[0026] Optionally, the distributed decomposition of the transformed optimization problem based on the signal interference between the drone base stations to obtain an objective optimization problem composed of global spatial resource variables and local spatial resource variables corresponding to each drone base station includes:

[0027] Determine the first signal interference of the drone base station on other drone base stations and the second signal interference of the other drone base stations on the drone base station, and determine the global spatial resource variables and the local spatial resource variables corresponding to each drone base station based on the first signal interference and the second signal interference;

[0028] Decompose and transform a number of constraint conditions in the transformed optimization problem based on the global spatial resource variables and the local spatial resource variables to obtain transformed constraint conditions, and construct an objective optimization problem based on the transformed constraint conditions, the global spatial resource variables, and the local spatial resource variables.

[0029] Optionally, the target optimization problem is iteratively updated based on the preset variable update method until the local space resource variable is equal to the global space resource variable to obtain the target data transmission rate, so as to determine the resource allocation strategy for wireless backhaul between the macro base station and the drone base station based on the target data transmission rate, including:

[0030] Convert the target optimization problem into a current Lagrangian function, and update the current local space resource variable in the current Lagrangian function based on the primal-dual interior point method to obtain the updated local space variable;

[0031] Update the current global space resource variable in the current Lagrangian function based on the updated local space variable to obtain the updated global space variable, and update the Lagrange multiplier of the current Lagrangian function based on the updated local space variable and the updated global space variable to obtain the updated Lagrangian function;

[0032] Determine whether the updated global space variable is equal to the updated local space variable;

[0033] If the updated global space variable is equal to the updated local space variable, determine the target data transmission rate based on the updated Lagrangian function;

[0034] If the updated global space variable is not equal to the updated local space variable, determine the updated local space variable as the new current local space resource variable, determine the updated global space variable as the new current global space resource variable, and determine the updated Lagrangian function as the new current Lagrangian function, and then jump to the step of updating the current local space resource variable in the current Lagrangian function based on the primal-dual interior point method to obtain the updated local space variable.

[0035] In a second aspect, the present application discloses a distributed drone base station wireless backhaul resource allocation device, including:

[0036] A transmission rate determination module, configured to determine the access transmission rate and the backhaul transmission rate corresponding to wireless backhaul between the macro base station and the drone base station based on the first deployment position of the macro base station, the second deployment position of the drone base station, the flight period, and the preset frequency resource allocation ratio;

[0037] An allocation problem generation module, configured to construct a plurality of constraint conditions based on the access transmission rate and the backhaul transmission rate, and construct a wireless backhaul resource allocation problem based on the plurality of constraint conditions and the access transmission rate;

[0038] An allocation problem transformation module, configured to transform the wireless backhaul resource allocation problem based on a preset slack variable and a preset data reconstruction method to obtain a transformed optimization problem;

[0039] An allocation problem decomposition module, configured to perform distributed decomposition on the transformed optimization problem based on signal interference between the UAV base stations, so as to obtain an objective optimization problem composed of global space resource variables and local space resource variables corresponding to each UAV base station;

[0040] A resource allocation strategy determination module, configured to perform iterative update on the objective optimization problem based on a preset variable update method until the local space resource variables are equal to the global space resource variables to obtain an objective data transmission rate, so as to determine a resource allocation strategy for wireless backhaul between the macro base station and the UAV base station based on the objective data transmission rate.

[0041] In a third aspect, the present application discloses an electronic device, including:

[0042] A memory, configured to store a computer program;

[0043] A processor, configured to execute the computer program to implement the foregoing distributed UAV base station wireless backhaul resource allocation method.

[0044] In a fourth aspect, the present application discloses a computer-readable storage medium, configured to store a computer program, where the computer program, when executed by a processor, implements the foregoing distributed UAV base station wireless backhaul resource allocation method.

[0045] It can be seen that in this application, based on the first deployment location of the macro base station, the second deployment location and flight cycle of the drone base station, and the preset frequency resource allocation ratio, the corresponding access transmission rate and backhaul transmission rate during wireless backhaul between the macro base station and the drone base station are determined; a number of constraint conditions are constructed, and a wireless backhaul resource allocation problem is constructed based on the number of constraint conditions, the access transmission rate, and the backhaul transmission rate; the wireless backhaul resource allocation problem is transformed based on a preset slack variable and a preset data reconstruction method to obtain a transformed optimization problem; the transformed optimization problem is disassembled distributively based on the signal interference between the drone base stations to obtain an objective optimization problem composed of global space resource variables and local space resource variables corresponding to each drone base station; the objective optimization problem is iteratively updated based on a preset variable update method until the local space resource variables are equal to the global space resource variables to obtain an objective data transmission rate, so as to determine the resource allocation strategy during wireless backhaul between the macro base station and the drone base station. That is, by considering the transmission rate during wireless backhaul between the macro base station and the drone base station, a corresponding optimization function is constructed, and when optimizing and solving the optimization function, the rate optimization problem is transformed into a communication network resource allocation problem through the signal interference problem between the micro base stations, and then the problem is optimized to obtain the corresponding objective data transmission rate, and finally the resource allocation strategy during wireless backhaul between the macro base station and the drone base station is determined according to the objective data transmission rate. In this way, while considering the limited wireless backhaul capacity of the drone base station, the communication network resource allocation is optimized in a distributed manner to increase the network parallel processing ability and improve the overall processing efficiency and response speed of the system. Description of the Drawings

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained according to the provided drawings without creative efforts.

[0047] Figure 1 Flowchart of a distributed drone base station wireless backhaul resource allocation method disclosed in this application;

[0048] Figure 2 Schematic diagram of a drone base station network model disclosed in this application;

[0049] Figure 3 Schematic diagram of the structure of a distributed drone base station wireless backhaul resource allocation device disclosed in this application;

[0050] Figure 4 A structural diagram of an electronic device disclosed in this application. Specific implementation manners

[0051] Next, the technical solutions in the embodiments of this application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of this application.

[0052] Currently, the link transmission for wireless backhaul to the drone base station brings more challenging problems. First, the use of limited spectrum resources for wireless backhaul inevitably results in limited link capacity, which restricts the transmission capacity through wireless backhaul. Second, existing research or methods on wireless backhaul of drone base stations mostly adopt a centralized processing method, where the calculations and decisions in the communication network are concentrated on the central node, which may lead to an overloaded transmission load on this node, easily causing communication delays and congestion, and becoming a bottleneck in system performance. Therefore, this application will specifically introduce a distributed resource allocation method for wireless backhaul of drone base stations, which can improve the overall processing efficiency of the wireless backhaul system.

[0053] See Figure 1 As shown, the embodiments of this application disclose a distributed resource allocation method for wireless backhaul of drone base stations, including:

[0054] Step S11: Determine the corresponding access transmission rate and backhaul transmission rate for wireless backhaul between the macro base station and the drone base station based on the first deployment location of the macro base station, the second deployment location of the drone base station, the flight cycle, and a preset frequency resource allocation ratio.

[0055] In this embodiment, first, as Figure 2 shown, a network model of the drone base station is established, including 1 macro base station and drone base stations. All base stations in the network are represented by the set , where represents the set of drone base stations, and the index number '0' represents the macro base station.

[0056] In this embodiment, determining the access transmission rate and the backhaul transmission rate corresponding to wireless backhaul between the macro base station and the drone base station based on the first deployment position of the macro base station, the second deployment position of the drone base station, the flight cycle, and a preset frequency resource allocation ratio includes: determining, based on the preset network resource allocation ratio, the first network frequency resource of the drone base station in the access transmission stage and the second network frequency resource in the backhaul transmission stage, and determining the base station frequency resource provided by the macro base station in the access transmission stage; determining the first channel gain of the macro base station based on the first deployment position of the macro base station, and determining the first transmission signal-to-interference-plus-noise ratio of the macro base station based on the first channel gain and the first preset signal-to-interference-plus-noise ratio model; inputting the first transmission signal-to-interference-plus-noise ratio and the base station frequency resource into a preset access transmission rate model to obtain a first access transmission rate, and inputting the second network frequency resource and the first channel gain into a preset backhaul transmission rate model to obtain a backhaul transmission rate; determining the second channel gain of the drone base station based on the second deployment position and the flight cycle of the drone base station, inputting the second channel gain into a second preset signal-to-interference-plus-noise ratio model to obtain the second transmission signal-to-interference-plus-noise ratio of the drone base station and inputting the second channel gain into a second preset signal-to-interference-plus-noise ratio model to obtain the second transmission signal-to-interference-plus-noise ratio of the drone base station; and inputting the first network frequency resource and the second transmission signal-to-interference-plus-noise ratio into the preset access transmission rate model to obtain a second access transmission rate.

[0057] In this embodiment, the macro base station provides first-stage access transmission services for macro base station terminal users (referred to as "macro users" for short) within its coverage area and provides second-stage backhaul transmission services for drone base stations. Each of the drone base stations provides first-stage access transmission services for drone base station terminal users (referred to as "micro users" for short) within its coverage area. The first-stage access transmission and the second-stage backhaul transmission share network frequency resources. Define as the frequency resource allocation ratio of the drone base station in the second-stage backhaul transmission, and the remaining frequency resources (the first network frequency resources) are used for the first-stage access transmission of the drone base station . For the macro base station, the frequency resources provided for the second-stage backhaul transmission of the drone base station are (the second network frequency resources), and the frequency resources provided for the first-stage access transmission of the macro users are (the base station frequency resources).

[0058] The macro base station is deployed at a horizontal position (the first deployment position), and the terminal users Deployed in the horizontal position The drone base station Deployed at the same vertical position within time L and different horizontal positions The drone base station Flies at a fixed altitude during each flight cycle above the ground. The flight cycle can be divided into N equal time slots (flight cycles). Considering safety factors such as terrain or obstacle avoidance, should be set as small as possible. Then, the horizontal position of the drone base station in the nth time slot can be expressed as (the second deployment position).

[0059] Subsequently, define as the channel matrix of the drone base station in the network model, where is the channel matrix of the drone base station The channel gain between the drone base station and the micro user k in the nth time slot (the second channel gain) is defined as:

[0060] ;

[0061] where is the reference channel power per unit distance.

[0062] The channel gain between the macro base station and the kth macro user in the tth time slot (the first channel gain) is defined as:

[0063] ;

[0064] where is the reference channel power per unit distance, is the small-scale channel fading between the macro base station and the kth macro user, the precoding vector between the macro base station and the kth macro user.

[0065] The average achievable rate formula (the first access transmission rate) for the first-stage access transmission between the macro base station and the macro user k is defined as:

[0066] ;

[0067] where , the signal-to-interference-plus-noise ratio between the macro base station and the macro user k is defined , is the transmit power of the macro base station, is the Gaussian white noise power.

[0068] Furthermore, the macro base station and the drone base station The formula for the achievable rate (backhaul transmission rate) of the second-stage backhaul transmission is defined as:

[0069] ;

[0070] where represents the capacity of the second-stage backhaul transmission of the drone base station in the t-th time slot, is the channel gain between the macro base station and the drone base station in the t-th time slot.

[0071] The drone base station The formula for the average transmission rate (second access transmission rate) of the first-stage access transmission between the drone base station and the micro user k is defined as:

[0072] ;

[0073] where , the signal-to-interference-plus-noise ratio between the drone base station and the micro user k in the t-th time slot is defined as , where represents the transmission power of the drone base station .

[0074] Step S12: Construct a number of constraint conditions, and construct a wireless backhaul resource allocation problem based on the number of constraint conditions, the access transmission rate, and the backhaul transmission rate.

[0075] In this embodiment, constructing a number of constraint conditions and constructing a wireless backhaul resource allocation problem based on the number of constraint conditions, the access transmission rate, and the backhaul transmission rate includes: constructing a number of constraint conditions based on the first channel gain and the second network frequency resource; constructing a wireless backhaul resource allocation problem based on the number of constraint conditions, the first access transmission rate, the second access transmission rate, and the backhaul transmission rate. Further, constructing a number of constraint conditions based on the first channel gain and the second network frequency resource includes: defining the transmission capacity of the drone base station for wireless backhaul based on the first access transmission rate, the backhaul transmission rate, and the first channel gain to obtain a first constraint condition; defining the moving distance and flight speed of the drone base station during the flight period to obtain a second constraint condition, and defining the value range of the second network frequency resource to obtain a third constraint condition.

[0076] Specifically, the following mathematical model (wireless backhaul resource allocation problem) for the UAV base station resource allocation optimization problem can be established as follows:

[0077] ;

[0078] Among them, the optimization objective C0 is to maximize the average transmission rate of the first-stage access of the network, is defined as the UAV base station trajectory variable vector, is defined as the bandwidth allocation ratio variable vector for the second-stage backhaul transmission; the constraint condition C1 is defined as the downlink transmission capacity of the UAV base station in the second-stage backhaul should not be less than the downlink capacity of its own first-stage access transmission ; the constraint condition C2 is that the moving distance of the UAV base station flight trajectory within the time slot n is less than the distance obtained by multiplying the maximum flight speed V by the time slot n; the constraint condition C3 represents the value range of the frequency resource allocation ratio for the UAV base station in the second-stage backhaul transmission.

[0079] Step S13: Transform the wireless backhaul resource allocation problem based on a preset slack variable and a preset data reconstruction method to obtain a transformed optimization problem.

[0080] In this embodiment, transforming the wireless backhaul resource allocation problem based on a preset slack variable and a preset data reconstruction method to obtain a transformed optimization problem includes: introducing a preset slack variable to reconstruct the wireless backhaul resource allocation problem to obtain a reconstructed optimization problem; performing convex optimization transformation on the objective constraint conditions in the reconstructed optimization problem by using the first-order Taylor approximation method to obtain a transformed optimization problem; the transformed optimization problem is an approximate convex optimization problem.

[0081] Specifically, set , introduce a slack variable Reconstruct the UAV base station resource allocation optimization problem as:

[0082] ;

[0083] Among them, , .

[0084] Among them, both sides of the constraint conditions , and include the product form of two variables on both sides of the formula, such as the product form AB of variable A and variable B. The product form AB of two variables has the following approximate convex upper bound:

[0085] ;

[0086] Among them, Given a series of feasible points can be defined , through the n - th iteration of convergence can be obtained.

[0087] The product form of two variables AB has the following approximate convex lower bound:

[0088] ;

[0089] Furthermore, the first - order Taylor approximation method is used to approximately optimize the constraint conditions near the point as:

[0090] ;

[0091] An approximately convex - optimized reconstructed problem is obtained near the given point:

[0092] .

[0093] Step S14: Based on the signal interference between the UAV base stations, the transformed optimization problem is disassembled distributively to obtain an objective optimization problem composed of global spatial resource variables and local spatial resource variables corresponding to each UAV base station.

[0094] In this embodiment, the step of disassembling the transformed optimization problem distributively based on the signal interference between the UAV base stations to obtain an objective optimization problem composed of global spatial resource variables and local spatial resource variables corresponding to each UAV base station includes: determining the first signal interference of the UAV base station on other UAV base stations and the second signal interference of other UAV base stations on the UAV base station, and determining the global spatial resource variables and local spatial resource variables corresponding to each UAV base station based on the first signal interference and the second signal interference; disassembling and transforming several constraint conditions in the transformed optimization problem based on the global spatial resource variables and the local spatial resource variables to obtain transformed constraint conditions, and constructing an objective optimization problem based on the transformed constraint conditions, the global spatial resource variables and the local spatial resource variables.

[0095] Specifically, the same - layer interference in the network model is split into two parts, that is, the interference of UAV base station j on other UAV base stations and the interference of other UAV base stations on UAV base station j , and the distributive disassembly of the approximately convex - optimized reconstruction problem is expressed as:

[0096] ;

[0097] Among them, the constraint condition in represents the local space of the network model, is a local space variable, and the specific form is shown in the following formula; the constraint condition and in and are local space variables limited in the drone base station j; the constraint conditions and in and are respectively and mappings in the global space.

[0098] ;

[0099] 。

[0100] Step S15: Iteratively update the target optimization problem based on a preset variable update method until the local space resource variable is equal to the global space resource variable to obtain a target data transmission rate, so as to determine a resource allocation strategy for wireless backhaul between the macro base station and the drone base station based on the target data transmission rate.

[0101] In this embodiment, the target optimization problem is iteratively updated based on the preset variable update method until the local space resource variable is equal to the global space resource variable to obtain the target data transmission rate, so as to determine the resource allocation strategy for wireless backhaul between the macro base station and the unmanned aerial vehicle (UAV) base station based on the target data transmission rate, including: transforming the target optimization problem into a current Lagrangian function, and updating the current local space resource variable in the current Lagrangian function based on the primal-dual interior point method to obtain the updated local space variable; updating the current global space resource variable in the current Lagrangian function based on the updated local space variable to obtain the updated global space variable, and updating the Lagrange multiplier of the current Lagrangian function based on the updated local space variable and the updated global space variable to obtain the updated Lagrangian function; determining whether the updated global space variable is equal to the updated local space variable; if the updated global space variable is equal to the updated local space variable, determining the target data transmission rate based on the updated Lagrangian function; if the updated global space variable is not equal to the updated local space variable, determining the updated local space variable as the new current local space resource variable, determining the updated global space variable as the new current global space resource variable, and determining the updated Lagrangian function as the new current Lagrangian function, and then jumping to the step of updating the current local space resource variable in the current Lagrangian function based on the primal-dual interior point method to obtain the updated local space variable. Specifically, the update formula of the local space variable in the m-th iteration using the primal-dual interior point method is expressed as:

[0102] ;

[0103] Furthermore, the update formula of the global space variable in the m-th iteration is expressed as:

[0104] ;

[0105] Finally, the Lagrange multiplier is updated in the (m + 1)-th iteration:

[0106] ;

[0107] Repeat updating the local space variable, the global space variable, and the Lagrange multiplier until the required convergence criterion is met.

[0108] It can be seen that in this embodiment, based on the first deployment location of the macro base station, the second deployment location of the drone base station and the flight cycle, as well as the preset frequency resource allocation ratio, the corresponding access transmission rate and backhaul transmission rate during wireless backhaul between the macro base station and the drone base station are determined; a number of constraint conditions are constructed, and a wireless backhaul resource allocation problem is constructed based on the number of constraint conditions, the access transmission rate and the backhaul transmission rate; the wireless backhaul resource allocation problem is transformed based on a preset relaxation variable and a preset data reconstruction method to obtain a transformed optimization problem; the transformed optimization problem is distributedly disassembled based on the signal interference between the drone base stations to obtain an objective optimization problem composed of global spatial resource variables and local spatial resource variables corresponding to each drone base station; the objective optimization problem is iteratively updated based on a preset variable update method until the local spatial resource variables are equal to the global spatial resource variables to obtain the target data transmission rate, so as to determine the resource allocation strategy during wireless backhaul between the macro base station and the drone base station. That is, by considering the transmission rate during wireless backhaul between the macro base station and the drone base station, a corresponding optimization function is constructed, and when optimizing and solving the optimization function, the rate optimization problem is transformed into a communication network resource allocation problem through the signal interference problem between the micro base stations, and then the problem is optimized to obtain the corresponding target data transmission rate, and finally the resource allocation strategy during wireless backhaul between the macro base station and the drone base station is determined according to the target data transmission rate. In this way, while considering the limited wireless backhaul capacity of the drone base station, the communication network resource allocation is optimized in a distributed manner to increase the network parallel processing ability and improve the overall processing efficiency and response speed of the system.

[0109] Reference Figure 3 As described above, an embodiment of the present application also correspondingly discloses a distributed drone base station wireless backhaul resource allocation device, including:

[0110] A transmission rate determination module 11, configured to determine the corresponding access transmission rate and backhaul transmission rate during wireless backhaul between the macro base station and the drone base station based on the first deployment location of the macro base station, the second deployment location of the drone base station and the flight cycle, as well as the preset frequency resource allocation ratio;

[0111] An allocation problem generation module 12, configured to construct a number of constraint conditions based on the access transmission rate and the backhaul transmission rate, and construct a wireless backhaul resource allocation problem based on the number of constraint conditions and the access transmission rate;

[0112] An allocation problem transformation module 13, configured to transform the wireless backhaul resource allocation problem based on a preset relaxation variable and a preset data reconstruction method to obtain a transformed optimization problem;

[0113] The allocation problem decomposition module 14 is configured to perform distributed decomposition on the transformed optimization problem based on the signal interference among the UAV base stations, so as to obtain an objective optimization problem composed of global space resource variables and local space resource variables corresponding to each UAV base station;

[0114] The resource allocation strategy determination module 15 is configured to perform iterative update on the objective optimization problem based on a preset variable update method until the local space resource variables are equal to the global space resource variables to obtain an objective data transmission rate, so as to determine a resource allocation strategy for wireless backhaul between the macro base station and the UAV base station based on the objective data transmission rate.

[0115] It can be seen that in this embodiment, by considering the transmission rate during wireless backhaul between the macro base station and the UAV base station, a corresponding optimization function is constructed, and when optimizing and solving the optimization function, the rate optimization problem is transformed into a communication network resource allocation problem through the signal interference problem among the micro base stations, and then the problem is optimized to obtain a corresponding objective data transmission rate. Finally, a resource allocation strategy for wireless backhaul between the macro base station and the UAV base station is determined according to the objective data transmission rate. In this way, while considering the limited wireless backhaul capacity of the UAV base station, a distributed method is adopted to optimize the communication network resource allocation to increase the network parallel processing ability and improve the overall processing efficiency and response speed of the system.

[0116] In some specific embodiments, the transmission rate determination module 11 may specifically include:

[0117] The frequency resource determination unit is configured to determine a first network frequency resource of the UAV base station in the access transmission stage and a second network frequency resource in the backhaul transmission stage based on a preset network resource allocation ratio, and determine a base station frequency resource provided by the macro base station in the access transmission stage;

[0118] The first signal-to-interference-plus-noise ratio determination unit is configured to determine a first channel gain of the macro base station based on a first deployment position of the macro base station, and determine a first transmission signal-to-interference-plus-noise ratio of the macro base station based on the first channel gain and a first preset signal-to-interference-plus-noise ratio model;

[0119] The first transmission rate determination unit is configured to input the first transmission signal-to-interference-plus-noise ratio and the base station frequency resource into a preset access transmission rate model to obtain a first access transmission rate, and input the second network frequency resource and the first channel gain into a preset backhaul transmission rate model to obtain a backhaul transmission rate;

[0120] A second signal-to-interference-plus-noise ratio determination unit, configured to determine a second channel gain of the drone base station based on a second deployment position and a flight period of the drone base station, and input the second channel gain into a second preset signal-to-interference-plus-noise ratio model to obtain a second transmission signal-to-interference-plus-noise ratio of the drone base station;

[0121] A second transmission rate determination unit, configured to input the first network frequency resource and the second transmission signal-to-interference-plus-noise ratio into the preset access transmission rate model to obtain a second access transmission rate.

[0122] In some specific embodiments, the allocation problem generation module 12 may specifically include:

[0123] A constraint condition determination sub-module, configured to construct a plurality of constraint conditions based on the first channel gain and the second network frequency resource;

[0124] A problem construction unit, configured to construct a wireless backhaul resource allocation problem based on the plurality of constraint conditions, based on the first access transmission rate, the second access transmission rate, and the backhaul transmission rate.

[0125] In some specific embodiments, the constraint condition determination sub-module may specifically include:

[0126] A first constraint condition definition unit, configured to define a transmission capacity during wireless backhaul of the drone base station based on the first access transmission rate, the backhaul transmission rate, and the first channel gain to obtain a first constraint condition;

[0127] A second constraint condition definition unit, configured to define a moving distance and a flight speed of the drone base station during the flight period to obtain a second constraint condition, and define a value range of the second network frequency resource to obtain a third constraint condition.

[0128] In some specific embodiments, the allocation problem transformation module 13 may specifically include:

[0129] A problem reconstruction unit, configured to introduce a preset slack variable to reconstruct the wireless backhaul resource allocation problem to obtain a reconstructed optimization problem;

[0130] A problem transformation unit, configured to perform convex optimization transformation on an objective constraint condition in the reconstructed optimization problem by using a first-order Taylor approximation method to obtain a transformed optimization problem; the transformed optimization problem is an approximate convex optimization problem.

[0131] In some specific embodiments, the allocation problem decomposition module 14 may specifically include:

[0132] A resource variable determination unit, configured to determine a first signal interference of the UAV base station on other UAV base stations and a second signal interference of the other UAV base stations on the UAV base station, and determine a global spatial resource variable and local spatial resource variables corresponding to each UAV base station based on the first signal interference and the second signal interference;

[0133] A target optimization problem construction unit, configured to disassemble and transform several constraint conditions in the transformed optimization problem based on the global spatial resource variable and the local spatial resource variables to obtain transformed constraint conditions, and construct a target optimization problem based on the transformed constraint conditions, the global spatial resource variable, and the local spatial resource variables.

[0134] In some specific embodiments, the resource allocation strategy determination module 15 may specifically include:

[0135] A resource variable update unit, configured to transform the target optimization problem into a current Lagrangian function, and update the current local spatial resource variables in the current Lagrangian function based on the primal-dual interior point method to obtain updated local spatial variables;

[0136] A function update unit, configured to update the current global spatial resource variables in the current Lagrangian function based on the updated local spatial variables to obtain updated global spatial variables, and update the Lagrange multipliers of the current Lagrangian function based on the updated local spatial variables and the updated global spatial variables to obtain an updated Lagrangian function;

[0137] A variable judgment unit, configured to judge whether the updated global spatial variables are equal to the updated local spatial variables;

[0138] A data transmission rate determination unit, configured to, if the updated global spatial variables are equal to the updated local spatial variables, determine a target data transmission rate based on the updated Lagrangian function;

[0139] A function update unit, configured to, if the updated global spatial variables are not equal to the updated local spatial variables, determine the updated local spatial variables as new current local spatial resource variables, determine the updated global spatial variables as new current global spatial resource variables, and determine the updated Lagrangian function as a new current Lagrangian function, and then jump to the step of updating the current local spatial resource variables in the current Lagrangian function based on the primal-dual interior point method to obtain updated local spatial variables.

[0140] Furthermore, an embodiment of the present application also discloses an electronic device, Figure 4It is a structural diagram of an electronic device 20 shown according to an exemplary embodiment. The content in the figure should not be regarded as any limitation on the scope of use of this application.

[0141] Figure 4 This is a schematic structural diagram of an electronic device 20 provided by an embodiment of this application. The electronic device 20 may specifically include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. Among them, the memory 22 is used to store a computer program, and the computer program is loaded and executed by the processor 21 to implement the relevant steps in the distributed UAV base station wireless backhaul resource allocation method disclosed in any of the foregoing embodiments. In addition, the electronic device 20 in this embodiment may specifically be an electronic computer.

[0142] In this embodiment, the power supply 23 is used to provide working voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and external devices, and the communication protocol it follows is any communication protocol applicable to the technical solution of this application, and no specific limitation is imposed on it here; the input / output interface 25 is used to obtain external input data or output data to the outside, and its specific interface type can be selected according to specific application needs, and no specific limitation is made here.

[0143] In addition, as a carrier for resource storage, the memory 22 may be a read-only memory, a random access memory, a magnetic disk, or an optical disc, etc. The resources stored thereon may include an operating system 221, a computer program 222, etc., and the storage method may be temporary storage or permanent storage.

[0144] Among them, the operating system 221 is used to manage and control each hardware device and the computer program 222 on the electronic device 20, and it may be Windows Server, Netware, Unix, Linux, etc. In addition to the computer program that can be used to complete the distributed UAV base station wireless backhaul resource allocation method executed by the electronic device 20 disclosed in any of the foregoing embodiments, the computer program 222 may further include computer programs that can be used to complete other specific tasks.

[0145] Furthermore, this application also discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the distributed UAV base station wireless backhaul resource allocation method disclosed above. For the specific steps of this method, reference may be made to the corresponding content disclosed in the foregoing embodiments, and details are not repeated here.

[0146] In this specification, the various embodiments are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. For the same or similar parts among the embodiments, reference can be made to each other. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple. For related parts, reference can be made to the description in the method section.

[0147] Those skilled in the art can further realize that the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the composition and steps of the examples have been generally described according to functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application of the technical solution and several design constraints. Those skilled in the art 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.

[0148] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be directly implemented by hardware, software modules executed by a processor, or a combination of both. The software modules can be placed in a random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium well-known in the technical field.

[0149] Finally, it should also be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variation thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including a..." does not exclude the existence of additional identical elements in the process, method, article or device including the element.

[0150] The technical solutions provided in this application have been introduced in detail above. Specific examples are used herein to elaborate on the principles and implementation manners of this application. The descriptions of the above embodiments are only used to help understand the method and its core idea of this application. At the same time, for those of ordinary skill in the art, based on the idea of this application, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to this application.

Claims

1. A distributed UAV base station wireless backhaul resource allocation method, characterized in that: include: Determine an access transmission rate and a backhaul transmission rate corresponding to wireless backhaul between the macro base station and the drone base station based on a first deployment position of the macro base station, a second deployment position and a flight period of the drone base station, and a preset frequency resource allocation ratio; Constructing a number of constraints, and constructing a wireless backhaul resource allocation problem based on the number of constraints, the access transmission rate, and the backhaul transmission rate; Based on preset slack variables and a preset data reconstruction method, the wireless backhaul resource allocation problem is transformed to obtain a transformed optimization problem; Distributed decomposition of the converted optimization problem is performed based on the signal interference between the drone base stations to obtain a target optimization problem consisting of a global space resource variable and a local space resource variable corresponding to each drone base station; The target optimization problem is iteratively updated based on a preset variable updating method until the local space resource variable is equal to the global space resource variable to obtain a target data transmission rate, so as to determine a resource allocation strategy for wireless backhaul between the macro base station and the drone base station based on the target data transmission rate.

2. The distributed UAV base station wireless backhaul resource allocation method according to claim 1 is characterized in that: The method of determining the access transmission rate and the backhaul transmission rate corresponding to the wireless backhaul between the macro base station and the drone base station based on the first deployment position of the macro base station, the second deployment position and the flight period of the drone base station, and the preset frequency resource allocation ratio includes: Determine the first network frequency resource of the drone base station in the access transmission phase and the second network frequency resource in the backhaul transmission phase based on the preset network resource allocation ratio, and determine the base station frequency resource provided by the macro base station in the access transmission phase; Determine a first channel gain of the macro base station based on a first deployment position of the macro base station, and determine a first transmission signal to interference and noise ratio of the macro base station based on the first channel gain and a first preset signal to interference and noise ratio model; Inputting the first transmission signal to interference and noise ratio and the base station frequency resource into a preset access transmission rate model to obtain a first access transmission rate, and inputting the second network frequency resource and the first channel gain into a preset backhaul transmission rate model to obtain a backhaul transmission rate; Determine a second channel gain of the drone base station based on a second deployment position and a flight period of the drone base station, and input the second channel gain into a second preset signal to interference plus noise ratio model to obtain a second transmission signal to interference plus noise ratio of the drone base station; The first network frequency resource and the second transmission signal to interference and noise ratio are input into the preset access transmission rate model to obtain a second access transmission rate.

3. The distributed UAV base station wireless backhaul resource allocation method according to claim 2 is characterized in that: The constructing of a plurality of constraints, and constructing a wireless backhaul resource allocation problem based on the plurality of constraints, the access transmission rate, and the backhaul transmission rate, includes: Establishing a plurality of constraints based on the first channel gain and the second network frequency resources; A wireless backhaul resource allocation problem is constructed based on the plurality of constraints, based on the first access transmission rate, the second access transmission rate and the backhaul transmission rate.

4. The distributed UAV base station wireless backhaul resource allocation method according to claim 3 is characterized in that: The constructing of a plurality of constraint conditions based on the first channel gain and the second network frequency resource comprises: Defining the transmission capacity of the drone base station when performing wireless backhaul based on the first access transmission rate, the backhaul transmission rate, and the first channel gain to obtain a first constraint condition; The moving distance and flight speed of the UAV base station during the flight cycle are defined to obtain the second constraint condition, and the value range of the second network frequency resource is defined to obtain the third constraint condition.

5. The distributed UAV base station wireless backhaul resource allocation method according to claim 3 is characterized in that: The method of converting the wireless backhaul resource allocation problem based on the preset slack variables and the preset data reconstruction method to obtain a converted optimization problem includes: Reconstructing the wireless backhaul resource allocation problem by introducing preset slack variables to obtain a reconstructed optimization problem; The objective constraint conditions in the reconstructed optimization problem are transformed into a convex optimization by using a first-order Taylor approximation method to obtain a transformed optimization problem; the transformed optimization problem is an approximate convex optimization problem.

6. The distributed UAV base station wireless backhaul resource allocation method according to claim 1 is characterized in that: The converted optimization problem is distributedly decomposed based on the signal interference between the drone base stations to obtain a target optimization problem consisting of a global space resource variable and a local space resource variable corresponding to each drone base station, including: Determine a first signal interference of the drone base station to other drone base stations and a second signal interference of the other drone base stations to the drone base station, and determine a global space resource variable and a local space resource variable corresponding to each of the drone base stations based on the first signal interference and the second signal interference; Based on the global space resource variables and the local space resource variables, several constraints in the post-conversion optimization problem are decomposed and transformed to obtain post-conversion constraints, and a target optimization problem is constructed based on the post-conversion constraints, the global space resource variables and the local space resource variables.

7. The distributed UAV base station wireless backhaul resource allocation method according to any one of claims 1 to 6, characterized in that: The method for updating the target optimization problem based on the preset variable is iteratively updated until the local space resource variable is equal to the global space resource variable to obtain a target data transmission rate, so as to determine a resource allocation strategy for wireless backhaul between the macro base station and the drone base station based on the target data transmission rate, including: Converting the target optimization problem into a current Lagrangian function, and updating the current local space resource variables in the current Lagrangian function based on the primal-dual interior point method to obtain updated local space variables; Based on the updated local spatial variables, the current global spatial resource variables in the current Lagrangian function are updated to obtain updated global spatial variables, and based on the updated local spatial variables and the updated global spatial variables, the Lagrangian multipliers of the current Lagrangian function are updated to obtain updated Lagrangian function; Determine whether the updated global space variable is equal to the updated local space variable; If the updated global spatial variable and the updated local spatial variable are equal, determining a target data transmission rate based on the updated Lagrangian function; If the updated global space variable and the updated local space variable are not equal, the updated local space variable is determined as the new current local space resource variable, the updated global space variable is determined as the new current global space resource variable, and the updated Lagrangian function is determined as the new current Lagrangian function, and then jump to the step of updating the current local space resource variable in the current Lagrangian function based on the primal-dual interior point method to obtain the updated local space variable.

8. A distributed UAV base station wireless backhaul resource allocation device, characterized in that: include: A transmission rate determination module, configured to determine an access transmission rate and a backhaul transmission rate corresponding to wireless backhaul between the macro base station and the drone base station based on a first deployment position of the macro base station, a second deployment position and a flight period of the drone base station, and a preset frequency resource allocation ratio; An allocation problem generating module, configured to construct a plurality of constraint conditions based on the access transmission rate and the backhaul transmission rate, and to construct a wireless backhaul resource allocation problem based on the plurality of constraint conditions and the access transmission rate; An allocation problem conversion module, used to convert the wireless backhaul resource allocation problem based on preset slack variables and a preset data reconstruction method to obtain a converted optimization problem; An allocation problem decomposition module is used to perform distributed decomposition on the converted optimization problem based on the signal interference between the drone base stations to obtain a target optimization problem consisting of a global space resource variable and a local space resource variable corresponding to each drone base station; A resource allocation strategy determination module is used to iteratively update the target optimization problem based on a preset variable updating method until the local space resource variable is equal to the global space resource variable to obtain a target data transmission rate, so as to determine the resource allocation strategy for wireless backhaul between the macro base station and the drone base station based on the target data transmission rate.

9. An electronic device, characterized in that: include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the distributed UAV base station wireless backhaul resource allocation method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: Used to store a computer program, which, when executed by a processor, implements the distributed UAV base station wireless backhaul resource allocation method as described in any one of claims 1 to 7.

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