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

Abstract

The application discloses a distributed unmanned aerial vehicle base station wireless backhaul resource allocation method and device, equipment and medium, relates to the wireless communication technical field, including: based on the deployment position and flight cycle of macro base station and unmanned aerial vehicle base station and the preset frequency resource allocation proportion determines the corresponding access transmission rate and backhaul transmission rate; based on a plurality of constraint conditions, access transmission rate and backhaul transmission rate, wireless backhaul resource allocation problem is constructed; based on the preset relaxation variable and the preset data reconstruction method, the wireless backhaul resource allocation problem is transformed to obtain the transformed optimization problem; based on the signal interference between each unmanned aerial vehicle base station, the transformed optimization problem is distributedly disassembled to obtain the target optimization problem; based on the preset variable updating method, the target optimization problem is iteratively updated until the target data transmission rate is obtained to determine the resource allocation strategy in wireless backhaul. In this way, the network parallel processing capability can be increased by optimizing the communication network resource allocation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wireless communication, in particular to a distributed unmanned aerial base station wireless backhaul resource allocation method, device, equipment and medium. BACKGROUND

[0002] As a key component of 5G (5th Generation Mobile Communication Technology, i.e. the fifth generation mobile communication technology) technology, the unmanned aerial base station can be flexibly and quickly deployed in areas such as disaster-stricken areas, remote mountainous areas and temporary activity sites where ground base stations cannot cover, to provide emergency and temporary communication, and has the advantages of relatively low construction cost, wide coverage range not limited by geographical environment, flexible adjustment of position to enhance capacity to alleviate network congestion, rapid response to communication guarantee in emergency situations, relatively simple maintenance and adaptation to large-scale temporary communication needs.

[0003] However, the link transmission of the wireless backhaul to the unmanned aerial base station brings more challenging problems. First, the limited frequency spectrum resources used by the wireless backhaul transmission inevitably limit the link capacity, limiting the transmission capacity through the wireless backhaul. Second, existing research or methods on unmanned aerial base station wireless backhaul are mostly centralized processing methods, with computation and decision-making in the communication network concentrated in the center node, which may cause excessive transmission load of the node and easily lead to communication delay and congestion, becoming a bottleneck of system performance. How to increase the network parallel processing capacity while considering the limited capacity of the unmanned aerial base station wireless backhaul, and improve the overall processing efficiency and response speed of the system is a problem to be solved. SUMMARY

[0004] Therefore, the purpose of the present application is to provide a distributed unmanned aerial base station wireless backhaul resource allocation method, device, equipment and medium, which can improve the overall processing efficiency and response speed of the communication system. The specific scheme is as follows:

[0005] In a first aspect, the present application discloses a distributed unmanned aerial base station wireless backhaul resource allocation method, comprising:

[0006] determining the corresponding access transmission rate and backhaul transmission rate when the wireless backhaul is performed between the macro base station and the unmanned aerial base station based on the first deployment position of the macro base station, the second deployment position of the unmanned aerial base station and the flight cycle, and the preset frequency resource allocation ratio;

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

[0008] 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;

[0009] distribute the transformed optimization problem based on signal interference between each of the UAV base stations to obtain a target optimization problem composed of global spatial resource variables and local spatial resource variables corresponding to each of the UAV base stations;

[0010] iteratively update the target optimization problem based on a preset variable updating method until the local spatial resource variables are equal to the global spatial resource variables to obtain a target data transmission rate, so as to determine a resource allocation strategy when the wireless backhaul is performed between the macro base station and the UAV base station based on the target data transmission rate.

[0011] Optionally, the access transmission rate and the backhaul transmission rate corresponding to the wireless backhaul performed between the macro base station and the UAV base station are determined based on the first deployment position of the macro base station, the second deployment position of the UAV base station, and the flight cycle, and a preset frequency resource allocation ratio, and the access transmission rate and the backhaul transmission rate corresponding to the wireless backhaul performed between the macro base station and the UAV base station include:

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

[0013] determining a first channel gain of the macro base station based on the first deployment position of the macro base station, and determining 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;

[0014] 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;

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

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

[0017] Optionally, the wireless backhaul resource allocation problem is constructed based on a plurality of constraint conditions and the access transmission rate and the backhaul transmission rate, and the plurality of constraint conditions include:

[0018] constructing a plurality of constraints based on the first channel gain and the second network frequency resource;

[0019] constructing a wireless backhaul resource allocation problem based on the plurality of constraints, the first access transmission rate, the second access transmission rate and the backhaul transmission rate.

[0020] Optionally, the constructing a plurality of constraints based on the first channel gain and the second network frequency resource comprises:

[0021] defining a transmission capacity of the unmanned 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;

[0022] defining a moving distance and a flight speed of the unmanned base station within the flight cycle to obtain a second constraint condition, and defining a 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 relaxation variable and a preset data reconstruction method to obtain a transformed optimization problem comprises:

[0024] reconstructing the wireless backhaul resource allocation problem by introducing a preset relaxation variable to obtain a reconstructed optimization problem;

[0025] transforming a target constraint condition in the reconstructed optimization problem by utilizing a first-order Taylor approximation method to obtain a transformed optimization problem; the transformed optimization problem is an approximately convex optimization problem.

[0026] Optionally, the distributing the transformed optimization problem based on signal interference between each of the unmanned base stations to obtain a target optimization problem composed of a global spatial resource variable and a local spatial resource variable corresponding to each of the unmanned base stations comprises:

[0027] determining a first signal interference of the unmanned base station to other unmanned base stations and a second signal interference of the other unmanned base stations to the unmanned base station, and determining a global spatial resource variable and a local spatial resource variable corresponding to each of the unmanned base stations based on the first signal interference and the second signal interference;

[0028] transforming a plurality of constraint conditions in the transformed optimization problem based on the global spatial resource variable and the local spatial resource variable to obtain transformed constraint conditions, and constructing a target optimization problem based on the transformed constraint conditions, the global spatial resource variable and the local spatial resource variable.

[0029] Optionally, the preset variable-based updating method iteratively updates the target optimization problem 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 when the wireless backhaul between the macro base station and the unmanned aerial base station is performed based on the target data transmission rate, comprising:

[0030] transforming the target optimization problem into a current Lagrangian function, and updating a current local space resource variable in the current Lagrangian function based on the original dual interior point method to obtain an updated local space variable;

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

[0032] determining whether the updated global space variable and the updated local space variable are equal;

[0033] if the updated global space variable and the updated local space variable are equal, determining a target data transmission rate based on the updated Lagrangian function;

[0034] if the updated global space variable and the updated local space variable are not equal, determining the updated local space variable as a new current local space resource variable, determining the updated global space variable as a new current global space resource variable, and determining the updated Lagrangian function as a 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 original dual interior point method to obtain an updated local space variable.

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

[0036] a transmission rate determination module, configured to determine corresponding access transmission rate and backhaul transmission rate when the wireless backhaul between the macro base station and the unmanned aerial base station is performed based on the first deployment position of the macro base station, the second deployment position of the unmanned aerial base station and the flight cycle, and a 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] The allocation problem conversion module is configured to convert the wireless backhaul resource allocation problem based on preset relaxation variables and a preset data reconstruction method to obtain a converted optimization problem.

[0039] The allocation problem decomposition module is configured to decompose the converted optimization problem based on signal interference between the unmanned base stations to obtain a target optimization problem composed of global spatial resource variables and local spatial resource variables corresponding to the unmanned base stations.

[0040] The resource allocation strategy determination module is configured to iteratively update the target optimization problem based on a preset variable updating method until the local spatial resource variables are equal to the global spatial resource variables 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 unmanned base stations based on the target data transmission rate.

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

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

[0043] A processor configured to execute the computer program to implement the foregoing distributed unmanned 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, which is executed by a processor to implement the foregoing distributed unmanned base station wireless backhaul resource allocation method.

[0045] It can be seen that, in the present application, the corresponding access transmission rate and backhaul transmission rate when the wireless backhaul is performed between the macro base station and the unmanned aerial base station are determined based on the first deployment position of the macro base station, the second deployment position of the unmanned aerial base station and the flight cycle, and the preset frequency resource allocation ratio; a plurality of constraint conditions are constructed, and the wireless backhaul resource allocation problem is constructed based on the plurality 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 each unmanned aerial base station to obtain a target optimization problem composed of global spatial resource variables and local spatial resource variables corresponding to each unmanned aerial base station; the target optimization problem is iteratively updated based on a preset variable updating method until the local spatial resource variables are equal to the global spatial resource variables to obtain a target data transmission rate, so as to determine the resource allocation strategy when the wireless backhaul is performed between the macro base station and the unmanned aerial base station based on the target data transmission rate. That is, by considering the transmission rate when the wireless backhaul is performed between the macro base station and the unmanned aerial base station, the corresponding optimization function is constructed, and when the optimization function is optimized and solved, the rate optimization problem is transformed into a communication network resource allocation problem through the signal interference problem between each micro base station, and then the problem is optimized to obtain the corresponding target data transmission rate, and finally the resource allocation strategy when the wireless backhaul is performed between the macro base station and the unmanned aerial base station is determined according to the target data transmission rate. In this way, while considering the limited wireless backhaul capacity of the unmanned aerial base station, the communication network resource allocation is optimized in a distributed manner to increase the network parallel processing capability and improve the overall processing efficiency and response speed of the system. BRIEF DESCRIPTION OF DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments or prior art description will be briefly introduced below. Obviously, the drawings in the following description are only embodiments of the present application, and those skilled in the art can also obtain other drawings according to the provided drawings without creative labor.

[0047] Figure 1 A flow chart of a distributed unmanned aerial base station wireless backhaul resource allocation method disclosed in the present application;

[0048] Figure 2 A schematic diagram of an unmanned aerial base station network model disclosed in the present application;

[0049] Figure 3 A schematic diagram of a distributed unmanned aerial base station wireless backhaul resource allocation device structure disclosed in the present application;

[0050] Figure 4 Figure 1 is a structural diagram of an electronic device disclosed in the present application. DETAILED DESCRIPTION

[0051] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0052] At present, the link transmission of wireless backhaul to the unmanned aerial base station brings more challenging problems. First, the limited frequency spectrum resources are used for transmission in wireless backhaul, and the link capacity is inevitably limited, which limits the transmission capacity through wireless backhaul. Second, the existing research or method about wireless backhaul of unmanned aerial base station is mostly centralized processing mode, and the calculation and decision in the communication network are concentrated in the center node, which may cause the transmission load of the node to be too heavy, easily leading to the delay and congestion of communication, and becoming the bottleneck of system performance. Therefore, the present application will specifically introduce a distributed wireless backhaul resource allocation method of unmanned aerial base station, which can improve the overall processing efficiency of the wireless backhaul system.

[0053] Referring to Figure 1 As shown in the figure, the embodiment of the present application discloses a distributed wireless backhaul resource allocation method of unmanned aerial base station, which comprises:

[0054] Step S11: determining the corresponding access transmission rate and backhaul transmission rate when the wireless backhaul is performed between the macro base station and the unmanned aerial base station based on the first deployment position of the macro base station, the second deployment position of the unmanned aerial base station and the flight cycle, and the preset frequency resource allocation ratio.

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

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

[0057] In the embodiment, the macro base station provides the first-stage access transmission service for end users (referred to as "macro users") within the coverage range of the macro base station, and provides the second-stage backhaul transmission service for UAV base stations. In the embodiment, the macro base station provides the first-stage access transmission service for end users (referred to as "macro users") within the coverage range of the macro base station, and provides the second-stage backhaul transmission service for UAV base stations. In the embodiment, the macro base station provides the first-stage access transmission service for end users (referred to as "macro users") within the coverage range of the macro base station, and provides the second-stage backhaul transmission service for UAV base stations. In the embodiment, the macro base station provides the first-stage access transmission service for end users (referred to as "macro users") within the coverage range of the macro base station, and provides the second-stage backhaul transmission service for

[0058] UAV base stations. In the embodiment, the macro base station provides the first-stage access transmission service for Deployed in horizontal position . UAV base station Deployed in the same vertical position within time L and different horizontal positions , UAV base station at a fixed height in each flight cycle, the flight cycle can be divided into N equal time slots (flight cycle). Considering the safety factors such as terrain or obstacle avoidance, should be set as small as possible. Then, the UAV base station The horizontal position of the nth time slot can be expressed as (second deployment position).

[0059] Subsequently, define as the channel matrix of the UAV base station in the network model, where is the channel matrix of the UAV base station , the channel gain between the UAV base station and the micro user k in the nth time slot (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 (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, is the precoding vector between the macro base station and the kth macro user.

[0065] The average achievable rate formula of the first stage access transmission between the macro base station and the macro user k (first access transmission rate) 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] Further, the macro base station and the UAV base station The second-stage backhaul transmission reachable rate (backhaul transmission rate) formula is defined as:

[0069] ;

[0070] Wherein, The capacity of the second-stage backhaul transmission in the tth time slot is denoted as The channel gain between the macro base station and the UAV base station in the tth time slot is denoted as

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

[0072] ;

[0073] Wherein The signal-to-interference-plus-noise ratio between the UAV base station and the micro user k in the tth time slot is denoted as , wherein The transmission power of the UAV base station is denoted as

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

[0075] In the embodiment, the constructing a plurality of constraint conditions, and constructing a wireless backhaul resource allocation problem based on the plurality of constraint conditions, the access transmission rate and the backhaul transmission rate comprises: constructing a plurality 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 plurality of constraint conditions, the first access transmission rate, the second access transmission rate and the backhaul transmission rate. Further, the constructing a plurality of constraint conditions based on the first channel gain and the second network frequency resource comprises: defining the transmission capacity of the UAV 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; defining the moving distance and the flight speed of the UAV base station in the flight cycle 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 unmanned aerial base station resource allocation optimization problem mathematical modeling (wireless backhaul resource allocation problem) can be established:

[0077] ;

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

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

[0080] In this embodiment, 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, which includes: introducing a preset relaxation variable to reconstruct the wireless backhaul resource allocation problem to obtain a reconstructed optimization problem; and performing convex optimization transformation on the 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.

[0081] Specifically, it is set that , a relaxation variable is introduced to reconstruct the unmanned aerial base station resource allocation optimization problem as follows:

[0082] ;

[0083] wherein, , .

[0084] wherein, the constraint conditions , and both include a two-variable product form, for example, a product form AB of a variable A and a variable B. The two-variable product form AB has the following approximate convex upper bound:

[0085] ;

[0086] wherein, . Given a set of feasible points can be defined by n iterations of converge.

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

[0088] ;

[0089] Further, using the first order Taylor approximation method, the constraint condition is approximated near the point as:

[0090] ;

[0091] The approximate convex optimization reconstruction problem is obtained near the given point:

[0092] .

[0093] Step S14: based on the signal interference between each of the UAV base stations, the transformed optimization problem is distributedly disassembled to obtain a target optimization problem composed of global spatial resource variables and local spatial resource variables corresponding to each of the UAV base stations.

[0094] In this embodiment, based on the signal interference between each of the UAV base stations, the transformed optimization problem is distributedly disassembled to obtain a target optimization problem composed of global spatial resource variables and local spatial resource variables corresponding to each of the UAV base stations, including: determining the first signal interference of the UAV base station to other UAV base stations and the second signal interference of the other UAV base stations to the UAV base station, and determining the global spatial resource variables and the local spatial resource variables corresponding to each of the UAV base stations based on the first signal interference and the second signal interference; based on the global spatial resource variables and the local spatial resource variables, disassembling and transforming a number of constraint conditions in the transformed optimization problem to obtain transformed constraint conditions, and constructing a target 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, i.e., the interference of the UAV base station j to other UAV base stations and the interference of other UAV base stations to the UAV base station j The distributed disassembly of the approximate convex optimization reconstruction problem is expressed as:

[0096] ;

[0097] wherein the constraint condition in the constraint condition represents a local space of the network model, is a local space variable, which is specifically represented in the following formula; the constraint condition and in the constraint condition and are local space variables limited in the unmanned aerial base station j; the constraint condition and in the constraint condition and are respectively and mapping in the global space.

[0098] ;

[0099] .

[0100] Step S15: iteratively updating 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 a resource allocation strategy when wireless backhaul is performed between the macro base station and the unmanned aerial 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 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 when wireless backhaul is performed between the macro base station and the unmanned aerial base station based on the target data transmission rate, including: converting the target optimization problem into a current Lagrangian function, and updating a current local space resource variable in the current Lagrangian function based on a primal-dual interior point method to obtain an updated local space variable; updating a current global space resource variable in the current Lagrangian function based on the updated local space variable to obtain an updated global space variable, and updating a Lagrange multiplier of the current Lagrangian function based on the updated local space variable and the updated global space variable to obtain an updated Lagrangian function; determining whether the updated global space variable and the updated local space variable are equal; if the updated global space variable and the updated local space 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, determining the updated local space variable as a new current local space resource variable, determining the updated global space variable as a new current global space resource variable, and determining the updated Lagrangian function as a 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 an updated local space variable. Specifically, the update formula of the local space variable in the mth iteration is represented as:

[0102] ;

[0103] Further, the update formula of the global space variable in the mth iteration is represented as:

[0104] ;

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

[0106] ;

[0107] The local space variable, the global space variable and the Lagrange multiplier are repeatedly updated until a required convergence criterion is reached.

[0108] It can be seen that, in the embodiment, the corresponding access transmission rate and backhaul transmission rate when the macro base station and the unmanned aerial base station perform wireless backhaul are determined based on the first deployment position of the macro base station, the second deployment position and the flight cycle of the unmanned aerial base station, and the preset frequency resource allocation ratio; a plurality of constraint conditions are constructed, and the wireless backhaul resource allocation problem is constructed based on the plurality 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 each unmanned aerial base station to obtain a target optimization problem composed of global space resource variables and local space resource variables corresponding to each unmanned aerial base station; the target optimization problem is iteratively updated based on a preset variable updating method until the local space resource variables are equal to the global space resource variables to obtain a target data transmission rate, so as to determine the resource allocation strategy when the macro base station and the unmanned aerial base station perform wireless backhaul based on the target data transmission rate. That is, by considering the transmission rate when the macro base station and the unmanned aerial base station perform wireless backhaul, the corresponding optimization function is constructed, and when the optimization function is optimized and solved, the rate optimization problem is transformed into a communication network resource allocation problem through the signal interference problem between each micro base station, and then the problem is optimized to obtain the corresponding target data transmission rate, and finally the resource allocation strategy when the macro base station and the unmanned aerial base station perform wireless backhaul is determined according to the target data transmission rate. In this way, while considering the limited wireless backhaul capacity of the unmanned aerial base station, the communication network resource allocation is optimized in a distributed manner to increase the network parallel processing capability and improve the overall processing efficiency and response speed of the system.

[0109] Reference Figure 3 As described above, the embodiments of the present application also correspondingly disclose a distributed unmanned aerial base station wireless backhaul resource allocation device, comprising:

[0110] The transmission rate determination module 11 is configured to determine the corresponding access transmission rate and backhaul transmission rate when the macro base station and the unmanned aerial base station perform wireless backhaul based on the first deployment position of the macro base station, the second deployment position and the flight cycle of the unmanned aerial base station, and the preset frequency resource allocation ratio;

[0111] The allocation problem generation module 12 is 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;

[0112] The allocation problem transformation module 13 is 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 decompose the transformed optimization problem in a distributed manner based on signal interference between the unmanned base stations to obtain a target optimization problem composed of global spatial resource variables and local spatial resource variables corresponding to the unmanned base stations;

[0114] The resource allocation strategy determination module 15 is configured to update the target optimization problem iteratively based on a preset variable updating method until the local spatial resource variables are equal to the global spatial resource variables to obtain a target data transmission rate, so as to determine a resource allocation strategy when the wireless backhaul is performed between the macro base station and the unmanned base station based on the target data transmission rate.

[0115] It can be seen that in the embodiment, the corresponding optimization function is constructed by considering the transmission rate when the wireless backhaul is performed between the macro base station and the unmanned base station, and when the optimization function is optimized and solved, the rate optimization problem is transformed into a communication network resource allocation problem by the signal interference between the micro base stations, then the problem is optimized to obtain the corresponding target data transmission rate, and finally the resource allocation strategy when the wireless backhaul is performed between the macro base station and the unmanned base station is determined according to the target data transmission rate. In this way, while considering the limited wireless backhaul capacity of the unmanned base station, the communication network resource allocation is optimized in a distributed manner to increase the network parallel processing capability and improve the overall processing efficiency and response speed of the system.

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

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

[0118] The first signal-to-interference-and-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-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;

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

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

[0121] a second transmission rate determination unit, configured to input the first network frequency resource and the second transmission signal-to-interference-and-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 can 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, 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 can specifically include:

[0126] a first constraint condition definition unit, configured to define a transmission capacity of the UAV 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;

[0127] a second constraint condition definition unit, configured to define a moving distance and a flight speed of the UAV base station within the flight cycle 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 can specifically include:

[0129] a problem reconstruction unit, configured to introduce a preset relaxation 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 a target 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 approximately convex optimization problem.

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

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

[0133] constructing a target optimization problem based on the global spatial resource variable and the local spatial resource variable to obtain a transformed constraint condition, and constructing the target optimization problem based on the transformed constraint condition, the global spatial resource variable and the local spatial resource variable.

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

[0135] updating the target optimization problem into a current Lagrange function, and updating a current local spatial resource variable in the current Lagrange function based on the original dual interior point method to obtain an updated local spatial variable;

[0136] updating a current global spatial resource variable in the current Lagrange function based on the updated local spatial variable to obtain an updated global spatial variable, and updating a Lagrange multiplier of the current Lagrange function based on the updated local spatial variable and the updated global spatial variable to obtain an updated Lagrange function;

[0137] judging whether the updated global spatial variable and the updated local spatial variable are equal;

[0138] determining a target data transmission rate based on the updated Lagrange function if the updated global spatial variable and the updated local spatial variable are equal;

[0139] updating the updated local spatial variable as a new current local spatial resource variable, the updated global spatial variable as a new current global spatial resource variable, and the updated Lagrange function as a new current Lagrange function if the updated global spatial variable and the updated local spatial variable are not equal, and then jumping to the step of updating the current local spatial resource variable in the current Lagrange function based on the original dual interior point method to obtain an updated local spatial variable.

[0140] Further, the embodiment of the present application further discloses an electronic device, Figure 4is a structural diagram of an electronic device 20 according to an exemplary embodiment, and the contents in the figure should not be considered as any limitation on the use scope of the present application.

[0141] Figure 4 A structural diagram of an electronic device 20 is provided in the present embodiment. The electronic device 20 can 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. The memory 22 is configured to store a computer program, which is loaded and executed by the processor 21 to implement the related steps in the distributed unmanned aerial base station wireless backhaul resource allocation method disclosed in any of the preceding embodiments. In addition, the electronic device 20 in the present embodiment can be specifically an electronic computer.

[0142] In the present embodiment, the power supply 23 is configured to provide operating voltage for each hardware device on the electronic device 20; the communication interface 24 is capable of creating a data transmission channel between the electronic device 20 and external devices, and the communication protocol followed by the communication interface 24 can be any communication protocol applicable to the technical solution of the present application, which is not specifically limited herein; the input / output interface 25 is configured to obtain external input data or output data to the outside, and the specific interface type can be selected according to the specific application needs, which is not specifically limited herein.

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

[0144] The operating system 221 is configured to manage and control each hardware device on the electronic device 20 and the computer program 222, and can be Windows Server, Netware, Unix, Linux, etc. In addition to the computer program capable of completing the distributed unmanned aerial base station wireless backhaul resource allocation method executed by the electronic device 20 disclosed in any of the preceding embodiments, the computer program 222 can further include a computer program capable of completing other specific work.

[0145] Further, the present application further discloses a computer readable storage medium for storing a computer program; wherein the computer program is executed by a processor to implement the distributed unmanned aerial base station wireless backhaul resource allocation method disclosed above. The specific steps of the method can refer to the corresponding contents disclosed in the preceding embodiments, which will not be repeated here.

[0146] The various embodiments described in the specification are progressive in nature, and each embodiment highlights the differences from other embodiments. The same or similar parts among the various embodiments can be mutually referred to. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the description of the method.

[0147] Those skilled in the art will further appreciate that the units and algorithm steps of the examples described in connection with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or both. To clearly illustrate the interchangeability of hardware and software, the description has generally been described in terms of exemplary components and steps that can comprise functionality implemented in hardware and / or software. Whether such functionality is implemented in hardware or software depends on the particular application and design constraints imposed on the overall architecture. Skilled persons can implement the described functionality in varying ways for each particular application, but such implementation should not be interpreted to change the scope of the application.

[0148] The steps of the methods or algorithms described in connection with the embodiments disclosed herein can be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module can be located in random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0149] Finally, it should be noted that the terms such as first and second, etc. are used herein only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between such entities or operations. Moreover, the terms "comprising", "including", or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or apparatus including a series of elements includes not only those elements, but also other elements not explicitly listed, or other elements inherent in such a process, method, article, or apparatus. Without more limitations, an element defined by the phrase "comprising a" does not exclude the presence of additional identical elements in the process, method, article, or apparatus including the element.

[0150] The above describes the technical solutions provided by the present application in detail, and the principles and implementation manners of the present application are described by using specific examples. The above description of the embodiments is only to help understand the method and its core idea of the present application; at the same time, for those skilled in the art, according to the idea of the present application, the specific implementation manner and application range can be changed, and the above description of the specification should not be understood as limiting the present application.

Claims

1. A method for allocating wireless backhaul resources for distributed unmanned aerial vehicle (UAV) base stations, characterized in that, include: The access transmission rate and backhaul transmission rate for wireless backhaul between the macro base station and the drone base station are determined 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. Several constraints are constructed, and a wireless backhaul resource allocation problem is constructed based on the constraints, the access transmission rate, and the backhaul transmission rate. The wireless backhaul resource allocation problem is transformed based on preset slack variables and preset data reconstruction methods to obtain the transformed optimization problem. The transformed optimization problem is decomposed in a distributed manner based on the signal interference between each of the UAV base stations to obtain the target optimization problem consisting of global spatial resource variables and local spatial resource variables corresponding to each of the UAV base stations. The target optimization problem is iteratively updated based on a preset variable update method until the local spatial resource variable is equal to the global spatial 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 UAV base station based on the target data transmission rate.

2. The distributed UAV base station wireless backhaul resource allocation method according to claim 1, characterized in that, The determination of the 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 includes: The first network frequency resources of the UAV base station in the access transmission phase and the second network frequency resources in the backhaul transmission phase are determined based on the preset network resource allocation ratio, and the base station frequency resources provided by the macro base station in the access transmission phase are also determined. The first channel gain of the macro base station is determined based on the first deployment location of the macro base station, and the first transmission signal-to-interference-plus-noise ratio (SINORR) of the macro base station is determined based on the first channel gain and the first preset SINORR model. The first transmission signal-to-interference-plus-noise ratio and the base station frequency resources are input into a preset access transmission rate model to obtain the first access transmission rate, and the second network frequency resources and the first channel gain are input into a preset backhaul transmission rate model to obtain the backhaul transmission rate. The second channel gain of the UAV base station is determined based on the second deployment location and flight cycle of the UAV base station, and the second channel gain is input into the 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; The first network frequency resource and the second transmission signal-to-interference-plus-noise ratio are input into the preset access transmission rate model to obtain the second access transmission rate.

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

4. The distributed UAV base station wireless backhaul resource allocation method according to claim 3, characterized in that, The constraint conditions constructed based on the first channel gain and the second network frequency resources include: The transmission capacity of the UAV base station for wireless backhaul is defined based on the first access transmission rate, the backhaul transmission rate and the first channel gain to obtain the first constraint condition. The movement 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, characterized in that, The transformation of the wireless backhaul resource allocation problem based on preset slack variables and preset data reconstruction methods to obtain the transformed optimization problem includes: The wireless backhaul resource allocation problem is reconstructed by introducing preset slack variables to obtain the reconstructed optimization problem. The target constraints in the reconstructed optimization problem are transformed into a convex optimization problem by using the first-order Taylor approximation method; 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, characterized in that, The distributed decomposition of the transformed optimization problem based on signal interference between the various UAV base stations yields a target optimization problem consisting of global spatial resource variables and local spatial resource variables corresponding to each UAV base station, including: The first signal interference of the UAV base station to other UAV base stations and the second signal interference of the other UAV base stations to the UAV base station are determined, and global spatial resource variables and local spatial resource variables corresponding to each UAV base station are determined based on the first signal interference and the second signal interference. Based on the global spatial resource variables and the local spatial resource variables, several constraints in the transformed optimization problem are decomposed and transformed to obtain the transformed constraints. Based on the transformed constraints, the global spatial resource variables, and the local spatial resource variables, the target optimization problem is constructed.

7. The distributed unmanned aerial vehicle (UAV) base station wireless backhaul resource allocation method according to any one of claims 1 to 6, characterized in that, The method of iteratively updating the target optimization problem based on a preset variable update method until the local spatial resource variable is equal to the global spatial 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 UAV base station based on the target data transmission rate, including: The objective optimization problem is transformed into the current Lagrangian function, and the current local space resource variables in the current Lagrangian function are updated based on the primal dual interior point method to obtain the updated local space variables; The current global space resource variable in the current Lagrange function is updated based on the updated local space variable to obtain the updated global space variable, and the Lagrange multipliers of the current Lagrange function are updated based on the updated local space variable and the updated global space variable to obtain the updated Lagrange function. Determine whether the updated global space variable and the updated local space variable are equal; If the updated global space variable and the updated local space variable are equal, the target data transmission rate is determined based on the updated Langron function. If the updated global space variable and the updated local space variable are not equal, then 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. Then, the process jumps 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 unmanned aerial vehicle (UAV) base station wireless backhaul resource allocation device, characterized in that, include: The transmission rate determination module is used to determine the access transmission rate and backhaul transmission rate corresponding to 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. The allocation problem generation module is used to construct several constraints based on the access transmission rate and the backhaul transmission rate, and to construct a wireless backhaul resource allocation problem based on the several constraints and the access transmission rate. The allocation problem transformation module is used to transform the wireless backhaul resource allocation problem based on preset slack variables and preset data reconstruction methods to obtain the transformed optimization problem; The allocation problem decomposition module is used to perform distributed decomposition of the transformed optimization problem based on the signal interference between each of the UAV base stations, so as to obtain the target optimization problem composed of global spatial resource variables and local spatial resource variables corresponding to each of the UAV base stations; The resource allocation strategy determination module is used to iteratively update the target optimization problem based on a preset variable update method until the local spatial resource variable is equal to the global spatial resource variable to obtain the target data transmission rate, so as to determine the resource allocation strategy when the macro base station and the UAV base station perform wireless backhaul based on the target data transmission rate.

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

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