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CN111835401B - Method for joint optimization of wireless resources and paths in unmanned aerial vehicle communication network - Google Patents

Method for joint optimization of wireless resources and paths in unmanned aerial vehicle communication network Download PDF

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CN111835401B
CN111835401B CN202010503621.7A CN202010503621A CN111835401B CN 111835401 B CN111835401 B CN 111835401B CN 202010503621 A CN202010503621 A CN 202010503621A CN 111835401 B CN111835401 B CN 111835401B
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CN111835401A (en
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张海君
李亚博
隆克平
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University of Science and Technology Beijing USTB
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/14—Relay systems
    • H04B7/15—Active relay systems
    • H04B7/185—Space-based or airborne stations; Stations for satellite systems
    • H04B7/1853—Satellite systems for providing telephony service to a mobile station, i.e. mobile satellite service
    • H04B7/18539—Arrangements for managing radio, resources, i.e. for establishing or releasing a connection
    • H04B7/18543—Arrangements for managing radio, resources, i.e. for establishing or releasing a connection for adaptation of transmission parameters, e.g. power control
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/14—Relay systems
    • H04B7/15—Active relay systems
    • H04B7/185—Space-based or airborne stations; Stations for satellite systems
    • H04B7/18578—Satellite systems for providing broadband data service to individual earth stations
    • H04B7/18595—Arrangements for adapting broadband applications to satellite systems
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/04—Wireless resource allocation
    • H04W72/044—Wireless resource allocation based on the type of the allocated resource
    • H04W72/0473—Wireless resource allocation based on the type of the allocated resource the resource being transmission power
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/50—Allocation or scheduling criteria for wireless resources
    • H04W72/54—Allocation or scheduling criteria for wireless resources based on quality criteria
    • H04W72/542—Allocation or scheduling criteria for wireless resources based on quality criteria using measured or perceived quality
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    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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Abstract

The invention provides a method for joint optimization of wireless resources and paths in an unmanned aerial vehicle communication network, which can improve the frequency spectrum utilization rate and improve the energy efficiency of the unmanned aerial vehicle wireless communication network. The method comprises the following steps: s1, dividing the flight path of the unmanned aerial vehicle in the flight period into a plurality of sub periods; s2, allocating channels to the end users in each sub-period; s3, according to the channel distribution result, calculating the approximate closed-form solution of the terminal user power, and distributing the power for the terminal user according to the closed-form solution; s4, establishing the path optimization problem as a convex optimization problem according to the channel and power distribution result, and performing path optimization according to the established convex optimization problem; and S5, returning to S2 for iterative optimization according to the power distribution result and the optimized path until the energy efficiency value is not changed or the iteration number reaches the maximum value of the iteration number, and obtaining the optimal channel and power distribution scheme and the optimal path optimization scheme. The invention relates to the technical field of unmanned aerial vehicle communication.

Description

Method for joint optimization of wireless resources and paths in unmanned aerial vehicle communication network
Technical Field
The invention relates to the technical field of unmanned aerial vehicle communication, in particular to a method for joint optimization of wireless resources and paths in an unmanned aerial vehicle communication network.
Background
As a newly derived wireless communication network, the unmanned aerial vehicle wireless communication network is more and more widely concerned by academic circles and industrial circles, and is expected to become a flexible and universal auxiliary communication mode in the future. Compared with the traditional wireless cellular network, the unmanned aerial vehicle communication network has the remarkable advantages of low cost, easiness in deployment, strong maneuverability, wide application range and the like, so that the unmanned aerial vehicle communication network has great application potential in the military and civil fields. According to the statistical data, the number of the unmanned aerial vehicles which are put into use in the next year can reach 2900 ten thousand, and the huge number of the unmanned aerial vehicles can urge more and more meaningful research.
Traditional unmanned aerial vehicle communication network needs artifical route to intervene, and in future wireless communication network scene, unmanned aerial vehicle's use will be more frequent, needs the flight more nimble freedom simultaneously to provide supplementary communication service for the ground user. Moreover, the addition of the communication of the unmanned aerial vehicle can aggravate the competition of scarce spectrum resources, so that the improvement of the spectrum utilization rate is necessary.
Disclosure of Invention
The embodiment of the invention provides a method for joint optimization of wireless resources and paths in an unmanned aerial vehicle communication network, which can improve the frequency spectrum utilization rate and improve the energy efficiency of the unmanned aerial vehicle wireless communication network. The technical scheme is as follows:
the embodiment of the invention provides a method for joint optimization of wireless resources and paths in an unmanned aerial vehicle communication network, which comprises the following steps:
s1, dividing the flight path of the unmanned aerial vehicle in the flight period into a plurality of sub periods;
s2, allocating channels to the end users in each sub-period;
s3, according to the channel distribution result, calculating the approximate closed-form solution of the terminal user power, and distributing the power for the terminal user according to the closed-form solution;
s4, establishing the path optimization problem as a convex optimization problem according to the channel and power distribution result, and performing path optimization according to the established convex optimization problem;
and S5, returning to S2 for iterative optimization according to the power distribution result and the optimized path until the energy efficiency value is not changed or the iteration number reaches the maximum value of the iteration number, and obtaining the optimal channel and power distribution scheme and the optimal path optimization scheme.
Further, the allocating the channel to the end user comprises:
and in each sub-period, allocating the channel to the terminal user with the maximum gain according to the channel gain maximization principle.
Further, the gain calculation formula is:
Figure BDA0002525754460000021
wherein, gk,mRepresenting the channel gain of end user m in the kth sub-period, epsilon being the path loss factor at a distance equal to 1, hk,mFor the distance between the terminal user m and the unmanned aerial vehicle in the kth sub-period
Further, the obtaining an approximate closed-form solution of the power of the terminal user according to the channel allocation result, and allocating the power to the terminal user according to the closed-form solution includes:
according to the channel distribution result, solving an approximate closed-form solution of the power of the terminal user by using a Lagrange dual function, and distributing the power to the terminal user according to the closed-form solution; wherein,
the approximate closed form solution for the power of end user m in the kth sub-period is given by:
Figure BDA0002525754460000022
wherein p isk,mThe power for end user m in the kth sub-period,
Figure BDA0002525754460000023
the signal-to-noise ratio of the iterative computation for the end user m in the kth sub-period, B is the bandwidth, etai-1Is energy efficiency in the i-1 st iteration, of the form [ p]+＝max{p,0}，λk,mAnd ωkBoth represent lagrangian factors.
Further, the signal to noise ratio
Figure BDA0002525754460000024
Expressed as:
Figure BDA0002525754460000025
wherein p isk,m′For the power of end user m' in the kth sub-period, gk,mDenotes the channel gain, σ, of end user m in the kth sub-period2As noise, MkThe number of users occupying the channel in the kth sub-period,
Figure BDA0002525754460000031
co-channel interference from end user m' is received for end user m.
Further, the establishing a path optimization problem as a convex optimization problem according to the channel and power distribution results, and performing path optimization according to the established convex optimization problem includes:
according to the channel and power distribution result, using the principle of approximate convex approximation to establish the path optimization problem as a convex optimization problem shown as the following formula:
Figure BDA0002525754460000032
wherein M represents the number of end users; k represents the number of sub-periods;
Figure BDA0002525754460000033
the lower bound of the data volume of the user m in the kth sub-period is set;
Figure BDA0002525754460000034
Figure BDA0002525754460000035
introducing a slow release variable l for the data rate of the terminal user m in the kth sub-periodk,mData rate expression of post-conversion, σ2As noise, pk,m′For the power of user m' in the kth sub-period, ε is the path loss factor, lk,mIs a slow release variable, and h is the flight height of the unmanned aerial vehicle; b is network bandwidth; etai-1The energy efficiency in the i-1 st iteration process; e is the total power of the terminal user;
and according to the obtained convex optimization problem, utilizing a convex optimization tool box to carry out path optimization.
Further, the constraint conditions of the convex optimization problem include:
Figure BDA0002525754460000036
Figure BDA0002525754460000037
wherein, γminIn order to be the minimum data rate,
Figure BDA0002525754460000038
for the position u of the unmanned plane in the kth sub-period in the process of t iterative optimizationmFor the location of end user m, qkThe position of the drone in the kth sub-period in the next iteration.
The technical scheme of the invention has the following beneficial effects:
in the scheme, the flight path of the unmanned aerial vehicle in the flight period is divided into a plurality of sub-periods; in each sub-period, allocating a channel to a terminal user, solving an approximate closed solution of the power of the terminal user by using a Lagrange dual function according to a channel allocation result, and allocating the power to the terminal user according to the closed solution, thereby realizing the optimal allocation of wireless resources in the unmanned aerial vehicle wireless communication network in each sub-period, namely the optimal allocation of the wireless channel and the terminal power; establishing a path optimization problem into a convex optimization problem according to a channel and power distribution result, and performing path optimization according to the established convex optimization problem; and returning to execute channel allocation operation according to the power allocation result and the optimized path, wherein the network parameters used by the channel allocation are optimized, and continuously executing the power allocation operation and the path optimization operation for circular optimization after obtaining a new channel allocation result until reaching a threshold value or energy efficiency convergence, so as to obtain an optimal channel and power allocation scheme and a path optimization scheme. Therefore, the flight path of the unmanned aerial vehicle is iteratively optimized through the optimized channel and power distribution scheme, the frequency spectrum utilization rate can be improved, and the energy efficiency of the wireless communication network of the unmanned aerial vehicle is improved.
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Fig. 1 is a schematic flowchart of a method for joint optimization of radio resources and paths in an unmanned aerial vehicle communication network according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of an architecture of an unmanned aerial vehicle communication network system according to an embodiment of the present invention.
Detailed Description
In order to make the technical problems, technical solutions and advantages of the present invention more apparent, the following detailed description is given with reference to the accompanying drawings and specific embodiments.
As shown in fig. 1, a method for joint optimization of radio resources and paths in an unmanned aerial vehicle communication network according to an embodiment of the present invention includes:
s1, dividing the flight path of the unmanned aerial vehicle in the flight period into a plurality of sub periods;
s2, allocating channels to the end users in each sub-period;
s3, according to the channel distribution result, calculating the approximate closed-form solution of the terminal user power, and distributing the power for the terminal user according to the closed-form solution;
s4, establishing the path optimization problem as a convex optimization problem according to the channel and power distribution result, and performing path optimization according to the established convex optimization problem;
and S5, returning to S2 for iterative optimization according to the power distribution result and the optimized path until the energy efficiency value is not changed or the iteration number reaches the maximum value of the iteration number, and obtaining the optimal channel and power distribution scheme and the optimal path optimization scheme.
The method for the joint optimization of the wireless resources and the paths in the unmanned aerial vehicle communication network divides the flight path of the unmanned aerial vehicle in the flight period into a plurality of sub-periods; in each sub-period, allocating a channel to a terminal user, solving an approximate closed solution of the power of the terminal user by using a Lagrange dual function according to a channel allocation result, and allocating the power to the terminal user according to the closed solution, thereby realizing the optimal allocation of wireless resources in the unmanned aerial vehicle wireless communication network in each sub-period, namely the optimal allocation of the wireless channel and the power of the terminal user; establishing a path optimization problem into a convex optimization problem according to a channel and power distribution result, and performing path optimization according to the established convex optimization problem; and returning to execute channel allocation operation according to the power allocation result and the optimized path, wherein the network parameters used by the channel allocation are optimized, and continuously executing the power allocation operation and the path optimization operation for circular optimization after obtaining a new channel allocation result until reaching a threshold value or energy efficiency convergence, so as to obtain an optimal channel and power allocation scheme and a path optimization scheme. Therefore, the flight path of the unmanned aerial vehicle is iteratively optimized through the optimized channel and power distribution scheme, the frequency spectrum utilization rate can be improved, and the energy efficiency of the wireless communication network of the unmanned aerial vehicle is improved.
In this embodiment, fig. 2 is an architecture diagram of an unmanned aerial vehicle communication network system including an unmanned aerial vehicle and an end user deployed at the same frequency, where the architecture diagram includes: the unmanned aerial vehicle base station and a plurality of terminal users consider the complexity of the dynamic change of the unmanned aerial vehicle, only one sub-channel is considered in the channel part, the unmanned aerial vehicle only occupies the sub-channel in different sub-periods, but the accessed terminal users can select the most appropriate terminal users in each sub-period according to the gain maximization principle of the channel distribution part.
In this embodiment, before S1, the path, the end user position, the path loss factor, the initial power, and the like of the drone in a complete flight cycle need to be initialized.
In this embodiment, in S1, the flight path within the complete flight period of the unmanned aerial vehicle is divided into a plurality of sub-periods, and thus the flight path of the unmanned aerial vehicle is set to be a closed-loop path for periodic flight.
In this embodiment, unmanned aerial vehicle's flight cycle is subdivided into a plurality of subcycles, and unmanned aerial vehicle is regarded as static in the twinkling of an eye in every subcycle, and the flight path of all subcycles is used for expressing unmanned aerial vehicle's dynamic position, and unmanned aerial vehicle and the terminal user on ground only consider the stadia influence, that is to say that unmanned aerial vehicle information transmission channel only receives path loss's effect.
In a specific embodiment of the foregoing method for joint optimization of radio resources and paths in a drone communication network, further, the allocating a channel to an end user includes:
and in each sub-period, allocating the channel to the terminal user with the maximum gain according to the channel gain maximization principle.
In this embodiment, the gain calculation formula is:
Figure BDA0002525754460000051
wherein, gk,mRepresenting the channel gain of end user m in the kth sub-period, epsilon being the path loss factor at a distance equal to 1, hk,mThe distance between the terminal user m and the unmanned aerial vehicle in the kth sub-period is shown.
In this embodiment, according to the formula
Figure BDA0002525754460000061
The channel allocation result in each sub-period can be obtained.
In a specific implementation manner of the foregoing method for jointly optimizing radio resources and paths in an unmanned aerial vehicle communication network, further, the obtaining an approximate closed-form solution of the power of the end user according to a result of channel allocation, and allocating the power to the end user according to the closed-form solution includes:
according to the channel distribution result, solving an approximate closed-form solution of the power of the terminal user by using a Lagrange dual function, and distributing the power to the terminal user according to the closed-form solution; wherein,
the approximate closed form solution for the power of end user m in the kth sub-period is given by:
Figure BDA0002525754460000062
and, instead,
Figure BDA0002525754460000063
wherein p isk,m、pk,m′The power for end users m, m' in the kth sub-period,
Figure BDA0002525754460000064
iterative computation of the signal-to-noise ratio for end user m in the kth sub-periodB is the bandwidth, ηi-1Is energy efficiency in the i-1 st iteration, of the form [ p]+＝max{p,0}，λk,mAnd ωkAll represent the Lagrange factor, gk,mDenotes the channel gain, σ, of end user m in the kth sub-period2As noise, MkThe number of users occupying the channel in the kth sub-period,
Figure BDA0002525754460000065
co-channel interference from end user m' is received for end user m.
In this embodiment, the formula
Figure BDA0002525754460000066
Calculating an approximate closed-form solution of the power of the end user m in the kth sub-period after the channel allocation; no power is required if the end user does not occupy the channel in the kth sub-period.
In this example, the form [ p ]]+Max { p,0} is used to denote the symbol [ ·]+Is the form [ p ]]+In which p is a positive value, [ p ]]+Is equal to p, otherwise [ p]+It is equal to 0.
In a specific implementation manner of the foregoing method for jointly optimizing radio resources and paths in an unmanned aerial vehicle communication network, further, the establishing a path optimization problem as a convex optimization problem according to a result of channel and power allocation, and performing path optimization according to the established convex optimization problem includes:
according to the channel and power distribution result, using the principle of approximate convex approximation to establish the path optimization problem as a convex optimization problem shown as the following formula:
Figure BDA0002525754460000071
wherein M represents the number of end users; k represents the number of sub-periods;
Figure BDA0002525754460000072
the lower bound of the data volume of the user m in the kth sub-period is set;
Figure BDA0002525754460000073
Figure BDA0002525754460000074
introducing a slow release variable l for the data rate of the terminal user m in the kth sub-periodk,mData rate expression of post-conversion, σ2As noise, pk,m′For the power of user m' in the kth sub-period, ε is the path loss factor, lk,mIs a slow release variable, and h is the flight height of the unmanned aerial vehicle; b is network bandwidth; etai-1The energy efficiency in the i-1 st iteration process; e is the total power of the terminal user;
and according to the obtained convex optimization problem, utilizing a convex optimization tool box to carry out path optimization.
In this embodiment, the formula
Figure BDA0002525754460000075
It is calculated that the data rate of end user m in the kth sub-period after channel allocation is subject to a slow release variable lk,mThe data rate of the post-conversion; if the end user does not occupy the channel in the kth sub-period, then
Figure BDA0002525754460000076
Is 0.
In this embodiment, according to the convex optimization problem that obtains, the place of injecing unmanned aerial vehicle's beginning and final position is the same simultaneously, utilizes convex optimization toolbox can obtain specific route optimization scheme.
In an embodiment of the foregoing method for jointly optimizing radio resources and paths in an unmanned aerial vehicle communication network, further, the limiting conditions of the convex optimization problem include:
Figure BDA0002525754460000077
Figure BDA0002525754460000078
wherein, γminIn order to be the minimum data rate,
Figure BDA0002525754460000079
for the position u of the unmanned plane in the kth sub-period in the process of t iterative optimizationmFor the location of end user m, qkThe position of the drone in the kth sub-period in the next iteration.
The method for jointly optimizing the wireless resources and the paths in the unmanned aerial vehicle communication network provided by the embodiment can effectively improve the spectrum utilization rate and improve the energy efficiency of the unmanned aerial vehicle wireless communication network on the premise of meeting the minimum data rate of a user and periodic flight.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention as defined in the appended claims.

Claims (6)

1. A method for joint optimization of radio resources and paths in an unmanned aerial vehicle communication network is characterized by comprising the following steps:
s1, dividing the flight path of the unmanned aerial vehicle in the flight period into a plurality of sub periods;
s2, allocating channels to the end users in each sub-period;
s3, according to the channel distribution result, calculating the approximate closed-form solution of the terminal user power, and distributing the power for the terminal user according to the closed-form solution;
s4, establishing the path optimization problem as a convex optimization problem according to the channel and power distribution result, and performing path optimization according to the established convex optimization problem;
s5, returning to S2 for iterative optimization according to the power distribution result and the optimized path until the energy efficiency value is not changed or the iteration times reach the maximum value of the iteration times, and obtaining an optimal channel and power distribution scheme and a path optimization scheme;
wherein, the calculating an approximate closed-form solution of the power of the terminal user according to the channel allocation result, and allocating the power to the terminal user according to the closed-form solution comprises:
according to the channel distribution result, solving an approximate closed-form solution of the power of the terminal user by using a Lagrange dual function, and distributing the power to the terminal user according to the closed-form solution; wherein,
the approximate closed form solution for the power of end user m in the kth sub-period is given by:
Figure FDA0003036161980000011
wherein p isk,mThe power for end user m in the kth sub-period,
Figure FDA0003036161980000012
the signal-to-noise ratio of the iterative computation for the end user m in the kth sub-period, B is the bandwidth, etai-1Is energy efficiency in the i-1 st iteration, of the form [ p]+＝max{p,0}，λk,mAnd ωkBoth represent lagrangian factors.
2. The method of joint optimization of radio resources and paths in a drone communication network of claim 1, wherein the allocating channels to end users comprises:
and in each sub-period, allocating the channel to the terminal user with the maximum gain according to the channel gain maximization principle.
3. The method of joint optimization of radio resources and paths in a drone communication network of claim 2, wherein the gain factor calculation formula is:
Figure FDA0003036161980000021
wherein, gk,mRepresents the channel gain factor of end user m in the kth sub-period, epsilon is the path loss factor when the distance equals 1, hk,mThe distance between the terminal user m and the unmanned aerial vehicle in the kth sub-period is shown.
4. The method of claim 1, wherein SNR is a signal-to-noise ratio for joint optimization of radio resources and paths in a drone communication network
Figure FDA0003036161980000022
Expressed as:
Figure FDA0003036161980000023
wherein p isk,m′For the power of end user m' in the kth sub-period, | gk,m|2Denotes the channel gain, σ, of end user m in the kth sub-period2As noise, MkThe number of users occupying the channel in the kth sub-period,
Figure FDA0003036161980000024
co-channel interference from end user m' is received for end user m.
5. The method of claim 1, wherein the establishing a path optimization problem as a convex optimization problem according to the channel and power allocation result, and performing path optimization according to the established convex optimization problem comprises:
according to the channel and power distribution result, using the principle of approximate convex approximation to establish the path optimization problem as a convex optimization problem shown as the following formula:
Figure FDA0003036161980000025
wherein M represents the number of end users; k represents the number of sub-periods;
Figure FDA0003036161980000026
the lower bound of the data volume of the user m in the kth sub-period is set;
Figure FDA0003036161980000027
Figure FDA0003036161980000028
introducing a slow release variable l for the data rate of the terminal user m in the kth sub-periodk,mData rate expression of post-conversion, σ2As noise, pk,m′For the power of user m' in the kth sub-period, ε is the path loss factor, lk,mIs a slow release variable, and h is the flight height of the unmanned aerial vehicle; b is network bandwidth; etai-1The energy efficiency in the i-1 st iteration process; e is the total power of the terminal user;
and according to the obtained convex optimization problem, utilizing a convex optimization tool box to carry out path optimization.
6. The method of joint optimization of radio resources and paths in a drone communication network of claim 5, wherein the constraints of the convex optimization problem include:
Figure FDA0003036161980000031
Figure FDA0003036161980000032
wherein, γminIn order to be the minimum data rate,
Figure FDA0003036161980000033
for the position u of the unmanned plane in the kth sub-period in the process of t iterative optimizationmFor the location of end user m, qkThe position of the drone in the kth sub-period in the next iteration.
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