CN110730031B - Unmanned aerial vehicle track and resource allocation joint optimization method for multi-carrier communication - Google Patents

Unmanned aerial vehicle track and resource allocation joint optimization method for multi-carrier communication Download PDF

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CN110730031B
CN110730031B CN201911007757.2A CN201911007757A CN110730031B CN 110730031 B CN110730031 B CN 110730031B CN 201911007757 A CN201911007757 A CN 201911007757A CN 110730031 B CN110730031 B CN 110730031B
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那振宇
王君
吴迪
刘玥
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Dalian Maritime University
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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
    • H04B7/18502—Airborne stations
    • H04B7/18506—Communications with or from aircraft, i.e. aeronautical mobile service
    • 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
    • H04B7/18502—Airborne stations
    • H04B7/18504—Aircraft used as relay or high altitude atmospheric platform
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/18—TPC being performed according to specific parameters
    • H04W52/26—TPC being performed according to specific parameters using transmission rate or quality of service QoS [Quality of Service]
    • H04W52/267—TPC being performed according to specific parameters using transmission rate or quality of service QoS [Quality of Service] taking into account the information rate
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/30—TPC using constraints in the total amount of available transmission power
    • H04W52/34—TPC management, i.e. sharing limited amount of power among users or channels or data types, e.g. cell loading
    • 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
    • H04W72/0453—Resources in frequency domain, e.g. a carrier in FDMA
    • 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
    • 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
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    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
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Abstract

The embodiment of the invention discloses a joint optimization method for trajectory and resource allocation of an unmanned aerial vehicle for multi-carrier wireless communication, which comprises the following steps of S1, creating an optimization model facing multi-node energy-carrying communication based on the unmanned aerial vehicle; s2, splitting the optimization model, and respectively carrying out iterative solution on the split sub-models; s3, fixing the track of the unmanned aerial vehicle, and optimizing the resource allocation variable of the unmanned aerial vehicle; s4, fixing unmanned aerial vehicle resource allocation and optimizing the flight trajectory of the unmanned aerial vehicle; and S5, carrying out the flight trajectory and resource allocation joint optimization of the unmanned aerial vehicle to obtain the optimal value of the optimization variable. The invention realizes the simultaneous transmission of information and energy of a plurality of ground nodes by the unmanned aerial vehicle; the problems of node information interaction and endurance time of the Internet of things are solved, and meanwhile, the design complexity of a receiver can be effectively reduced; and the flight path of the unmanned aerial vehicle improves a communication link, improves the utilization rate of wireless resources and realizes the maximization of data transmission rate.

Description

Unmanned aerial vehicle track and resource allocation joint optimization method for multi-carrier communication
Technical Field
The invention relates to the technical field of wireless energy-carrying communication technology and unmanned aerial vehicle communication in wireless communication technology, in particular to a joint optimization method for unmanned aerial vehicle track and resource allocation for multi-carrier communication.
Background
Recently, with the development of unmanned aerial vehicle technology, the internet of things assisted by unmanned aerial vehicles has attracted extensive attention in academic and industrial fields as a new emerging communication field. Traditional ground thing networking receives natural disasters to destroy easily. And for the recovery of the Internet of things in disaster areas and emergency scenes, the unmanned aerial vehicle communication can quickly establish network connection. At present, space internet of things in other forms mainly depend on satellite communication, and are time-delay, weak in signal, high in cost and difficult to control. In contrast, cost effective, steerable drones can be used as flexible aerial base stations. The Internet of things enabled by the unmanned aerial vehicle can be more flexibly applied to various Internet of things scenes with complex environments.
Different from the traditional thing networking based on fixed base station, unmanned aerial vehicle has advantages such as high mobility, low cost, high performance price ratio to can carry on equipment such as GPS locater, camera, carry out the interaction of information with ground thing networking node anytime and anywhere. In the aspect of remote sensing mapping, an unmanned aerial vehicle is used as a flying camera, a real capturing technology is applied, and collected information is downloaded to an internet of things node; in the aspects of military investigation and safety prevention and control, the unmanned aerial vehicle can execute tasks such as aerial surveillance, information collection and the like by virtue of concealment and controllable mobility of the unmanned aerial vehicle, and transmits picture and video information to nodes of the Internet of things; in emergency rescue and disaster relief, the unmanned aerial vehicle can execute tasks such as disaster detection and auxiliary rescue, and rapidly downloads the data collected in the air to the Internet of things node, so as to provide real-time rescue information for disaster areas.
But the energy limitation of the nodes of the internet of things still is a practical problem for restricting the development of the nodes of the internet of things. Unlike traditional energy sources, such as solar energy and wind energy, Wireless energy communication (SWIPT) can utilize radio frequency signals to simultaneously transmit Information and energy. The problem of high-density deployment in a traditional energy supply network is effectively solved, and information transmission and stable and reliable energy supply to low-power-consumption Internet of things nodes in a complex environment are achieved.
The traditional wireless energy-carrying communication technology mainly has two types: based on slot switching and power allocation methods. However, both of these methods require additional time slot switches and power dividers at the receiving end, so it can be said that the prior art cannot effectively improve the utilization rate of radio resources and maximize the data transmission rate.
Disclosure of Invention
Based on the above, in order to solve the defects existing in the prior art, a joint optimization method for unmanned aerial vehicle trajectory and resource allocation for multi-carrier wireless communication is provided.
The invention provides an unmanned aerial vehicle track and resource allocation joint optimization method for multi-carrier wireless communication, which is characterized by comprising the following steps:
s1, creating an optimization model facing multi-node energy-carrying communication based on the unmanned aerial vehicle;
s2, splitting the optimization model, and respectively carrying out iterative solution on the split sub-models;
s3, fixing the track of the unmanned aerial vehicle, and optimizing the resource allocation variable of the unmanned aerial vehicle, wherein the resource allocation variable of the unmanned aerial vehicle comprises an information transmission subcarrier set
Figure BDA0002243262260000021
And energy transmission carrier set
Figure BDA0002243262260000022
Power of subcarrier allocation
Figure BDA0002243262260000023
And subcarrier scheduling variables of nodes
Figure BDA0002243262260000024
S4, fixing unmanned aerial vehicle resource allocation and optimizing the flight trajectory of the unmanned aerial vehicle;
and S5, carrying out the flight trajectory and resource allocation joint optimization of the unmanned aerial vehicle to obtain the optimal value of the optimization variable.
Optionally, in one embodiment, the specific step of creating an optimization model for multi-node portable communication based on drones in S1 includes:
s11, determining an optimization target and an optimization variable corresponding to the creation of the optimization model, wherein the optimization target refers to the average speed of the unmanned aerial vehicle transmitted to the ground node in all time slots, and the optimization variable refers to the position q [ M ] of the unmanned aerial vehicle at each time slot M is 1, …, M]And in the m-th time slot, respectively used for information transmission subcarrier sets
Figure BDA0002243262260000025
And energy ofSubcarrier set for transmission
Figure BDA0002243262260000026
Power of subcarrier allocation
Figure BDA0002243262260000027
And subcarrier scheduling variables of nodes
Figure BDA0002243262260000028
Meanwhile, suppose that K ground nodes are randomly distributed in a circular area with the radius of r, the position of each ground node is known, and the position of the kth ground node is wk(ii) a The unmanned aerial vehicle is limited on a plane with the height of H for periodic flight, and the time of one flight circle is T; the time T is divided into M time slots, each time slot having a length deltatAt any time t equal to m δtM is 1, …, M; then in the mth time slot, the position of the drone is qm]The maximum speed of the unmanned plane is VmaxIn each time slot, the total power transmitted by the unmanned aerial vehicle is PmaxThe lower limit of the total energy collected by the K nodes is Emin；
S12, determining constraint conditions corresponding to the creation of the optimization model, wherein the constraint conditions comprise: (1) constraint conditions for constraining the subcarrier allocation under each time slot; (2) the constraint condition is used for constraining the maximum transmitting power of the unmanned aerial vehicle under each time slot; (3) constraint conditions for constraining the energy collected by each node under each time slot; (4) constraint conditions for constraining the flying speed and state of the unmanned aerial vehicle;
s13, determining to create the optimization model based on S11-S12, wherein the optimization model comprises a resource allocation optimization model of the unmanned aerial vehicle and a flight path optimization model of the unmanned aerial vehicle;
the model formula corresponding to the resource allocation optimization model of the unmanned aerial vehicle is as follows (1):
Figure BDA0002243262260000031
the model formula corresponding to the optimization model of the flight path of the unmanned aerial vehicle is as follows (2):
Figure BDA0002243262260000032
wherein,
Figure BDA0002243262260000033
Figure BDA0002243262260000034
in the formula (1), C represents a total set of subcarriers,
Figure BDA0002243262260000035
and
Figure BDA0002243262260000036
respectively, the subcarrier sets for information transmission and energy transmission in the mth slot,
Figure BDA0002243262260000037
is an identification variable for the node's schedule,
Figure BDA0002243262260000038
the expression that in the mth time slot, the subcarrier n is allocated to the node k, and in each time slot, the subcarrier n can be allocated to only one node, namely, a plurality of nodes cannot use the same subcarrier;
in formula (2), q [1 ]]＝q[M]The starting position and the key position of the unmanned aerial vehicle are the same to ensure that the unmanned aerial vehicle flies periodically, | q [ m +1 ]]-q[m]||2≤(Vmaxδt)2M-1 indicates that the distance between two adjacent positions of the unmanned aerial vehicle is smaller than the distance of the unmanned aerial vehicle flying at the maximum speed in a time slot, namely, the optimization of the track of the unmanned aerial vehicle is ensured to meet the requirement of the actual flying speed of the unmanned aerial vehicle;
in equation (3), equation (3) represents the rates of all nodes in the mth slot; wherein,
Figure BDA0002243262260000041
Figure BDA0002243262260000042
indicating the channel gain between the node k and the unmanned aerial vehicle when the sub-carrier n receives the information of the unmanned aerial vehicle in the mth time slot; gk,nA channel gain coefficient indicating that the nth subcarrier is allocated to the kth node; g0And GnRepresenting the directional antenna gains at the node and at the drone, respectively; beta is a0Represents the channel power gain at a reference position of 1 meter; n is a radical of0Representing a noise power spectral density; b represents a subcarrier bandwidth;
in equation (4), equation (4) represents the energy collected by K nodes in the mth slot, and the threshold is Emin。
Optionally, in one embodiment, the specific steps of splitting the optimization model in S2 and respectively performing iterative solution on the split sub-models include:
splitting the model into two submodels and carrying out iterative solution to obtain corresponding suboptimal solutions,
the original optimization problem can be decomposed into the following two subproblems to be solved respectively:
the first submodel corresponds to a calculation formula of
Figure BDA0002243262260000043
subject to：
Figure BDA0002243262260000044
Figure BDA0002243262260000045
Figure BDA0002243262260000046
Figure BDA0002243262260000047
Figure BDA0002243262260000048
E[m]≥Emin,m＝1,..,M
The second submodel corresponds to a calculation formula of
Figure BDA0002243262260000051
Optionally, in one embodiment, in S3, the trajectory of the drone is fixed, and the specific step of optimizing the resource allocation variable of the drone includes;
s31, setting the total power of the unmanned aerial vehicle in each time slot to be PmaxAnd the lower limit of the total energy collected by K nodes in each time slot is fixed as Emin(ii) a Due to optimization of the objective
Figure BDA0002243262260000052
Middle T, K is a fixed value, therefore
Figure BDA0002243262260000053
S32, converting the channel model of the unmanned aerial vehicle and the node k on the nth subcarrier of the mth time slot into the following formula, wherein the channel model of the unmanned aerial vehicle and the node k on the nth subcarrier of the mth time slot is
Figure BDA0002243262260000054
Wherein, gk,nIndicating the channel gain coefficient of the nth sub-carrier distributed to the kth node; g0And GnRepresenting the directional antenna gain at the node and the directional antenna gain at the drone, respectively; beta is a0Representing the channel power gain at 1 meter of the reference position,
Figure BDA0002243262260000055
Figure BDA0002243262260000056
indicates that in the mth time slot, the unmanned plane and the node wkWherein H is the flying height of the drone;
s33, converting the channel models of the unmanned aerial vehicle and the node k on the nth subcarrier of the mth time slot into corresponding equivalent transformation formulas, performing iterative computation, and solving the maximum value of the objective function, wherein the equivalent transformation formula is
Figure BDA0002243262260000061
Optionally, in one embodiment, the specific step of solving the maximum value of the objective function includes:
s331, given
Figure BDA0002243262260000062
And
Figure BDA0002243262260000063
based on an optimization condition that allocates subcarriers n to designated nodes so that the total information rate and collected energy of the nodes are maximized, a node subcarrier scheduling variable is determined
Figure BDA0002243262260000064
And (3) optimizing the value, wherein the corresponding optimization formula is as follows:
Figure BDA0002243262260000065
subject to：
Figure BDA0002243262260000066
Figure BDA0002243262260000067
Figure BDA0002243262260000068
wherein,
Figure BDA0002243262260000069
and is
Figure BDA00022432622600000610
S332, scheduling variables based on optimized node subcarriers
Figure BDA00022432622600000611
And given
Figure BDA00022432622600000612
And
Figure BDA00022432622600000613
determining subcarrier allocation power
Figure BDA00022432622600000614
The corresponding optimization formula is as follows:
Figure BDA00022432622600000615
subject to：
Figure BDA00022432622600000616
Figure BDA00022432622600000617
wherein,
Figure BDA0002243262260000071
representing a node-passed subcarrier scheduling variable
Figure BDA0002243262260000072
After optimization, the nth subcarrier is allocated to the kth subcarrier*When a node is in use, the channel gain of the subcarrier to the node is obtained;
s333, optimizing the first sub-model by a Lagrange dual decomposition method to obtain the corresponding Lagrange multiplier rho1And ρ2Then, according to the KKT condition and through iterative optimization of Lagrange multiplier and power, solving and solving
Figure BDA0002243262260000073
To obtain the corresponding optimal distribution of power, the corresponding solving formula is:
Figure BDA0002243262260000074
wherein, PmaxAnd PminMaximum and minimum values of power for energy harvesting are respectively represented, N is 1, …, N;
s334, based on optimization
Figure BDA0002243262260000075
And
Figure BDA0002243262260000076
optimizing subcarrier sets
Figure BDA0002243262260000077
And
Figure BDA0002243262260000078
the corresponding optimization formula is as follows:
Figure BDA0002243262260000079
wherein,
Figure BDA00022432622600000710
represents the optimized set of subcarriers for energy harvesting,
Figure BDA00022432622600000711
s335, repeating the steps S331-S334 until the target function R[m]And (6) converging.
Optionally, in one embodiment, the calculating of the optimal allocation of power includes: s3331, the calculation process of the optimal allocation of the power comprises the following steps: s3331, initializing Lagrange multiplier rho1And rho2The initial value of the Lagrange multiplier is expressed as
Figure BDA00022432622600000712
And
Figure BDA00022432622600000713
and set ρ1Corresponding to an iteration precision of mu1，ρ2Has an iteration precision of mu2The iteration times t corresponding to the two are 0; s3332, carrying out iterative calculation, and judging whether t is 0 or whether the Lagrange multiplier can not reach convergence precision, namely
Figure BDA00022432622600000714
And is
Figure BDA00022432622600000715
Figure BDA00022432622600000716
If so, then the Lagrangian function is utilized
Figure BDA00022432622600000717
Calculating the corresponding power according to the formula
Figure BDA0002243262260000081
S3333, respectively substituting the calculated power into two gradient solving formulas to obtain the optimized Lagrange multiplier rho1And ρ2And updating the Lagrange multiplier
Figure BDA0002243262260000082
And
Figure BDA0002243262260000083
t +1, and the gradient solving formula is as follows:
Figure BDA0002243262260000084
Figure BDA0002243262260000085
s3334, returning to S3332, and judging whether the conditions are satisfied
Figure BDA0002243262260000086
Or
Figure BDA0002243262260000087
Figure BDA0002243262260000088
If the conditions are met, continuing iteration; otherwise, the iteration is terminated, and the power allocation in the last iteration is the optimal power allocation.
Optionally, in one embodiment, in the step S4, the resource allocation of the drone is fixed, and in optimizing the flight trajectory of the drone, the corresponding optimization calculation formula includes:
Figure BDA0002243262260000089
optionally, in one embodiment, in S5, the resource allocation of the drone and the flight of the drone are performedThe track joint optimization specific operation steps comprise: s51, carrying out initialization processing of unmanned aerial vehicle resource allocation and unmanned aerial vehicle flight trajectory combined optimization process, namely according to the position w of the nodekK1, …, K, initializing drone trajectory q m](0)M is 1, …, M; and solving variables based on model formulas corresponding to optimized models of flight trajectories of the unmanned aerial vehicle
Figure BDA00022432622600000810
And
Figure BDA00022432622600000811
the values after the solution are respectively taken as
Figure BDA00022432622600000812
Figure BDA00022432622600000813
And
Figure BDA00022432622600000814
while solving the objective function value
Figure BDA00022432622600000815
Wherein an error precision tau is setobjThe iteration number l is 0; s52, carrying out iterative operation of the unmanned aerial vehicle resource allocation and unmanned aerial vehicle flight trajectory combined optimization process, namely: s521, giving
Figure BDA0002243262260000091
And
Figure BDA0002243262260000092
solving a model formula corresponding to the optimization model of the flight path of the unmanned aerial vehicle to obtain an optimization variable q [ m ]]Of (2) an optimal solution q [ m ]]*And update q [ m ]](l+1)＝q[m]*(ii) a S522, fixing q [ m ]](l+1)Solving a model formula corresponding to the resource allocation optimization model of the unmanned aerial vehicle to obtain an optimized variable
Figure BDA0002243262260000093
And
Figure BDA0002243262260000094
of (2) an optimal solution
Figure BDA0002243262260000095
And
Figure BDA0002243262260000096
and update
Figure BDA0002243262260000097
Figure BDA0002243262260000098
And
Figure BDA0002243262260000099
s523, calculating an objective function
Figure BDA00022432622600000910
A corresponding objective function value; s524, judging whether the requirements are met
Figure BDA00022432622600000911
Figure BDA00022432622600000912
If yes, updating l to l +1, and going to S521; otherwise, the iteration is terminated; the optimal solution of the last iteration in S53 and S52 is the optimal value of the optimization variable, and the optimal value includes: optimal trajectory q [ m ] of unmanned aerial vehicle]＝q[m](l+1)Set of subcarriers for information transmission
Figure BDA00022432622600000913
Subcarrier set for energy transmission
Figure BDA00022432622600000914
Subcarrier scheduling variables for nodes
Figure BDA00022432622600000915
Allocated power of sub-carriers
Figure BDA00022432622600000916
The embodiment of the invention has the following beneficial effects:
the invention provides a multi-node-oriented data distribution method for an unmanned aerial vehicle in a multi-carrier communication system, namely, the invention realizes the simultaneous transmission of information and energy of a plurality of ground nodes by the unmanned aerial vehicle through jointly optimizing the flight track and resource distribution of the unmanned aerial vehicle; the problems of node information interaction and endurance time of the Internet of things are solved, and meanwhile, the design complexity of a receiver can be effectively reduced; and the flight path of the unmanned aerial vehicle improves a communication link, improves the utilization rate of wireless resources and realizes the maximization of data transmission rate.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Wherein:
FIG. 1 is a flow diagram of an implementation technique in one embodiment;
FIG. 2 is a schematic diagram of an embodiment in which an UAV is flying in the air to transmit information and energy to multiple ground nodes simultaneously;
FIG. 3 is a schematic diagram of an embodiment of the initial trajectory of the drone and the optimized trajectory obtained by the proposed method;
fig. 4 is a schematic diagram illustrating a trajectory change of the unmanned aerial vehicle after the node 2 moves in the embodiment;
fig. 5 is a schematic diagram of energy collection conditions of different time slots under T ═ 20s in the embodiment;
fig. 6 is a diagram illustrating an average achievable rate of all nodes in each timeslot when T is 20s in the embodiment;
fig. 7 is a schematic diagram illustrating a relationship between an average reachable rate of a node and a flight period T of the unmanned aerial vehicle in three different flight scenarios.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. It will be understood that, as used herein, the terms "first," "second," and the like may be used herein to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish one element from another. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, without departing from the scope of the present application. The first and second elements are both elements, but they are not the same element.
In order to solve the defects existing in the prior art, in the embodiment, a joint optimization method for trajectory and resource allocation of an unmanned aerial vehicle for multi-carrier wireless communication is provided, the design goal is to design an unmanned aerial vehicle facing multiple ground nodes, a system for simultaneously transmitting information and energy based on a multi-carrier wireless energy-carrying communication technology is adopted, and the optimization goal is to maximize the average transmission rate of the nodes on the premise of ensuring that the ground nodes collect quantitative energy. Specifically, as shown in fig. 1, the method includes the following steps: s1, creating an optimization model facing multi-node energy-carrying communication based on the unmanned aerial vehicle; s2, splitting the optimization model, and respectively carrying out iterative solution on the split sub-models; s3, fixing the track of the unmanned aerial vehicle, optimizing the resource allocation variable of the unmanned aerial vehicle, and enabling the unmanned aerial vehicle to be unmannedThe resource allocation variable of the machine comprises a set of information transmission subcarriers
Figure BDA0002243262260000111
And energy transmission carrier set
Figure BDA0002243262260000112
Power of subcarrier allocation
Figure BDA0002243262260000113
And subcarrier scheduling variables of nodes
Figure BDA0002243262260000114
S4, fixing unmanned aerial vehicle resource allocation and optimizing the flight trajectory of the unmanned aerial vehicle; and S5, carrying out the flight trajectory and resource allocation joint optimization of the unmanned aerial vehicle to obtain the optimal value of the optimization variable. In the scheme, the flight path and resource allocation joint allocation optimization process of the unmanned aerial vehicle is realized through the following design process, namely the optimization problem modeling process comprising 1 and oriented to multi-node energy-carrying communication based on the unmanned aerial vehicle; 2. optimizing a problem analysis derivation process; 3. fixing the flight track of the unmanned aerial vehicle, and updating the resource allocation process of the unmanned aerial vehicle; 4. fixing the resource allocation of the unmanned aerial vehicle, and updating the flight track process of the unmanned aerial vehicle; 5. and (3) performing a combined optimization process of the flight trajectory and the resource allocation of the unmanned aerial vehicle.
By adopting the scheme, the invention can realize wireless energy-carrying communication based on multiple carriers, does not need additional receiver design, only needs to divide the subcarriers into two sets for information transmission and energy transmission, respectively applies different subcarriers to realize simultaneous transmission of information and energy, and reduces the complexity and cost of system design.
In some specific embodiments, in the step of S1, the objective is to model an optimization problem for multi-node portable communication based on drones as a mathematical optimization problem, the optimization problem at least includes determining an optimization goal, an optimization variable, and a constraint condition. Firstly, suppose that K nodes are randomly distributed in a circular area with the radius of r, the positions of the nodes are known, and the position of the kth node is wk＝[x(k),y(k)]. Each node receives information distributed by the unmanned aerial vehicle. In order to avoid the energy loss of the unmanned aerial vehicle in climbing and descending, the unmanned aerial vehicle is limited to fly periodically on a plane with the height H. Assuming that the time of a week of flight of the drone is, we divide T into M sufficiently small time slots, each time slot having a length δtThus, any time 0 ≦ T may be expressed as T ═ m δtAnd M is 1, …, M. Suppose that during a time slot, the drone remains stationary relative to the ground, with a fixed position. In the mth time slot, the position of the unmanned aerial vehicle is qm]＝[xu(m),yu(m)]. The maximum flying speed of the unmanned aerial vehicle is Vmax。
The flight path of the unmanned aerial vehicle needs to meet some practical constraint conditions, and the invention expresses the flight path as the following formula:
Figure BDA0002243262260000115
wherein, the initial position that first item represents unmanned aerial vehicle is the same with key position, guarantees that unmanned aerial vehicle can do periodic flight. The second term indicates that the distance between two adjacent positions of the unmanned aerial vehicle is less than the distance of the unmanned aerial vehicle flying at the maximum speed in a time slot, that is, the optimization of the track of the unmanned aerial vehicle is ensured to meet the requirement of the actual flying speed of the unmanned aerial vehicle. Secondly, setting in each time slot, adopting a wireless energy-carrying communication technology based on subcarrier allocation, simultaneously transmitting information and energy for a plurality of nodes, wherein the total power transmitted by the unmanned aerial vehicle is PmaxThe lower limit of the energy collected by each node is Emin. In the mth slot, we divide the total channel into N subcarriers, represented by set C. The subcarrier sets are divided into two groups for information transmission
Figure BDA0002243262260000121
Indicating the set of sub-carriers used for energy transmission
Figure BDA0002243262260000122
Indicating that two subcarrier sets do not existAt the intersection, and add to the total set C. Further, we will assemble the subcarriers
Figure BDA0002243262260000123
And
Figure BDA0002243262260000124
the subcarriers in the system are allocated to K nodes, and variables are scheduled by the subcarriers
Figure BDA0002243262260000125
Indicating that node k decodes information on subcarrier n during the mth slot. To avoid interference between different nodes, each subcarrier can be used for only one node to decode information. Variables of
Figure BDA0002243262260000126
Indicating the power allocated to the nth subcarrier in the mth slot.
Therefore, in the mth time slot, the unmanned aerial vehicle adopts the multi-carrier wireless energy-carrying communication technology to transmit information and energy to a plurality of nodes simultaneously, and the following constraint conditions need to be satisfied:
Figure BDA0002243262260000127
the unmanned aerial vehicle transmits information and energy through a downlink channel, and the channel modeling of the unmanned aerial vehicle and a node k on the nth subcarrier of the mth time slot is as follows:
Figure BDA0002243262260000128
wherein, gk,nIndicating the channel gain coefficient of the nth sub-carrier distributed to the kth node; g0And GnRepresenting the directional antenna gains at the node and at the drone, respectively; beta is a0Representing the channel power gain at 1 meter of the reference position,
Figure BDA0002243262260000129
indicates that in the mth time slot, the unmanned plane and the node wkWherein H is the drone flight height.
Then in the mth time slot, K nodes are in the subcarrier set
Figure BDA00022432622600001210
The information rate of the received data can be expressed as
Figure BDA0002243262260000131
In the mth time slot, K nodes are in the subcarrier set
Figure BDA0002243262260000132
The total energy received can be expressed as
Figure BDA0002243262260000133
In summary, in the optimization problem, the optimization target is the average rate of K nodes in all time slots. The corresponding optimization variables may include: the position q [ M ] of the drone under each time slot M-1, …, M]And a set of subcarriers for information transmission and energy transmission, respectively, in time slot m
Figure BDA0002243262260000134
And
Figure BDA0002243262260000135
power of subcarrier allocation
Figure BDA0002243262260000136
And subcarrier scheduling variables of nodes
Figure BDA0002243262260000137
The constraint conditions include: (1) constraint conditions of subcarrier allocation under each time slot; (2) unmanned plane in each time slotA lower maximum transmit power; (3) the total energy collected by the K nodes under each time slot; (4) the speed and state of flight of the drone.
Based on the above design scheme, the specific step of creating the optimization model for multi-node-oriented energy-carrying communication based on the unmanned aerial vehicle in S1 includes: s11, determining an optimization target and an optimization variable corresponding to the creation of the optimization model, wherein the optimization target refers to the average speed of the unmanned aerial vehicle transmitted to the ground node in all time slots, and the optimization variable refers to the position q [ M ] of the unmanned aerial vehicle at each time slot M is 1, …, M]And in the m-th time slot, respectively used for information transmission subcarrier sets
Figure BDA0002243262260000138
And subcarrier set for energy transmission
Figure BDA0002243262260000139
Power of subcarrier allocation
Figure BDA00022432622600001310
And subcarrier scheduling variables of nodes
Figure BDA00022432622600001311
Meanwhile, suppose that K ground nodes are randomly distributed in a circular area with the radius of r, the position of each ground node is known, and the position of the kth ground node is wk(ii) a The unmanned aerial vehicle is limited on a plane with the height of H for periodic flight, and the time of one flight circle is T; the time T is divided into M time slots, each time slot having a length deltatAt any time t equal to m δtM is 1, …, M; then in the mth time slot, the position of the drone is qm]The maximum speed of the unmanned plane is VmaxIn each time slot, the total power transmitted by the unmanned aerial vehicle is PmaxThe lower limit of the total energy collected by the K nodes is Emin；
S12, determining constraint conditions corresponding to the creation of the optimization model, wherein the constraint conditions comprise: (1) constraint conditions for constraining the subcarrier allocation under each time slot; (2) the constraint condition is used for constraining the maximum transmitting power of the unmanned aerial vehicle under each time slot; (3) constraint conditions for constraining the energy collected by each node under each time slot; (4) constraint conditions for constraining the flying speed and state of the unmanned aerial vehicle; s13, determining to create the optimization model based on S11-S12, wherein the optimization model comprises a resource allocation optimization model of the unmanned aerial vehicle and a flight path optimization model of the unmanned aerial vehicle; the model formula corresponding to the resource allocation optimization model of the unmanned aerial vehicle is as follows (1):
Figure BDA0002243262260000141
the model formula corresponding to the optimization model of the flight path of the unmanned aerial vehicle is as follows (2):
Figure BDA0002243262260000142
wherein,
Figure BDA0002243262260000143
Figure BDA0002243262260000144
in the formula (1), C represents a total set of subcarriers,
Figure BDA0002243262260000145
and
Figure BDA0002243262260000146
respectively, the subcarrier sets for information transmission and energy transmission in the mth slot,
Figure BDA0002243262260000147
is an identification variable for the node's schedule,
Figure BDA0002243262260000148
the expression that in the mth time slot, the subcarrier n is allocated to the node k, and in each time slot, the subcarrier n can be allocated to only one node, namely, a plurality of nodes can not use the same subcarrier;
in formula (2), q [1 ]]＝q[M]The starting position and the key position of the unmanned aerial vehicle are the same to ensure that the unmanned aerial vehicle flies periodically, | q [ m +1 ]]-q[m]||2≤(Vmaxδt)2M-1 indicates that the distance between two adjacent positions of the unmanned aerial vehicle is smaller than the distance of the unmanned aerial vehicle flying at the maximum speed in a time slot, namely, the optimization of the track of the unmanned aerial vehicle is ensured to meet the requirement of the actual flying speed of the unmanned aerial vehicle;
in equation (3), equation (3) represents the rates of all nodes in the mth slot; wherein,
Figure BDA0002243262260000151
Figure BDA0002243262260000152
indicating the channel gain between the node k and the unmanned aerial vehicle when the sub-carrier n receives the information of the unmanned aerial vehicle in the mth time slot; gk,nA channel gain coefficient indicating that the nth subcarrier is allocated to the kth node; g0And GnRepresenting the directional antenna gains at the node and at the drone, respectively; beta is a0Represents the channel power gain at a reference position of 1 meter; n is a radical of0Representing a noise power spectral density;
in equation (4), equation (4) represents the energy collected by K nodes in the mth slot, and the threshold is Emin。
In some specific embodiments, since in step 1, the present invention has given a mathematical model that the drone performs communication and energy collection facing multiple nodes, however, the optimization problem (10) is difficult to solve due to the complex structure. The optimization problem is divided into two sub-problems, and the suboptimal solution of the original problem is solved through iteration of the two sub-problems. Based on the above principle, the S2 includes splitting the optimization model, and performing iterative solution on the split sub-models respectively, and the specific steps include: splitting the model into two submodels and carrying out iterative solution to obtain corresponding suboptimal solutions:
the first submodel corresponds to a calculation formula of
Figure BDA0002243262260000153
subject to：
Figure BDA0002243262260000154
Figure BDA0002243262260000155
Figure BDA0002243262260000156
Figure BDA0002243262260000157
Figure BDA0002243262260000158
E[m]≥Emin,m＝1,..,M
The second submodel corresponds to a calculation formula of
Figure BDA0002243262260000159
In some specific embodiments, the purpose of setting step S3 is to fix the flight trajectory of the drone and update the resource allocation of the drone, i.e. in this step, the flight trajectory of the drone is first fixed and the resource allocation variables of the drone are optimized, the variables at least including the subcarrier sets for information transmission and energy transmission
Figure BDA0002243262260000161
And
Figure BDA0002243262260000162
power of subcarrier allocation
Figure BDA0002243262260000163
And subcarrier scheduling variables of nodes
Figure BDA0002243262260000164
As the basis for subsequent processing. In the optimization processing process of the step, a wireless energy-carrying communication technology based on multiple carriers is adopted in each time slot, namely, subcarriers and power are required to be reallocated in each time slot, and node subcarrier scheduling variables are re-optimized. To simplify the complexity of the problem, we specify that the total power of the drones in each slot is fixed to PmaxAnd the lower limit of the total energy collected by K nodes in each time slot is fixed as Emin. Therefore, the constraint conditions under each time slot are the same, the optimization method is the same, and the optimization target of the original optimization problem is
Figure BDA0002243262260000165
The middle T, K is a fixed value, so that the target can be converted into
Figure BDA0002243262260000166
Therefore, it is
Figure BDA0002243262260000167
Equivalently transformed into the following equation:
Figure BDA0002243262260000168
subject to：
Figure BDA00022432622600001616
Figure BDA0002243262260000169
Figure BDA00022432622600001610
Figure BDA00022432622600001611
Figure BDA00022432622600001612
Figure BDA00022432622600001613
according to the formula, the optimization problem in the formula is a non-convex problem, the suboptimal solution can be solved through an iterative algorithm, namely the optimization problem is divided into three steps for optimization, and the maximum value of the objective function is solved through iterative calculation.
Fixing the trajectory of the unmanned aerial vehicle in the step S3, wherein the specific step of optimizing the resource allocation variables of the unmanned aerial vehicle includes; s31, setting the total power of the unmanned aerial vehicle in each time slot to be PmaxAnd the lower limit of the total energy collected by K nodes in each time slot is fixed as Emin(ii) a Due to optimization of the objective
Figure BDA00022432622600001614
Middle T, K is a fixed value, therefore
Figure BDA00022432622600001615
Figure BDA0002243262260000171
S32, converting the channel model of the unmanned aerial vehicle and the node k on the nth subcarrier of the mth time slot into the following formula, wherein the channel model of the unmanned aerial vehicle and the node k on the nth subcarrier of the mth time slot is
Figure BDA0002243262260000172
Wherein, gk,nIndicating the channel gain coefficient of the nth sub-carrier distributed to the kth node; g0And GnRepresenting the directional antenna gain at the node and the directional antenna gain at the drone, respectively; beta is a0Representing the channel power gain at 1 meter of the reference position,
Figure BDA0002243262260000173
Figure BDA0002243262260000174
indicates that in the mth time slot, the unmanned plane and the node wkThe distance between them;
s33, converting the channel models of the unmanned aerial vehicle and the node k on the nth subcarrier of the mth time slot into corresponding equivalent transformation formulas, performing iterative computation, and solving the maximum value of the objective function, wherein the equivalent transformation formula is
Figure BDA0002243262260000175
subject to：
Figure BDA0002243262260000176
Figure BDA0002243262260000177
Figure BDA0002243262260000178
Figure BDA0002243262260000179
Figure BDA00022432622600001710
Figure BDA00022432622600001711
In some more specific embodiments, the specific step of solving the maximum value of the objective function includes:
s331, given
Figure BDA00022432622600001712
And
Figure BDA00022432622600001713
based on an optimization condition that allocates subcarriers n to designated nodes so that the total information rate and collected energy of the nodes are maximized, a node subcarrier scheduling variable is determined
Figure BDA00022432622600001714
And (3) optimizing the value, wherein the corresponding optimization formula is as follows:
Figure BDA0002243262260000181
subject to：
Figure BDA0002243262260000182
Figure BDA0002243262260000183
Figure BDA0002243262260000184
wherein,
Figure BDA0002243262260000185
and is
Figure BDA0002243262260000186
The purpose of this step is to allocate subcarrier n to a given node such that the total information rate and the collected energy of the node are maximized, i.e. subcarrier n is allocated to node k such that
Figure BDA0002243262260000187
The maximum value is taken. It can thus be concluded that as mentioned above,
Figure BDA0002243262260000188
and is
Figure BDA0002243262260000189
S332, scheduling variables based on optimized node subcarriers
Figure BDA00022432622600001810
And given
Figure BDA00022432622600001811
And
Figure BDA00022432622600001812
determining subcarrier allocation power
Figure BDA00022432622600001813
By changing the following formula A to the optimization formula corresponding to the following formula B
Figure BDA00022432622600001814
subject to：
Figure BDA00022432622600001815
Figure BDA00022432622600001816
In the formula (II), the compound (II) is shown in the specification,
Figure BDA00022432622600001817
Figure BDA00022432622600001818
Figure BDA00022432622600001819
Figure BDA00022432622600001820
Figure BDA00022432622600001821
subject to：
Figure BDA00022432622600001822
Figure BDA00022432622600001823
wherein,
Figure BDA00022432622600001824
representing a node-passed subcarrier scheduling variable
Figure BDA00022432622600001825
After optimization, the nth subcarrier is allocated to the kth subcarrier*When a node is in use, the channel gain of the subcarrier to the node is obtained;
and because the following formula represents a convex optimization problem, the optimization can be carried out by Lagrange dual decomposition, and the formula is
Figure BDA0002243262260000191
Then the lagrange dual function can be expressed as:
Figure BDA0002243262260000192
where ρ is1And ρ2Being non-negative Lagrangian multipliers, then Lagrangian functions
Figure BDA0002243262260000193
Can be expressed as:
Figure BDA0002243262260000194
it follows that the lagrange dual function can be simplified as:
Figure BDA0002243262260000195
subject to:ρ1≥0
ρ2≥0
since the dual function is differentiable, a suitable gradient can be selected by using a secondary gradient method to solve, wherein the secondary gradient can be expressed as:
Figure BDA0002243262260000196
Figure BDA0002243262260000197
obtaining the optimized Lagrange multiplier rho through the calculation of the two formulas1And ρ2Then, the solution can be obtained according to the KKT condition
Figure BDA0002243262260000201
Namely, the optimal distribution of power is solved through the Lagrange multiplier and the iterative optimization of power.
S333, based on the principle, the step is led out, namely the corresponding Lagrange multiplier rho is obtained by optimizing the first sub-model through a Lagrange dual decomposition method1And ρ2Then, according to the KKT condition and through iterative optimization of Lagrange multiplier and power, solving and solving
Figure BDA0002243262260000202
To obtain the corresponding optimal distribution of power, the corresponding solving formula is:
Figure BDA0002243262260000203
wherein, PmaxAnd PminMaximum and minimum values of power for energy harvesting are respectively represented, N is 1, …, N; in one embodiment, the calculation of the optimal allocation of power comprises: s3331, initializing Lagrange multiplier
Figure BDA0002243262260000204
And
Figure BDA0002243262260000205
and set ρ1Corresponding to an iteration precision of mu1，ρ2Has an iteration precision of mu2The iteration times t corresponding to the two are 0; s3332, carrying out iterative calculation, and judging whether t is 0 or whether the Lagrange multiplier can not reach convergence precision, namely
Figure BDA0002243262260000206
And is
Figure BDA0002243262260000207
Figure BDA0002243262260000208
If so, then the Lagrangian function is utilized
Figure BDA0002243262260000209
Calculating the corresponding power according to the formula
Figure BDA00022432622600002010
S3333, respectively substituting the calculated power into two gradient solving formulas to obtain the optimized Lagrange multiplier rho1And ρ2And updating the Lagrange multiplier
Figure BDA00022432622600002011
And
Figure BDA00022432622600002012
t +1, and the gradient solving formula is as follows:
Figure BDA00022432622600002013
Figure BDA00022432622600002014
s3334, returning to S3332, and judging whether the conditions are satisfied
Figure BDA00022432622600002015
Or
Figure BDA00022432622600002016
Figure BDA0002243262260000211
If the conditions are met, continuing iteration; otherwise, the iteration is terminated, and the power distribution in the last iteration is the optimal power distribution;
s334, based on optimization
Figure BDA0002243262260000212
And
Figure BDA0002243262260000213
optimizing subcarrier sets
Figure BDA0002243262260000214
And
Figure BDA0002243262260000215
the corresponding optimization formula is as follows:
Figure BDA0002243262260000216
wherein,
Figure BDA0002243262260000217
represents the optimized set of subcarriers for energy harvesting,
Figure BDA0002243262260000218
the meaning of this step is based on optimization
Figure BDA0002243262260000219
And
Figure BDA00022432622600002110
bringing formula (21) into formula (17) and further simplifying Lagrangian function expressions such as
Figure BDA00022432622600002111
Wherein,
Figure BDA00022432622600002112
it can be observed that only one term on the right side of the above equation is related to the subcarrier set
Figure BDA00022432622600002113
Others are constant terms. Thus, optimization of the set of subcarriers can be translated into solving for
Figure BDA00022432622600002114
The maximum value of (a) is:
Figure BDA0002243262260000221
wherein,
Figure BDA0002243262260000222
representing the set of sub-carriers optimized for energy harvesting. The optimization problem represented by equation (20) can be described as: bringing all n into
Figure BDA0002243262260000223
So that
Figure BDA0002243262260000224
A subcarrier n that takes a larger value is more suitable for energy harvesting. The specific optimization method comprises the following steps: bringing all N, N-1, …, N into formula
Figure BDA0002243262260000225
Get N
Figure BDA0002243262260000226
N is 1, …, and N is arranged in a row from large to small. Can satisfy E[m]≥EminOn the premise of allocating as few subcarriers as possible in the subcarrier sequence to the set in turn
Figure BDA0002243262260000227
In (1). The remaining subcarriers are allocated to the set
Figure BDA0002243262260000228
In (1).
S335, through the three steps, obtaining an initial value of iteration, and through the iterative optimization among three variables, namely repeating the steps S331-S334 until the target function R[m]And (6) converging.
In some specific embodiments, the M time slots obtained by the optimization process of step S3 correspond to each otherOptimized variable machine node subcarrier scheduling variable
Figure BDA0002243262260000229
Subcarrier power
Figure BDA00022432622600002210
Subcarrier set
Figure BDA00022432622600002211
And
Figure BDA00022432622600002212
m is 1, …, M; specifically, in the resource allocation of the fixed unmanned aerial vehicle in S4, and in optimizing the flight trajectory of the unmanned aerial vehicle, the corresponding optimization calculation formula includes:
Figure BDA00022432622600002213
q[m]，m＝1,…,M。
the above process is obtained by firstly collecting K nodes in subcarrier set in m-th time slot
Figure BDA00022432622600002214
The formula corresponding to the information rate of the received data is the following formula
Figure BDA00022432622600002215
The equivalence is transformed into the following equation:
Figure BDA0002243262260000231
wherein,
Figure BDA0002243262260000232
Figure BDA0002243262260000233
due to in the objective function
Figure BDA0002243262260000234
And in a third constraint
Figure BDA0002243262260000235
Non-convex, the optimization problem shown in this formula is not related to q [ m ]]The convex optimization problem of (1). To convert the optimization problem into a convex optimization problem, the variables are solved separately here
Figure BDA0002243262260000236
And
Figure BDA0002243262260000237
to obtain their lower bounds, the corresponding steps include:
variables of
Figure BDA0002243262260000238
First order taylor expansion of (1):
Figure BDA0002243262260000239
Figure BDA00022432622600002310
Figure BDA00022432622600002311
variables of
Figure BDA00022432622600002312
First order taylor expansion of (1):
Figure BDA00022432622600002313
Figure BDA00022432622600002314
Figure BDA00022432622600002315
wherein q is(l)[m]Indicating the trajectory position of the drone at the mth slot in the ith iteration. Although the original optimization problem is not a convex optimization problem, variables may be used
Figure BDA00022432622600002316
And
Figure BDA00022432622600002317
lower boundary of (1)
Figure BDA00022432622600002318
And
Figure BDA00022432622600002319
instead of them, the original problem is converted into a convex optimization problem, i.e. formula (C) is replaced by the following formula:
Figure BDA0002243262260000241
subject to：q[1]＝q[M]
||q[m+1]-q[m]||2≤(Vmaxδt)2,m＝1,...,M-1
Figure BDA0002243262260000242
the optimization problem can be solved directly by the tool kit CVX.
In some specific embodiments, the specific operation step of jointly optimizing the resource allocation of the drone and the flight trajectory of the drone in S5 includes: s51,Carrying out initialization processing of unmanned aerial vehicle resource allocation and unmanned aerial vehicle flight track combined optimization process, namely according to the position w of the nodekK1, …, K, initializing drone trajectory q m](0)M is 1, …, M; and solving variables based on model formulas corresponding to optimized models of flight trajectories of the unmanned aerial vehicle
Figure BDA0002243262260000243
And
Figure BDA0002243262260000244
the values after the solution are respectively taken as
Figure BDA0002243262260000245
Figure BDA0002243262260000246
And
Figure BDA0002243262260000247
while solving the objective function value
Figure BDA0002243262260000248
Wherein an error precision tau is setobjThe iteration number l is 0; s52, carrying out iterative operation of the unmanned aerial vehicle resource allocation and unmanned aerial vehicle flight trajectory combined optimization process, namely: s521, giving
Figure BDA0002243262260000249
And
Figure BDA00022432622600002410
solving a model formula corresponding to the optimization model of the flight path of the unmanned aerial vehicle to obtain an optimization variable q [ m ]]Of (2) an optimal solution q [ m ]]*And update q [ m ]](l+1)＝q[m]*(ii) a S522, fixing q [ m ]](l+1)Solving a model formula corresponding to the resource allocation optimization model of the unmanned aerial vehicle to obtain an optimized variable
Figure BDA00022432622600002411
And
Figure BDA00022432622600002412
of (2) an optimal solution
Figure BDA00022432622600002413
And
Figure BDA00022432622600002414
and update
Figure BDA00022432622600002415
Figure BDA00022432622600002416
And
Figure BDA00022432622600002417
s523, calculating an objective function
Figure BDA00022432622600002418
A corresponding objective function value; s524, judging whether the requirements are met
Figure BDA00022432622600002419
Figure BDA00022432622600002420
If yes, updating l to l +1, and going to S521; otherwise, the iteration is terminated; the optimal solution of the last iteration in S53 and S52 is the optimal value of the optimization variable, and the optimal value includes: optimal trajectory q [ m ] of unmanned aerial vehicle]＝q[m](l+1)Set of subcarriers for information transmission
Figure BDA00022432622600002421
Subcarrier set for energy transmission
Figure BDA00022432622600002422
Subcarrier scheduling variables for nodes
Figure BDA00022432622600002423
Sub-carrier waveDistributed power of
Figure BDA00022432622600002424
The design scheme is further verified through a specific simulation case, MATLAB software is adopted for the simulation of the system in the case, and a CVX software package is adopted for solving the optimization problem. The following embodiments examine the effectiveness of the joint optimization method for unmanned aerial vehicle trajectory and resource allocation in multi-carrier communication designed by the present invention.
In this embodiment, as shown in fig. 2, an unmanned aerial vehicle flies in the air and transmits information and energy to a plurality of ground nodes at the same time, in the simulation, it is considered that K-5 ground nodes are randomly distributed in a circular area with radius r-150 m, the flying height of the unmanned aerial vehicle is H-50 m, an initial trajectory is set as a circle, the center of the circle is the center of gravity of the nodes, and the radius is half of the farthest distance between the nodes; considering the total time T of one flight turn of the drone 20s, the time slot interval δt0.5s, maximum flying speed Vmax30 m/s; system bandwidth Btot1MHz, subcarrier bandwidth
Figure BDA0002243262260000251
The number N of the subcarriers is 16; the gain of the directional antenna at the unmanned aerial vehicle and the node is G0＝Gn10dB, reference channel power gain β at position 1 meter0-30dB, noise power spectral density N at ground node receiver0＝-70dBm；
Fig. 3 shows the initial trajectory of the drone and the trajectory after optimization by the proposed method. In the unmanned aerial vehicle track obtained by the method, the unmanned aerial vehicle always flies as close to the nodes as possible, and particularly in the dense node areas, the unmanned aerial vehicle has long flying time in the sky and even can be coiled in the sky. This is mainly to improve the channel condition between nodes, increase the average reachable rate of all nodes and the energy collected by the nodes.
Fig. 4 shows the trajectory change of the drone after the node 2 moves. As shown in FIG. 4, the positions of the remaining nodes remain unchanged, and the position of node 2 remains unchangedFrom the point [ -97, -48]TMove to the point [ -20, -20 [ ]]TAfterwards, the unmanned aerial vehicle orbit also changes along with it. Because the new position of the node 2 is closer to the geometric center of the node, and the distance between the nodes is reduced, all the nodes can be covered by the unmanned aerial vehicle track obtained by optimizing the new node position in the same flight period. Therefore, the proposed scheme can adaptively optimize the unmanned aerial vehicle trajectory according to the node position.
Fig. 5 shows the energy collection situation of different time slots under T ═ 20 s. It can be seen that the collected energy of the nodes in each time slot satisfies E[m]≥EminAnd the energy collected in different time slots has little change. This is because in each time slot, a set of sub-carriers allocated for energy transmission
Figure BDA0002243262260000252
The remaining majority of subcarriers are allocated to a set of subcarriers for information transmission
Figure BDA0002243262260000253
In (1).
Fig. 6 shows the average achievable rate for all nodes in each slot at T-20 s. With reference to fig. 3, it can be seen that the average reachable rates in different time slots are different greatly, which is mainly influenced by the distance between the drone and the node. When the flight position of the drone is directly above the node 5, as shown in fig. 2, the time slot m is 40, since the channel gains of the node 5 and the drone are far better than those of other nodes, most of subcarriers and power are allocated to the node 5 at this time, the information rate of the node 5 increases, and the average reachable rate also increases. When the flight position of the drone is far away from the node, as shown in fig. 2, the time slot m is 33, at this time, the channel gain between the drone and all the nodes is generally poor, so that both the collected energy and the node rate are low, and in order to ensure that the collected energy reaches the energy threshold, at this time, more subcarriers and power are allocated to the drone
Figure BDA0002243262260000261
Resulting in a significant reduction in the average achievable rate.
Fig. 7 shows the relationship between the node average reachable rate and the flight period T of the unmanned aerial vehicle under three different flight schemes. The three different flight schemes used for comparison are, respectively, scheme 1: hovering at a center of gravity of the K node positions; scheme 2: flying along the initial circular track; scheme 3: and flying along the optimized track. It can be seen from fig. 5 that the average achievable rate for scenario 3 presented herein is much higher than for the other two scenarios. The average achievable rates for scenarios 1 and 2 did not change significantly over time, and the average achievable rate for scenario 3 increased with increasing time. This is because as time increases, the optimized trajectory of the drone increases the time of flight in the dense node area, so that the channel gain between the drone and the node is at a higher level for a longer time, improving the path loss, and thus increasing the average reachable rate.
Therefore, it can be said that implementing the embodiment of the present invention will have the following beneficial effects:
1. the multi-node-oriented data distribution method for the unmanned aerial vehicle has the advantages of flexible maneuverability, low cost, high cost performance and strong adaptability to network dynamic change, and can be applied to various scenes of the Internet of things such as environment detection, safety management, emergency rescue and the like.
2. The invention adopts a multi-carrier wireless energy-carrying communication technology, can effectively solve the problems of small energy storage and short service life of the nodes of the Internet of things, and greatly reduces the complexity and cost of the design of the receiver compared with the wireless energy-carrying communication technology based on time switching and power distribution.
3. The invention can improve the communication link by using the flight characteristics of the unmanned aerial vehicle, improve the utilization rate of wireless resources by optimizing the track of the unmanned aerial vehicle, and realize the maximization of the data transmission rate.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present application. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims (7)

1. A joint optimization method for unmanned aerial vehicle track and resource allocation for multi-carrier wireless communication is characterized by comprising the following steps:
s1, creating an optimization model facing multi-node energy-carrying communication based on the unmanned aerial vehicle;
s2, splitting the optimization model, and respectively carrying out iterative solution on the split sub-models;
s3, fixing the track of the unmanned aerial vehicle, and optimizing the resource allocation variable of the unmanned aerial vehicle, wherein the resource allocation variable of the unmanned aerial vehicle comprises a subcarrier set for information transmission
Figure FDA0003446346190000011
And subcarrier set for energy transmission
Figure FDA0003446346190000012
Power of subcarrier allocation
Figure FDA0003446346190000013
And subcarrier scheduling variables of nodes
Figure FDA0003446346190000014
S4, fixing unmanned aerial vehicle resource allocation and optimizing the flight trajectory of the unmanned aerial vehicle;
s5, carrying out combined optimization of the flight trajectory and the resource allocation of the unmanned aerial vehicle to obtain the optimal value of the optimization variable;
the specific step of creating an optimization model for multi-node-oriented energy-carrying communication based on the unmanned aerial vehicle in S1 includes:
s11, determining an optimization target and an optimization variable corresponding to the creation of the optimization model, wherein the optimization target refers to the average transmission rate of the unmanned aerial vehicle to the ground node in all time slots, and the optimization variable refers to the average transmission rate of the unmanned aerial vehicle to the ground node in each time slot m-1, …Position q [ M ] at M]And in the mth time slot, the subcarrier sets respectively used for information transmission
Figure FDA0003446346190000015
And subcarrier set for energy transmission
Figure FDA0003446346190000016
Power of subcarrier allocation
Figure FDA0003446346190000017
And subcarrier scheduling variables of nodes
Figure FDA0003446346190000018
Meanwhile, suppose that K ground nodes are randomly distributed in a circular area with the radius of r, the position of each ground node is known, and the position of the kth ground node is wk(ii) a The unmanned aerial vehicle is limited on a plane with the height of H for periodic flight, and the time of one flight circle is T; the time T is divided into M time slots, each time slot having a length deltatAt any time t equal to m δtM is 1, …, M; then in the mth time slot, the position of the drone is qm]The maximum speed of the unmanned plane is VmaxIn each time slot, the total power transmitted by the unmanned aerial vehicle is PmaxThe lower limit of the total energy collected by the K nodes is Emin；
S12, determining constraint conditions corresponding to the creation of the optimization model, wherein the constraint conditions comprise: (1) constraint conditions for constraining the subcarrier allocation under each time slot; (2) the constraint condition is used for constraining the maximum transmitting power of the unmanned aerial vehicle under each time slot; (3) constraint conditions for constraining the energy collected by each node under each time slot; (4) constraint conditions for constraining the flying speed and state of the unmanned aerial vehicle;
s13, determining to create the optimization model based on S11-S12, wherein the optimization model comprises a resource allocation optimization model of the unmanned aerial vehicle and a flight path optimization model of the unmanned aerial vehicle;
the model formula corresponding to the resource allocation optimization model of the unmanned aerial vehicle is as follows (1):
Figure FDA0003446346190000021
the model formula corresponding to the optimization model of the flight path of the unmanned aerial vehicle is as follows (2):
Figure FDA0003446346190000022
wherein,
Figure FDA0003446346190000023
Figure FDA0003446346190000024
in the formula (1), C represents a total set of subcarriers,
Figure FDA0003446346190000025
and
Figure FDA0003446346190000026
respectively, the subcarrier sets for information transmission and energy transmission in the mth slot,
Figure FDA0003446346190000027
is an identification variable for the node's schedule,
Figure FDA0003446346190000028
the expression that in the mth time slot, the subcarrier n is allocated to the node k, and in each time slot, the subcarrier n can be allocated to only one node, namely, a plurality of nodes cannot use the same subcarrier;
in formula (2), q [1 ]]＝q[M]To indicate nobodyThe starting position and the end position of the unmanned aerial vehicle are the same to ensure that the unmanned aerial vehicle flies periodically, | q [ m +1 ]]-q[m]||2≤(Vmaxδt)2M-1 indicates that the distance between two adjacent positions of the unmanned aerial vehicle is smaller than the distance of the unmanned aerial vehicle flying at the maximum speed in a time slot, namely, the optimization of the track of the unmanned aerial vehicle is ensured to meet the requirement of the actual flying speed of the unmanned aerial vehicle;
in equation (3), equation (3) represents the rates of all nodes in the mth slot; wherein,
Figure FDA0003446346190000031
indicating the channel gain between the node k and the unmanned aerial vehicle when the sub-carrier n receives the information of the unmanned aerial vehicle in the mth time slot; gk,nA channel gain coefficient indicating that the nth subcarrier is allocated to the kth node; g0And GnRepresenting the directional antenna gains at the node and at the drone, respectively; beta is a0Represents the channel power gain at a reference position of 1 meter; n is a radical of0Representing a noise power spectral density; b represents a subcarrier bandwidth;
in equation (4), equation (4) represents the energy collected by K nodes in the mth slot, and the threshold is Emin。
2. The method of claim 1, wherein the step of splitting the optimization model and performing iterative solution on the split submodels in S2 comprises:
splitting the model into two submodels and carrying out iterative solution to obtain corresponding suboptimal solutions,
the original optimization problem can be decomposed into the following two subproblems to be solved respectively:
the first submodel corresponds to a calculation formula of
Figure FDA0003446346190000032
The second submodel corresponds to a calculation formula of
Figure FDA0003446346190000041
3. The joint optimization method of drone trajectory and resource allocation for multi-carrier wireless communication according to claim 1, characterized in that: in the step S3, fixing the trajectory of the unmanned aerial vehicle, and the specific step of optimizing the resource allocation variables of the unmanned aerial vehicle includes;
s31, setting the total power of the unmanned aerial vehicle in each time slot to be PmaxAnd the lower limit of the total energy collected by K nodes in each time slot is fixed as Emin(ii) a Due to optimization of the objective
Figure FDA0003446346190000042
Middle T, K is a fixed value, therefore
Figure FDA0003446346190000043
S32, converting the channel model of the unmanned aerial vehicle and the node k on the nth subcarrier of the mth time slot into the following formula, wherein the channel model of the unmanned aerial vehicle and the node k on the nth subcarrier of the mth time slot is
Figure FDA0003446346190000044
Wherein, gk,nIndicating the channel gain coefficient of the nth sub-carrier distributed to the kth node; g0And GnRepresenting the directional antenna gain at the node and the directional antenna gain at the drone, respectively; beta is a0Representing the channel power gain at 1 meter of the reference position,
Figure FDA0003446346190000045
indicates that in the mth time slot, the unmanned plane and the node wkWherein H represents the flight of the droneA height;
s33, converting the channel models of the unmanned aerial vehicle and the node k on the nth subcarrier of the mth time slot into corresponding equivalent transformation formulas, performing iterative computation, and solving the maximum value of the objective function, wherein the equivalent transformation formula is
Figure FDA0003446346190000051
4. The joint optimization method of unmanned aerial vehicle trajectory and resource allocation for multi-carrier wireless communication of claim 3, wherein: the specific step of solving the maximum value of the objective function includes:
s331, given
Figure FDA0003446346190000052
And
Figure FDA0003446346190000053
based on an optimization condition that allocates subcarriers n to designated nodes so that the total information rate and collected energy of the nodes are maximized, a node subcarrier scheduling variable is determined
Figure FDA0003446346190000054
And (3) optimizing the value, wherein the corresponding optimization formula is as follows:
Figure FDA0003446346190000055
wherein,
Figure FDA0003446346190000056
and is
Figure FDA0003446346190000057
S332, optimizing the node subcarriersWave scheduling variables
Figure FDA0003446346190000058
And given
Figure FDA0003446346190000059
And
Figure FDA00034463461900000510
determining subcarrier allocation power
Figure FDA00034463461900000511
The corresponding optimization formula is as follows:
Figure FDA00034463461900000512
wherein,
Figure FDA0003446346190000061
representing a node-passed subcarrier scheduling variable
Figure FDA0003446346190000062
After optimization, the nth subcarrier is allocated to the kth subcarrier*When a node is in use, the channel gain of the subcarrier to the node is obtained;
s333, optimizing the first sub-model by a Lagrange dual decomposition method to obtain the corresponding Lagrange multiplier rho1And ρ2Then, according to the KKT condition and through iterative optimization of Lagrange multiplier and power, solving
Figure FDA0003446346190000063
To obtain the corresponding optimal distribution of power, the corresponding solving formula is:
Figure FDA0003446346190000064
wherein, PmaxAnd PminMaximum and minimum values of power for energy harvesting are respectively represented, N is 1, …, N;
s334, based on optimization
Figure FDA0003446346190000065
And
Figure FDA0003446346190000066
optimizing subcarrier sets
Figure FDA0003446346190000067
And
Figure FDA0003446346190000068
the corresponding optimization formula is as follows:
Figure FDA0003446346190000069
wherein,
Figure FDA00034463461900000610
represents the optimized set of subcarriers for energy harvesting,
Figure FDA00034463461900000611
s335, repeating the steps S331-S334 until the target function R[m]And (6) converging.
5. The joint optimization method of unmanned aerial vehicle trajectory and resource allocation for multi-carrier wireless communication of claim 4, wherein: the calculation process of the optimal allocation of power comprises: s3331, initializing Lagrange multiplier rho1And rho2The initial value of the Lagrange multiplier is expressed as
Figure FDA00034463461900000612
And
Figure FDA00034463461900000613
and set ρ1Corresponding to an iteration precision of mu1，ρ2Has an iteration precision of mu2The iteration times t corresponding to the two are 0; s3332, carrying out iterative calculation, and judging whether t is 0 or whether the Lagrange multiplier can not reach convergence precision, namely
Figure FDA00034463461900000614
Figure FDA00034463461900000615
And is
Figure FDA00034463461900000616
If so, then the Lagrangian function is utilized
Figure FDA00034463461900000617
Calculating corresponding power, wherein the corresponding power calculation formula is
Figure FDA0003446346190000071
S3333, respectively substituting the calculated power into two gradient solving formulas to obtain the optimized Lagrange multiplier rho1And ρ2And updating the Lagrange multiplier
Figure FDA0003446346190000072
And
Figure FDA0003446346190000073
t +1, and the gradient solving formula is as follows:
Figure FDA0003446346190000074
Figure FDA0003446346190000075
s3334, returning to S3332, and judging whether the conditions are satisfied
Figure FDA0003446346190000076
Or
Figure FDA0003446346190000077
Figure FDA0003446346190000078
If the conditions are met, continuing iteration; otherwise, the iteration is terminated, and the power allocation in the last iteration is the optimal power allocation.
6. The joint optimization method of drone trajectory and resource allocation for multi-carrier wireless communication according to claim 1, characterized in that: in the step S4, resource allocation of the fixed unmanned aerial vehicle is performed, and in optimizing the flight trajectory of the unmanned aerial vehicle, the corresponding optimization calculation formula includes:
Figure FDA0003446346190000079
7. the joint optimization method of drone trajectory and resource allocation for multi-carrier wireless communication according to claim 1, characterized in that: the specific operation steps of the unmanned aerial vehicle resource allocation and unmanned aerial vehicle flight trajectory joint optimization in the step S5 include: s51, carrying out initialization processing of unmanned aerial vehicle resource allocation and unmanned aerial vehicle flight trajectory combined optimization process, namely according to the position w of the nodekK1, …, K, initializing drone trajectory q m](0)M is 1, …, M; and solving variables based on model formulas corresponding to optimized models of flight trajectories of the unmanned aerial vehicle
Figure FDA00034463461900000710
And
Figure FDA00034463461900000711
the values after the solution are respectively taken as
Figure FDA00034463461900000712
Figure FDA00034463461900000713
And
Figure FDA00034463461900000714
while solving the objective function value
Figure FDA00034463461900000715
Wherein an error precision tau is setobjThe iteration number l is 0; s52, carrying out iterative operation of the unmanned aerial vehicle resource allocation and unmanned aerial vehicle flight trajectory combined optimization process, namely: s521, giving
Figure FDA0003446346190000081
And
Figure FDA0003446346190000082
solving a model formula corresponding to the optimization model of the flight path of the unmanned aerial vehicle to obtain an optimization variable q [ m ]]Of (2) an optimal solution q [ m ]]*And update q [ m ]](l+1)＝q[m]*(ii) a S522, fixing q [ m ]](l+1)Solving a model formula corresponding to the resource allocation optimization model of the unmanned aerial vehicle to obtain an optimized variable
Figure FDA0003446346190000083
And
Figure FDA0003446346190000084
of (2) an optimal solution
Figure FDA0003446346190000085
And
Figure FDA0003446346190000086
and update
Figure FDA0003446346190000087
Figure FDA0003446346190000088
And
Figure FDA0003446346190000089
s523, calculating an objective function
Figure FDA00034463461900000810
A corresponding objective function value; s524, judging whether the requirements are met
Figure FDA00034463461900000811
If yes, updating l to l +1, and going to S521; otherwise, the iteration is terminated; the optimal solution of the last iteration in S53 and S52 is the optimal value of the optimization variable, and the optimal value includes: optimal trajectory q [ m ] of unmanned aerial vehicle]＝q[m](l+1)Set of subcarriers for information transmission
Figure FDA00034463461900000812
Subcarrier set for energy transmission
Figure FDA00034463461900000813
Subcarrier scheduling variables for nodes
Figure FDA00034463461900000814
Allocated power of sub-carriers
Figure FDA00034463461900000815
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