CN110730031B - Unmanned aerial vehicle track and resource allocation joint optimization method for multi-carrier communication - Google Patents
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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
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 setAnd energy transmission carrier setPower of subcarrier allocationAnd subcarrier scheduling variables of nodes
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 setsAnd energy ofSubcarrier set for transmissionPower of subcarrier allocationAnd subcarrier scheduling variables of nodesMeanwhile, 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):
the model formula corresponding to the optimization model of the flight path of the unmanned aerial vehicle is as follows (2):
wherein,
in the formula (1), C represents a total set of subcarriers,andrespectively, the subcarrier sets for information transmission and energy transmission in the mth slot,is an identification variable for the node's schedule,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, 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
E[m]≥Emin,m=1,..,M
The second submodel corresponds to a calculation formula of
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 objectiveMiddle T, K is a fixed value, therefore
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
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, 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
Optionally, in one embodiment, the specific step of solving the maximum value of the objective function includes:
s331, givenAndbased 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 determinedAnd (3) optimizing the value, wherein the corresponding optimization formula is as follows:
S332, scheduling variables based on optimized node subcarriersAnd givenAnddetermining subcarrier allocation powerThe corresponding optimization formula is as follows:
wherein,representing a node-passed subcarrier scheduling variableAfter 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 solvingTo obtain the corresponding optimal distribution of power, the corresponding solving formula is:
wherein, PmaxAnd PminMaximum and minimum values of power for energy harvesting are respectively represented, N is 1, …, N;
s334, based on optimizationAndoptimizing subcarrier setsAndthe corresponding optimization formula is as follows:
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 asAndand 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, namelyAnd is If so, then the Lagrangian function is utilizedCalculating the corresponding power according to the formulaS3333, respectively substituting the calculated power into two gradient solving formulas to obtain the optimized Lagrange multiplier rho1And ρ2And updating the Lagrange multiplierAndt +1, and the gradient solving formula is as follows:
s3334, returning to S3332, and judging whether the conditions are satisfiedOr 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:
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 vehicleAndthe values after the solution are respectively taken as Andwhile solving the objective function valueWherein 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, givingAndsolving 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 variableAndof (2) an optimal solutionAndand update Ands523, calculating an objective functionA corresponding objective function value; s524, judging whether the requirements are met 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 transmissionSubcarrier set for energy transmissionSubcarrier scheduling variables for nodesAllocated power of sub-carriers
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.
Drawings
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 subcarriersAnd energy transmission carrier setPower of subcarrier allocationAnd subcarrier scheduling variables of nodesS4, 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:
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 transmissionIndicating the set of sub-carriers used for energy transmissionIndicating that two subcarrier sets do not existAt the intersection, and add to the total set C. Further, we will assemble the subcarriersAndthe subcarriers in the system are allocated to K nodes, and variables are scheduled by the subcarriersIndicating 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 ofIndicating 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:
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:
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,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 setThe information rate of the received data can be expressed as
In the mth time slot, K nodes are in the subcarrier setThe total energy received can be expressed as
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 mAndpower of subcarrier allocationAnd subcarrier scheduling variables of nodesThe 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 setsAnd subcarrier set for energy transmissionPower of subcarrier allocationAnd subcarrier scheduling variables of nodesMeanwhile, 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):
the model formula corresponding to the optimization model of the flight path of the unmanned aerial vehicle is as follows (2):
wherein,
in the formula (1), C represents a total set of subcarriers,andrespectively, the subcarrier sets for information transmission and energy transmission in the mth slot,is an identification variable for the node's schedule,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, 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
E[m]≥Emin,m=1,..,M
The second submodel corresponds to a calculation formula of
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 transmissionAndpower of subcarrier allocationAnd subcarrier scheduling variables of nodesAs 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 isThe middle T, K is a fixed value, so that the target can be converted intoTherefore, it isEquivalently transformed into the following equation:
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 objectiveMiddle T, K is a fixed value, therefore
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
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, 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
In some more specific embodiments, the specific step of solving the maximum value of the objective function includes:
s331, givenAndbased 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 determinedAnd (3) optimizing the value, wherein the corresponding optimization formula is as follows:
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 thatThe maximum value is taken. It can thus be concluded that as mentioned above,and is
S332, scheduling variables based on optimized node subcarriersAnd givenAnddetermining subcarrier allocation powerBy changing the following formula A to the optimization formula corresponding to the following formula B
wherein,representing a node-passed subcarrier scheduling variableAfter 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
Then the lagrange dual function can be expressed as:
where ρ is1And ρ2Being non-negative Lagrangian multipliers, then Lagrangian functionsCan be expressed as:
it follows that the lagrange dual function can be simplified as:
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:
obtaining the optimized Lagrange multiplier rho through the calculation of the two formulas1And ρ2Then, the solution can be obtained according to the KKT conditionNamely, 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 solvingTo obtain the corresponding optimal distribution of power, the corresponding solving formula is:
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 multiplierAndand 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, namelyAnd is If so, then the Lagrangian function is utilizedCalculating the corresponding power according to the formulaS3333, respectively substituting the calculated power into two gradient solving formulas to obtain the optimized Lagrange multiplier rho1And ρ2And updating the Lagrange multiplierAndt +1, and the gradient solving formula is as follows:
s3334, returning to S3332, and judging whether the conditions are satisfiedOr 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 optimizationAndoptimizing subcarrier setsAndthe corresponding optimization formula is as follows:
wherein,represents the optimized set of subcarriers for energy harvesting,the meaning of this step is based on optimizationAndbringing formula (21) into formula (17) and further simplifying Lagrangian function expressions such as
Wherein,it can be observed that only one term on the right side of the above equation is related to the subcarrier setOthers are constant terms. Thus, optimization of the set of subcarriers can be translated into solving forThe maximum value of (a) is:
wherein,representing the set of sub-carriers optimized for energy harvesting. The optimization problem represented by equation (20) can be described as: bringing all n intoSo thatA 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 formulaGet NN 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 turnIn (1). The remaining subcarriers are allocated to the setIn (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 variableSubcarrier powerSubcarrier setAndm 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:
q[m],m=1,…,M。
the above process is obtained by firstly collecting K nodes in subcarrier set in m-th time slotThe formula corresponding to the information rate of the received data is the following formula
The equivalence is transformed into the following equation:
wherein, due to in the objective functionAnd in a third constraintNon-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 hereAndto obtain their lower bounds, the corresponding steps include:
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 usedAndlower boundary of (1)Andinstead of them, the original problem is converted into a convex optimization problem, i.e. formula (C) is replaced by the following formula:
subject to:q[1]=q[M]
||q[m+1]-q[m]||2≤(Vmaxδt)2,m=1,...,M-1
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 vehicleAndthe values after the solution are respectively taken as Andwhile solving the objective function valueWherein 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, givingAndsolving 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 variableAndof (2) an optimal solutionAndand update Ands523, calculating an objective functionA corresponding objective function value; s524, judging whether the requirements are met 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 transmissionSubcarrier set for energy transmissionSubcarrier scheduling variables for nodesSub-carrier waveDistributed power of
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 bandwidthThe 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 transmissionThe remaining majority of subcarriers are allocated to a set of subcarriers for information transmissionIn (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 droneResulting 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 transmissionAnd subcarrier set for energy transmissionPower of subcarrier allocationAnd subcarrier scheduling variables of nodes
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 transmissionAnd subcarrier set for energy transmissionPower of subcarrier allocationAnd subcarrier scheduling variables of nodesMeanwhile, 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):
the model formula corresponding to the optimization model of the flight path of the unmanned aerial vehicle is as follows (2):
wherein,
in the formula (1), C represents a total set of subcarriers,andrespectively, the subcarrier sets for information transmission and energy transmission in the mth slot,is an identification variable for the node's schedule,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,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
The second submodel corresponds to a calculation formula of
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 objectiveMiddle T, K is a fixed value, therefore
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
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,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
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, givenAndbased 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 determinedAnd (3) optimizing the value, wherein the corresponding optimization formula is as follows:
S332, optimizing the node subcarriersWave scheduling variablesAnd givenAnddetermining subcarrier allocation powerThe corresponding optimization formula is as follows:
wherein,representing a node-passed subcarrier scheduling variableAfter 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, solvingTo obtain the corresponding optimal distribution of power, the corresponding solving formula is:
wherein, PmaxAnd PminMaximum and minimum values of power for energy harvesting are respectively represented, N is 1, …, N;
s334, based on optimizationAndoptimizing subcarrier setsAndthe corresponding optimization formula is as follows:
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 asAndand 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 And isIf so, then the Lagrangian function is utilizedCalculating corresponding power, wherein the corresponding power calculation formula isS3333, respectively substituting the calculated power into two gradient solving formulas to obtain the optimized Lagrange multiplier rho1And ρ2And updating the Lagrange multiplierAndt +1, and the gradient solving formula is as follows:
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:
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 vehicleAndthe values after the solution are respectively taken as Andwhile solving the objective function valueWherein 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, givingAndsolving 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 variableAndof (2) an optimal solutionAndand update Ands523, calculating an objective functionA corresponding objective function value; s524, judging whether the requirements are metIf 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 transmissionSubcarrier set for energy transmissionSubcarrier scheduling variables for nodesAllocated power of sub-carriers
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CN113498018B (en) * | 2020-03-19 | 2022-04-05 | 湖南智领通信科技有限公司 | Unmanned aerial vehicle track optimization method and system for assisting coverage enhancement of intelligent Internet of things |
CN111835401B (en) * | 2020-06-05 | 2021-07-09 | 北京科技大学 | Method for joint optimization of wireless resources and paths in unmanned aerial vehicle communication network |
CN112055310B (en) * | 2020-07-30 | 2021-07-09 | 中国科学院上海微系统与信息技术研究所 | Trajectory design and power distribution method in unmanned aerial vehicle CR-NOMA network |
CN111885504B (en) * | 2020-08-05 | 2022-08-02 | 广州大学 | Unmanned aerial vehicle track optimization method for assisting wireless communication of mobile vehicle |
CN112256054B (en) * | 2020-10-09 | 2022-03-29 | 北京邮电大学 | Unmanned aerial vehicle trajectory planning method and device |
CN112995913B (en) * | 2021-03-08 | 2022-04-08 | 南京航空航天大学 | Unmanned aerial vehicle track, user association and resource allocation joint optimization method |
CN113365288B (en) * | 2021-04-30 | 2023-04-07 | 中山大学 | NB-IoT system uplink resource allocation method based on SWIPT |
CN113743795B (en) * | 2021-09-08 | 2024-09-20 | 北京京东振世信息技术有限公司 | Method and device for determining task scheduling result |
CN113825143B (en) * | 2021-10-15 | 2023-07-14 | 西北工业大学 | Position optimization and resource allocation method and system based on collaborative heterogeneous air network |
CN115278849B (en) * | 2022-09-29 | 2022-12-20 | 香港中文大学(深圳) | Transmission opportunity and power control method for dynamic topology of unmanned aerial vehicle |
CN115802494B (en) * | 2023-02-03 | 2023-05-19 | 南京邮电大学 | Unmanned aerial vehicle hidden communication system track optimization and communication resource allocation method and system |
Citations (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN109600828A (en) * | 2018-11-19 | 2019-04-09 | 赣南师范大学 | The Adaptive Transmission power distribution method of unmanned plane downlink |
Family Cites Families (7)
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US9621254B2 (en) * | 2012-09-21 | 2017-04-11 | Spatial Digital Systems, Inc. | Communications architectures via UAV |
CN106304362A (en) * | 2016-08-14 | 2017-01-04 | 辛建芳 | A kind of relay system efficiency optimization method based on OFDM |
US10389432B2 (en) * | 2017-06-22 | 2019-08-20 | At&T Intellectual Property I, L.P. | Maintaining network connectivity of aerial devices during unmanned flight |
CN109348532B (en) * | 2018-10-26 | 2021-07-09 | 南京航空航天大学 | Cognitive Internet of vehicles efficient combined resource allocation method based on asymmetric relay transmission |
CN110147040B (en) * | 2019-04-10 | 2022-05-20 | 中国人民解放军陆军工程大学 | Flight trajectory and power distribution joint optimization method for energy-carrying transmission of unmanned aerial vehicle |
CN110166107A (en) * | 2019-05-17 | 2019-08-23 | 武汉大学 | Based on the unmanned plane relay system resource allocation method for wirelessly taking energy communication network |
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