CN113498018B - Unmanned aerial vehicle track optimization method and system for assisting coverage enhancement of intelligent Internet of things - Google Patents
Unmanned aerial vehicle track optimization method and system for assisting coverage enhancement of intelligent Internet of things Download PDFInfo
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Abstract
The invention discloses an unmanned aerial vehicle track optimization method and system for assisting coverage enhancement of an intelligent Internet of things, wherein the method comprises the steps of determining a constraint condition for unmanned aerial vehicle track optimization according to a communication relation between an unmanned aerial vehicle and a preset intelligent Internet of things system and unmanned aerial vehicle attributes of the unmanned aerial vehicle; constructing a communication scheduling and association model of the unmanned aerial vehicle and the ground Internet of things terminal equipment according to the constraint conditions; according to the intelligent Internet of things system and the communication scheduling and correlation model, a total energy consumption model of all unmanned aerial vehicles and a total throughput model of the ground Internet of things terminal equipment in a task period are established; and establishing an unmanned aerial vehicle track optimization model according to the total energy consumption model and the total throughput model, and obtaining the unmanned aerial vehicle optimized track meeting the constraint condition by iteratively solving the unmanned aerial vehicle track optimization model. The method provided by the invention can realize the maximization of system benefit and simultaneously ensure the service quality of equipment.
Description
Technical Field
The invention relates to the technical field of wireless communication, in particular to an unmanned aerial vehicle track optimization method and system for assisting coverage enhancement of an intelligent Internet of things.
Background
With the rapid development of communication technology and electronic devices, more and more devices are connected to a network, which creates a huge challenge for a communication system. Next generation wireless communications (5G) are required to be able to provide ubiquitous coverage and sustainable high data transfer rates. Conventional wireless communication systems are primarily composed of fixed terrestrial infrastructure, such as terrestrial base stations, access points, and relays. However, traditional base station deployments are mostly static and deployed on the ground, which makes them unsatisfactory for certain applications in specific scenarios, such as hot spot area traffic surge (e.g. stadiums, holidays, etc.), infrastructure destruction in emergency situations (e.g. flood, earthquake-stricken area, etc.), and no base station coverage in remote areas (e.g. mountainous area, desert, gobi, etc.). The above problem is that 5G wireless communication must be solved.
Recently, the rapid development of electronic devices, sensors and communication technologies has greatly promoted the application of unmanned aerial vehicles in various fields, such as navigation, precision agriculture, aerial photography, and the like. In view of the advantages of unmanned aerial vehicles having rapid deployment, controllable movement, low cost and high probability of line-of-sight communication, unmanned aerial vehicles are applied to the field of wireless communication. The unmanned aerial vehicle is used as a relay, an access point, a base station and an edge server by carrying different communication equipment. Because unmanned aerial vehicle can be deployed anytime and anywhere, as required. Thus, drone airborne base stations are considered to be an effective solution to the deficiencies of traditional cellular networks. Currently, there are some successful drone communication prototypes, such as Facebook solar drones and the Google Loon program. The unmanned aerial base station is divided into the following parts according to whether mobility is used: stationary air base stations and mobile air base stations. Stationary airborne base stations, while capable of providing seamless coverage, are less flexible and do not have adaptive capabilities. The air base station is moved, the unmanned aerial vehicle makes full use of the flexible mobility of the base station, the air-ground communication distance can be shortened, and the channel quality can be improved in time. However, implementing drone-assisted smart internet of things also faces a number of challenges.
Unmanned aerial vehicle orbit design is the key to realize unmanned aerial vehicle supplementary wireless network. Because, the drone 3D trajectory position has a significant impact on channel links, communication coverage, and energy consumption. Likewise, the trajectory of the drone is also affected by the particular quality of service of the internet of things devices, the distribution of the device locations, the system radio resources, and the onboard energy. How to design a proper unmanned aerial vehicle track is the focus of current attention. Trajectories generated by different communication targets and constraints are also different, and unmanned aerial vehicle trajectory optimization schemes can be roughly divided into the following two types: 2D trajectory optimization, e.g., minimum throughput maximization, fixed drone altitude, optimization of its horizontal flight trajectory ground user reception service fairness; the 3D orbit is optimized, for example, the mobile unmanned aerial vehicle realizes energy-saving Internet of things communication, the position information of the Internet of things equipment is stored in a cloud end controller, the unmanned aerial vehicle obtains the position of the Internet of things equipment from a controller, and the 3D position of the unmanned aerial vehicle is timely adjusted according to the number of the equipment needing service at present, so that the total energy consumption of the Internet of things equipment is minimum.
The airborne energy of the unmanned aerial vehicle is a key difficulty of the unmanned aerial vehicle auxiliary wireless network. The onboard energy of the unmanned aerial vehicle has important influence on the operation and the endurance of the unmanned aerial vehicle. Different applications in the internet of things network have different quality of service requirements. How to design an unmanned aerial vehicle flight trajectory which can ensure the service quality and simultaneously enable the system energy efficiency to be maximum is one of the key points concerned at present. Radio resource allocation is a key technology for drone-assisted wireless networks. How to guarantee the service quality of service users so that resources are reasonably allocated according to needs is also the focus of current attention.
Although there are many methods for resource allocation and trajectory optimization, they are not very efficient.
Disclosure of Invention
The invention provides an unmanned aerial vehicle track optimization method and system for assisting coverage enhancement of an intelligent Internet of things, which are used for overcoming the defects of poor system benefit and the like in the prior art.
In order to achieve the purpose, the invention provides an unmanned aerial vehicle track optimization method for assisting coverage enhancement of an intelligent internet of things, which comprises the following steps:
determining a constraint condition for unmanned aerial vehicle track optimization according to a communication relation between an unmanned aerial vehicle and a preset intelligent Internet of things system and unmanned aerial vehicle attributes of the unmanned aerial vehicle; the intelligent Internet of things system comprises: ground internet of things terminal equipment; the unmanned aerial vehicle is communicated with the ground Internet of things terminal equipment;
constructing a communication scheduling and association model of the unmanned aerial vehicle and the ground Internet of things terminal equipment according to the constraint conditions;
according to the intelligent Internet of things system and the communication scheduling and correlation model, a total energy consumption model of all unmanned aerial vehicles and a total throughput model of the ground Internet of things terminal equipment in a task period are established;
and establishing an unmanned aerial vehicle track optimization model according to the total energy consumption model and the total throughput model, and obtaining the unmanned aerial vehicle optimized track meeting the constraint condition by iteratively solving the unmanned aerial vehicle track optimization model.
In order to achieve the above object, the present invention further provides an unmanned aerial vehicle trajectory optimization system for assisting coverage enhancement of an intelligent internet of things, including:
the system comprises a limiting module, a track optimizing module and a track optimizing module, wherein the limiting module is used for determining a constraint condition for optimizing the track of the unmanned aerial vehicle according to the communication relationship between the unmanned aerial vehicle and a preset intelligent Internet of things system and the unmanned aerial vehicle attribute of the unmanned aerial vehicle; the intelligent Internet of things system comprises: ground internet of things terminal equipment; the unmanned aerial vehicle is communicated with the ground Internet of things terminal equipment;
the model building module is used for building a communication scheduling and association model of the unmanned aerial vehicle and the ground Internet of things terminal equipment according to the constraint conditions; according to the intelligent Internet of things system and the communication scheduling and correlation model, a total energy consumption model of all unmanned aerial vehicles and a total throughput model of the ground Internet of things terminal equipment in a task period are established;
and the track optimization module is used for establishing an unmanned aerial vehicle track optimization model according to the total energy consumption model and the total throughput model, and obtaining the unmanned aerial vehicle optimized track meeting the constraint condition by solving the unmanned aerial vehicle track optimization model in an iterative manner.
To achieve the above object, the present invention further provides a computer device, which includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method when executing the computer program.
Compared with the prior art, the invention has the beneficial effects that:
the unmanned aerial vehicle track optimization method for assisting coverage enhancement of the intelligent Internet of things provided by the invention is used for timely adjusting the position of the unmanned aerial vehicle aerial base station by combining the known position of the ground Internet of things terminal equipment, service quality demand information (mainly data transmission rate) and available radio resources of the intelligent Internet of things system, so that the service quality can be effectively guaranteed; in addition, the method realizes the optimization of the unmanned aerial vehicle track by coordinating the relation between the total energy consumption and the total system throughput of the unmanned aerial vehicle in the intelligent Internet of things system, and can save the energy of the unmanned aerial vehicle so as to effectively prolong the service life of the wireless communication network of the unmanned aerial vehicle. By the unmanned aerial vehicle track optimization method for assisting coverage enhancement of the intelligent Internet of things, system benefit maximization can be realized, and meanwhile, the service quality of equipment is guaranteed.
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 structures shown in the drawings without creative efforts.
Fig. 1 is a flowchart of an unmanned aerial vehicle trajectory optimization method for assisting coverage enhancement of an intelligent internet of things, provided by the invention;
FIG. 2 is a diagram of the components of the system of the smart Internet of things in example 1;
fig. 3a is a 3D trajectory diagram of a drone under a throughput maximization scheme;
fig. 3b is a 3D trajectory diagram of the unmanned aerial vehicle under the unmanned aerial vehicle trajectory optimization method for assisting coverage enhancement of the intelligent internet of things provided by the invention;
FIG. 4 is a schematic diagram showing the comparison between the method provided in example 1 and the system of the prior art;
fig. 5 is a schematic diagram of energy consumption of the drone under different maximum transmission powers in the method provided in embodiment 1 and the existing method.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In addition, the technical solutions in the embodiments of the present invention may be combined with each other, but it must be based on the realization of those skilled in the art, and when the technical solutions are contradictory or cannot be realized, such a combination of technical solutions should not be considered to exist, and is not within the protection scope of the present invention.
The invention provides an unmanned aerial vehicle track optimization method for assisting coverage enhancement of an intelligent Internet of things, which comprises the following steps of:
101, determining a constraint condition for unmanned aerial vehicle track optimization according to a communication relation between an unmanned aerial vehicle and a preset intelligent Internet of things system and unmanned aerial vehicle attributes of the unmanned aerial vehicle; the intelligent Internet of things system comprises: ground internet of things terminal equipment; the unmanned aerial vehicle is communicated with the ground Internet of things terminal equipment;
the intelligent Internet of things system comprises ground Internet of things terminal equipment and an unmanned aerial vehicle aerial base station. The information such as the number, material property, spiral blade tip rotating speed, body resistance ratio, rotor hardness, rotor area size, thrust of the unmanned aerial vehicles and the like can be obtained from the intelligent Internet of things system; the density of the air; unmanned plane service period; coordinates of ground internet-of-things terminal equipment; half wave width of an aerial base station antenna of the unmanned aerial vehicle; the minimum data transmission rate requirement of the specific service of the ground Internet of things terminal equipment; the maximum flight speed of the unmanned aerial vehicle; information such as the maximum transmitting power of the unmanned aerial vehicle.
The unmanned aerial vehicle trajectory optimization needs to meet the constraint condition so as to ensure the safe flight of the unmanned aerial vehicle, guarantee the communication service quality of the system and the like.
102, constructing a communication scheduling and association model of the unmanned aerial vehicle and the ground internet of things terminal equipment according to the constraint conditions;
according to the communication scheduling and association model, the communication condition of the unmanned aerial vehicle aerial base station and the ground Internet of things terminal equipment can be adjusted in real time.
103, according to the intelligent Internet of things system and the communication scheduling and correlation model, constructing a total energy consumption model of all unmanned aerial vehicles and a total throughput model of the ground Internet of things terminal equipment in a task period;
the unmanned aerial vehicle in the intelligent Internet of things system carries out periodic service, so that a total energy consumption model of the unmanned aerial vehicle and a total throughput model of the ground Internet of things terminal equipment in a period only need to be established.
104, establishing an unmanned aerial vehicle track optimization model according to the total energy consumption model and the total throughput model, and obtaining an unmanned aerial vehicle optimized track meeting the constraint condition by iteratively solving the unmanned aerial vehicle track optimization model;
in the intelligent Internet of things system, M pieces of ground Internet of things terminal equipment are randomly and uniformly distributed on the ground. In the task period, the unmanned aerial vehicle is communicated with the ground Internet of things terminal equipment by combining a time division multiple access technology and a frequency division multiple access technology. Assuming that the position of the ground internet-of-things terminal device is fixed and the position information is known by the unmanned aerial vehicle, the experience quality requirements of the ground internet-of-things terminal device are diverse and randomly distributed, and each task period is different, and the unmanned aerial vehicle can collect the flow requirements of each ground internet-of-things terminal device from the cloud server in each task period.
The unmanned aerial vehicle track refers to a position time sequence of the unmanned aerial vehicle in a task period. And in the track optimization process, the track position is determined according to the state of the intelligent Internet of things system.
In one embodiment, for step 101, determining a constraint condition for trajectory optimization of the drone according to a communication relationship between the drone and a preset intelligent internet of things system and a drone attribute of the drone includes:
(1) the air-to-ground link can meet the requirement that the data transmission rate is greater than or equal to the minimum data transmission rate of a specific service;
Ri(t)≥Ri,th (7)
in the formula, Ri(t) the achievable data transmission rate of the air-ground link; ri,thThe minimum data transmission rate requirement of a specific service for a ground Internet of things terminal device i;
(2) the moving speed of the unmanned aerial vehicle is less than or equal to the maximum flying speed of the unmanned aerial vehicle;
wherein q (t) is time-varying coordinates of the drone, q (t) { x (t), y (t), z (t) };
is the moving speed of the unmanned aerial vehicle,
vmaxthe maximum flying speed of the unmanned aerial vehicle.
(3) The transmitting power of the unmanned aerial vehicle is less than or equal to the maximum transmitting power of the unmanned aerial vehicle;
pi(t)≤pmax (9)
in the formula, pi(t) transmitting power for the unmanned aerial vehicle; p is a radical ofmaxThe maximum transmitting power of the unmanned aerial vehicle.
(4) Ground thing networking terminal equipment is in unmanned aerial vehicle coverage.
In another embodiment, the data transfer rate R may be achieved for a constraint where the air-to-ground link isi(t) dependent on the distance d between the drone and the ground internet of things terminal devicei(t), unmanned aerial vehicle emission power pi(t) and drone channel bandwidth bi(t) of (d). The minimum data transmission rate requirement of a specific service of a known ground internet of things terminal device i is assumed to be Ri,thTo ensureQuality of service, air-to-ground link achievable data transmission rate Ri(t) minimum data transfer rate requirement R ≧ specific servicei,th. Wherein the air-to-ground link can achieve a data transmission rate Ri(t) is
In the formula, Ri(t) the achievable data transmission rate of the air-ground link; bi(t) channel bandwidth for the drone; p is a radical ofi(t) transmitting power for the unmanned aerial vehicle; l isi(t) unmanned plane path loss; delta2Is gaussian white noise power.
In the next embodiment, for drone path loss LiAnd (t), the air-ground link in the intelligent Internet of things system comprises a line-of-sight link and a non-line-of-sight link, which are based on an unavoidable communication mode in the unmanned aerial vehicle wireless communication system, so that the unmanned aerial vehicle path loss comprises line-of-sight link path loss and non-line-of-sight link path loss. The half-power wave width elevation psi of the antenna used by the unmanned aerial vehicle base station is 3dB, so that the line-of-sight link probability p is within the coverage range of the unmanned aerial vehicle base stationLoSApproximately equals 1, and the unmanned aerial vehicle path loss L of the intelligent Internet of things system is equal to the weight of the line-of-sight link and the non-line-of-sight link in the coverage range of the unmanned aerial vehicle aerial base stationi(t) distance d between main unmanned aerial vehicle and ground internet of things terminal equipmenti(t) related to
In the formula, Li(t) unmanned plane path loss;
c is the speed of light, fcIs the carrier frequency; di(t) is the distance between the unmanned aerial vehicle and the ground internet of things terminal equipment; mu.sLoSIs the line-of-sight link attenuation coefficient; { x (t), y (t), z (t) } are time-varying coordinates of the unmanned aerial vehicle in the air; (x)i,yi) And the coordinates of the ith ground internet of things terminal equipment.
Thus, the air-to-ground link can achieve a data transmission rate Ri(t) can be simplified as:
in the formula, bi(t) channel bandwidth for the drone; p is a radical ofi(t) transmitting power for the unmanned aerial vehicle; delta2Is gaussian white noise power;
c is the speed of light, fcIs the carrier frequency; di(t) is the distance between the unmanned aerial vehicle and the ground internet of things terminal equipment; mu.sLoSIs the line-of-sight link attenuation coefficient.
In a certain embodiment, for the constraint condition, wherein, the ground internet of things terminal device is in the coverage of the unmanned aerial vehicle, specifically:
di(t)≤H(t)tanθ (4)
in the formula (d)i(t) is the distance between the unmanned aerial vehicle and the ground internet of things terminal equipment; h (t) is the height of the unmanned aerial vehicle at the moment t; theta is the half wave width of the aerial base station antenna of the unmanned aerial vehicle.
In a next embodiment, for step 102, constructing a communication scheduling and association model of the unmanned aerial vehicle and the ground internet of things terminal device according to the constraint condition, including:
constructing a communication scheduling and association model of the unmanned aerial vehicle and the ground Internet of things terminal equipment according to two constraint conditions that the reachable data transmission rate of the air-ground link is more than or equal to the minimum data transmission rate requirement of a specific service and the ground Internet of things terminal equipment is within the coverage range of the unmanned aerial vehicle,
in the formula, λi(t) ═ 1 is that ground thing networking terminal equipment i communicates with unmanned aerial vehicle at time t, lambdai(t) ═ 0 is that the ground internet of things terminal device i does not communicate with the unmanned aerial vehicle at the moment t; ri(t) the achievable data transmission rate of the air-ground link; ri,thThe minimum data transmission rate requirement of a specific service for a ground Internet of things terminal device i; di(t) is the distance between the unmanned aerial vehicle and the ground internet of things terminal equipment; h (t) is the height of the unmanned aerial vehicle at the moment t; theta is the half wave width of the aerial base station antenna of the unmanned aerial vehicle.
When the link capacity between the ground Internet of things terminal device i and the unmanned aerial vehicle (namely the air-ground link can reach the data transmission rate R)i(t)) at time t, the minimum data transmission rate requirement R for a particular service of a terminal device i of the terrestrial internet of things is meti,thAnd the ground internet of things terminal device i is within the coverage range of the unmanned aerial vehicle, the ground internet of things terminal device i is arranged to communicate with the unmanned aerial vehicle, otherwise, the unmanned aerial vehicle does not communicate with the ground internet of things terminal device i.
In a next embodiment, for step 103, constructing a total energy consumption model of all drones in a task period according to the smart internet of things system and the communication scheduling and association model, including:
301, obtaining the total energy consumption composition of the unmanned aerial vehicle in the task period according to the intelligent Internet of things system;
according to the intelligent Internet of things system, the total energy consumption of the unmanned aerial vehicle in the task period is calculated by communication energy consumption PcAnd thrust energy consumption P (v (t)).
302, according to the total energy consumption composition and the communication scheduling and correlation model, constructing a total energy consumption model of the unmanned aerial vehicle in the task period,
wherein,
in the formula, E is the total energy consumption of the unmanned aerial vehicle in the task period; pc(t) communication energy consumption; p (v (t)) is thrust energy consumption; t is a task period, and T is the time in the task period; m is the number of unmanned aerial vehicles; lambda [ alpha ]i(t) a communication scheduling and association model of the unmanned aerial vehicle and the ground Internet of things terminal equipment; p is a radical ofi,c(t) communication energy consumption of the ith unmanned aerial vehicle; v (t) is the speed of the unmanned aerial vehicle at the moment t, including the vertical direction and the horizontal direction; p is a radical ofo，piIs a known quantity and depends on the material property of the unmanned aerial vehicle; k is the ratio of the thrust to the gravity of the unmanned aerial vehicle; u shapetipThe rotational speed of the spiral blade tip; v. of0Hovering speed of the unmanned aerial vehicle; v. ofz(t) the vertical direction speed of the unmanned aerial vehicle; d0The resistance ratio of the unmanned plane body; ρ is the air density; s is the hardness of the rotor of the unmanned aerial vehicle; a is the rotor area of the unmanned aerial vehicle;
is the thrust of the unmanned plane.
In one embodiment, considering that the rotor disk area of the unmanned aerial vehicle is small, the fuselage drag is also small, so the thrust-to-gravity ratio k ≈ 1. Equation (13) can be further simplified as:
in the formula, W is the gravity of the unmanned aerial vehicle; the other symbols have the same meaning as formula (13).
In a certain embodiment, for step 103, the total throughput model of the ground internet of things terminal device in the task period is
In the formula, R (t) is the total throughput of the ground Internet of things terminal equipment in the task period; m is the number of unmanned aerial vehicles; riAnd (t) the achievable data transmission rate of the air-ground link.
In the next embodiment, for step 104, an unmanned aerial vehicle trajectory optimization model is established according to the total energy consumption model and the total throughput model, and the unmanned aerial vehicle trajectory optimization model is solved iteratively to obtain an unmanned aerial vehicle optimized trajectory meeting the constraint condition, wherein the unmanned aerial vehicle trajectory optimization model is
Wherein E (t) is the total energy consumption of all unmanned aerial vehicles in the task period; and R (t) is the total throughput of the ground Internet of things terminal equipment in the task period.
The drone trajectory is a continuous time variable, so the solution set of the drone trajectory optimization model is infinite. In addition, the unmanned aerial vehicle trajectory optimization model is a fractional function, the denominator of the fractional function is a non-convex function, and the numerator of the fractional function is a non-convex non-concave function. In the embodiment, the unmanned aerial vehicle trajectory optimization model is solved iteratively through a coordinate fast descent algorithm and a Dinkelbach algorithm, and the unmanned aerial vehicle optimized trajectory meeting the constraint condition is finally obtained.
The invention also provides an unmanned aerial vehicle track optimization system assisting in coverage enhancement of the intelligent Internet of things, which comprises the following components:
the system comprises a limiting module, a track optimizing module and a track optimizing module, wherein the limiting module is used for determining a constraint condition for optimizing the track of the unmanned aerial vehicle according to the communication relationship between the unmanned aerial vehicle and a preset intelligent Internet of things system and the unmanned aerial vehicle attribute of the unmanned aerial vehicle; the intelligent Internet of things system comprises: ground internet of things terminal equipment; the unmanned aerial vehicle is communicated with the ground Internet of things terminal equipment;
the model building module is used for building a communication scheduling and association model of the unmanned aerial vehicle and the ground Internet of things terminal equipment according to the constraint conditions; according to the intelligent Internet of things system and the communication scheduling and correlation model, a total energy consumption model of all unmanned aerial vehicles and a total throughput model of the ground Internet of things terminal equipment in a task period are established;
and the track optimization module is used for establishing an unmanned aerial vehicle track optimization model according to the total energy consumption model and the total throughput model, and obtaining the unmanned aerial vehicle optimized track meeting the constraint condition by solving the unmanned aerial vehicle track optimization model in an iterative manner.
The invention further provides a computer device, which includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method when executing the computer program.
Example 1
The embodiment provides an unmanned aerial vehicle trajectory optimization method assisting coverage enhancement of an intelligent internet of things, which comprises the following steps:
determining a constraint condition for unmanned aerial vehicle track optimization according to a communication relation between an unmanned aerial vehicle and a preset intelligent Internet of things system and unmanned aerial vehicle attributes of the unmanned aerial vehicle; the intelligent Internet of things system comprises: ground internet of things terminal equipment; the unmanned aerial vehicle is communicated with the ground Internet of things terminal equipment;
constructing a communication scheduling and association model of the unmanned aerial vehicle and the ground Internet of things terminal equipment according to the constraint conditions;
according to the intelligent Internet of things system and the communication scheduling and correlation model, a total energy consumption model of all unmanned aerial vehicles and a total throughput model of the ground Internet of things terminal equipment in a task period are established;
and establishing an unmanned aerial vehicle track optimization model according to the total energy consumption model and the total throughput model, and obtaining the unmanned aerial vehicle optimized track meeting the constraint condition by iteratively solving the unmanned aerial vehicle track optimization model.
The intelligent internet of things system obtained in the embodiment is shown in fig. 2, wherein theta is the half wave width of the aerial base station antenna of the unmanned aerial vehicle, psi is the half power wave width elevation angle of the aerial base station antenna of the unmanned aerial vehicle,
the simulation scenario of this embodiment provides an on-demand wireless communication service for the drone in a traffic hot zone (e.g., a large sporting event, a concert, etc.). Assume that there are 10 terrestrial mobile users that are randomly and evenly distributed and have different quality of service requirements. The total bandwidth B of the system is 67kHz, and the noise power is delta2Maximum transmission power p of-90 dbmmax100dbm, carrier frequency f02e +09Hz, line-of-sight communication link attenuation coefficient muLoS3db, the maximum moving speed of the unmanned plane in the horizontal direction and the maximum moving speed of the unmanned plane in the vertical direction are respectively
The task period T is 60s, and the minimum speed requirement R of the real-time userth100kbits, non-real time user rate requirement Rth＝1kbits。
In order to evaluate the performance of the unmanned aerial vehicle trajectory optimization method for assisting coverage enhancement of the intelligent internet of things, the method (EE maximization) provided by the embodiment is compared with an existing throughput maximization scheme (throughput maximization), a resource random allocation scheme (resources allocation) and a benchmark scheme (baseline). Wherein the throughput maximization scheme does not consider the energy consumption of the unmanned aerial vehicle; the resource random allocation scheme does not consider the requirements of the quality of service experience of the users; the benchmark scheme does not consider maximum throughput nor energy consumption of the unmanned aerial vehicle, and only considers the user service experience quality requirement.
Fig. 3a is a 3D trajectory of a drone under a throughput maximization scheme, where the acquired trajectory of the drone mostly stays at one position and rises almost vertically. Fig. 3b is a 3D trajectory of the unmanned aerial vehicle according to the technical scheme of the present invention, and the 3D trajectory of the unmanned aerial vehicle according to the technical scheme of the present invention is relatively smooth and rises or falls at a small angle.
In fig. 3a and 3b, users are classified into 3 categories, the first category is real-time users, which need to be always connected online and have high data transmission rate (such as online games and watching live videos); the second category of users are non-real time users, which in turn include low data transmission rates (e.g., sending text messages, browsing web pages) and high data transmission rates (e.g., viewing regular videos).
Compared with the technical scheme of the invention, the throughput maximization scheme has the advantages that the unmanned aerial vehicle tracks are different in the two schemes, because:
the throughput maximization scheme does not take into account drone energy consumption. According to the scheme of the invention, the energy consumption of the unmanned aerial vehicle is considered in the process of designing the track of the unmanned aerial vehicle. The hovering state of the unmanned aerial vehicle consumes more energy than the flying state, and the vertical up-and-down flying consumes more energy than the oblique flying. In order to save the energy of the unmanned aerial vehicle, the unmanned aerial vehicle track under the scheme of the invention is smoother than the track under the scheme of maximizing the throughput. The unmanned aerial vehicle track under the scheme provided by the invention has another characteristic: when the non-real-time user among the users needing service is close to the real-time user and the speed requirement is high, the unmanned aerial vehicle performs descending flight; when the non-real-time user is far away from the real-time user and the speed requirement is low in the users needing service, the unmanned aerial vehicle climbs and flies. According to the simulation result, the scheme provided by the invention can well guarantee the service quality of the equipment. To further evaluate the performance of the proposed solution, the system energy efficiency was next examined.
Fig. 4 is a schematic diagram illustrating comparison between the system energy efficiency obtained by the method provided by the present embodiment and the system energy efficiency obtained by the existing method, and it can be known that the system energy efficiency obtained by the method provided by the present embodiment is higher than that obtained by the existing throughput maximization scheme (throughput maximization) and the base-level scheme (baseline), and is close to that obtained by the resource random allocation scheme (resources allocation), but the resource random allocation scheme (resources allocation) cannot ensure the user experience quality requirement. The system energy efficiency obtained by the reference scheme (baseline) is lowest, and the trend of the iteration curve is opposite to that of the other three schemes, because system resources are all distributed to two real-time users in the experiment initialization process, the initial track of the unmanned aerial vehicle is a 2D track with fixed height, and all track points are very close to the real-time users. Therefore, in order to ensure the experience quality requirement of the device in the iteration process, the unmanned aerial vehicle flies in 3D, the distance between the unmanned aerial vehicle and the real-time user is increased, the energy consumption is larger than the initial situation, the throughput is smaller than the initial situation, and the energy efficiency is reduced along with the increase of the iteration times in the previous 3 iteration processes. According to the simulation result, the method provided by the embodiment can obtain good system benefit.
Fig. 5 is a schematic diagram of energy consumption of the unmanned aerial vehicle under different maximum transmission powers in the method provided in this embodiment and the existing method, and it can be known from the diagram that the energy consumption of the unmanned aerial vehicle under different maximum transmission powers in the method provided in this embodiment is significantly reduced compared with the existing throughput maximization scheme (throughput maximization) and the reference scheme (baseline); compared with the existing resource random allocation scheme (resources allocation), the energy consumption of the unmanned aerial vehicle in the embodiment is linearly changed along with the maximum transmission power (the energy consumption is increased along with the increase of the transmission power) and is adjusted to control the energy consumption, and the change of the existing resource random allocation scheme (resources allocation) is not obviously regular. The simulation result fully shows that the method provided by the embodiment can achieve a good energy-saving effect.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention, and all modifications and equivalents of the present invention, which are made by the contents of the present specification and the accompanying drawings, or directly/indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (6)
1. An unmanned aerial vehicle track optimization method assisting coverage enhancement of an intelligent Internet of things is characterized by comprising the following steps:
determining a constraint condition for unmanned aerial vehicle track optimization according to a communication relation between an unmanned aerial vehicle and a preset intelligent Internet of things system and unmanned aerial vehicle attributes of the unmanned aerial vehicle; the intelligent Internet of things system comprises: ground internet of things terminal equipment; the unmanned aerial vehicle is communicated with the ground Internet of things terminal equipment; the constraint conditions include: the air-to-ground link can meet the requirement that the data transmission rate is greater than or equal to the minimum data transmission rate of a specific service; the moving speed of the unmanned aerial vehicle is less than or equal to the maximum flying speed of the unmanned aerial vehicle; the transmitting power of the unmanned aerial vehicle is less than or equal to the maximum transmitting power of the unmanned aerial vehicle; the ground internet of things terminal equipment is in the coverage range of the unmanned aerial vehicle;
according to the constraint conditions, a communication scheduling and association model of the unmanned aerial vehicle and the ground Internet of things terminal equipment is constructed, and the method comprises the following steps:
constructing a communication scheduling and association model of the unmanned aerial vehicle and the ground Internet of things terminal equipment according to two constraint conditions that the reachable data transmission rate of the air-ground link is more than or equal to the minimum data transmission rate requirement of a specific service and the ground Internet of things terminal equipment is within the coverage range of the unmanned aerial vehicle,
in the formula, λi(t) ═ 1 is that ground thing networking terminal equipment i communicates with unmanned aerial vehicle at time t, lambdai(t) ═ 0 is that the ground internet of things terminal device i does not communicate with the unmanned aerial vehicle at the moment t; ri(t) the achievable data transmission rate of the air-ground link; ri,thThe minimum data transmission rate requirement of a specific service for a ground Internet of things terminal device i; di(t) is the distance between the unmanned aerial vehicle and the ground internet of things terminal equipment; h (t) is the height of the unmanned aerial vehicle at the moment t; theta is the half wave width of the aerial base station antenna of the unmanned aerial vehicle;
according to the intelligent Internet of things system and the communication scheduling and correlation model, a total energy consumption model of all unmanned aerial vehicles and a total throughput model of the ground Internet of things terminal equipment in a task period are established; according to the intelligent Internet of things system and the communication scheduling and correlation model, a total energy consumption model of all unmanned aerial vehicles in a task period is constructed, and the method comprises the following steps:
obtaining the total energy consumption composition of the unmanned aerial vehicle in the task period according to the intelligent Internet of things system;
constructing a total energy consumption model of the unmanned aerial vehicle in a task period according to the total energy consumption composition and the communication scheduling and correlation model,
wherein,
in the formula, E is the total energy consumption of the unmanned aerial vehicle in the task period; pc(t) communication energy consumption; p (v (t)) is thrust energy consumption; t is a task period, and T is the time in the task period; m is the number of unmanned aerial vehicles; lambda [ alpha ]i(t) a communication scheduling and association model of the unmanned aerial vehicle and the ground Internet of things terminal equipment; p is a radical ofi,c(t) communication energy consumption of the ith unmanned aerial vehicle; v (t) is the speed of the unmanned aerial vehicle at the moment t, including the vertical direction and the horizontal direction; p is a radical ofo，piIs a known quantity and depends on the material property of the unmanned aerial vehicle; k is the ratio of the thrust to the gravity of the unmanned aerial vehicle; u shapetipThe rotational speed of the spiral blade tip; v. of0Hovering speed of the unmanned aerial vehicle; v. ofz(t) the vertical direction speed of the unmanned aerial vehicle; d0The resistance ratio of the unmanned plane body; ρ is the air density; s is the hardness of the rotor of the unmanned aerial vehicle; a is the rotor area of the unmanned aerial vehicle;
thrust of the unmanned aerial vehicle;
the total throughput model of the ground internet of things terminal equipment in the task period is
Wherein R (t) is in the task periodThe total throughput of the ground internet of things terminal equipment; m is the number of unmanned aerial vehicles; ri(t) the achievable data transmission rate of the air-ground link;
establishing an unmanned aerial vehicle track optimization model according to the total energy consumption model and the total throughput model, and obtaining an unmanned aerial vehicle optimized track meeting the constraint condition by iteratively solving the unmanned aerial vehicle track optimization model; wherein the unmanned aerial vehicle track optimization model is
Wherein E (t) is the total energy consumption of all unmanned aerial vehicles in the task period; and R (t) is the total throughput of the ground Internet of things terminal equipment in the task period.
2. The unmanned aerial vehicle trajectory optimization method for assisting coverage enhancement of intelligent internet of things as claimed in claim 1, wherein the air-to-ground link reachable data transmission rate is
In the formula, Ri(t) the achievable data transmission rate of the air-ground link; bi(t) channel bandwidth for the drone; p is a radical ofi(t) transmitting power for the unmanned aerial vehicle; l isi(t) unmanned plane path loss; delta2Is gaussian white noise power.
3. The method for unmanned aerial vehicle trajectory optimization with coverage enhancement for the auxiliary intelligent internet of things as claimed in claim 2, wherein the unmanned aerial vehicle path loss comprises a line-of-sight link path loss and a non-line-of-sight link path loss, and the unmanned aerial vehicle path loss is the unmanned aerial vehicle path loss according to weights of the line-of-sight link and the non-line-of-sight link in the coverage range of an unmanned aerial vehicle air base station
In the formula, Li(t) unmanned plane path loss;
c is the speed of light, fcIs the carrier frequency; di(t) is the distance between the unmanned aerial vehicle and the ground internet of things terminal equipment; mu.sLoSIs the line-of-sight link attenuation coefficient; { x (t), y (t), z (t) } are time-varying coordinates of the unmanned aerial vehicle in the air; (x)i,yi) And the coordinates of the ith ground internet of things terminal equipment.
4. The unmanned aerial vehicle trajectory optimization method for assisting coverage enhancement of the intelligent internet of things as claimed in claim 1, wherein the ground internet of things terminal device is in the coverage range of the unmanned aerial vehicle, and specifically comprises:
di(t)≤H(t)tanθ (4)
in the formula (d)i(t) is the distance between the unmanned aerial vehicle and the ground internet of things terminal equipment; h (t) is the height of the unmanned aerial vehicle at the moment t; theta is the half wave width of the aerial base station antenna of the unmanned aerial vehicle.
5. The utility model provides an unmanned aerial vehicle orbit optimization system of supplementary intelligence thing networking coverage reinforcing which characterized in that includes:
the system comprises a limiting module, a track optimizing module and a track optimizing module, wherein the limiting module is used for determining a constraint condition for optimizing the track of the unmanned aerial vehicle according to the communication relationship between the unmanned aerial vehicle and a preset intelligent Internet of things system and the unmanned aerial vehicle attribute of the unmanned aerial vehicle; the intelligent Internet of things system comprises: ground internet of things terminal equipment; the unmanned aerial vehicle is communicated with the ground Internet of things terminal equipment; the constraint conditions include: the air-to-ground link can meet the requirement that the data transmission rate is greater than or equal to the minimum data transmission rate of a specific service; the moving speed of the unmanned aerial vehicle is less than or equal to the maximum flying speed of the unmanned aerial vehicle; the transmitting power of the unmanned aerial vehicle is less than or equal to the maximum transmitting power of the unmanned aerial vehicle; the ground internet of things terminal equipment is in the coverage range of the unmanned aerial vehicle;
the model building module is used for building a communication scheduling and association model of the unmanned aerial vehicle and the ground Internet of things terminal equipment according to the constraint conditions, and comprises the following steps:
constructing a communication scheduling and association model of the unmanned aerial vehicle and the ground Internet of things terminal equipment according to two constraint conditions that the reachable data transmission rate of the air-ground link is more than or equal to the minimum data transmission rate requirement of a specific service and the ground Internet of things terminal equipment is within the coverage range of the unmanned aerial vehicle,
in the formula, λi(t) ═ 1 is that ground thing networking terminal equipment i communicates with unmanned aerial vehicle at time t, lambdai(t) ═ 0 is that the ground internet of things terminal device i does not communicate with the unmanned aerial vehicle at the moment t; ri(t) the achievable data transmission rate of the air-ground link; ri,thThe minimum data transmission rate requirement of a specific service for a ground Internet of things terminal device i; di(t) is the distance between the unmanned aerial vehicle and the ground internet of things terminal equipment; h (t) is the height of the unmanned aerial vehicle at the moment t; theta is the half wave width of the aerial base station antenna of the unmanned aerial vehicle; according to the intelligent Internet of things system and the communication scheduling and correlation model, a total energy consumption model of all unmanned aerial vehicles and a total throughput model of the ground Internet of things terminal equipment in a task period are established; according to the intelligent Internet of things system and the communication scheduling and correlation model, a total energy consumption model of all unmanned aerial vehicles in a task period is constructed, and the method comprises the following steps:
obtaining the total energy consumption composition of the unmanned aerial vehicle in the task period according to the intelligent Internet of things system;
constructing a total energy consumption model of the unmanned aerial vehicle in a task period according to the total energy consumption composition and the communication scheduling and correlation model,
wherein,
in the formula, E is the total energy consumption of the unmanned aerial vehicle in the task period; pc(t) communication energy consumption; p (v (t)) is thrust energy consumption; t is a task period, and T is the time in the task period; m is the number of unmanned aerial vehicles; lambda [ alpha ]i(t) a communication scheduling and association model of the unmanned aerial vehicle and the ground Internet of things terminal equipment; p is a radical ofi,c(t) communication energy consumption of the ith unmanned aerial vehicle; v (t) is the speed of the unmanned aerial vehicle at the moment t, including the vertical direction and the horizontal direction; p is a radical ofo，piIs a known quantity and depends on the material property of the unmanned aerial vehicle; k is the ratio of the thrust to the gravity of the unmanned aerial vehicle; u shapetipThe rotational speed of the spiral blade tip; v. of0Hovering speed of the unmanned aerial vehicle; v. ofz(t) the vertical direction speed of the unmanned aerial vehicle; d0The resistance ratio of the unmanned plane body; ρ is the air density; s is the hardness of the rotor of the unmanned aerial vehicle; a is the rotor area of the unmanned aerial vehicle;
thrust of the unmanned aerial vehicle;
the total throughput model of the ground internet of things terminal equipment in the task period is
In the formula, R (t) is the total throughput of the ground Internet of things terminal equipment in the task period; m is the number of unmanned aerial vehicles; ri(t) the achievable data transmission rate of the air-ground link;
the track optimization module is used for establishing an unmanned aerial vehicle track optimization model according to the total energy consumption model and the total throughput model, and obtaining an unmanned aerial vehicle optimized track meeting the constraint condition by solving the unmanned aerial vehicle track optimization model in an iterative manner; wherein the unmanned aerial vehicle track optimization model is
Wherein E (t) is the total energy consumption of all unmanned aerial vehicles in the task period; and R (t) is the total throughput of the ground Internet of things terminal equipment in the task period.
6. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor when executing the computer program implements the steps of the method of any of claims 1 to 4.
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