CN108924791B

CN108924791B - Wireless communication method, device, equipment and readable storage medium

Info

Publication number: CN108924791B
Application number: CN201810769377.1A
Authority: CN
Inventors: 张广驰; 严海强; 崔苗; 林凡
Original assignee: Guangdong University of Technology
Current assignee: Guangdong University of Technology
Priority date: 2018-07-13
Filing date: 2018-07-13
Publication date: 2021-09-03
Anticipated expiration: 2038-07-13
Also published as: CN108924791A

Abstract

The invention discloses a wireless communication method in a communication network for realizing multi-hop relay by utilizing an unmanned aerial vehicle, which comprises the following steps: acquiring channel information and the number of unmanned aerial vehicles, and creating a system throughput calculation model by using the channel information and the number of the unmanned aerial vehicles; carrying out iterative optimization on the transmitting power and the flight track in the system throughput calculation model by using a block coordinate descent method and a continuous convex optimization method to obtain an optimized flight track and optimized transmitting power; in the communication network, communication is realized by utilizing optimized transmitting power and optimized flight path. So, can realize remote communication in the unable signal blind area that covers of basic station with make full use of unmanned aerial vehicle's degree of freedom, can also optimize system throughput simultaneously. Correspondingly, the embodiment of the invention also provides the wireless communication device, the equipment and the readable storage medium, and the corresponding technical effects are achieved.

Description

Wireless communication method, device, equipment and readable storage medium

Technical Field

The present invention relates to the field of communications technologies, and in particular, to a wireless communication method, apparatus, device, and readable storage medium.

Background

Unmanned Aerial Vehicles (UAVs) have been widely used in military and civilian fields, such as search and rescue, inspection, surveillance, and cargo transportation, due to their features such as low cost, high mobility, and on-demand deployment.

In recent years, unmanned aerial vehicles have more and more practical applications in the field of wireless communication. Unmanned aerial vehicles are also a focus of recent research as relay-assisted communications. Compare traditional fixed position's relay node, unmanned aerial vehicle relay can utilize its motion degree of freedom to solve the communication problem of the unable signal blind area that covers of fixed base station.

Currently, a single drone is mainly focused as relay auxiliary communication, which greatly limits the range of use of the drone. In the case where the two communication parties are far apart, for example, in a desert area without communication infrastructure or in an earthquake-stricken area with a seriously damaged base station, a single drone relay cannot guarantee real-time and reliable communication.

To sum up, how to effectively solve the problems of utilizing the unmanned aerial vehicle to solve the long-distance communication and the like is a technical problem which needs to be solved urgently by technical personnel in the field at present.

Disclosure of Invention

The invention aims to provide a wireless communication method, a wireless communication device, wireless communication equipment and a readable storage medium, so that a unmanned aerial vehicle is used for realizing long-distance communication.

In order to solve the technical problems, the invention provides the following technical scheme:

a wireless communication method is applied to a communication network for realizing multi-hop relay by utilizing an unmanned aerial vehicle, and comprises the following steps:

acquiring channel information and the number of unmanned aerial vehicles, and creating a system throughput calculation model by using the channel information and the number of the unmanned aerial vehicles; the system throughput calculation model is a model for calculating system throughput by using the transmitting power distributed to the information source and each unmanned aerial vehicle and the flight path of each unmanned aerial vehicle;

performing iterative optimization on the transmitting power and the flight path in the system throughput calculation model by using a block coordinate descent method and a continuous convex optimization method to obtain an optimized flight path and optimized transmitting power;

and in the communication network, the optimized transmitting power and the optimized flight path are utilized to realize communication.

Preferably, the performing iterative optimization on the transmitting power and the flight trajectory in the system throughput calculation model by using a block coordinate descent method and a continuous convex optimization method to obtain an optimized flight trajectory and an optimized transmitting power includes:

inputting a preset initial flight trajectory into the system throughput calculation model to optimize the transmitting power, and obtaining optimized transmitting power and corresponding first throughput;

inputting the optimized transmitting power into the system throughput calculation model to optimize the flight trajectory to obtain an optimized flight trajectory and a corresponding second throughput;

and iteratively executing the operation of optimizing the transmitting power and the operation of optimizing the flight trajectory until the absolute value of the difference between the first throughput and the second throughput is smaller than a first preset difference threshold.

inputting preset initial transmitting power into the system throughput calculation model to optimize the flight trajectory, and obtaining an optimized flight trajectory and a corresponding third throughput;

inputting the optimized flight trajectory into the system throughput calculation model to optimize the transmitting power to obtain optimized transmitting power and a corresponding fourth throughput;

and iteratively executing the operation of optimizing the transmitting power and the operation of optimizing the flight trajectory until the absolute value of the difference between the first throughput and the second throughput is smaller than a second preset difference threshold.

Preferably, the inputting a preset initial flight trajectory into the system throughput calculation model to optimize the transmit power to obtain an optimized transmit power and a corresponding first throughput includes:

inputting a preset initial flight trajectory into the system throughput calculation model;

under the constraint condition of preset transmitting power, introducing a first relaxation variable, and converting a non-convex transmitting power optimization problem into a first convex optimization problem by using the continuous convex optimization method;

and solving the first convex optimization problem by utilizing an interior point method or an optimization tool packet to obtain the optimized transmitting power and the corresponding first throughput.

Preferably, inputting the optimized transmitting power into the system throughput calculation model to optimize the flight trajectory, and obtaining an optimized flight trajectory and a corresponding second throughput, includes:

inputting the optimized transmit power into the system throughput computation model;

under the condition of preset motion constraint, introducing a second relaxation variable, and converting the non-convex flight trajectory optimization problem into a second convex optimization problem by using the continuous convex optimization method;

and solving the second convex optimization problem by utilizing an interior point method or an optimization tool packet to obtain the optimized transmitting power and the corresponding first throughput.

Preferably, in the communication network, the communication is implemented by using the current optimized transmission power and the optimized flight trajectory, and the communication comprises the following steps:

in the communication network, an information source and each unmanned aerial vehicle are respectively enabled to send signals to the outside by using corresponding transmitting power in the current optimized transmitting power, and each unmanned aerial vehicle is enabled to fly according to the current optimized flight trajectory;

the optimized transmitting power comprises the information source and the transmitting power corresponding to the unmanned aerial vehicles respectively, and the optimized flight trajectory comprises the flight trajectories corresponding to the unmanned aerial vehicles respectively.

Preferably, creating a system throughput computation model using the channel information and the number of drones includes:

determining the flight height, the starting position and the end position of the unmanned aerial vehicle and the power gain of the channel by using the channel information;

and creating a system throughput calculation model by using the flying height, the starting position, the ending position and the number of the unmanned aerial vehicles and the channel power gain.

A wireless communication device is applied to a communication network for realizing multi-hop relay by utilizing an unmanned aerial vehicle, and comprises the following components:

the model creating module is used for acquiring channel information and the number of the unmanned aerial vehicles and creating a system throughput calculation model by using the channel information and the number of the unmanned aerial vehicles; the system throughput calculation model is a model for calculating system throughput by using the transmitting power distributed to the information source and each unmanned aerial vehicle and the flight path of each unmanned aerial vehicle;

the optimization module is used for performing iterative optimization on the transmitting power and the flight track in the system throughput calculation model by using a block coordinate descent method and a continuous convex optimization method to obtain an optimized flight track and optimized transmitting power;

and the communication module is used for realizing communication by utilizing the optimized transmitting power and the optimized flight path in the communication network.

A wireless communication device, comprising:

a memory for storing a computer program;

a processor for implementing the steps of the above-described wireless communication method when executing the computer program.

A readable storage medium having stored thereon a computer program which, when executed by a processor, carries out the steps of the above-mentioned wireless communication method.

The method applied to the communication network for realizing the multi-hop relay by utilizing the unmanned aerial vehicle comprises the following steps: acquiring channel information and the number of unmanned aerial vehicles, and creating a system throughput calculation model by using the channel information and the number of the unmanned aerial vehicles; the system throughput calculation model is a model for calculating the system throughput by using the transmitting power distributed to the information source and each unmanned aerial vehicle and the flight path of each unmanned aerial vehicle; carrying out iterative optimization on the transmitting power and the flight track in the system throughput calculation model by using a block coordinate descent method and a continuous convex optimization method to obtain an optimized flight track and optimized transmitting power; in the communication network, communication is realized by utilizing optimized transmitting power and optimized flight path.

Specifically, when the multi-hop relay is implemented by using the drones, a system throughput calculation model can be created by using the channel information and the number of the drones. And carrying out iterative optimization on the transmitting power and the flight trajectory in the system throughput calculation model by using a block coordinate descent method so as to enable the system throughput to reach an optimized value. The optimized transmitting power is substituted into the system throughput calculation model to optimize the flight track, the optimized flight track is substituted into the system throughput calculation model to optimize the transmitting power, and finally the system throughput is optimized in a mode of optimizing the flight track and optimizing the transmitting power. And then, in a communication network for realizing multi-hop relay by using the unmanned aerial vehicle, the remote communication for optimizing the system throughput is realized by using the optimized flight path and the optimized transmitting power. So, can realize remote communication in the unable signal blind area that covers of basic station with make full use of unmanned aerial vehicle's degree of freedom, can also optimize system throughput simultaneously.

Accordingly, embodiments of the present invention further provide a wireless communication apparatus, a device and a readable storage medium corresponding to the wireless communication method, which have the above technical effects and are not described herein again.

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.

Fig. 1 is a flowchart illustrating an implementation of a wireless communication method according to an embodiment of the present invention;

fig. 2 is a schematic structural diagram of a communication network for implementing multihop relay by using an unmanned aerial vehicle according to an embodiment of the present invention;

FIG. 3 is a flow chart of another method of wireless communication in accordance with an embodiment of the present invention;

FIG. 4 is a flow chart of another method of wireless communication in accordance with an embodiment of the present invention;

FIG. 5 is a flow chart of another method of wireless communication in accordance with an embodiment of the present invention;

fig. 6 is a flowchart illustrating a specific implementation of a wireless communication method according to an embodiment of the present invention;

fig. 7 shows a schematic diagram of the trajectory of 100 seconds of flight of 2 drones under different scenarios;

fig. 8 shows a schematic diagram of the trajectories of 400 seconds of flight of 2 drones under different scenarios;

fig. 9 is a graph of the variation trend of the throughput of the system with the flight time of the drone at different average transmit powers;

fig. 10 is a schematic structural diagram of a wireless communication device according to an embodiment of the present invention;

fig. 11 is a schematic structural diagram of a wireless communication device according to an embodiment of the present invention.

Detailed Description

The core of the invention is to provide a method for realizing wireless communication in a communication network for realizing multi-hop relay by utilizing an unmanned aerial vehicle. The core of the method is that a system throughput calculation model is established by utilizing channel information and the number of unmanned aerial vehicles; and then carrying out iterative optimization on the transmitting power and the flight trajectory in the system throughput calculation model, and finally obtaining the optimized transmitting power and the optimized flight trajectory which achieve the maximized system throughput. And the optimized transmitting power and the optimized flight path are utilized to realize long-distance wireless communication.

Correspondingly, the embodiment of the invention also provides a wireless communication device, equipment and a readable storage medium.

In order that those skilled in the art will better understand the disclosure, the invention will be described in further detail with reference to the accompanying drawings and specific embodiments. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. 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.

The first embodiment is as follows:

referring to fig. 1, fig. 1 is a flowchart illustrating a wireless communication method according to an embodiment of the invention. The method can be applied to a communication network for realizing multi-hop relay by utilizing unmanned planes as shown in fig. 2. The UAV (Unmanned Aerial Vehicle/Drones) serves as a relay, the Source is a Source of a fixed base station, and the Destination is a sink of a mobile communication terminal (such as a mobile phone, a communication watch and other communication devices).

The method comprises the following steps:

s101, acquiring channel information and the number of unmanned aerial vehicles, and creating a system throughput calculation model by using the channel information and the number of the unmanned aerial vehicles.

The system throughput calculation model is a model for calculating the system throughput by using the transmitting power distributed to the information source and each unmanned aerial vehicle and the flight path of each unmanned aerial vehicle; .

In the embodiment of the invention, the channel information and the number of the unmanned aerial vehicles can be acquired by receiving the channel information input by the user and the number of the unmanned aerial vehicles; the channel information and the number of the unmanned aerial vehicles can be acquired by reading the channel information and the number of the unmanned aerial vehicles in a preset information table. The channel information may include information such as location information, maximum transmission power, signal coverage, and the like of the signal source, location information of the signal sink, minimum signal strength that the signal sink can normally receive, maximum flight speed, maximum transmission power, and the like of each drone, and channel conditions between the signal source and the signal sink (for example, a distance between the signal source and the signal sink, a landform between the signal source and the signal sink, and whether a radiation area exists between the signal source and the channel). In addition, the number of the unmanned aerial vehicles can be preset, and can also be determined and adjusted according to actual conditions, and is not limited herein. Accordingly, the model of the unmanned aerial vehicle is not limited.

After obtaining the channel information and the number of drones, a system throughput computation model may be created based on utilizing the channel information and the number of drones. Specifically, the system throughput calculation model is a system throughput calculation formula based on the flight trajectory of each drone and the variables of the transmission power of the information source and each drone. That is, in the system throughput calculation model, two variables of flight trajectory and transmission power are included, and each variable includes a plurality of sub-variables. Specifically, the flight trajectory comprises flight trajectories respectively corresponding to all the unmanned aerial vehicles; the transmitting power comprises transmitting power respectively corresponding to the information source and each unmanned aerial vehicle.

And S102, carrying out iterative optimization on the transmitting power and the flight track in the system throughput calculation model by using a block coordinate descent method and a continuous convex optimization method to obtain an optimized flight track and optimized transmitting power.

From the above, the system throughput calculation model includes two variables, namely flight trajectory and transmission power. The optimization can be performed from both the aspect of flight trajectory and the aspect of transmission power in order to optimize the system throughput or maximize the system throughput. In order to improve the optimization efficiency, the used optimization method is to use a block coordinate Descent method (block coordinate decline), that is, to divide the problem of optimizing the system throughput into two sub-problems (namely, a transmission power optimization problem and a flight trajectory optimization problem) and then solve the sub-problems.

Specifically, the transmission power and the flight trajectory in the system throughput calculation model are optimized by using an iterative method. For example, the transmission power may be optimized, the optimized transmission power may be substituted into the system throughput calculation model to optimize the flight trajectory, the optimized flight trajectory may be substituted into the system throughput calculation model to optimize the transmission power again, and the iteration is repeated until the system throughput is optimized, and the current transmission power and flight trajectory are used as the optimized transmission power and the optimized flight trajectory. Or, the flight trajectory may be optimized first, then the optimized flight trajectory is substituted into the system throughput calculation model, the transmission power is optimized, then the optimized transmission power is substituted into the system throughput calculation model to optimize the flight trajectory again, and the iteration is repeated until the system throughput optimization is completed, and the current transmission power and flight trajectory are used as the optimized transmission power and the optimized flight trajectory. It should be noted that, when optimizing the system throughput, optimizing the transmission power and optimizing the sequence of the flight trajectory do not affect the implementation of the embodiments of the present invention to improve the system throughput and complete the remote communication. Thus, the order of iterative optimization is not limited herein.

And S103, realizing communication by utilizing the optimized transmitting power and the optimized flight path in the communication network.

The current optimized transmitting power and optimized flight trajectory are applied to the communication network shown in fig. 2, and the unmanned aerial vehicle is used for realizing multi-hop relay and further realizing long-distance wireless communication.

Specifically, the information source and each unmanned aerial vehicle in the communication network are enabled to transmit signals to the outside according to the transmitting power respectively corresponding to the optimized transmitting power, and each unmanned aerial vehicle is enabled to fly according to the flight trajectories respectively corresponding to the optimized flight trajectories. That is, a plurality of drones are used as multi-hop relays, the number of drones is the hop count of the relays, that is, one drone is one relay, so as to transmit signals to a destination far away from the signal source. The optimized transmitting power and flight path can also improve the throughput of the system on the basis of ensuring the basic communication requirement.

It should be noted that, based on the first embodiment, the embodiment of the present invention further provides a corresponding improvement scheme. In the following embodiments, steps that are the same as or correspond to those in the first embodiment may be referred to each other, and corresponding advantageous effects may also be referred to each other, which are not described in detail in the following modified embodiments.

Example two:

referring to fig. 3, fig. 3 is a flowchart of another wireless communication method according to an embodiment of the present invention, the method including the following steps:

s201, acquiring channel information and the number of the unmanned aerial vehicles, and determining the flight height, the starting position and the ending position of the unmanned aerial vehicles and the channel power gain by using the channel information.

S202, a system throughput calculation model is created by using the flight altitude, the starting position, the ending position, the number of unmanned aerial vehicles and the channel power gain.

For convenience of description, the above-described step S201 and step S202 will be explained in combination.

When the landform between the information source and the information sink in the channel information is a landform with large topographic relief such as mountainous regions, disaster areas and the like, the flying height of the unmanned aerial vehicle can be changed along with the change of the landform; when the landform between the information source and the information sink in the channel information is a landform with small fluctuation such as a plain or a hill, the flying height of the unmanned aerial vehicle can be fixed.

The position information of the information source can be used as the starting position of the unmanned aerial vehicle, and the position information of the information sink can be used as the terminal position of the unmanned aerial vehicle. In addition the number of drones should be greater than or equal to 2. Using the flying height, starting position and ending positionThe number of drones, and the channel power gain, create a system throughput computation model. For convenience of description, the following describes the creation of the system throughput calculation model in detail, taking as an example the case where the flying height H of the unmanned aerial vehicle is constant and the number of the unmanned aerial vehicles is M, specifically as follows: when M unmanned aerial vehicles serve as multi-hop relay auxiliary communication, the set is used

Representing individual drones. The flight time of the unmanned aerial vehicle is T, the T is divided into N time slots, and the length of each time slot is delta small enough_t. Therefore, the real-time position of the unmanned aerial vehicle can be regarded as unchanged in each time slot, and the relay flight track of the mth unmanned aerial vehicle is expressed as a sequence

The starting point position of the mth unmanned plane is

(x_0,m,y_0,mH), the end point position is (x)_F,m,y_F,mH). And the maximum flying speed of the unmanned aerial vehicle is v_maxIf V is equal to V_maxδ_tRepresenting the maximum distance that the drone can fly within each time slot. The motion constraint of the drone is then:

when the communication channel is a line-of-sight channel, the channel power gain conforms to the free space path loss model. That is, the source to 1 st drone relay channel power gain may be expressed as

Wherein beta is₀Denotes the channel power gain at a reference distance of 1meter (m), d_s,1[n]And (3) representing the distance between the information source at the nth moment and the 1 st unmanned aerial vehicle, wherein the transmission rate of the link information from the information source to the 1 st unmanned aerial vehicle is as follows:

wherein, γ₀＝β₀/σ²，P_s[n]Representing the transmission power, σ, at the nth time instant of the source²Representing an additive white gaussian noise power. Similarly, the link information transmission rates from the mth drone to the (m + 1) th drone and from the last drone to the sink are respectively:

and because the unmanned aerial vehicle relay forwarding information is limited by the information causal constraints as shown in formula 5, formula 6 and formula 7:

formula 5, formula 6, and formula 7 represent the 1 st drone, the 2 nd drone to the mth drone respectively and correspond to the information cause and effect constraint.

When the information processing delay of each unmanned aerial vehicle relay is one time slot, further transmit power constraint is as shown in formula 8, formula 9, and formula 10:

wherein, P_s[n]Representing the transmission power of the source at the nth time, similarly, P_m[n]M1, M-1 denotes the transmit power of the mth drone at the nth time, P_M[n]Representing the transmit power at the nth time instant of the mth (i.e., the last 1) drone. According to the above constraint, the throughput from source to sink can be obtained, that is, the average information transmission rate of the last drone is as follows:

the core of the embodiment of the invention is that the throughput of the system is maximized by optimizing the flight trajectory and the transmitting power, so a system throughput calculation model can be represented by formula 11, and the system throughput optimization problem is represented by P1:

(formula 1), (formula 5) - (formula 10);

wherein, formula 12 and formula 13 respectively show that the relay transmission power of the information source and the unmanned aerial vehicle is limited by the average transmission power

And peak transmit power constraint P_max. In addition, it is shown that the calculation is performed by (equation 1), (equation 5) to (equation 10) (the same applies below).

As can be seen from the description of step S201 and step S202, the system throughput calculation model includes two variables, namely, the flight trajectory and the transmission power. The optimization problem P1 that wants to optimize the system throughput through the system throughput calculation model is non-convex, so the embodiment of the present invention finally achieves the purpose of optimizing the system throughput (hereinafter, expressed as throughput) by fixing one of the variables, optimizing the other variable, and repeating iterations. Specifically, after the system throughput calculation model is created, the operation of step S203 is performed.

S203, inputting the preset initial flight trajectory into the system throughput calculation model to optimize the transmitting power, and obtaining the optimized transmitting power and the corresponding first throughput.

In this embodiment, the initial flight trajectory of each drone may be set in advance. For example, the unmanned aerial vehicle flight trajectory may be used

Is shown in which

The preset initial flight trajectory is input into the system throughput calculation model, that is, the variables regarding the flight trajectory in the system throughput calculation model are replaced with a specific initial flight trajectory. In this case, the system throughput calculation model has only one variable of the transmission power. At this time, specific data of the optimized transmission power that maximizes the system throughput can be calculated. When the flight trajectory variable is the initial flight trajectory, the corresponding maximum system throughput may be determined as the first throughput, and the transmit power corresponding to the first throughput may be determined as the optimized transmit power.

And S204, inputting the optimized transmitting power into the system throughput calculation model to optimize the flight trajectory, and obtaining the optimized flight trajectory and the corresponding second throughput.

After obtaining the optimized transmit power, the variables in the system throughput calculation model regarding transmit power may be replaced with the specific optimized transmit power. Accordingly, there is only one variable in the flight trajectory in the system throughput calculation model at this time. At this time, specific data of the flight trajectory that maximizes the system throughput can be calculated. When the transmission power variable is the optimized transmission power, the corresponding maximum system throughput is determined as the second throughput, and the flight trajectory corresponding to the second throughput is determined as the optimized flight trajectory.

S205, judging whether the absolute value of the difference between the first throughput and the second throughput is smaller than a first preset difference threshold value.

If yes, step S206 is executed, and if no, step S203 is executed to perform iterative optimization.

It should be noted that step S203 is a step of optimizing the transmission power operation, and step S204 is a step of optimizing the flight trajectory operation. And step S203 is executed, and performing iterative optimization, that is, iteratively executing operations of optimizing the transmission power and optimizing the flight trajectory until the absolute value of the difference between the first throughput and the second throughput is smaller than the preset difference threshold.

In the embodiment of the present invention, a first preset difference threshold may be preset to determine whether the current optimization operation meets the requirement. The specific value of the difference threshold value can be preset, and can also be determined and adjusted according to the actual situation. Of course, the smaller the value of the error threshold, the greater the calculated system throughput, and in addition, the smaller the value of the error threshold, the greater the number of iterationsThe more, the slower the optimization process. Preferably, the error threshold may be ∈ 10^-3。

Specifically, the steps S203 and S204 are iteratively executed until the absolute value of the difference between the first throughput and the second throughput is smaller than the first preset difference threshold. That is, it is necessary to consider that the optimization of the system throughput is achieved when the variation of the system throughput is smaller than the first preset difference threshold value during the optimization of the flight trajectory and the optimization of the transmission power, and the optimization operation may be stopped.

S206, in the communication network, the information source and each unmanned aerial vehicle are respectively enabled to send signals outwards at the corresponding transmitting power in the current optimized transmitting power, and each unmanned aerial vehicle is enabled to fly according to the current optimized flight trajectory.

The optimized transmitting power comprises transmitting power corresponding to the information source and each unmanned aerial vehicle respectively, and the optimized flight trajectory comprises flight trajectories corresponding to the unmanned aerial vehicles respectively.

Specifically, the signal source and each unmanned aerial vehicle can be instructed to send signals to the outside at the corresponding transmitting power in the current optimized transmitting power respectively in a mode of sending power transmitting instructions to the signal source and the unmanned aerial vehicle; correspondingly, each unmanned aerial vehicle can fly according to the corresponding flight track in the current optimized flight track flight in a mode of sending a flight track instruction to the unmanned aerial vehicle. That is, the source and drone receive the corresponding commands, i.e., transmit signals and/or fly outwardly in accordance with the commands.

Example three:

referring to fig. 4, fig. 4 is a flowchart of another wireless communication method according to an embodiment of the present invention, the method including the following steps:

s301, acquiring channel information and the number of the unmanned aerial vehicles, and creating a system throughput calculation model by using the channel information and the number of the unmanned aerial vehicles.

The solution is then carried out using the block coordinate descent method, i.e. the problem of optimizing the system throughput (P1) divided into two sub-problems (problem P2: transmit power optimization problem; problem P4: flight trajectory optimization problem).

And S302, inputting the preset initial flight trajectory into a system throughput calculation model.

S303, under the constraint condition of preset transmitting power, introducing a first relaxation variable, and converting a non-convex transmitting power optimization problem into a first convex optimization problem by using a continuous convex optimization method.

S304, solving the first convex optimization problem by utilizing an interior point method or an optimization tool packet to obtain optimized transmitting power and corresponding first throughput.

For convenience of description, the following description will be made in conjunction with step S302 to step S304 involved in the problem P2 of optimizing transmission power.

Under the condition that the operation track of the unmanned aerial vehicle is given (when the step S302 is executed for the first time, the operation track is an initial operation track, and when the step S302 is executed again, the operation track is an optimized operation track optimized for the last time), the throughput is maximized by optimizing the transmission power P [ n ] of the information source and each unmanned aerial vehicle:

(equation 8), (equation 9), (equation 10), (equation 12), (equation 13).

In solving problem P2, P is related to the left equation of equations 15-17 due to the constraint_m[n]Is notConvex, and therefore introduces a first relaxation variable t_m[n]Then the question P2 can be re-expressed as:

(equation 8), (equation 9), (equation 10), (equation 12), (equation 13).

The above problem constraints are shown in equations 22, 23, and 24 by the inverse method, and there is always an optimal solution so that they satisfy strict inequalities, respectively, so that problem P2 is equivalent to problem P3. The evidence process of the syndrome differentiation can be referred to a common syndrome differentiation application process, and is not described herein again. Problem P3, obtained by transforming problem P2, is a convex optimization problem that can be solved by interior point method or toolkit CVX. That is, a transmit power that maximizes system throughput with the initial flight trajectory may be obtained. At this time, the maximum system throughput may be used as the first throughput, and the optimal transmission power may be determined according to the transmission power corresponding to the first throughput.

S305, inputting the optimized transmitting power into a system throughput calculation model;

s306, introducing a second relaxation variable under a preset motion constraint condition, and converting a non-convex flight trajectory optimization problem into a second convex optimization problem by using a continuous convex optimization method;

s307, solving the second convex optimization problem by using an interior point method or an optimization tool package to obtain the optimized transmitting power and the corresponding first throughput.

For convenience of description, the following description will be made in conjunction with steps S305 to S307 of optimizing the flight trajectory.

Maximizing the throughput by optimizing the trajectory of each drone, given the transmit power of the drone (i.e. replacing the transmit power variable in the system throughput calculation model with an optimized transmit power):

in particular, continuous convex planning is used to optimize trajectories given the source and individual drone power allocations

Is expressed by P4 (as equation 25):

(equation 1).

Wherein, formula 1 refers to formula 1a, formula 1b, and formula 1 c. Because problem P4 is a non-convex problem, a second slack variable q is introduced_m[n]Question P4 may re-represent question P5:

(equation 1).

It can be shown by a back-up method that there is always an optimal solution so that the constraint, such as formula 33, formula 34, formula 35, etc., is equal, and the problem P5 is equivalent to the problem P4. Because the constraints are as in equation 30, equation 33, equation 34, and the equation to the right of equation 35 with respect to x_m,y_mIs non-concave and therefore problem P5 is difficult to solve optimally. By means of a representation

As the trajectory of the mth drone at the ith iteration,

the trace increment at the l +1 th iteration is expressed, and the coordinates of the trace at the l +1 th iteration can be respectively expressed by a formula

Thus obtaining the product. Therefore, the reachable rate from the source to the 1 st drone at the (l + 1) th iteration is obtained as follows:

wherein,

represents the distance from the source to the 1 st drone in the ith iteration:

the formula 36 applies the formula

The 2 nd equal sign in equation 36 holds. And due to the function

The function is convex in the case of z ≧ -A. From the first order Taylor expansion of the convex function as a globally estimated property, expanding the function at z equal to 0 yields the following inequality:

based on the inequality (equation 37), let

The following inequality can be further derived:

similarly, at the l +1 th iteration, the information reachable rates of the mth drone to the M +1 th drone (M1, …, M-1) satisfy the following inequality:

wherein,

represents the distance of the mth drone to the m +1 th drone at the ith iteration. During the (l + 1) th iteration, the information reachable rate from the last 1 unmanned aerial vehicle M to the information sink meets the following inequality:

wherein,

and the distance from the source end to the 1 st unmanned aerial vehicle in the ith iteration is shown. Thus, the trajectory at the first iteration is given

Then the trace at the l +1 th iteration

This can be obtained by solving the following optimization problem P6:

problem P6 is a convex optimization problem and therefore can be solved by the interior point method or optimization tool package CVX. At iteration (i + 1), the inequality constraints of the problem P6, such as formula 42 and formulae 45-47, are satisfied, i.e. it can be inferred that the inequality constraints of the problem P5, such as formulae 30, formulae 33-35, are also satisfied, and then the trajectory obtained by solving the problem P6 is satisfied

A feasible solution to the problem P5. And because of the point of

Where, there are formula 43, formula 44, formula 45 taking equal sign, and at this point

And the respective corresponding lower bound

The gradient of (a) takes an equal sign. The optimal solution obtained by solving problem P6, the target value obtained by bringing it into problem P5, is therefore no longer at point than problem P5

The optimum value of (c) is smaller.

I.e. a flight trajectory that maximizes the system throughput with a determined transmit power allocation. At this time, the maximum system throughput may be used as the second throughput, and the flight trajectory corresponding to the second throughput may be determined as the optimized flight trajectory.

S308, judging whether the absolute value of the difference between the first throughput and the second throughput is smaller than a first preset difference threshold value.

If so, the operation of step S309 is performed; if not, the optimized flight trajectory is taken as the initial flight trajectory, and the operation of step S302 is repeatedly performed, that is, the operation of optimizing the transmitting power and optimizing the flight trajectory is iteratively performed.

S309, in the communication network, the information source and each unmanned aerial vehicle are respectively enabled to send signals to the outside by the corresponding transmitting power in the current optimized transmitting power, and each unmanned aerial vehicle is enabled to fly according to the current optimized flight trajectory.

Example four:

referring to fig. 4, fig. 4 is a flowchart illustrating another wireless communication method according to an embodiment of the present invention, which can be used in the communication network shown in fig. 2 for implementing multihop relay by using a drone. The method comprises the following steps:

s401, acquiring channel information and the number of the unmanned aerial vehicles, and creating a system throughput calculation model by using the channel information and the number of the unmanned aerial vehicles.

S402, inputting the preset initial transmitting power into the system throughput calculation model to optimize the flight trajectory, and obtaining the optimized flight trajectory and the corresponding third throughput.

And S403, inputting the optimized flight trajectory into a system throughput calculation model to optimize the transmitting power, and obtaining the optimized transmitting power and the corresponding fourth throughput.

S404, judging whether the absolute value of the difference between the first throughput and the second throughput is smaller than a second preset difference threshold value.

In this embodiment, the setting manner of the second preset difference threshold may refer to the setting manner of the first preset difference threshold, which is not described herein again. It should be noted that the first preset difference threshold and the second preset difference threshold may be the same or different in value.

When the judgment result is yes, the operation of step S405 is performed; when the judgment result is no, the operation of step S402 is repeatedly performed. Specifically, the operation of optimizing the transmission power and optimizing the flight trajectory is iteratively performed until the third throughput and the fourth throughput are smaller than a second preset difference threshold.

And S405, in the communication network, realizing communication by utilizing the optimized transmitting power and the optimized flight path.

Since the difference between the wireless communication method and the wireless communication method is only the sequence difference between the iterative optimization transmission power and the flight trajectory, the corresponding technical effects of the embodiments of the present invention are also specified, and may be mutually referred to with the above technical effects, and no further description is given here.

For convenience of understanding, the following describes the technical solutions provided by the embodiments of the present invention in detail by taking specific implementation flows and simulation experimental data as examples.

Referring to fig. 6, an alternating optimization algorithm for jointly optimizing the trajectory of the drone and the transmission power of the source (source)/drone is adopted to maximize the throughput of the system, and the specific steps are as follows:

(1) initialization: setting an initial unmanned aerial vehicle flight trajectory

l is 0 and the error threshold e is 10^-3。

(2) Flying track of unmanned aerial vehicle

Substituting the problem (P3), solving the optimization problem P3 by the interior point method, and obtaining the optimal solution to be expressed as

(3) The obtained source end and the emission power of the unmanned aerial vehicle

Solving the optimization problem P6 by the interior point method by substituting the problem P6, and obtaining the optimal solution to be expressed as

Updating trajectory variables (i.e. fly)The line track) is expressed as

And obtaining the value of the objective function expressed as

(4) Let l be l + 1.

(5) If it is not

Obtaining the optimal flight path of the unmanned aerial vehicle

Communication power distribution with source/drone

Otherwise, repeating the steps (2), (3) and (4).

In the simulation experiment, three schemes are proposed for comparison: "Line traj.w/pow.alloc." indicates a straight flight trajectory scenario when power is distributed; "traj.opt.w/o pow.alloc." represents the optimal flight trajectory scenario without power allocation; "Joint traj.opt. & point.alloc." represents the proposed Joint trajectory optimization and power allocation scheme. Fig. 7 shows a schematic diagram of 2 trajectories of drones under different schemes, and the flight times are all set to be T ═ 100 s. Fig. 8 is a schematic diagram of a flight trajectory when the flight time T is 400 s. Wherein, Initial Location is the Initial position, Final Location is the terminal position.

Referring to fig. 9, fig. 9 is a graph showing the variation of the throughput of the system with the flight time of the drone at different average transmit powers.

The specific simulation parameters are respectively as follows: the distance between the source and the sink is 1000m, the flying height H is 100m, and the time slot length delta_t1s, 25m/s at maximum flying speed v, and reference signal-to-noise ratio gamma₀80dB and assuming the peak transmit power of the source and all drones, the average transmit power is the same.

static relay refers to two drones fixed at (L/3,0, H) and (2L/3,0, H) as stationary relays. Compared with the scheme of only optimizing the track or only distributing the power, the scheme of jointly optimizing the track and the power distribution fully utilizes the degree of freedom of the unmanned aerial vehicle and obviously improves the throughput of the system.

Corresponding to the above method embodiments, the present invention further provides a wireless communication device, and the wireless communication device described below and the corresponding wireless communication method described above may be referred to correspondingly.

Referring to fig. 10, a wireless communication apparatus applicable to a communication network implementing multi-hop relay by using a drone includes the following modules:

the model creating module 101 is configured to obtain channel information and the number of the drones, and create a system throughput calculation model by using the channel information and the number of the drones; the system throughput calculation model is a model for calculating the system throughput by using the transmitting power distributed to the information source and each unmanned aerial vehicle and the flight path of each unmanned aerial vehicle;

the optimization module 102 is configured to perform iterative optimization on the transmit power and the flight trajectory in the system throughput calculation model by using a block coordinate descent method and a continuous convex optimization method to obtain an optimized flight trajectory and an optimized transmit power;

and the communication module 103 is used for realizing communication by utilizing the optimized transmitting power and the optimized flight trajectory in the communication network.

The device which can be applied to the communication network for realizing the multi-hop relay by utilizing the unmanned aerial vehicle comprises the following steps: acquiring channel information and the number of unmanned aerial vehicles, and creating a system throughput calculation model by using the channel information and the number of the unmanned aerial vehicles; the system throughput calculation model is a model for calculating the system throughput by using the transmitting power and flight path of each unmanned aerial vehicle distributed to the information source and each unmanned aerial vehicle; carrying out iterative optimization on the transmitting power and the flight track in the system throughput calculation model by using a block coordinate descent method and a continuous convex optimization method to obtain an optimized flight track and optimized transmitting power; in the communication network, communication is realized by utilizing optimized transmitting power and optimized flight path.

In an embodiment of the invention, the optimization module 102 includes:

the first transmitting power optimizing unit is used for inputting a preset initial flight track into the system throughput calculation model to optimize transmitting power and obtain optimized transmitting power and corresponding first throughput;

the first flight path optimization unit is used for inputting the optimized transmitting power into the system throughput calculation model to optimize the flight path so as to obtain the optimized flight path and the corresponding second throughput;

and the first optimization iteration unit is used for iteratively executing the operation of optimizing the transmitting power and the flight trajectory until the absolute value of the difference between the first throughput and the second throughput is smaller than a first preset difference threshold.

In an embodiment of the invention, the optimization module 102 includes:

the second flight path optimization unit is used for inputting the preset initial transmitting power into the system throughput calculation model to optimize the flight path, and obtaining the optimized flight path and the corresponding third throughput;

the second transmitting power optimizing unit is used for inputting the optimized flight trajectory into the system throughput calculation model to optimize the transmitting power and obtain the optimized transmitting power and the corresponding fourth throughput;

and the second optimization iteration unit is used for iteratively executing the operation of optimizing the transmitting power and the flight trajectory until the absolute value of the difference between the first throughput and the second throughput is smaller than a second preset difference threshold.

In an embodiment of the present invention, the first transmit power optimization unit is specifically configured to input a preset initial flight trajectory into the system throughput calculation model; introducing a first relaxation variable under a preset transmitting power constraint condition, and converting a non-convex transmitting power optimization problem into a first convex optimization problem by using a continuous convex optimization method; and solving the first convex optimization problem by using an interior point method or an optimization tool packet to obtain the optimized transmitting power and the corresponding first throughput.

In an embodiment of the present invention, the first flight trajectory optimization unit is specifically configured to input the optimized transmit power into a system throughput calculation model; under the condition of preset motion constraint, introducing a second relaxation variable, and converting a non-convex flight trajectory optimization problem into a second convex optimization problem by using a continuous convex optimization method; and solving the second convex optimization problem by using an interior point method or an optimization tool packet to obtain the optimized transmitting power and the corresponding first throughput.

In a specific embodiment of the present invention, the communication module 103 is specifically configured to enable the information source and each unmanned aerial vehicle to send a signal to the outside at a corresponding transmitting power in the current optimized transmitting power, and enable each unmanned aerial vehicle to fly according to the current optimized flight trajectory in the communication network; the optimized transmitting power comprises transmitting power corresponding to the information source and each unmanned aerial vehicle respectively, and the optimized flight trajectory comprises flight trajectories corresponding to the unmanned aerial vehicles respectively.

In a specific embodiment of the present invention, the model creating module 101 is specifically configured to determine a flight altitude, a start position, and an end position of the drone, and a channel power gain by using channel information; a system throughput computation model is created using the flight altitude, start position, end position, and number of drones, as well as the channel power gain.

Corresponding to the above method embodiments, the present invention further provides a wireless communication device, and a wireless communication device described below and a wireless communication method described above may be referred to correspondingly.

Referring to fig. 11, the wireless communication apparatus includes:

a memory D1 for storing computer programs;

a processor D2 for implementing the steps of the wireless communication method of the above-described method embodiments when executing the computer program.

Corresponding to the above method embodiment, the present invention further provides a readable storage medium, and a readable storage medium described below and the above wireless communication method may be referred to correspondingly.

A readable storage medium, having stored thereon a computer program which, when executed by a processor, carries out the steps of the wireless communication method of the above-described method embodiments.

The computer-readable storage medium may be a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and various other readable storage media capable of storing program codes.

The embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same or similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.

Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

It is further noted that, in the present specification, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions.

The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

The principle and the implementation of the present invention are explained in the present application by using specific examples, and the above description of the embodiments is only used to help understanding the technical solution and the core idea of the present invention. It should be noted that, for those skilled in the art, it is possible to make various improvements and modifications to the present invention without departing from the principle of the present invention, and those improvements and modifications also fall within the scope of the claims of the present invention.

Claims

1. A wireless communication method is applied to a communication network for realizing multi-hop relay by using an unmanned aerial vehicle, and comprises the following steps:

in the communication network, communication is realized by utilizing the optimized transmitting power and the optimized flight track;

the method for obtaining the optimal flight trajectory and the optimal transmitting power by using the block coordinate descent method and the continuous convex optimization method to iteratively optimize the transmitting power and the flight trajectory in the system throughput calculation model comprises the following steps:

iteratively executing the operation of optimizing the transmitting power and optimizing the flight trajectory until the absolute value of the difference between the first throughput and the second throughput is smaller than a first preset difference threshold;

the steps are described additionally by using a specific formula:

creating a system throughput calculation model by using the channel information and the number of the drones, which is specifically described as follows:

with unmanned aerial vehicle flying height H unchangeable, when having M unmanned aerial vehicle as multihop relay auxiliary communication, with set

Representing individual drones; the flight time of the unmanned aerial vehicle is T, the T is divided into N time slots, and the length of each time slot is small enough; the mth unmanned aerial vehicle relay flight path is expressed as a sequence

The starting point of the mth unmanned aerial vehicle is (x)_0,m,y_0,mH), the end point position is (x)_F,m,y_F,mH); and the maximum flying speed of the unmanned aerial vehicle is v_maxIf V is equal to V_maxδ_tRepresenting the maximum distance that the unmanned aerial vehicle can fly in each time slot; the motion constraint of the unmanned aerial vehicle is as follows:

when the communication channel is a line-of-sight channel, namely the channel power gain conforms to a free space path loss model; that is, the source to 1 st drone relay channel power gain may be expressed as

wherein, γ₀＝β₀/σ²，P_s[n]Representing the transmission power, σ, at the nth time instant of the source²Representing an additive white gaussian noise power; similarly, the mth drone to the m +1 th drone link information transfer rate, and the last drone to the beaconThe link information transmission rates of the sink are respectively:

formula 5, formula 6 and formula 7 respectively represent information causal constraints corresponding to the 1 st unmanned aerial vehicle, the 2 nd unmanned aerial vehicle to the m th unmanned aerial vehicle;

wherein, P_s[n]Representing the transmission power of the source at the nth time, similarly, P_m[n]M1, M-1 denotes the transmit power of the mth drone at the nth time, P_M[n]Represents the transmit power of the mth (i.e., the last 1) drone at the nth time; according to the above constraint, the throughput from source to sink can be obtained, that is, the average information transmission rate of the last drone is as follows:

maximizing the system throughput by optimizing flight trajectory and optimizing transmit power, the system throughput computation model is represented by equation 11, and the system throughput optimization problem is represented by P1:

P1：

performing constraint using (formula 1), (formula 5) - (formula 10);

performing iterative optimization on the transmitting power and the flight trajectory in the system throughput calculation model to obtain an optimized flight trajectory and optimized transmitting power, which is specifically described as follows:

throughput is maximized by optimizing the source and the transmit power P [ n ] of the individual drones given their trajectory:

P2：

using (formula 8), (formula 9), (formula 10), (formula 12), (formula 13) for constraint;

in solving problem P2, P is related to the left equation of equations 15-17 due to the constraint_m[n]Is non-convex and therefore introduces a first relaxation variable t_m[n]Then the question P2 can be re-expressed as:

P2：

optimizing trajectory using continuous convex programming given source and individual drone power allocations

Is expressed by P4 (as equation 25):

and constraining with (equation 1);

until the absolute value of the difference between the first throughput and the second throughput is smaller than a first preset difference threshold, the following is specifically described:

if it is not

Obtaining the optimal flight path of the unmanned aerial vehicle

Communication power distribution with source/drone

2. The wireless communication method of claim 1, wherein the iterative optimization of the transmit power and the flight trajectory in the system throughput computation model by using a block coordinate descent method and a continuous convex optimization method to obtain an optimized flight trajectory and an optimized transmit power comprises:

3. The wireless communication method of claim 1, wherein the inputting of the preset initial flight trajectory into the system throughput calculation model optimizes the transmit power to obtain an optimized transmit power and a corresponding first throughput, comprising:

4. The wireless communication method of claim 1, wherein inputting the optimized transmit power into the system throughput computation model to optimize the flight trajectory to obtain an optimized flight trajectory and a corresponding second throughput comprises:

5. The wireless communication method according to any one of claims 1 to 4, wherein the communication is implemented by using the current optimized transmission power and the optimized flight trajectory in the communication network, and comprises the following steps:

6. The wireless communication method of claim 5, wherein creating a system throughput computation model using the channel information and the number of drones comprises:

7. A wireless communication device, applied to a communication network that implements multihop relay by using an unmanned aerial vehicle, includes:

the communication module is used for realizing communication by utilizing the optimized transmitting power and the optimized flight path in the communication network;

the optimization module specifically comprises:

the first transmitting power optimizing unit is used for inputting a preset initial flight track into the system throughput computing model to optimize the transmitting power to obtain optimized transmitting power and corresponding first throughput;

the first flight trajectory optimization unit is used for inputting the optimized transmitting power into the system throughput calculation model to optimize the flight trajectory to obtain an optimized flight trajectory and corresponding second throughput;

the first optimization iteration unit is used for iteratively executing the operation of optimizing the transmitting power and the flight trajectory until the absolute value of the difference between the first throughput and the second throughput is smaller than a first preset difference threshold;

the modules are described in a supplementary mode by using a specific formula:

Wherein beta is₀Represents the channel power gain at a reference distance of 1meter (m)，d_s,1[n]And (3) representing the distance between the information source at the nth moment and the 1 st unmanned aerial vehicle, wherein the transmission rate of the link information from the information source to the 1 st unmanned aerial vehicle is as follows:

wherein, γ₀＝β₀/σ²，P_s[n]Representing the transmission power, σ, at the nth time instant of the source²Representing an additive white gaussian noise power; similarly, the link information transmission rates from the mth drone to the (m + 1) th drone and from the last drone to the sink are respectively:

P1：

performing constraint using (formula 1), (formula 5) - (formula 10);

P2：

P2：

Is expressed by P4 (as equation 25):

and constraining with (equation 1);

if it is not

Obtaining the optimal flight path of the unmanned aerial vehicle

Communication power distribution with source/drone

8. A wireless communication device, comprising:

a memory for storing a computer program;

a processor for implementing the steps of the wireless communication method according to any of claims 1 to 6 when executing the computer program.

9. A readable storage medium, having stored thereon a computer program which, when being executed by a processor, carries out the steps of the wireless communication method according to any one of claims 1 to 6.