KR102560726B1 - Method and Apparatus for Optimizing Energy Efficiency of UAV Communication Using Intelligent Reflecting Surface - Google Patents

Abstract

A method and apparatus for optimizing the energy efficiency of unmanned aerial vehicle communication using an intelligent reflective surface are presented. An energy efficiency optimization method for unmanned aerial vehicle communication using an intelligent reflecting surface (IRS) performed by a computer device according to an embodiment of the present invention considers a plurality of intelligent reflecting surfaces in an unmanned aerial vehicle communication environment. providing a system model; and optimizing a phase set value of the intelligent reflective surface and a path, speed, and communication time of the unmanned aerial vehicle based on the system model to minimize energy consumption of the unmanned aerial vehicle.

Description

Method and Apparatus for Optimizing Energy Efficiency of UAV Communication Using Intelligent Reflecting Surface}

Embodiments of the present invention relate to a method and apparatus for optimizing energy efficiency of unmanned aerial vehicle communication using an intelligent reflective surface, and more particularly, to minimize energy consumption of an unmanned aerial vehicle by considering an intelligent reflective surface in an unmanned aerial vehicle communication environment. It relates to a method and apparatus for optimizing energy efficiency of aircraft communication.

Current wireless communication systems are stepping into millimeter-wave (mmWave) communication through fifth-generation (5G) technology. MmWave communication is expected to be able to satisfy various communication requirements such as a high information transmission rate (achievable rate) while utilizing a very wide bandwidth. However, mmWave bandwidth also has limitations due to high propagation attenuation and low transmittance, and various methods are being studied to solve this problem.

Technologies that stand out among various studies include Intelligent Reflecting Surface (IRS) and Unmanned Aerial Vehicle (UAV). An intelligent reflective surface is a flat device with multiple passive elements, each of which can independently change the phase of electromagnetic waves reflected on it. Using these properties, intelligent reflective surfaces can advantageously change wireless communication channels, such as creating virtual Line of Sight (LoS) paths. An unmanned aerial vehicle (UAV) is an object that flies freely in the sky through control, and it can be used as a relay or base station in the sky for communication. The unmanned aerial vehicle can obtain a high-quality channel through altitude and can actively avoid radio interference factors such as buildings through manipulation. Using these features, unmanned aerial vehicles are considered as candidates that can overcome the limitations of mmWave band communication.

Despite these possibilities, unmanned aerial vehicle communication still has limitations such as energy consumption for commercialization. Existing communication devices can directly receive supplemental power, but unmanned aerial vehicles have difficulty receiving supplemental power due to the nature of aerial flight. In addition, the unmanned aerial vehicle consumes a lot of power by flight in addition to communication. In conclusion, while unmanned aerial vehicles consume very high power, their use is limited because energy is limited.

S. Li, B. Duo, X. Yuan, Y. Liang, and M. D. Renzo, "Reconfigurable Intelligent Surface Assisted UAV Communication: Joint Trajectory Design and Passive Beamforming," IEEE Wireless Communications Letters, vol. 9, no. 5, p. 716-720, Jan. 2020.

Embodiments of the present invention describe a method and apparatus for optimizing energy efficiency of unmanned aerial vehicle communication using an intelligent reflective surface, and more specifically, propose a new system model that considers an intelligent reflective surface in an unmanned aerial vehicle communication environment. It provides a technology that minimizes the energy consumption of unmanned aerial vehicles by simultaneously using multiple intelligent reflective surfaces.

Embodiments of the present invention effectively utilize intelligent reflective surfaces on the ground to supplement energy consumption, which is a limitation of UAV communication, and a method for optimizing energy efficiency of UAV communication using an intelligent reflective surface that significantly lowers energy consumption of UAVs. and to provide an apparatus.

An energy efficiency optimization method for unmanned aerial vehicle communication using an intelligent reflecting surface (IRS) performed by a computer device according to an embodiment of the present invention considers a plurality of intelligent reflecting surfaces in an unmanned aerial vehicle communication environment. providing a system model; and optimizing a phase set value of the intelligent reflective surface and a path, speed, and communication time of the unmanned aerial vehicle based on the system model to minimize energy consumption of the unmanned aerial vehicle.

The unmanned aerial vehicle may serve as a relay or a base station.

The intelligent reflective surface is composed of a flat device including a plurality of passive elements, each of which can independently change the phase of reflected electromagnetic waves.

The system model is based on a probabilistic LoS model, expresses the probability that a LoS (Line of Sight) path between the unmanned aerial vehicle and a designated object exists as a function of elevation angle, Information including building density and average height of the place You can set environment variables using .

The system model may be set to change the environment variable by treating at least one of a height at which the intelligent reflective surface is located, a height of the receiving device, and a height set for an arbitrary object as a new effective ground.

Optimizing the phase setting value of the intelligent reflective surface and the path, speed, and communication time of the unmanned aerial vehicle may include: calculating an information transfer rate of a receiving device in a communication environment of the unmanned aerial vehicle; designing an optimization problem based on the information transfer rate; and solving the optimization problem.

Calculating the information transmission rate of the receiving device in the communication environment of the unmanned aerial vehicle includes: calculating a phase setting value of the intelligent reflective surface; maximizing the information transfer rate by setting the calculated phase setting value and maximizing signal strength; and calculating the information transfer rate when the intelligent reflective surface supports a receiving device.

In the step of optimizing the phase setting value of the intelligent reflective surface and the path, speed, and communication time of the UAV, energy consumption of the UAV can be minimized by simultaneously using a plurality of the intelligent reflective surfaces.

In the step of optimizing the phase setting value of the intelligent reflective surface and the path, speed, and communication time of the UAV, energy consumption of the UAV can be minimized by using one intelligent reflective surface at a time.

An apparatus for optimizing energy efficiency of unmanned aerial vehicle communication using an intelligent reflecting surface (IRS) according to another embodiment of the present invention is a system that provides a system model considering a plurality of intelligent reflecting surfaces in an unmanned aerial vehicle communication environment. model part; and an optimization unit that optimizes a phase setting value of the intelligent reflective surface and a route, speed, and communication time of the unmanned aerial vehicle based on the system model to minimize energy consumption of the unmanned aerial vehicle.

The system model is based on a probabilistic LoS model, expresses the probability that a LoS (Line of Sight) path between the unmanned aerial vehicle and a designated object exists as a function of elevation angle, Information including building density and average height of the place You can set environment variables using .

The optimization unit may include: an information transfer rate calculation unit that calculates an information transfer rate of a receiving device in the communication environment of the unmanned aerial vehicle; an optimization problem designing unit that designs an optimization problem based on the information transfer rate; and an optimization problem calculation unit that solves the optimization problem.

The information transmission rate calculation unit calculates the phase setting value of the intelligent reflective surface, sets the calculated phase setting value and maximizes the signal strength to maximize the information transmission rate, and when the intelligent reflective surface supports the receiving device The information transmission rate of can be calculated.

The optimization unit may minimize energy consumption of the unmanned aerial vehicle by simultaneously using a plurality of the intelligent reflective surfaces.

The optimizer can minimize energy consumption of the unmanned aerial vehicle by using one intelligent reflective surface at a time.

According to embodiments of the present invention, energy efficiency of UAV communication using an intelligent reflective surface that significantly lowers the energy consumption of an UAV by effectively utilizing an intelligent reflective surface on the ground to supplement energy consumption, which is a limitation of UAV communication. Optimization methods and devices can be provided.

1 is a diagram schematically illustrating an unmanned aerial vehicle downlink communication system according to an embodiment of the present invention.
2A is a flowchart illustrating a method for optimizing energy efficiency of unmanned aerial vehicle communication using an intelligent reflective surface according to an embodiment of the present invention.
2B is a flowchart illustrating a method for optimizing a phase setting value of an intelligent reflective surface and a path, speed, and communication time of an unmanned aerial vehicle according to an embodiment of the present invention.
3 is a block diagram illustrating an energy efficiency optimization device for unmanned aerial vehicle communication using an intelligent reflective surface according to an embodiment of the present invention.
4 is a diagram showing an actual information transmission rate and an approximate information transmission rate when there is one intelligent reflective surface according to an embodiment of the present invention.
5 is a diagram illustrating an unmanned aerial vehicle path according to an amount of information requested by a receiving device according to an embodiment of the present invention.
6 is a diagram illustrating energy consumption of an unmanned aerial vehicle according to an amount of information requested by a receiving device according to an embodiment of the present invention.

Hereinafter, embodiments will be described with reference to the accompanying drawings. However, the described embodiments may be modified in many different forms, and the scope of the present invention is not limited by the embodiments described below. In addition, several embodiments are provided to more completely explain the present invention to those skilled in the art. The shapes and sizes of elements in the drawings may be exaggerated for clarity.

An unmanned aerial vehicle communication system considered in an embodiment of the present invention is a single antenna unmanned aerial vehicle. downlink situation supporting single-antenna receiving devices, each with two passive elements Two intelligent reflective surfaces assist the drone.

The technical problem to be achieved by the present invention is to minimize the energy consumption of an unmanned aerial vehicle by utilizing intelligent reflective surfaces. In the present invention, a new system model considering the characteristics of the UAV and intelligent reflective surfaces is proposed to achieve the purpose, and an algorithm that optimizes the phase setting values of the intelligent reflective surfaces and the path, speed, and communication time of the UAV based on the proposed model. to effectively minimize the energy consumption of the unmanned aerial vehicle. In addition, we propose a low-complexity energy consumption minimization algorithm that has a very low complexity compared to the optimization algorithm and produces satisfactory performance.

1 is a diagram schematically illustrating an unmanned aerial vehicle downlink communication system according to an embodiment of the present invention.

Referring to Figure 1, An unmanned aerial vehicle downlink communication system supporting two receiving devices 120 through the assistance of W intelligent reflective surfaces 130 is shown.

In this environment, the unmanned aerial vehicle 110 can move freely in a horizontal space, and the location in am. and, The location of the first receiving device 120 and The positions of the third intelligent reflective surface 130 are respectively , And, the unmanned aerial vehicle (110), th receiving device 120, The elevation and height of the third intelligent reflective surface 130 are respectively am.

It is assumed that the unmanned aerial vehicle 110 uses a rotary-wing model, and accordingly, a previously known model is used as a power use model as follows.

[Equation 1]

here, is the speed of the UAV, and the remaining variables are determined by the structure of the UAV. Therefore, the total energy used by the unmanned aerial vehicle can be expressed as follows.

[Equation 2]

At this time, are the communication time, transmit power, and flight time of the UAV, respectively.

The channel between the drone, the intelligent reflective surface, and the receiving device in the system can be represented as:

[Equation 3]

,

.

here, , , are respectively in the unmanned aerial vehicle th intelligent reflective surface, on the second intelligent reflective surface In the second receiving device, unmanned aerial vehicle It is a channel between the first receiving devices. also, is the propagation attenuation in unit distance, are the distance between devices, the path attenuation index, and the small-scale fading factor, respectively. At this time, follows a Rician distribution, and between each path -factor is has a value of

2A is a flowchart illustrating a method for optimizing energy efficiency of unmanned aerial vehicle communication using an intelligent reflective surface according to an embodiment of the present invention. 2B is a flowchart illustrating a method for optimizing a phase setting value of an intelligent reflective surface and a path, speed, and communication time of an unmanned aerial vehicle according to an embodiment of the present invention.

Referring to FIG. 2A , a method for optimizing energy efficiency of unmanned aerial vehicle communication using an intelligent reflective surface (IRS) performed by a computer device according to an embodiment of the present invention includes a plurality of intelligent reflective surfaces in an unmanned aerial vehicle communication environment. providing a system model considering the UAV (S110), and optimizing the phase setting value of the intelligent reflective surface and the path, speed, and communication time of the UAV based on the system model to minimize the energy consumption of the UAV (S120). ) can be made including.

Referring to FIG. 2B, the step of optimizing the phase setting value of the intelligent reflective surface and the route, speed, and communication time of the unmanned aerial vehicle (S120) is the step of calculating the information transfer rate of the receiving device in the communication environment of the unmanned aerial vehicle (S121). , designing an optimization problem based on the information transfer rate (S122), and solving the optimization problem (S123).

Here, the step of calculating the information transmission rate of the receiving device in the communication environment of the unmanned aerial vehicle (S121) includes calculating the phase setting value of the intelligent reflective surface, setting the calculated phase setting value and maximizing the signal strength to maximize the information transmission rate. maximizing , and calculating the information transfer rate when the intelligent reflective surface supports the receiving device.

A method for optimizing energy efficiency of unmanned aerial vehicle communication using an intelligent reflective surface according to an embodiment of the present invention may be described using an energy efficiency optimizing apparatus for unmanned aerial vehicle communication using an intelligent reflective surface as an example.

3 is a block diagram illustrating an energy efficiency optimization device for unmanned aerial vehicle communication using an intelligent reflective surface according to an embodiment of the present invention.

Referring to FIG. 3 , an apparatus 300 for optimizing energy efficiency of unmanned aerial vehicle communication using an intelligent reflective surface according to an embodiment may include a system model unit 310 and an optimizer 320.

In step S110, the system model unit 310 may provide a system model considering a plurality of intelligent reflective surfaces in the communication environment of the unmanned aerial vehicle.

Here, the unmanned aerial vehicle is moving in the sky and may serve as a relay or a base station. Also, the intelligent reflective surface is composed of a planar device including a plurality of passive elements, and each passive element can independently change the phase of an electromagnetic wave reflected on it.

The system model is based on a probabilistic LoS model, expresses the probability that a LoS path exists between an unmanned aerial vehicle and a designated object as a function of altitude angle, and can set environmental variables using information including the density and average height of buildings in a place. there is. In addition, the system model can be set to change environmental variables by treating the height at which the intelligent reflective surface is located as a new valid ground. In addition to the height of the intelligent reflective surface, the system model can be set for any object, such as the height of the receiving device.

In step S120, the optimizer 320 may optimize the phase set value of the intelligent reflective surface, the path, speed, and communication time of the UAV based on the system model in order to minimize the energy consumption of the UAV.

Here, the optimization unit 320 may include an information transfer rate calculation unit 321 , an optimization problem design unit 322 , and an optimization problem calculation unit 323 .

In step S121, the information transmission rate calculation unit 321 may calculate the information transmission rate of the receiving device in the communication environment of the unmanned aerial vehicle. More specifically, the information transmission rate calculation unit 321 calculates the phase setting value of the intelligent reflective surface, sets the calculated phase setting value and maximizes the signal strength to maximize the information transmission rate, and the intelligent reflective surface determines the receiving device. Information delivery rate can be calculated when applying.

In step S122, the optimization problem design unit 322 may design an optimization problem based on the information transfer rate.

Then, in step S123, the optimization problem calculation unit 323 may solve the optimization problem.

On the other hand, the optimization technique can be divided into a case of utilizing all of the intelligent reflective surfaces at the same time and a case of utilizing one by one.

For example, the optimizer 320 may minimize energy consumption of the unmanned aerial vehicle by simultaneously using a plurality of intelligent reflective surfaces. That is, the optimizer 320 may minimize the energy consumption of the unmanned aerial vehicle by designing and solving an optimization problem when a plurality of intelligent reflective surfaces are simultaneously used.

As another example, the optimizer 320 may use one intelligent reflective surface at a time to minimize energy consumption of the unmanned aerial vehicle. That is, the optimizer 320 can minimize energy consumption of the unmanned aerial vehicle by designing and solving an optimization problem when using one intelligent reflective surface at a time.

In addition, it is possible to provide a low-complexity energy consumption minimization algorithm that has a very low complexity compared to these optimization algorithms and produces satisfactory performance.

Hereinafter, a method and apparatus for optimizing energy efficiency of unmanned aerial vehicle communication using an intelligent reflective surface performed by a computer device according to an embodiment of the present invention will be described in more detail.

system model

Due to the characteristics of MmWave, a large loss occurs when a signal of the corresponding bandwidth encounters an obstacle such as a building. At this time, there are situations where loss occurs due to buildings when communicating while moving through an unmanned aerial vehicle, and the probabilistic LoS model effectively reflects this. This model expresses the probability of existence of a LoS path between an unmanned aerial vehicle and a designated object as a function of altitude angle, and sets environmental variables using information such as the density and average height of buildings in a place. However, the existing probabilistic LoS model only considers the altitude of the UAV and does not consider the height of the specified object.

The proposed model is a generalization of the probabilistic LoS model, and is a model that can consider the advantage of objects located high. Specifically, the generalization is performed with a model that expresses a higher LoS path existence probability for objects located high, and using this, the advantage of an intelligent reflective surface installed high on the outer wall of a building is expressed. Specifically, the height at which the intelligent reflective surface is located is treated as a new effective ground, and environmental variables are set to change accordingly. Therefore, the probability of existence of a LoS path between the UAV, the intelligent reflective surface, and the receiving device can be expressed as follows.

[Equation 4]

.

here, is an environment variable determined by the environment, is the elevation angle, express it as At this time, unlike the existing probabilistic LoS model, environmental variables go It becomes a variable according to and considers the situation flexibly.

Due to this, the actual channel value can be expressed as follows.

[Equation 5]

At this time, is the loss rate due to the absence of the LoS path. For the convenience of proceeding with the formula thereafter, Set to and proceed. but, Even so, this technique can proceed without problems.

Combining the above, unmanned aerial vehicles and The channel between the first receiving device can be expressed as follows.

[Equation 6]

here, Is is the phase setting matrix of the second intelligent reflective surface, , can be expressed as At this time, Is second intelligent reflective surface It is the phase setting value of the th passive element.

Finally, it is assumed that the communication system uses the Time-Division Multiple Access (TDMA) technique, and accordingly The information transmission rate of the th receiving device can be expressed as follows.

[Equation 7]

here, is the available bandwidth, is the white noise power density in the frequency band.

optimization algorithm

The proposed optimization technique can be divided into two types: when all intelligent reflective surfaces are used simultaneously and when used one by one. Both problems optimize the phase setpoint of the intelligent reflective surface, the drone's speed, route and communication time. At this time, a total of two assumptions are made for the tractability of the problem. First, the expected value of the channel is used instead of the instantaneous channel to eliminate the randomness of the channel. Second, it is assumed that the elevation angle of the unmanned aerial vehicle and the object is constant in order to deal with the complex form of [Equation 4]. For reference, even if the elevation angle is assumed to be a constant, the system model maintains the feature of considering the advantage of the object height.

Since the present invention uses the TDMA technique, communication is performed with one receiving device at each moment. This makes it possible to calculate the phase setpoint of an intelligent reflective surface in closed-form. second intelligent reflective surface Assuming that the second receiving device is supported second intelligent reflective surface The th passive element may have the following phase setting values.

[Equation 8]

here, are respectively of is the second term

Therefore, if the phase value is set using [Equation 8], the signal intensity is maximized to optimally maximize the information transfer rate.

To simplify the expression of the channel mathematically, only the wth intelligent reflective surface Assuming that the second receiving device is assisted, the state variable can be defined as follows.

[Equation 9]

At this time, If , it means that the LoS path of the corresponding channel exists. When , it means that the channel's LoS path does not exist. Then, using state variables and Jensen's inequality, second receiving device The expected value of the information transmission rate when only the second intelligent reflective surface is used can be approximated as follows.

[Equation 10]

here, Is class length consisting of vector of means, UAV considering the state of the channels and Represents a channel between the first receiving device. Looking at [Equation 10], If you calculate can be calculated in closed-form.

Then, using mathematical techniques such as the central limit theorem, second intelligent reflective surface The information transfer rate when supporting the th receiving device can be derived as follows.

[Equation 11]

Here, the overall signal-to-noise ratio (SNR) can be expressed as follows.

[Equation 12]

Here, the time-independent values and can be defined as below.

[Equation 13]

here, Is -factor is the mean value of the Rician distribution, am.

4 is a diagram showing an actual information transmission rate and an approximate information transmission rate when there is one intelligent reflective surface according to an embodiment of the present invention.

Referring to Fig. 4, an actual information transmission rate and an approximate information transmission rate when there is one intelligent reflective surface are shown, where the receiving device is located at 250m and the intelligent reflective surface is located at 245m.

In an environment with one receiving device and an intelligent reflective surface, it can be seen that the difference between the approximate information transfer rate and the actual information transfer rate is very small, so it is safe to use the approximate value instead of the actual value.

Finally, while the example above is a situation where one intelligent reflective surface supports a receiving device, any number of intelligent reflective surfaces can be assumed, as another example a situation where all intelligent reflective surfaces support a receiving device is a state variable. person cast It can be derived in the same way by extending it as

From now on, the optimization problem is designed based on the information transfer rate derived. At this time, prior to defining the expression, the time variable is discretized to avoid an infinite number of variables.

In the present invention, a path discretization technique is used, and through this, the entire path It proceeds by dividing it into segments. First, assuming a situation in which all intelligent reflective surfaces are used simultaneously, the optimization problem can be expressed as follows.

[Equation 14]

here, are respectively Position, flight time, and speed of the UAV in the second segment, are respectively in the second segment It is the communication time and information transfer rate with the second receiving device. also, are respectively It is the amount of information requested by the second receiving device and the maximum speed of the unmanned aerial vehicle. finally, are the start and end points of the UAV, respectively, and is the length of the second segment. At this time, as one characteristic of the path discretization technique, the length within each segment is sufficiently short so that the channel can be assumed to be constant. to meet this condition is defined to limit the segment length to short.

In [Equation 14], the form of the problem (P1) cannot be directly solved because it does not satisfy the form of the convex optimization problem, but the following optimization problem is induced in this proposal using a slack variable, and A solution of (P1) is obtained in [Equation 14] through one problem.

[Equation 15]

At this time, is a slack variable, am. also, is the number of iterations of the optimization problem, silver This is the result of solving the second optimization problem. The remaining variables can be defined as follows.

[Equation 16]

finally, of [Equation 11] distance value each slack variable is the value replaced by

In this proposal, when the problem of (P2) in [Equation 15] is repeatedly solved using the Successive Convex Approximation (SCA) technique and mathematical derivation, the result converges to the local optimum of the problem in [Equation 14] (P1). [Table 1] shows the comprehensive algorithm.

[Table 1] shows the UAV energy consumption minimization algorithm when using all intelligent reflective surfaces.

[Table 1]

Second, in situations where one intelligent reflective surface is being utilized at a time, the problem can be expressed as:

[Equation 17]

At this time, Is in the second segment second receiving device It is the information transmission rate when supported using the th intelligent reflective surface, and it can be seen from [Equation 17] that the best intelligent reflective surface is selected and communicated through (1) constraint.

In this case, the (1) constraint in [Equation 17] can be equally expressed as the following constraints.

[Equation 18]

here, Is in the second segment second receiving device This is the communication time when assisted by utilizing the second intelligent reflective surface.

Finally, solving (P3) in [Equation 17], where (1) in [Equation 17] is changed to [Equation 18], is the same as solving (P1) in [Equation 14]. can be solved

low complexity algorithm

As a last technique, we propose an UAV energy minimization algorithm that can operate with low complexity without using optimization techniques. The proposed method utilizes one intelligent reflective surface at a time.

First, the intelligent reflective surface closest to each receiving device is grouped, and through 1-dimensional search, the point with the highest information transfer rate on a straight line between the receiving device and the intelligent reflective surface bound together save After that, the starting point and ending point The straight line that connects is designated as a toy trajectory. after, point closest to the temporary route in designate as

First of all, the speed at which the unmanned aerial vehicle uses the least energy per unit distance along the temporary path and communicates for the same time to all receiving devices ( ), the simulation proceeds. Through simulation, it is possible to calculate the amount of information that can reach each receiving device, which can be expressed as follows.

[Equation 19]

here, Is The amount of information reaching the first receiving device This is the value when normalized for . At this time, , communication can proceed immediately by using the temporary route as the actual route. if If there is a receiving device, a new communication location can be defined as follows.

[Equation 20]

here, is, Is and It is an arbitrary function that finds the dot product between is a design parameter that can be set according to the situation, and in this proposal, to show an actual application case, is defined as

If the communication location is defined as in [Equation 20], the new path is Set by solving the Traveling Salesman Problem (TSP) so that can be moved with the minimum distance. Finally, the speed along the new path move to is temporarily stopped, and communication proceeds when moving and at the moment of stopping.

More specifically, by pre-calculating the amount of information that can be delivered when moving, the amount of remaining information is Stop at and communicate. In conclusion, the low-complexity algorithm is starting from move the shortest distance and communicate, and finally reach A comprehensive algorithm can be found in Table 2.

[Table 2] shows a low-complexity algorithm for minimizing the energy consumption of an unmanned aerial vehicle.

[Table 2]

Performance Comparison

For performance comparison, a situation without an intelligent reflective surface was implemented, which is indicated as No IRS in the graph. In addition, optimization techniques that utilize all intelligent reflective surfaces at the same time, optimization techniques that utilize one intelligent reflective surface at a time, and low-complexity techniques are marked as General IRS, IRS matching, and Low complexity, respectively. In addition, the receiving device and the intelligent reflective surface are marked with a circle and a diamond, respectively; am.

5 is a diagram illustrating an unmanned aerial vehicle path according to an amount of information requested by a receiving device according to an embodiment of the present invention.

Referring to FIG. 5 , paths of unmanned aerial vehicles were compared for all techniques. If you check the results, you can see that all of them moved directly to the end point when a low amount of information was requested, and each acted differently when a high amount of information was requested. This can be interpreted as the optimal way to move on the fastest path possible when a low amount of information is requested, since the amount of information can be easily satisfied. It can be interpreted as moving closer to the receiving device. In addition, since the behavior pattern is different for each technique, it can be confirmed that different strategies should be adopted according to communication conditions.

6 is a diagram illustrating energy consumption of an unmanned aerial vehicle according to an amount of information requested by a receiving device according to an embodiment of the present invention.

Referring to FIG. 6 , it is possible to check the energy consumption of the unmanned aerial vehicle according to the amount of information requested by the receiving device. First of all, it can be confirmed that the performance of all proposed techniques is significantly higher than No IRS, which is the target of comparison. However, simply set It can be confirmed that there is a performance degradation of low complexity in the low information amount range. In the case of General IRS, since all intelligent reflective surfaces were used, it can be seen that it serves as a lower bound, and even in the case of IRS matching, it can be seen that the performance degradation is very small compared to General IRS.

Through this, it was verified that the use of intelligent reflective surfaces to reduce the energy consumption of unmanned aerial vehicles is very effective.

According to embodiments of the present invention, unmanned aerial vehicle technology and intelligent reflective surface technology, which are popular in wireless communication systems, are utilized. Specifically, in order to compensate for energy consumption, which is a limitation of unmanned aerial vehicle communication, we have provided a technology that significantly lowers the energy consumption of unmanned aerial vehicles by effectively utilizing intelligent reflective surfaces on the ground.

Embodiments of the present invention can be applied to a communication system using an unmanned aerial vehicle as a base station, and can be applied to any number of intelligent reflective surfaces or receiving devices. In particular, due to the low cost of intelligent reflective surfaces, high efficiency will be achieved if popularization proceeds. In addition, the embodiments are presented in a low-complexity, low-energy technique, which is likely to be borrowed when an intelligent reflective surface is introduced in the future.

As described above, in the embodiment of the present invention, three techniques for minimizing energy consumption of an unmanned aerial vehicle in an unmanned aerial vehicle communication system using a plurality of intelligent reflective surfaces have been proposed. At this time, the embodiments of the present invention generalized the probabilistic LoS model to express the height of the intelligent reflective surface, and it was confirmed that the energy consumption of the unmanned aerial vehicle can be greatly reduced by utilizing a plurality of intelligent reflective surfaces.

Specifically, an optimization technique to reduce the energy consumption of an unmanned aerial vehicle by simultaneously utilizing multiple intelligent reflective surfaces was proposed, followed by an optimization technique to reduce energy consumption by utilizing multiple intelligent reflective surfaces one by one in real time. Finally, we proposed a low-complexity technique whose performance is comparable to that of the optimization technique. This can supplement energy consumption, which is one of the implementation limitations of unmanned aerial vehicle communication.

The devices described above may be implemented as hardware components, software components, and/or a combination of hardware components and software components. For example, devices and components described in the embodiments may include, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable array (FPA), It may be implemented using one or more general purpose or special purpose computers, such as a programmable logic unit (PLU), microprocessor, or any other device capable of executing and responding to instructions. A processing device may run an operating system (OS) and one or more software applications running on the operating system. A processing device may also access, store, manipulate, process, and generate data in response to execution of software. For convenience of understanding, there are cases in which one processing device is used, but those skilled in the art will understand that the processing device includes a plurality of processing elements and/or a plurality of types of processing elements. It can be seen that it can include. For example, a processing device may include a plurality of processors or a processor and a controller. Other processing configurations are also possible, such as parallel processors.

Software may include a computer program, code, instructions, or a combination of one or more of the foregoing, which configures a processing device to operate as desired or processes independently or collectively. The device can be commanded. Software and/or data may be any tangible machine, component, physical device, virtual equipment, computer storage medium or device, intended to be interpreted by or provide instructions or data to a processing device. can be embodied in Software may be distributed on networked computer systems and stored or executed in a distributed manner. Software and data may be stored on one or more computer readable media.

The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded on a computer readable medium. The computer readable medium may include program instructions, data files, data structures, etc. alone or in combination. Program commands recorded on the medium may be specially designed and configured for the embodiment or may be known and usable to those skilled in computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks and magnetic tapes, optical media such as CD-ROMs and DVDs, and magnetic media such as floptical disks. - includes hardware devices specially configured to store and execute program instructions, such as magneto-optical media, and ROM, RAM, flash memory, and the like. Examples of program instructions include high-level language codes that can be executed by a computer using an interpreter, as well as machine language codes such as those produced by a compiler.

As described above, although the embodiments have been described with limited examples and drawings, those skilled in the art can make various modifications and variations from the above description. For example, the described techniques may be performed in an order different from the method described, and/or components of the described system, structure, device, circuit, etc. may be combined or combined in a different form than the method described, or other components may be used. Or even if it is replaced or substituted by equivalents, appropriate results can be achieved.

Therefore, other implementations, other embodiments, and equivalents of the claims are within the scope of the following claims.

Claims (15)

In the energy efficiency optimization method of unmanned aerial vehicle communication using an intelligent reflecting surface (IRS) performed by a computer device,
providing a system model considering a plurality of intelligent reflective surfaces in a communication environment of an unmanned aerial vehicle; and
Optimizing a phase setting value of the intelligent reflective surface and a path, speed, and communication time of the UAV based on the system model to minimize energy consumption of the UAV
including,
Providing the system model
Energy efficiency optimization of unmanned aerial vehicle communication using an intelligent reflective surface, characterized in that the system model is provided by expressing the probability that a line of sight (LoS) path exists between the unmanned aerial vehicle and a designated object as a function of elevation angle method According to claim 1,
The unmanned aerial vehicle,
Acting as a relay or base station
A method for optimizing the energy efficiency of unmanned aerial vehicle communication using an intelligent reflective surface, characterized by According to claim 1,
The intelligent reflective surface,
It consists of a flat device including a plurality of passive elements, and each of the passive elements independently changes the phase of reflected electromagnetic waves.
A method for optimizing the energy efficiency of unmanned aerial vehicle communication using an intelligent reflective surface, characterized by According to claim 1,
The system model is
Based on a probabilistic LoS model, setting environmental variables using information including the density and average height of buildings in a place
A method for optimizing the energy efficiency of unmanned aerial vehicle communication using an intelligent reflective surface, characterized by According to claim 4,
The system model is
The intelligent reflective surface is set to change the environmental variable by treating at least one of the height at which the intelligent reflective surface is located, the height of the receiving device, and the height set for an arbitrary object as a new effective ground. Energy Efficiency Optimization Method of Utilized Unmanned Aerial Vehicle Communication. According to claim 1,
Optimizing the phase setting value of the intelligent reflective surface and the route, speed, and communication time of the unmanned aerial vehicle,
Calculating an information transmission rate of a receiving device in the communication environment of the unmanned aerial vehicle;
designing an optimization problem based on the information transfer rate; and
Steps to solve the above optimization problem
A method for optimizing energy efficiency of unmanned aerial vehicle communication using an intelligent reflective surface, including a. According to claim 6,
The step of calculating the information transmission rate of the receiving device in the communication environment of the unmanned aerial vehicle,
Calculating a phase setting value of the intelligent reflective surface;
maximizing the information transfer rate by setting the calculated phase setting value and maximizing signal strength; and
calculating the information transmission rate when the intelligent reflective surface supports a receiving device;
A method for optimizing energy efficiency of unmanned aerial vehicle communication using an intelligent reflective surface, including a. According to claim 1,
Optimizing the phase setting value of the intelligent reflective surface and the route, speed, and communication time of the unmanned aerial vehicle,
Minimizing energy consumption of the unmanned aerial vehicle by simultaneously using a plurality of the intelligent reflective surfaces.
A method for optimizing the energy efficiency of unmanned aerial vehicle communication using an intelligent reflective surface, characterized by According to claim 1,
Optimizing the phase setting value of the intelligent reflective surface and the route, speed, and communication time of the unmanned aerial vehicle,
Minimizing the energy consumption of the unmanned aerial vehicle by using one of the intelligent reflective surfaces at a time.
A method for optimizing the energy efficiency of unmanned aerial vehicle communication using an intelligent reflective surface, characterized by In the energy efficiency optimization device of unmanned aerial vehicle communication using an intelligent reflecting surface (IRS),
a system model unit providing a system model considering a plurality of intelligent reflective surfaces in an unmanned aerial vehicle communication environment; and
Optimization unit for optimizing the phase setting value of the intelligent reflective surface and the path, speed and communication time of the UAV based on the system model to minimize energy consumption of the UAV
including,
The system model part
Energy efficiency optimization of unmanned aerial vehicle communication using an intelligent reflective surface, characterized in that the system model is provided by expressing the probability that a line of sight (LoS) path exists between the unmanned aerial vehicle and a designated object as a function of elevation angle Device. According to claim 10,
The system model is
Based on a probabilistic LoS model, setting environmental variables using information including the density and average height of buildings in a place
An apparatus for optimizing energy efficiency of unmanned aerial vehicle communication using an intelligent reflective surface. According to claim 10,
The optimization unit,
an information transfer rate calculation unit that calculates an information transfer rate of a receiving device in the communication environment of the unmanned aerial vehicle;
an optimization problem designing unit that designs an optimization problem based on the information transfer rate; and
Optimization problem calculation unit for solving the optimization problem
An energy efficiency optimization device for unmanned aerial vehicle communication using an intelligent reflective surface, including a. According to claim 12,
The information transfer rate calculation unit,
Calculate the phase setting value of the intelligent reflective surface, set the calculated phase setting value and maximize the signal strength to maximize the information transmission rate, and calculate the information transmission rate when the intelligent reflective surface supports a receiving device to do
An apparatus for optimizing energy efficiency of unmanned aerial vehicle communication using an intelligent reflective surface. According to claim 10,
The optimization unit,
Minimizing energy consumption of the unmanned aerial vehicle by simultaneously using a plurality of the intelligent reflective surfaces.
An apparatus for optimizing energy efficiency of unmanned aerial vehicle communication using an intelligent reflective surface. According to claim 10,
The optimization unit,
Minimizing the energy consumption of the unmanned aerial vehicle by using one of the intelligent reflective surfaces at a time.
An apparatus for optimizing energy efficiency of unmanned aerial vehicle communication using an intelligent reflective surface.

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