Intelligent reflecting surface based unmanned aerial vehicle communication method and device, and electronic device

Abstract

The application discloses a UAV communication method and device based on an intelligent reflecting surface and electronic equipment, the method comprising: determining a first communication channel coefficient from a base station to a UAV and a second communication channel coefficient from the UAV to an Internet of Things device according to the position and height of the UAV in each time slot, the position of the base station, and the position of the Internet of Things device; wherein the UAV is configured with an intelligent reflecting surface; determining an energy consumption function of the UAV and the Internet of Things device according to the first communication channel coefficient from the base station to the UAV and the second communication channel coefficient from the UAV to the Internet of Things device; constructing a joint optimization model of a communication strategy with the minimum energy consumption function as the target and the communication scheduling, position, speed of the UAV in each time slot, the data upload rate of the Internet of Things device, and the phase shift matrix of the intelligent reflecting surface as the constraint conditions; and solving the joint optimization model of the communication strategy to determine a target communication strategy of the UAV and the Internet of Things device.

Description

Technical Field

[0001] This application belongs to the field of wireless communication technology, specifically relating to a UAV communication method, device, and electronic device based on an intelligent reflective surface. Background Technology

[0002] In communication systems assisted by intelligent reflective surfaces (IRS) provided by related technologies, when there are obstacles between the base station and the IoT device, such as hills, tall buildings, or forests between them, the communication channel quality of the entire communication system is poor due to the obstruction of the obstacles. Poor communication channel quality will cause the IoT device to transmit data at excessively high power, thereby consuming the stored energy more quickly and stopping working, resulting in poor battery life of the communication system. Summary of the Invention

[0003] The purpose of this application is to provide a UAV communication method, device, and electronic device based on a smart reflective surface, which can solve the problem of poor battery life of the communication system.

[0004] In a first aspect, embodiments of this application provide a UAV communication method based on a smart reflector. The method includes: determining a first communication channel coefficient from the base station to the UAV and a second communication channel coefficient from the UAV to the IoT device based on the UAV's position and altitude in each time slot during flight time, the location of a base station, and the location of an IoT device; wherein the UAV is equipped with a smart reflector; determining the energy consumption function of the UAV and the IoT device based on the first communication channel coefficient from the base station to the UAV and the second communication channel coefficient from the UAV to the IoT device; constructing a joint optimization model of the communication strategy with the goal of minimizing the energy consumption function, and using the UAV's communication scheduling, position, speed, the IoT device's data upload rate, and the smart reflector's phase shift matrix as constraints; solving the joint optimization model of the communication strategy to determine the target communication strategy between the UAV and the IoT device; wherein the target communication strategy includes the UAV's target flight trajectory, the target communication scheduling in each time slot, and the smart reflector's target phase shift matrix.

[0005] Secondly, embodiments of this application provide a drone communication device based on a smart reflector. The device includes: a first determining module, configured to determine a first communication channel coefficient from the base station to the drone and a second communication channel coefficient from the drone to the IoT device based on the drone's position and altitude in each time slot during flight time, the location of the base station, and the location of the IoT device; wherein the drone is equipped with a smart reflector; a construction module, configured to determine the energy consumption function of the drone and the IoT device based on the first communication channel coefficient from the base station to the drone and the second communication channel coefficient from the drone to the IoT device, aiming to minimize the energy consumption function, and using the communication scheduling, position, and speed of the drone in each time slot, the data upload rate of the IoT device, and the phase shift matrix of the smart reflector as constraints, to construct a joint optimization model of the communication strategy; and a second determining module, configured to solve the joint optimization model of the communication strategy to determine the target communication strategy between the drone and the IoT device; wherein the target communication strategy includes the target flight trajectory of the drone, the target communication scheduling in each time slot, and the target phase shift matrix of the smart reflector.

[0006] Thirdly, embodiments of this application provide an electronic device including a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the method described in the first aspect.

[0007] Fourthly, embodiments of this application provide a readable storage medium on which a program or instructions are stored, which, when executed by a processor, implement the steps of the method described in the first aspect.

[0008] Fifthly, embodiments of this application provide a chip, the chip including a processor and a communication interface, the communication interface being coupled to the processor, the processor being used to run programs or instructions to implement the steps of the method described in the first aspect.

[0009] In a sixth aspect, embodiments of this application provide a computer program product, the computer program product including a computer program stored on a non-transitory computer-readable storage medium, the computer program including a program or instructions, which, when executed, implement the steps of the method described in the first aspect.

[0010] In this embodiment, based on the location and altitude of the UAV in each time slot during its flight time, the location of the base station, and the location of the IoT device, a first communication channel coefficient from the base station to the UAV and a second communication channel coefficient from the UAV to the IoT device are determined, respectively. The UAV is equipped with a smart reflector. Then, based on the first communication channel coefficient from the base station to the UAV and the second communication channel coefficient from the UAV to the IoT device, the energy consumption function of the UAV and the IoT device is determined. With minimizing the energy consumption function as the objective, and using the communication scheduling, location, and speed of the UAV in each time slot, the data upload rate of the IoT device, and the phase shift matrix of the smart reflector as constraints, a joint optimization model of the communication strategy is constructed to jointly optimize the UAV's trajectory, communication scheduling, and the phase shift of the smart reflector. By solving the joint optimization model of the communication strategy, the target communication strategy between the UAV and the IoT device can be determined. Through the embodiments of this application, a smart reflective surface is configured on a drone. By aiming to minimize the energy consumption of the drone and IoT devices, the drone's flight trajectory, communication scheduling, and the phase shift of the smart reflective surface are jointly optimized to provide a target communication strategy that minimizes the energy consumption of the drone and IoT devices. This enables communication through the target communication strategy that minimizes energy consumption, thereby improving the endurance of the drone and IoT devices. Attached Figure Description

[0011] Figure 1 This is a flowchart illustrating a UAV communication method based on a smart reflective surface provided in an embodiment of this application; Figure 2 This is a flowchart illustrating another UAV communication method based on a smart reflective surface provided in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of an IRS-assisted UAV communication system provided in an embodiment of this application; Figure 4 This is a schematic diagram illustrating the variation of unmanned aerial vehicle (UAV) flight power with speed, provided in an embodiment of this application. Figure 5 This is a schematic diagram of the flight trajectory of a drone provided in an embodiment of this application; Figure 6 This is a schematic diagram illustrating the impact of the number of IRS reflective units on the energy consumption of IoT devices in the embodiments of this application and different methods; Figure 7 This is a schematic diagram illustrating the impact of the number of IoT devices on UAV flight energy consumption in the embodiments of this application and different methods; Figure 8 This is a schematic diagram of the structure of a drone communication device based on a smart reflective surface provided in an embodiment of this application; Figure 9 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0012] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0013] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such terms can be used interchangeably where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0014] The following description, in conjunction with the accompanying drawings, details the UAV communication method, apparatus, and electronic equipment based on intelligent reflective surfaces provided in this application through specific embodiments and application scenarios.

[0015] Figure 1 This diagram illustrates a flowchart of a UAV communication method based on a smart reflective surface, provided in an embodiment of this application. This method can be executed by an electronic device. See also... Figure 1 The method may include the following steps.

[0016] Step 102: Based on the position and altitude of the UAV in each time slot during its flight time, the position of the base station, and the position of the IoT device, determine the first communication channel coefficient from the base station to the UAV and the second communication channel coefficient from the UAV to the IoT device, respectively; wherein, the UAV is equipped with an intelligent reflective surface.

[0017] With the large-scale deployment of 5G, IoT, and UAV technologies, especially UAVs as airborne wireless communication nodes in applications such as smart cities, intelligent transportation, and disaster management, the combination of Intelligent Reflecting Surfaces (IRS) and UAVs expands network coverage while enhancing communication performance. Furthermore, IRS offers advantages in low cost and low power consumption; each reflective element can adjust beamforming through phase shifting to improve communication channel quality, increasing the data upload rate of IoT devices. However, this also increases the energy consumed in uploading data, potentially reducing the battery life of IoT devices. In this embodiment, by constructing a target that minimizes the energy consumption of UAVs and IoT devices, this approach determines which UAV flight path, communication scheduling, and IRS phase shift can balance communication channel quality and energy consumption, thereby improving the battery life of IoT devices.

[0018] Step 104: Determine the energy consumption function of the UAV and the IoT device based on the first communication channel coefficient from the base station to the UAV and the second communication channel coefficient from the UAV to the IoT device. With minimizing the energy consumption function as the objective, and with the communication scheduling, position, speed of the UAV, the data upload rate of the IoT device, and the phase shift matrix of the smart reflector in each time slot as constraints, construct a joint optimization model of the communication strategy.

[0019] The joint optimization model of this communication strategy is used to jointly optimize the drone's flight trajectory, communication scheduling, and phase shift of the smart reflector while minimizing the energy consumption of the drone and IoT devices, so as to provide a communication strategy that meets the minimum energy consumption requirement and improves the endurance of the communication system.

[0020] Step 106: Solve the joint optimization model of the communication strategy to determine the target communication strategy between the UAV and the IoT device; wherein, the target communication strategy includes the target flight trajectory of the UAV, the target communication scheduling of each time slot, and the target phase shift matrix of the smart reflector.

[0021] In this embodiment, based on the location and altitude of the UAV in each time slot during its flight time, the location of the base station, and the location of the IoT device, a first communication channel coefficient from the base station to the UAV and a second communication channel coefficient from the UAV to the IoT device are determined, respectively. The UAV is equipped with a smart reflector. Then, based on the first communication channel coefficient from the base station to the UAV and the second communication channel coefficient from the UAV to the IoT device, the energy consumption function of the UAV and the IoT device is determined. With minimizing the energy consumption function as the objective, and using the communication scheduling, location, and speed of the UAV in each time slot, the data upload rate of the IoT device, and the phase shift matrix of the smart reflector as constraints, a joint optimization model of the communication strategy is constructed to jointly optimize the UAV's trajectory, communication scheduling, and the phase shift of the smart reflector. By solving the joint optimization model of the communication strategy, the target communication strategy between the UAV and the IoT device can be determined. Through the embodiments of this application, a smart reflective surface is configured on a drone. By aiming to minimize the energy consumption of the drone and IoT devices, the drone's flight trajectory, communication scheduling, and the phase shift of the smart reflective surface are jointly optimized to provide a target communication strategy that minimizes the energy consumption of the drone and IoT devices. This enables communication through the target communication strategy that minimizes energy consumption, thereby improving the endurance of the drone and IoT devices.

[0022] In one implementation, step 102 above, which determines the first communication channel coefficient from the base station to the drone and the second communication channel coefficient from the drone to the IoT device based on the drone's position and altitude in each time slot during flight time, the base station's position, and the IoT device's position, may include the following steps.

[0023] Step 1021: For the channel between the base station and the smart reflector, based on the position and altitude of the UAV in each time slot during its flight time and the position of the base station, determine the channel coefficient corresponding to the direct wave channel and the channel coefficient corresponding to the multipath reflection wave channel between the base station and the smart reflector.

[0024] Based on the drone's trajectory in various time slots, the channels between the base station and the intelligent reflective surface configured on the drone include direct wave channels and multipath reflected wave channels. When the drone flies at higher altitudes and there are no tall buildings, mountains, or other obstructions between it and the ground equipment (base station), there is usually a strong direct signal wave. When there are obstacles between the drone and the ground equipment (base station), the signal wave is reflected, diffracted, and scattered, generating multiple channels to the ground equipment (base station).

[0025] Step 1022: Determine the first communication channel coefficient based on the channel coefficient corresponding to the direct wave channel between the base station and the smart reflector and the channel coefficient corresponding to the multipath reflection channel.

[0026] The first communication channel coefficient integrates the channel coefficients corresponding to the direct wave channel and the multipath reflected wave channel between the base station and the smart reflector, and adds the Rice fading factor. The value of the Rice fading factor changes with the altitude of the UAV, thereby more accurately describing the channel conditions between the base station and the smart reflector.

[0027] Step 1023: For the channel between the smart reflector and the IoT device, based on the position and altitude of the UAV in each time slot during the flight time and the position of the IoT device, determine the channel coefficient corresponding to the direct wave channel and the channel coefficient corresponding to the multipath reflection wave channel between the smart reflector and the IoT device.

[0028] Similarly, based on the drone's trajectory in various time slots, the channels between the smart reflective surface configured on the drone and the IoT device also include direct wave channels and multipath reflected wave channels. When the drone flies in a higher altitude and there are no tall buildings, mountains, or other obstructions between it and the ground device (IoT device), there is usually a strong direct signal wave. When there are obstacles between the drone and the ground device (IoT device), the signal wave is reflected, diffracted, and scattered, generating multiple channels to the ground device.

[0029] Step 1024: Determine the second communication channel coefficient based on the channel coefficient corresponding to the direct wave channel and the channel coefficient corresponding to the multipath reflection wave channel between the smart reflector and the IoT device.

[0030] The second communication channel coefficient integrates the channel coefficients corresponding to the direct wave channel and the multipath reflected wave channel between the smart reflector and the IoT device, and adds the Rice fading factor. The value of the Rice fading factor changes with the altitude of the UAV, thereby more accurately describing the channel conditions between the smart reflector and the IoT device.

[0031] In this embodiment, based on the flight trajectory of the UAV during flight time, the channel between the intelligent reflector configured on the UAV and the base station may be a direct wave channel or a multipath reflected wave channel. Similarly, the channel between the intelligent reflector configured on the UAV and the IoT device may also be a direct wave channel or a multipath reflected wave channel. Therefore, for the channel between the base station and the intelligent reflector, the first communication channel coefficient is determined based on the channel coefficients corresponding to the direct wave channel and the multipath reflected wave channel. Similarly, for the channel between the intelligent reflector and the IoT device, the second communication channel coefficient is determined based on the channel coefficients corresponding to the direct wave channel and the multipath reflected wave channel. By comprehensively considering the different channels that may correspond to different flight states of the UAV and fusing the channel coefficients corresponding to the possible channels, the determined first and second communication channel coefficients can more accurately describe the channel conditions between the base station and the intelligent reflector, as well as between the intelligent reflector and the IoT device, providing accurate support for subsequent communication strategy decisions.

[0032] In one implementation, the constraints when constructing the joint optimization model of the communication strategy may include the following:

[0033] (1) The communication scheduling of the UAV in each time slot is less than or equal to 1; wherein the communication scheduling of the UAV is a binary variable.

[0034] In this embodiment, the communication is carried out using the Time Division Multiple Access (TDMA) method, which allows one IoT device to access the drone and communicate with the base station in each time slot.

[0035] (2) The drone takes off from the same location as it lands.

[0036] Among them, the standard constraint is that the take-off location and the landing location of the drone are the same, to ensure compliance with safety requirements and regulations.

[0037] (3) The speed of the UAV is less than or equal to the preset speed.

[0038] The preset speed can be the maximum flight speed that the drone can achieve.

[0039] (4) The amount of data uploaded by the IoT device is greater than or equal to the preset amount of data; wherein the amount of data uploaded is determined based on the communication scheduling of the UAV and the rate at which the IoT device uploads data.

[0040] The preset data volume is the amount of data that IoT devices need to upload. In reality, in addition to the data that needs to be uploaded, it also includes data used for communication overhead, such as source address, destination address, data number, communication protocol used, cyclic redundancy check code, etc., to ensure the reliability and security of communication.

[0041] (5) The phase value in the phase shift matrix of the intelligent reflective surface is [0, 2π).

[0042] In this embodiment, each element of the phase shift matrix of the smart reflector (corresponding to each reflective unit) is a complex number, including amplitude and phase. In this application, the amplitude is 1, meaning the signal wave is not amplified. The phase is changed to control whether the signal wave is enhanced or weakened in various directions; for example, it can be enhanced in the direction of normal IoT devices and weakened in the direction of eavesdropping IoT devices.

[0043] In one implementation, before solving the joint optimization model of the communication strategy in step 106 above to determine the target communication strategy between the UAV and the IoT device, the method may further include the following steps.

[0044] Step 1051: Adjust the array of the smart reflective surface and configure the phase shift matrix of the smart reflective surface as the optimal phase shift matrix.

[0045] In this way, by optimizing the phase shift matrix of the IRS configured in the UAV, that is, adjusting the array of reflection units in the intelligent reflection, the signal wave is focused in one direction, so that the signal and the receiving antenna accumulate in the same phase, thereby maximizing the intensity of the received signal wave, further improving the communication channel quality and increasing the data transmission rate.

[0046] Step 1052: Adjust the communication scheduling of the UAV in each time slot from a binary variable to a continuous variable.

[0047] In this process, the communication scheduling of binary variables is made continuous to transform the joint optimization model of the communication strategy, so as to facilitate the solution.

[0048] Step 1053: Based on the optimal phase shift matrix and the communication scheduling of the continuous variables, the joint optimization model of the communication strategy is transformed from a mixed integer non-convex optimization model into a continuous non-convex optimization model.

[0049] In this embodiment, the phase shift matrix of the intelligent reflector configured on the UAV is optimized, i.e., the array of intelligent reflectors is adjusted so that the signal waves arriving at the intelligent reflectors converge in one direction and accumulate on the same phase, thereby strengthening the signal wave intensity. Furthermore, the UAV's communication scheduling is changed from a binary variable to a continuous variable. Based on the above adjustments and optimizations to the array of intelligent reflectors and the UAV's communication scheduling, the joint optimization model of the communication strategy is transformed from a mixed-integer non-convex optimization model into a continuous non-convex optimization model, facilitating the subsequent solution of the joint optimization model of the communication strategy.

[0050] In one implementation, based on the above-mentioned transformation of the joint optimization model of the communication strategy from a mixed integer nonconvex optimization model to a continuous nonconvex optimization model, step 106 above-mentioned solving the joint optimization model of the communication strategy to determine the target communication strategy between the UAV and the IoT device may include the following steps.

[0051] Step 1061: Introduce a first relaxation variable to relax the energy consumption function of the joint optimization model of the communication strategy.

[0052] In this process, the first relaxation variable is introduced to transform the non-convex energy consumption function into a convex energy consumption function.

[0053] Step 1062: Introduce a second relaxation variable to relax the constraints of the joint optimization model of the communication strategy.

[0054] In this way, the introduction of a second relaxation variable transforms the constraints of non-convex characteristics into constraints of convex characteristics.

[0055] Step 1063: Based on the relaxed energy consumption function and the constraints, the joint optimization model of the communication strategy is transformed from the continuous non-convex optimization model into a convex optimization model.

[0056] Specifically, based on the energy consumption function and constraints after the transformation characteristics, the joint optimization model of the communication strategy with continuous non-convex characteristics is transformed into a convex optimization model, which can be directly solved using the CVX toolbox to obtain the target communication strategy corresponding to the minimum energy consumption of UAV and IoT devices.

[0057] Step 1064: Solve the convex optimization model until convergence to obtain the target communication strategy between the UAV and the IoT device.

[0058] In this embodiment, since the joint optimization model of the communication strategy is a non-convex optimization model, a first relaxation variable is introduced to relax the energy consumption function as the objective, and a second relaxation variable is introduced to relax the constraints. Based on the relaxed energy consumption function and constraints, the joint optimization model of the communication strategy is transformed from the continuous non-convex optimization model into a convex optimization model, thereby realizing the feature transformation of the joint optimization model of the communication strategy, so as to facilitate solving the joint optimization model of the communication strategy.

[0059] In one implementation, step 104 above, which determines the energy consumption functions of the drone and the IoT device based on the first communication channel coefficient from the base station to the drone and the second communication channel coefficient from the drone to the IoT device, may include the following steps: Step 1041: Determine the transmission power of the drone and the transmission power of the IoT device based on the first communication channel coefficient from the base station to the drone and the second communication channel coefficient from the drone to the IoT device, respectively. Step 1042: Determine the energy consumption function of the IoT device based on the transmission power of the IoT device, the communication scheduling of the UAV in each time slot, and the length of each time slot; Step 1043: Determine the energy consumption function of the UAV based on the UAV's transmission power, the length of each time slot, and the UAV's flight energy consumption in each time slot; wherein, the UAV's flight energy consumption is determined based on the UAV's flight information in each time slot. Step 1044: Determine the energy consumption functions of the drone and the IoT device based on the energy consumption function of the IoT device, the energy consumption weight corresponding to the IoT device, the energy consumption function of the drone, and the energy consumption weight corresponding to the drone.

[0060] In this embodiment, the transmission power of the UAV and the IoT device are first determined based on the first and second communication channel coefficients, respectively. For the IoT device, the energy consumption function is then determined by combining the UAV's communication scheduling and the time slot length. For the UAV, the energy consumption function is then determined by combining the time slot length and the UAV's flight energy consumption. Since the energy consumption of the IoT device and the UAV are not on the same order of magnitude, different energy consumption weights are introduced to balance the energy consumption impact of the two, and the energy consumption functions of the target UAV and IoT device are comprehensively determined.

[0061] Figure 2 This diagram illustrates another UAV communication method based on a smart reflective surface provided in an embodiment of this application. This method can be executed by an electronic device. See also... Figure 2 The method may include the following steps.

[0062] Among them, see Figure 3 , Figure 3 This illustration shows a structural diagram of an IRS-assisted UAV communication system according to an embodiment of this application. The application scenario of this embodiment is: IoT devices transmitting data uplink. The IRS-assisted UAV communication system includes: one base station, one UAV equipped with an IRS, and K IoT devices. The IRS consists of an array of M intelligent reflective units. Due to obstacles between the K IoT devices and the base station, the communication channel quality deteriorates. In this case, the UAV configured with the IRS uses TDMA to perform communication tasks, maintaining a height H, and providing services to multiple IoT devices.

[0063] Step 201: Determine the communication channel coefficients from the base station to the UAV and from the UAV to the IoT device.

[0064] In this embodiment of the application, the flight time of the UAV is... T Divided into N equal time intervals Assuming a time slot Small enough that the UAV's position remains unchanged within each time slot. The base station's location is... Ground-based IoT devices The horizontal coordinate is The UAV configured with IRS in the first n The position of each time slot is Assuming the UAV takes off and lands at the same location, it can be represented as: The maximum flight speed of a UAV is expressed as: Among them, in the first n The transmission scheduling for each time slot is as follows: (1) in, This represents the complete occupation of the UAV. n Each time slot communicates with IoT devices; This means that the UAV does not occupy the first position. n Each time slot communicates with IoT devices.

[0065] Furthermore, only one IoT device can upload data to the base station in the same time slot, that is: (2) Among them, IRS consists of M It consists of an array of intelligent reflective units, the first n The distance from each time-slot base station to the UAV is: The distance from the UAV to the IoT device is: .

[0066] For the direct wave channel, the communication channel coefficients from the base station to the UAV are shown in (3), and the communication channel coefficients from the UAV to the IoT device are shown in (4): (3) (4) Where j is the imaginary unit, describing the phase change of the signal. D is the spacing between the reflective elements in the IRS.

[0067] However, based on the flight trajectory of the UAV during flight time, the channel between the intelligent reflector configured by the UAV and the base station may be a direct wave channel or a multipath reflected wave channel. Thus, the communication channel coefficient from the base station to the UAV is shown in (5), and the communication channel coefficient from the UAV to the IoT device is shown in (6). (5) (6) in, Indicates wavelength. The value represents the average power gain at a distance of 1m. The azimuth angle between the base station and the IRS is: The azimuth angles between the IRS and the IoT device are: , Rice's fading factor and For the direct wave component of the Ricean fading channel model, and It represents the channel of the multipath reflected wave components and follows a Gaussian distribution.

[0068] Furthermore, when the UAV configured with an IRS flies at a sufficiently high altitude, the channels from the base station to the IRS and from the IRS to the IoT device are primarily direct wave components. As the frequency increases, the communication channel coefficient will become more dependent on free-space path loss, i.e., the direct wave component channel. When hour, Similarly, we can obtain That is, the communication channel approximates a Rayleigh fading channel. Therefore, the communication channel coefficients (5) and (6) can be rewritten as: (7) (8) Furthermore, it can be deduced that the signal-to-noise ratio of the signal received by the base station is: (9) Therefore, in the n In each time slot, the data upload rate of IoT devices is: (10) Step 202: Construct a joint optimization model for the communication strategy with the objective of minimizing the energy consumption function of UAV and IoT devices.

[0069] In this embodiment, the drone is a rotary-wing drone, such as... Figure 4 As shown, Figure 4 This diagram illustrates the variation of a drone's flight power with speed, as provided in an embodiment of this application. The drone's energy consumption includes communication energy consumption and flight energy consumption. Communication energy consumption is determined based on the aforementioned communication channel coefficient, while flight energy consumption is determined based on the drone's flight information, such as rotor induced power, drone airframe drag power, and rotor blade drag power.

[0070] The energy consumption function of the drone is shown in (11), and the energy consumption function of the IoT device is shown in (12): (11) in, and The induced resistance and induced power of the UAV in hovering state. The tip angular velocity (m / s) The average rotor induced velocity during hovering ( m / s ), It is the fuselage drag ratio. It is the ratio of the total blade area to the area swept by the blade. It is the area swept by the drone's blades.

[0071] (12) in, It is the first k The transmission power when an IoT device uploads data.

[0072] Furthermore, since the energy consumption of UAVs and IoT devices are not on the same order of magnitude, a weighting factor needs to be added to the energy consumption of IoT devices. , , Therefore, the energy consumption functions of UAV and IoT devices are as follows: (13) The joint optimization model for the communication strategy thus constructed is shown below: (14) Wherein, C1 is the constraint on the amount of data uploaded by the IoT device, C2 and C3 are the constraints on the communication scheduling of each time slot UAV, C4 is the constraint on the speed of the UAV, C5 is the constraint on the take-off and landing positions of the UAV, and C6 is the constraint on the phase shift matrix of the IRS.

[0073] Due to the existence of binary variables And the UAV trajectory and data transmission scheduling The model is highly coupled and is a mixed-integer non-convex problem, which is difficult to solve directly. Therefore, this application provides a method based on continuous convex approximation to solve the above model, as shown in steps 203 to 204.

[0074] Step 203: Relax the energy consumption function and constraints of the joint optimization model of the communication strategy, and transform the joint optimization model of the communication strategy from a mixed integer non-convex optimization model into a continuous convex optimization model.

[0075] Specifically, step 203 may include the following steps.

[0076] S1, Adjust the IRS array to configure the phase shift matrix of the IRS as the optimal phase shift matrix.

[0077] From (9), we can obtain: (15) IRS (Inductively Reflective Array) focuses the signal wave in one direction by adjusting the reflective element array of the smart reflector, so that the signal and the receiving antenna accumulate in the same phase, thereby maximizing the intensity of the received signal wave. m The phase shift configuration of each reflecting unit is as follows: (16) Given the optimal phase shift matrix, Rayleigh fading can be expressed as: (17) S2 changes the communication scheduling from a binary variable to a continuous variable.

[0078] Among them, Convert to .

[0079] Thus, the joint optimization model of the communication strategy is transformed from a mixed-integer nonconvex optimization model to a continuous nonconvex optimization model: (19) S3, introduce the first slack variable to relax the energy consumption function of the joint optimization model of the communication strategy.

[0080] Among them, the first slack variable ,in: (20) Equation (20) can be equivalent to: (twenty one) Given the first rLocal points in the next iteration and ,because and Regarding respectively and For convex functions, based on the continuous convex approximation method, ... , Performing a first-order Taylor expansion yields an approximate problem for a convex optimization problem: (twenty two) Therefore, the flight energy consumption of a UAV can be expressed as: (twenty three) At this point, the UAV's flight energy consumption function is transformed into a convex optimization term.

[0081] S4. A second slack variable is introduced to relax the constraints of the joint optimization model of the communication strategy.

[0082] Among them, the second slack variable ,in: (twenty four) For a given number r Local points in the next iteration and ,right A first-order Taylor expansion yields: (25) From equation (23), the following inequalities hold: (26) At this point, constraint C1 is transformed into a convex constraint.

[0083] S5, verify the data upload rate of IoT devices. The convexity of .

[0084] The data upload rate of IoT devices can be obtained from (5). : (27) in, , Equation (27) for Taking the second derivative, we get... Clearly, the second derivative is always greater than 0. Therefore, the data upload rate of IoT devices is a convex function. Taking its lower bound through a first-order Taylor expansion, we obtain: (28) in: (29) Among them, due to It is still a non-convex term, given the first term. l Local point in the next iteration and Approximately: (30) After the transformation, It is about The convex function, equation (28) is transformed into a convex constraint.

[0085] S6, based on the transformation from S1 to S5, yields a continuous convex optimization model: (31) Step 204: Solve the continuous convex optimization model until convergence, and obtain the target flight trajectory of the UAV, the target communication scheduling of each time slot, and the target phase shift matrix of the IRS.

[0086] The methods for solving convex optimization models can include least squares method, gradient descent method, and conjugate gradient method.

[0087] In some embodiments, a two-dimensional region is defined, see [reference] Figure 5 , Figure 5 This illustration shows a schematic diagram of the flight trajectory of a drone according to an embodiment of this application, wherein five IoT devices are randomly distributed within an area and their locations are known, and a single UAV equipped with an IRS operates at a constant altitude. Perform a communication task. The communication parameter is: additive white Gaussian noise. Channel power gain , , , Weight parameters , , The flight energy consumption parameters for rotary-wing UAVs are as follows: , , , , , , .like Figure 5 As shown, weighting factors In this scenario, a single UAV server can provide UAV trajectory maps for four IoT devices. The value of determines the optimization weights of UAV and IoT devices, and with the weights With increased power consumption, UAVs have a greater weight in energy consumption. UAVs perform communication tasks at greater distances from IoT devices, resulting in longer battery life.

[0088] like Figure 6 As shown, Figure 6 The illustration shows the impact of the number of IRS reflective units on the power consumption of IoT devices, comparing embodiments of this application with different methods. It demonstrates that phase shift optimization can improve communication channel quality, thereby reducing the transmit power of IoT devices, and thus reducing their power consumption. The comparison methods include: 1) IRS single-bit reference method: each IRS reflective unit has 2 phase shift levels; 2) IRS random phase shift method: the phase shift of the IRS reflective unit is... 3) No IRS method: The UAV is not equipped with an IRS, and the UAV forwards the received information to the base station. Figure 7 It can be seen that, Figure 7 The diagram illustrates the impact of the number of IoT devices on UAV flight energy consumption in embodiments of this application and different methods. Except for the method without an IRS, the energy consumption of IoT devices decreases with the increase of IRS reflection units in the other methods. The random phase-shift method significantly reduces IoT device energy consumption compared to the method without an IRS, but it does not fully utilize the advantages of the adjustable IRS reflection unit technology. For the single-bit reference method, the IRS phase-shift capability is insufficient, and multiple reflected signals deviate significantly from the ideal direction, resulting in performance loss. Compared to the comparative methods, the embodiments of this application fully utilize the advantages of optimized phase shift, and with the increase of IRS reflection units... M With the increase in gain, passive beamforming gains are enhanced, resulting in outstanding performance. This significantly reduces the energy consumption of IoT devices, extends the lifespan of IoT networks, and improves the stability of communication systems.

[0089] according to Figure 7 It is known that UAV flight energy consumption increases with the number of IoT devices. This paper aims to reduce UAV flight energy consumption by optimizing IRS phase shift, UAV flight trajectory, and communication scheduling. Simulation results show that in unoptimized UAV flight energy consumption methods, the excessively high UAV flight speed and distance reduce the UAV's endurance. Both the single-bit reference method and the random phase method result in poor communication channel quality due to the inability of reflected signals to converge at the base station, extending data transmission time (i.e., the time for UAV to execute tasks) and increasing UAV energy consumption. This application's embodiment utilizes the advantages of IRS phase shift optimization while ensuring the sustainability and stability of the IRS-assisted UAV communication system, and this embodiment can handle situations with a large number of IoT devices.

[0090] Furthermore, the application scenarios of this application embodiment may include: (1) Internet of Things and Smart Cities: In the construction of Internet of Things and smart cities, a large number of sensors and IoT devices need to transmit data in real time. UAVs can serve as mobile relay nodes and provide reliable data transmission services for these devices through IRS-assisted communication systems, enabling rapid data collection and analysis.

[0091] (2) Emergency communication and search and rescue operations: Natural disasters or accident sites may cause traditional communication facilities to be interrupted. At this time, UAVs can act as communication relay nodes, providing stable communication support to rescue teams through the IRS-assisted covert communication system, ensuring timely information transmission and efficient search and rescue operations.

[0092] (3) Communication in remote areas: In some remote areas, traditional communication methods may not be able to cover due to complex terrain or insufficient infrastructure. UAVs can serve as airborne base stations or relay nodes to provide temporary communication services to these areas and meet the communication needs of local residents or scientific research teams.

[0093] (4) Special reconnaissance and communication: In special fields, UAVs can be used as reconnaissance platforms to collect highly confidential information. At the same time, through the IRS-based UAV communication system, UAVs can also act as relay nodes to transmit information to the command center in real time, improving utilization efficiency.

[0094] (5) Temporary events and large-scale competitions: In some temporary events or large-scale competitions, due to the large number of people and the high demand for communication, traditional communication facilities may not be able to meet the needs. At this time, UAVs can act as aerial relay nodes, providing stable communication services to on-site personnel through IRS-assisted communication systems, ensuring the smooth progress of the event.

[0095] Through the embodiments of this application, it is possible to: 1. Improve the performance of wireless communication networks: IRS significantly improves the performance of wireless communication networks by intelligently reconfiguring the wireless propagation environment through the integration of a large number of low-cost passive reflective elements on a plane. These passive reflective elements can adjust the phase and amplitude of the incident signal to achieve beamforming, optimize the signal transmission path, and reduce signal attenuation and interference.

[0096] 2. Improve energy efficiency: IRS optimizes signal communication channel quality by intelligently adjusting the phase and amplitude of reflective elements, reducing signal attenuation and interference during transmission, thereby reducing energy consumption of UAV and IoT devices during communication and improving energy efficiency.

[0097] 3. Extended IoT Device Lifespan and UAV Endurance: By reducing energy consumption during communication, the IRS-assisted UAV communication system can significantly extend the lifespan of UAVs and IoT devices. In the UAV field, longer flight time means more tasks can be completed, improving work efficiency; in the IoT field, extended device lifespan means lower maintenance and replacement costs, reducing overall operating costs.

[0098] 4. Promoting UAV Communication Technology Innovation: The research and application of IRS-assisted UAV communication systems will drive the innovation and development of UAV communication technology. With continuous technological advancements and market expansion, UAV communication systems will play a vital role in more fields, bringing more business opportunities and value to various industries.

[0099] 5. Expanding Application Areas: Because IRS-assisted UAV communication strategies optimize communication channel quality, reduce signal attenuation and interference, and UAVs have longer battery life, they can be applied to more application scenarios requiring high-speed data transmission and real-time processing, such as environmental monitoring, agricultural monitoring, disaster relief, and emergency response. This expansion of application areas will further drive the development of the UAV industry.

[0100] It should be noted that the UAV communication method based on a smart reflector provided in this application embodiment can be executed by a UAV communication device based on a smart reflector, or by a control module within that device for executing the UAV communication method based on a smart reflector. This application embodiment uses the execution method of a UAV communication device based on a smart reflector as an example to illustrate the UAV communication device based on a smart reflector provided in this application embodiment.

[0101] Figure 8 This illustration shows a structural schematic diagram of a UAV communication device based on a smart reflective surface, according to an embodiment of this application. (See attached diagram.) Figure 8 The device 800 may include: a first determining module 81, a constructing module 82, and a second determining module 83.

[0102] The system comprises the following components: a first determining module 81, configured to determine a first communication channel coefficient from the base station to the drone and a second communication channel coefficient from the drone to the IoT device based on the drone's position and altitude in each time slot during its flight, the base station's position, and the IoT device's position; wherein the drone is equipped with a smart reflector; a construction module 82, configured to determine the energy consumption function of the drone and the IoT device based on the first and second communication channel coefficients from the base station to the drone and from the drone to the IoT device, and to construct a joint optimization model of the communication strategy with the goal of minimizing the energy consumption function, and with constraints including the drone's communication scheduling, position, speed, the IoT device's data upload rate, and the smart reflector's phase shift matrix in each time slot; and a second determining module 83, configured to solve the joint optimization model of the communication strategy to determine the target communication strategy between the drone and the IoT device; wherein the target communication strategy includes the drone's target flight trajectory, the target communication scheduling in each time slot, and the smart reflector's target phase shift matrix.

[0103] In one implementation, the first determining module 81 can be used to: for the channel between the base station and the smart reflector, determine the channel coefficient corresponding to the direct wave channel and the channel coefficient corresponding to the multipath reflection channel between the base station and the smart reflector based on the position and altitude of the UAV in each time slot during its flight time and the position of the base station; determine the first communication channel coefficient based on the channel coefficient corresponding to the direct wave channel and the multipath reflection channel between the base station and the smart reflector; for the channel between the smart reflector and the IoT device, determine the channel coefficient corresponding to the direct wave channel and the multipath reflection channel between the smart reflector and the IoT device based on the position and altitude of the UAV in each time slot during its flight time and the position of the IoT device; and determine the second communication channel coefficient based on the channel coefficient corresponding to the direct wave channel and the multipath reflection channel between the smart reflector and the IoT device.

[0104] In one implementation, the constraints include: (1) The communication scheduling of the UAV in each time slot is less than or equal to 1; wherein, the communication scheduling of the UAV is a binary variable; (2) The take-off position of the UAV is the same as the landing position; (3) The speed of the UAV is less than or equal to the preset speed; (4) The amount of data uploaded by the IoT device is greater than or equal to a preset amount of data; wherein, the amount of data uploaded is determined based on the communication scheduling of the UAV and the data upload rate of the IoT device; (5) The phase value in the phase shift matrix of the intelligent reflective surface is [0, 2π).

[0105] In one implementation, the second determining module 83 can also be used to: adjust the array of the intelligent reflective surface, configure the phase shift matrix of the intelligent reflective surface as the optimal phase shift matrix; adjust the communication scheduling of the UAV in each time slot from binary variables to continuous variables; and based on the optimal phase shift matrix and the communication scheduling of the continuous variables, transform the joint optimization model of the communication strategy from a mixed integer non-convex optimization model to a continuous non-convex optimization model.

[0106] In one implementation, the second determining module 83 can be specifically used to: introduce a first relaxation variable to relax the energy consumption function of the joint optimization model of the communication strategy; introduce a second relaxation variable to relax the constraints of the joint optimization model of the communication strategy; based on the relaxed energy consumption function and the constraints, transform the joint optimization model of the communication strategy from the continuous non-convex optimization model into a convex optimization model; solve the convex optimization model until convergence, and obtain the target communication strategy between the UAV and the IoT device.

[0107] In one implementation, the construction module 82 can be used to: determine the transmission power of the UAV and the transmission power of the IoT device based on the first communication channel coefficient from the base station to the UAV and the second communication channel coefficient from the UAV to the IoT device, respectively; determine the energy consumption function of the IoT device based on the transmission power of the IoT device, the communication scheduling of the UAV in each time slot, and the length of each time slot; determine the energy consumption function of the UAV based on the transmission power of the UAV, the length of each time slot, and the flight energy consumption of the UAV in each time slot; wherein the flight energy consumption of the UAV is determined based on the flight information of the UAV in each time slot; and determine the energy consumption functions of the UAV and the IoT device based on the energy consumption function of the IoT device, the energy consumption weight corresponding to the IoT device, the energy consumption function of the UAV, and the energy consumption weight corresponding to the UAV.

[0108] The UAV communication device based on a smart reflective surface in this application embodiment can be an electronic device or a component within an electronic device, such as an integrated circuit or a chip. The electronic device can be a terminal or other devices besides a terminal. For example, the electronic device can be a mobile phone, tablet computer, laptop computer, PDA, in-vehicle electronic device, mobile internet device (MID), augmented reality (AR) / virtual reality (VR) device, robot, wearable device, ultra-mobile personal computer (UMPC), netbook, or personal digital assistant (PDA), etc. It can also be a server, network attached storage (NAS), personal computer (PC), television (TV), ATM, or self-service machine, etc. This application embodiment does not specifically limit the specific type of device.

[0109] The UAV communication device based on a smart reflective surface in this application embodiment can be a device with an operating system. This operating system can be Android, iOS, or other possible operating systems; this application embodiment does not specifically limit it.

[0110] The UAV communication device based on a smart reflector provided in this application embodiment can achieve... Figures 1 to 2 The various processes implemented in the method implementation examples will not be described again here to avoid repetition.

[0111] Based on the same technical concept, embodiments of this application also provide an electronic device for executing the aforementioned UAV communication method based on a smart reflective surface. Figure 9 This is a schematic diagram of the structure of an electronic device to implement the various embodiments of this application. The electronic device can vary significantly due to differences in configuration or performance, and may include a processor 901, a communications interface 902, a memory 903, and a communication bus 904. The processor 901, communications interface 902, and memory 903 communicate with each other via the communication bus 904. The processor 901 can call a computer program stored in the memory 903 and executable on the processor 901 to perform the various steps of the above-described embodiments of the UAV communication method based on a smart reflector, achieving the same technical effects. To avoid repetition, further details are omitted here.

[0112] It should be noted that the electronic devices in the embodiments of this application include servers, terminals, or other devices besides terminals. For example, automobiles, robots, and handheld devices.

[0113] The above electronic device structure does not constitute a limitation on the electronic device. An electronic device may include more or fewer components than illustrated, or combine certain components, or arrange them differently. For example, an input unit may include a Graphics Processing Unit (GPU) and a microphone, and a display unit may use a liquid crystal display (LCD), organic light-emitting diode (OLED), or other similar display panels. User input units include at least one of a touch panel and other input devices. A touch panel is also called a touchscreen. Other input devices may include, but are not limited to, physical keyboards, function keys (such as volume control buttons, power buttons, etc.), trackballs, mice, and joysticks, which will not be elaborated further here.

[0114] Memory can be used to store software programs and various data. Memory can primarily include a first storage area for storing programs or instructions and a second storage area for storing data. The first storage area can store the operating system, application programs or instructions required for at least one function (such as sound playback, image playback, etc.). Furthermore, memory can include volatile memory or non-volatile memory, or both. Non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory can be random access memory (RAM), static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous linked dynamic random access memory (Synchlink DRAM, SLDRAM), and direct memory bus RAM (DRRAM).

[0115] The processor may include one or more processing units; optionally, the processor integrates an application processor and a modem processor, wherein the application processor mainly handles operations related to the operating system, user interface, and applications, while the modem processor mainly handles wireless communication signals, such as a baseband processor. It is understood that the aforementioned modem processor may also not be integrated into the processor.

[0116] This application also provides a readable storage medium storing a program or instructions. When the program or instructions are executed by a processor, they implement the various processes of the above-described UAV communication method embodiment based on a smart reflective surface and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0117] The processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0118] This application embodiment also provides a chip, which includes a processor and a communication interface. The communication interface and the processor are coupled. The processor is used to run programs or instructions to implement the various processes of the above-described UAV communication method embodiment based on intelligent reflective surface, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0119] It should be understood that the chip mentioned in the embodiments of this application may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.

[0120] This application also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes a program or instructions. When the program or instructions are executed, they implement the various processes of the above-described embodiments of the UAV communication method based on intelligent reflective surfaces and achieve the same technical effects. To avoid repetition, they will not be described again here.

[0121] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0122] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0123] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A UAV communication method based on a smart reflective surface, characterized in that, include: Based on the location and altitude of the UAV in each time slot during its flight time, the location of the base station, and the location of the IoT device, a first communication channel coefficient from the base station to the UAV and a second communication channel coefficient from the UAV to the IoT device are determined respectively; wherein, the UAV is equipped with an intelligent reflective surface; Based on the first communication channel coefficient from the base station to the UAV and the second communication channel coefficient from the UAV to the IoT device, the energy consumption function of the UAV and the IoT device is determined. With minimizing the energy consumption function as the objective, and with the communication scheduling, position, speed of the UAV, the data upload rate of the IoT device, and the phase shift matrix of the smart reflector as constraints, a joint optimization model of the communication strategy is constructed. Solve the joint optimization model of the communication strategy to determine the target communication strategy between the UAV and the IoT device; wherein, the target communication strategy includes the target flight trajectory of the UAV, the target communication scheduling of each time slot, and the target phase shift matrix of the smart reflector.

2. The method according to claim 1, characterized in that, The step of determining the first communication channel coefficient from the base station to the drone and the second communication channel coefficient from the drone to the IoT device based on the drone's position and altitude in each time slot during its flight time, the base station's position, and the IoT device's position includes: For the channel between the base station and the smart reflector, the channel coefficients corresponding to the direct wave channel and the multipath reflection wave channel between the base station and the smart reflector are determined based on the position and altitude of the UAV in each time slot during its flight time and the position of the base station. The first communication channel coefficient is determined based on the channel coefficient corresponding to the direct wave channel and the channel coefficient corresponding to the multipath reflection wave channel between the base station and the smart reflector. For the channel between the smart reflector and the IoT device, the channel coefficients corresponding to the direct wave channel and the multipath reflection wave channel are determined based on the position and altitude of the UAV in each time slot during its flight time and the position of the IoT device. The second communication channel coefficient is determined based on the channel coefficient corresponding to the direct wave channel and the multipath reflection wave channel between the smart reflector and the IoT device.

3. The method according to claim 1, characterized in that, The constraints include: The communication schedule of the UAV in each time slot is less than or equal to 1; wherein, the communication schedule of the UAV is a binary variable; The drone takes off from the same location as it lands. The drone's flight speed is less than or equal to a preset speed; The amount of data uploaded by the IoT device is greater than or equal to a preset amount of data; wherein, the amount of data uploaded is determined based on the communication scheduling of the UAV and the data upload rate of the IoT device; The phase value in the phase shift matrix of the intelligent reflective surface is [0, 2π].

4. The method according to claim 1, characterized in that, Before solving the joint optimization model of the communication strategy to determine the target communication strategy between the UAV and the IoT device, the method further includes: Adjust the array of the intelligent reflective surfaces to configure the phase shift matrix of the intelligent reflective surfaces as the optimal phase shift matrix; The communication scheduling of the UAV in each time slot is adjusted from a binary variable to a continuous variable; Based on the optimal phase shift matrix and the communication scheduling of the continuous variables, the joint optimization model of the communication strategy is transformed from a mixed integer nonconvex optimization model into a continuous nonconvex optimization model.

5. The method according to claim 4, characterized in that, Solving the joint optimization model of the communication strategy to determine the target communication strategy between the UAV and the IoT device includes: The energy consumption function of the joint optimization model of the communication strategy is relaxed by introducing a first relaxation variable; A second relaxation variable is introduced to relax the constraints of the joint optimization model of the communication strategy; Based on the relaxed energy consumption function and the constraints, the joint optimization model of the communication strategy is transformed from the continuous non-convex optimization model into a convex optimization model. The convex optimization model is solved until convergence, thus obtaining the target communication strategy between the UAV and the IoT device.

6. The method according to claim 1, characterized in that, The step of determining the energy consumption function of the drone and the IoT device based on the first communication channel coefficient from the base station to the drone and the second communication channel coefficient from the drone to the IoT device includes: The transmission power of the drone and the transmission power of the IoT device are determined based on the first communication channel coefficient from the base station to the drone and the second communication channel coefficient from the drone to the IoT device, respectively. The energy consumption function of the IoT device is determined based on the transmission power of the IoT device, the communication scheduling of the UAV in each time slot, and the length of each time slot. The energy consumption function of the UAV is determined based on the UAV's transmission power, the length of each time slot, and the UAV's flight energy consumption in each time slot; wherein, the UAV's flight energy consumption is determined based on the UAV's flight information in each time slot. The energy consumption functions of the drone and the IoT device are determined based on the energy consumption function of the IoT device, the energy consumption weight of the IoT device, the energy consumption function of the drone, and the energy consumption weight of the drone.

7. A UAV communication device based on an intelligent reflective surface, characterized in that, include: The first determining module is used to determine, based on the position and altitude of the UAV in each time slot during its flight time, the position of the base station, and the position of the IoT device, a first communication channel coefficient from the base station to the UAV and a second communication channel coefficient from the UAV to the IoT device, respectively; wherein the UAV is equipped with an intelligent reflective surface; The construction module is used to determine the energy consumption function of the UAV and the IoT device based on the first communication channel coefficient from the base station to the UAV and the second communication channel coefficient from the UAV to the IoT device. With the goal of minimizing the energy consumption function, and with the communication scheduling, position, speed of the UAV, the data upload rate of the IoT device and the phase shift matrix of the smart reflector in each time slot as constraints, a joint optimization model of the communication strategy is constructed. The second determining module is used to solve the joint optimization model of the communication strategy to determine the target communication strategy between the UAV and the IoT device; wherein, the target communication strategy includes the target flight trajectory of the UAV, the target communication scheduling of each time slot, and the target phase shift matrix of the smart reflector.

8. An electronic device, characterized in that, It includes a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the UAV communication method based on a smart reflective surface as described in any one of claims 1 to 6.

9. A readable storage medium, characterized in that, The readable storage medium stores a program or instructions that, when executed by a processor, implement the steps of the UAV communication method based on a smart reflective surface as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, The computer program product includes a computer program stored on a non-transitory computer-readable storage medium, the computer program including programs or instructions that, when executed, implement the steps of the UAV communication method based on a smart reflector as described in any one of claims 1 to 6.

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