Reconfigurable smart surface UAV collaborative communication methods, systems, devices and media

Abstract

This application provides a method, system, device, and medium for cooperative communication between UAVs on reconfigurable smart surfaces. By iteratively optimizing the base station's transmit power allocation coefficient, the pairing relationship between sub-channels and communication devices, the phase shift coefficient matrix of the reconfigurable smart surface, and the UAV's flight trajectory, the minimum average transmission rate among all communication devices located at the cell edge is maximized within a flight cycle. This improves the fairness of communication devices located at the cell edge and the coverage capability of the cell edge, enabling all communication devices located at the cell edge to have high channel quality and transmission rate.

Description

Technical Field

[0001] This application relates to the field of communication technology, and in particular to a method, system, device and medium for collaborative communication between reconfigurable smart surface unmanned aerial vehicles. Background Technology

[0002] In wireless communication networks, terminal devices located at the cell edge often experience poor channel quality and low transmission rates. To improve the channel quality and transmission rate of these devices, related technologies employ cooperative multipoint transmission techniques and reconfigurable smart surfaces deployed in fixed locations to establish wireless communication networks. While this method partially addresses the aforementioned issues, the fixed-location reconfigurable smart surfaces struggle to ensure fairness among different terminal devices, resulting in some cell-edge terminal devices still exhibiting poor channel quality and low transmission rates. Summary of the Invention

[0003] In view of this, the purpose of this application is to propose a method, system, device and medium for collaborative communication between reconfigurable smart surface unmanned aerial vehicles.

[0004] To achieve the above objectives, this application provides a cooperative communication method for unmanned aerial vehicles (UAVs) with reconfigurable smart surfaces. This method is applied to a communication system comprising a UAV equipped with a reconfigurable smart surface, multiple base stations, multiple communication devices, and multiple sub-channels. The multiple communication devices include multiple first devices, where the distance between each first device and any base station is greater than a distance threshold. The communication method includes:

[0005] The system acquires the base station's transmit power allocation coefficient, the pairing relationship between sub-channels and communication equipment, the phase shift coefficient matrix of the reconfigurable smart surface, and the UAV's flight trajectory. Among these, one flight cycle of the UAV includes multiple equidistant time slots, and the flight trajectory includes the UAV's position information in each equidistant time slot.

[0006] For any one of the equidistant time slots, the transmission power allocation coefficient of that equidistant time slot is calculated based on the pairing relationship of the previous equidistant time slot, the phase shift coefficient matrix, and the position information of the UAV in that equidistant time slot.

[0007] The pairing relationship of the equidistant time slots is obtained by matching the transmission rates of the first devices in all the equidistant time slots preceding the equidistant time slot.

[0008] The phase shift coefficient matrix of the equidistant time slot is obtained by calculating the transmission power allocation coefficient, pairing relationship and UAV position information of the equidistant time slot.

[0009] The optimized flight trajectory is obtained by performing optimization calculations based on the transmission power allocation coefficient, pairing relationship, and phase shift coefficient matrix of each equidistant time slot;

[0010] Communication is performed based on the transmission power allocation coefficient, pairing relationship, and phase shift coefficient matrix of each equidistant time slot, as well as the optimized flight trajectory.

[0011] Furthermore, the communication method also includes calculating the minimum transmission rate of the first device by means of the following method:

[0012] The transmission rate of each first device in each equidistant time slot is calculated based on the transmission power allocation coefficient, pairing relationship, phase shift coefficient matrix and the position information of the UAV in each equidistant time slot;

[0013] The average transmission rate of each first device in each equidistant time slot is calculated to obtain the average transmission rate of each first device in one flight cycle.

[0014] The minimum of all average transmission rates is taken as the minimum transmission rate of the first device.

[0015] Furthermore, when multiple base stations include a first base station and a second base station, and multiple communication devices include multiple second devices whose distance from the first base station is less than a first distance threshold, and multiple third devices whose distance from the second base station is less than a second distance threshold, the transmission rate of each first device in each equidistant time slot is calculated using the following formula:

[0016]

[0017] in,

[0018]

[0019]

[0020] Let S be the transmission rate of the f-th first device in the t-th equidistant time slot, S be the total number of sub-channels, M be the total number of second devices, N be the total number of third devices, and x be the transmission rate of the first device in the t-th equidistant time slot. s,m (t) represents the pairing coefficient between the s-th sub-channel and the m-th second device, x s,n (t) represents the pairing coefficient between the s-th sub-channel and the n-th third device, x s,f (t) represents the pairing coefficient between the s-th sub-channel and the f-th first device. Let be the signal-to-noise ratio of the f-th first device in the s-th sub-channel within the t-th equidistant time slot, where the m-th second device, the n-th third device, and the f-th first device are connected in the s-th sub-channel. Let be the equivalent channel gain between the i-th base station and the f-th first device in the t-th equidistant time slot on the s-th sub-channel. This refers to the channel between the i-th base station and the f-th first device on the s-th sub-channel within the t-th equidistant time slot. Let be the channel gain vector between the UAV and the f-th first device in the t-th equidistant time slot on the s-th sub-channel. H Let θ(t) be the conjugate transpose of the matrix, and let Θ(t) be the phase shift coefficient matrix in the t-th equidistant time slot. p is an auxiliary quantity for computation. i Let σ be the transmission power of the i-th base station. 2 For noise power, Let be the transmission power allocation coefficient between the first base station and the m-th second device in the t-th equidistant time slot. It is the transmission power allocation coefficient between the second base station and the nth third device in the t-th equidistant time slot.

[0021] Furthermore, pairing is performed based on the transmission rates of the first devices in all equidistant time slots preceding the equidistant time slot to obtain the pairing relationship of the equidistant time slots, including:

[0022] Initialize the pairing relationship between sub-channels and communication devices to obtain a sub-channel set, a first device set, a second device set, and a third device set;

[0023] For any subchannel in the subchannel set, the first device with the smallest total transmission rate among all equidistant time slots before the equidistant time slot in the first device set, and the second and third devices in the second and third device sets respectively that enable the first device to have the largest transmission rate are connected to the subchannel. The first, second, and third devices connected to the subchannel are removed from the first, second, and third device sets respectively, and the subchannel is removed from the subchannel set.

[0024] In response to determining that the sub-channel set is empty, pairing is completed, and the pairing relationship of the equidistant time slots is obtained.

[0025] Furthermore, based on the pairing relationship of the preceding equidistant time slot, the phase shift coefficient matrix, and the location information of the UAV in the equidistant time slot, the transmission power allocation coefficient of the equidistant time slot is calculated, including:

[0026] The relationship between the transmission rate of the first device and the transmission power allocation coefficient in the equidistant time slot is calculated based on the pairing relationship of the previous equidistant time slot, the phase shift coefficient matrix, and the position information of the UAV in the equidistant time slot.

[0027] Based on the threshold of the transmission power allocation coefficient, the value of the transmission power allocation coefficient that maximizes the transmission rate of the first device in the equidistant time slot is calculated according to the relationship between the transmission rate of the first device in the equidistant time slot and the transmission power allocation coefficient, and is used as the transmission power allocation coefficient of the equidistant time slot.

[0028] Furthermore, based on the transmission power allocation coefficient, pairing relationship, and UAV location information of the equidistant time slot, the phase shift coefficient matrix of the equidistant time slot is calculated, including:

[0029] The relationship between the transmission rate of the first device and the phase shift coefficient matrix in the equidistant time slot is calculated based on the transmission power allocation coefficient, pairing relationship and UAV location information of the equidistant time slot.

[0030] Establish a first relaxation variable to convert the transmission rate of the first device in the equidistant time slot into a first lower bound;

[0031] The semidefinite relaxation algorithm is used to calculate the value of the first relaxation variable when the first lower bound is maximized.

[0032] The value of the first relaxation variable is substituted into the relationship between the transmission rate of the first device and the phase shift coefficient matrix in the equidistant time slot to calculate the phase shift coefficient matrix of the equidistant time slot.

[0033] Furthermore, optimization calculations are performed based on the transmission power allocation coefficient, pairing relationship, and phase shift coefficient matrix of each equidistant time slot to obtain the optimized flight trajectory, including:

[0034] The relationship between the transmission rate of the first device in each equidistant time slot and the location information of the UAV is obtained by calculating the transmission power allocation coefficient, pairing relationship and phase shift coefficient matrix of each equidistant time slot.

[0035] Establish a second relaxation variable to convert the first device transmission rate in each equidistant time slot into a second lower bound;

[0036] The value of the second relaxation variable is calculated using the successive convex approximation algorithm to maximize the second lower bound.

[0037] The value of the second relaxation variable is substituted into the relationship between the transmission rate of the first device and the position information of the UAV in each equidistant time slot to calculate the position information of the UAV in each equidistant time slot.

[0038] The optimized flight trajectory is obtained based on the position information of the UAV in all equidistant time slots.

[0039] This application also provides a communication system, including a drone equipped with a reconfigurable smart surface, multiple base stations, multiple communication devices, and multiple sub-channels. The multiple communication devices include multiple first devices, and the distance between each first device and any base station is greater than a distance threshold. The communication system further includes:

[0040] The parameter acquisition module is used to acquire the base station's transmit power allocation coefficient, the pairing relationship between the sub-channel and the communication equipment, the phase shift coefficient matrix of the reconfigurable smart surface, and the UAV's flight trajectory; wherein, one flight cycle of the UAV includes multiple equidistant time slots, and the flight trajectory includes the UAV's position information in each equidistant time slot;

[0041] The transmit power allocation coefficient calculation module is used to calculate the transmit power allocation coefficient of any one of the equidistant time slots based on the pairing relationship of the previous equidistant time slot, the phase shift coefficient matrix, and the position information of the UAV in the equidistant time slot.

[0042] The pairing relationship acquisition module is used to pair the first devices in all equidistant time slots before the equidistant time slot according to their transmission rates, and obtain the pairing relationship of the equidistant time slot.

[0043] The phase shift coefficient matrix calculation module is used to calculate the phase shift coefficient matrix of the equidistant time slot based on the transmission power allocation coefficient, pairing relationship and UAV position information of the equidistant time slot.

[0044] The flight trajectory optimization calculation module is used to perform optimization calculations based on the transmission power allocation coefficient, pairing relationship, and phase shift coefficient matrix of each equidistant time slot to obtain the optimized flight trajectory;

[0045] The communication module is used to communicate based on the transmission power allocation coefficient, pairing relationship, phase shift coefficient matrix of each equidistant time slot, and the optimized flight trajectory.

[0046] This application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the above-described method.

[0047] This application also provides a non-transitory computer-readable storage medium that stores computer instructions for causing a computer to perform the above-described method.

[0048] As can be seen from the above, the reconfigurable smart surface UAV cooperative communication method, system, device and medium provided in this application, through iterative optimization of the base station's transmit power allocation coefficient, the pairing relationship between sub-channels and communication devices, the phase shift coefficient matrix of the reconfigurable smart surface and the UAV's flight trajectory, maximizes the minimum average transmission rate among all communication devices located at the cell edge within a flight cycle, improves the fairness of communication devices located at the cell edge and the cell edge coverage capability, and enables communication devices located at the cell edge to have high channel quality and transmission rate. Attached Figure Description

[0049] To more clearly illustrate the technical solutions in this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0050] Figure 1 This is a schematic diagram of the communication system of the reconfigurable smart surface UAV cooperative communication method according to an embodiment of this application.

[0051] Figure 2 This is a flowchart illustrating the reconfigurable smart surface UAV cooperative communication method according to an embodiment of this application.

[0052] Figure 3 This is a flowchart illustrating the pairing method of the reconfigurable smart surface UAV cooperative communication method according to an embodiment of this application.

[0053] Figure 4 This is a flowchart of the two-layer iterative algorithm of the reconfigurable smart surface UAV cooperative communication method according to an embodiment of this application.

[0054] Figure 5 This is a first simulation effect diagram of the reconfigurable smart surface UAV cooperative communication method and comparison algorithm according to an embodiment of this application.

[0055] Figure 6 This is a second simulation effect diagram of the reconfigurable smart surface UAV cooperative communication method and comparison algorithm according to an embodiment of this application.

[0056] Figure 7 This is a schematic diagram of the communication system according to an embodiment of this application.

[0057] Figure 8 This is a schematic diagram of the hardware structure of the server in an embodiment of this application. Detailed Implementation

[0058] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with specific embodiments and the accompanying drawings.

[0059] It should be noted that, unless otherwise defined, the technical or scientific terms used in the embodiments of this application should have the ordinary meaning understood by one of ordinary skill in the art to which this application pertains. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect.

[0060] With the rapid development of mobile internet and IoT technologies, and the growing need for wireless communication services across all sectors of society, an increasing number of machine-type communication terminal devices are being deployed and applied in wireless communication networks. Terminal devices located at the cell edge often suffer from poor channel quality and are also subject to interference from other cells, limiting their transmission capabilities. Cooperative multipoint transmission technology aims to provide services to devices at the cell edge through cooperation among multiple cell base stations, mitigating inter-cell interference and improving received signal quality. Meanwhile, drones, due to their high flexibility, adaptive altitude, and line-of-sight air-to-ground links, can act as flying base stations and airborne relays to extend wireless coverage and provide data transmission services, bringing significant flexibility to network deployment. Furthermore, reconfigurable smart surface technology can proactively adjust the wireless propagation environment with extremely low power consumption, thereby achieving controllable and intelligent wireless environment management.

[0061] However, in machine-to-machine communication scenarios, devices are geographically dispersed and their communication requests are discontinuous in time, making it difficult to ensure fairness when deploying reconfigurable smart surfaces in fixed locations. How to effectively leverage the advantages of drones and reconfigurable smart surfaces to provide better services to edge devices remains a problem to be solved.

[0062] Based on the aforementioned deficiencies in related technologies, embodiments of this application provide a reconfigurable smart surface UAV collaborative communication method, system, device, and medium.

[0063] The reconfigurable smart surface UAV cooperative communication method, system, device and medium provided in this application, through iterative optimization of the base station's transmit power allocation coefficient, the pairing relationship between sub-channels and communication devices, the phase shift coefficient matrix of the reconfigurable smart surface and the UAV's flight trajectory, maximizes the minimum average transmission rate among all communication devices located at the cell edge within a flight cycle, improves the fairness of communication devices located at the cell edge and the cell edge coverage capability, and enables communication devices located at the cell edge to have high channel quality and transmission rate.

[0064] Figure 1 The structure of the communication system of the reconfigurable smart surface UAV cooperative communication method provided in the embodiments of this application is shown. Figure 2 The flowchart of the reconfigurable smart surface UAV cooperative communication method provided in the embodiments of this application is shown.

[0065] This application provides a reconfigurable smart surface UAV cooperative communication method. The communication system using this method may include a UAV equipped with a reconfigurable smart surface, multiple base stations, multiple communication devices, and multiple sub-channels. The multiple communication devices include multiple first devices, and the distance between each first device and any base station is greater than a distance threshold.

[0066] In this communication system, communication devices are categorized into two types based on their distance from base stations. One type consists of devices whose distance to any base station is greater than a distance threshold, known as Cell Edge Devices (CEDs). These devices are located at the edge of the cell formed by the base stations. The other type consists of devices whose distance to any base station is less than or equal to the distance threshold, known as Cell Center Devices (CCDs). Base stations use NOMA (Normally Oscillating Multi-mode) to transmit signals to the CCDs and CEDs they communicate with. This means that the signals from the CCDs and CEDs can overlap in both the time and frequency domains, but their power levels differ. Upon receiving the signal from the base station, the communication device decodes its own signal based on the power difference. The system utilizes drones equipped with reconfigurable smart surfaces as supplementary equipment to the ground-based base stations, passively relaying signals and enhancing the CED channels. Furthermore, the drones move along a pre-defined flight path, moving the reconfigurable smart surfaces mounted on them, thereby enhancing the channels of different CEDs during this movement and improving cell edge coverage.

[0067] For example, refer to Figure 1In a specific communication system, there are two adjacent cells, each equipped with a base station that transmits information to machine-type communication devices (MCDs). MCDs are divided into two categories: Cell Center Devices (CCDs) and Cell Edge Devices (CEDs). A CCD is a MCD closer to one of the two base stations, while a CED is a MCD farther from both base stations and located in the overlapping area of ​​the two cells. To overcome inter-cell interference, each CED is associated with both cells and is serviced by the two base stations through coordinated multipoint transmission. Both base stations and MCDs are equipped with a single antenna. Furthermore, drones equipped with Reconfigurable Smart Surfaces (RIS) can supplement ground base stations, acting as mobile passive relays to assist CED transmission. The RIS deployed on the drone can move along the drone's trajectory and actively adjust to the wireless propagation environment. The RIS has K reflectors. Let j represent the phase shift coefficient matrix of RIS, where j is the imaginary unit.

[0068] The two cell base stations are denoted as BS1 and BS2, respectively, and the number of corresponding central devices are M and N, respectively. The m-th and n-th devices are denoted as... and The number of edge devices is F, and the f-th device is denoted as Ff. The available set of sub-channels is: Considering the limited resources in practical communication, we have S ≤ M, N, F. Let the horizontal coordinate of BSi be w. i =[x i ,y i ] T , The horizontal coordinate is w 1,m =[x 1,m ,y 1,m ] T , The horizontal coordinate is w 2,n =[x 2,n ,y 2,n ] T , The horizontal coordinate is w e,f =[x e,f ,y e,f ] T .

[0069] The UAV's flight cycle consists of T equidistant time slots with a step size δ. The UAV's horizontal coordinates in the t-th time slot are plotted as q(t) = [q x (t),q y (t)] TThe drone's flight altitude is fixed at H. To provide periodic service to users, the drone needs to return to its initial position at the end of cycle T, i.e., q(T) = q(0). The drone's maximum flight speed is v. max The maximum flight distance within a time slot is d max =v max δ, then we have

[0070] The channel model comprises two parts: path loss and small-scale fading. Using BSi and... Taking the channel model on subchannel s as an example, it can be derived from... Given, where h0 represents the road loss at a unit distance d0 = 1m, α ≥ 2 is the road loss coefficient, and d i,m =||w i -w 1,m ||. These are small-scale fading components. From BSi to The channel is recorded as From BSi to The channel is recorded as The channel gain vector from BSi to RIS is denoted as From RIS to The channel gain vector is denoted as The generation method is similar, also including both path loss and small-scale fading. Thus, we can obtain the values ​​from BSi to... The equivalent channel gain in time slot t,

[0071] Define binary subchannel-device pairing coefficient x s,j (t), j∈{m,n,f}. x s,m (t)=1, x s,n (t)=1, x s,f (t) = 1, representing the time slot t, respectively. and On subchannel s, otherwise x s,m (t)=0, x s,n (t)=0, x s,f (t) = 0. To reduce interference and reception complexity, each sub-channel is constrained to have one edge device and each of the two cells has one central device, and each device is constrained to be on at most one sub-channel.

[0072] The transmit power p1 of BS1 and the transmit power p2 of BS2 are known and fixed, independent of time slot t, and serve the corresponding central and edge devices using NOMA. For a given NOMA cluster on a subchannel s... In other words, Signal received via subchannel s:

[0073]

[0074] According to the downlink NOMA decoding order First, solve The signal, at which point SINR is... Correct decoding After the signal is eliminated Decoding its own signal, at which point SINR is in It is the BS1 transmit power allocation coefficient. For calculating auxiliary quantities.

[0075] Similarly, After receiving the superimposed signal, first decode it. The signal, at which point SINR is... Correct decoding After the signal is eliminated Decoding its own signal, at which point SINR is in It is the BS2 transmit power allocation factor. To calculate the auxiliary quantity, σ 2 This represents noise power.

[0076] Because of the need for coordinated multi-point transmission, the CED will receive useful signals from two base stations, as well as interference and noise. Received SINR:

[0077]

[0078] in

[0079] Considering the uncertainty of the grouping, The transmission rate in time slot t can be expressed as:

[0080]

[0081] like Figure 2 As shown, the communication method includes: acquiring the base station's transmit power allocation coefficient, the pairing relationship between the sub-channel and the communication device, the phase shift coefficient matrix of the reconfigurable smart surface, and the UAV's flight trajectory. Specifically, one flight cycle of the UAV includes multiple equidistant time slots, and the flight trajectory includes the UAV's position information in each equidistant time slot.

[0082] In this embodiment, the base station's transmit power allocation coefficient is the ratio of the transmit power allocated to the central device to the total transmit power when the base station transmits signals to the central and edge devices that overlap in the time and frequency domains. Because the edge devices are farther from the base station and experience greater signal attenuation, a higher transmit power needs to be allocated to them; that is, the transmit power allocation coefficient is often less than 50%. The pairing relationship between the sub-channel and the communication device is a coefficient indicating whether the communication device is connected to the sub-channel. This coefficient can be 1 or 0. When the coefficient is 1, it indicates that the communication device is connected to the sub-channel; when the coefficient is 0, it indicates that the communication device is not connected to the sub-channel. The phase shift coefficient matrix of the reconfigurable smart surface represents the angle of each reflective element constituting the reconfigurable smart surface, with a value ranging from 0 to 2π.

[0083] Based on the above formula (3), the expression for the transmission rate of the CED is obtained. It can be seen that the transmission rate of the CED is related to the transmission power allocation coefficient, pairing relationship, phase shift coefficient matrix, and flight trajectory. In order to improve the transmission rate of the CED and obtain better communication performance, the transmission power allocation coefficient, pairing relationship, phase shift coefficient matrix, and flight trajectory can be optimized to obtain a higher transmission rate of the CED. The expression is as follows:

[0084]

[0085] Where, η m η n and η f They are respectively and The signal-to-noise ratio constraint threshold is a constant related to the communication equipment.

[0086] To achieve the above optimization and improve the communication effect of the communication method, the optimized parameters are obtained through the following method for communication.

[0087] For any one of the equidistant time slots, the transmission power allocation coefficient of that equidistant time slot is calculated based on the pairing relationship of the previous equidistant time slot, the phase shift coefficient matrix, and the position information of the UAV in that equidistant time slot.

[0088] The pairing relationship of the equidistant time slots is obtained by matching the transmission rates of the first devices in all the equidistant time slots preceding the equidistant time slot.

[0089] The phase shift coefficient matrix of the equidistant time slot is obtained by calculating the transmission power allocation coefficient, pairing relationship and UAV position information of the equidistant time slot.

[0090] The optimized flight trajectory is obtained by performing optimization calculations based on the transmission power allocation coefficient, pairing relationship, and phase shift coefficient matrix of each equidistant time slot.

[0091] Communication is performed based on the transmission power allocation coefficient, pairing relationship, and phase shift coefficient matrix of each equidistant time slot, as well as the optimized flight trajectory.

[0092] In this embodiment, following the order of equidistant time slots, optimization calculations are performed in each equidistant time slot based on the transmission power allocation coefficient, pairing relationship, and phase shift coefficient matrix of the previous equidistant time slot and the current equidistant time slot. This yields the optimized transmission power allocation coefficient, pairing relationship, and phase shift coefficient matrix for the current equidistant time slot. Furthermore, optimization calculations are performed on the flight trajectory to increase the minimum transmission rate of the CED after each optimization calculation.

[0093] In some implementations, the flight trajectory can be iteratively calculated to further optimize the flight trajectory, and the iterative calculation of the flight trajectory can be terminated when the change in two adjacent iterations is less than a preset threshold or the number of iterations reaches a preset upper limit.

[0094] As an optional embodiment, the transmission power allocation coefficient of the equidistant time slot is calculated based on the pairing relationship of the preceding equidistant time slot, the phase shift coefficient matrix, and the location information of the UAV in the equidistant time slot, including:

[0095] Based on the pairing relationship of the previous equidistant time slot, the phase shift coefficient matrix, and the location information of the UAV in the equidistant time slot, the relationship between the transmission rate of the edge device and the transmission power allocation coefficient in the equidistant time slot is calculated.

[0096] Based on the threshold of the transmit power allocation coefficient, the transmit power allocation coefficient is calculated according to the relationship between the transmission rate of the edge device in the equidistant time slot and the transmit power allocation coefficient. The value of the transmit power allocation coefficient that maximizes the transmission rate of the edge device in the equidistant time slot is then used as the transmit power allocation coefficient of the equidistant time slot.

[0097] The transmission rate of edge devices in equidistant time slots decreases as the transmit power allocation coefficient increases; that is, the transmission rate of edge devices in equidistant time slots is maximized when the transmit power is minimum. The transmit power allocation coefficient is positively correlated with the signal-to-noise ratio (SNR) of the communication equipment; the transmit power allocation coefficient is minimized when the SNR of the communication equipment is minimum. The minimum SNR of the communication equipment is the SNR constraint threshold, which is a constant value related to the communication equipment. Therefore, the optimized transmit power allocation coefficient is calculated based on the SNR constraint threshold of the communication equipment.

[0098] The formula can be expressed as:

[0099]

[0100]

[0101] As an optional embodiment, pairing is performed based on the transmission rates of the first devices in all equidistant time slots preceding the equidistant time slot to obtain the pairing relationship of the equidistant time slots, including:

[0102] Initialize the pairing relationships between sub-channels and communication devices to obtain a sub-channel set, a first device set, a second device set, and a third device set. The second device is the CCD that communicates with BS1, and the second device set is the set of CCDs that communicate with BS1. The third device is the CCD that communicates with BS2, and the third device set is the set of CCDs that communicate with BS2.

[0103] For any subchannel in the subchannel set, the first device with the smallest total transmission rate among all equidistant time slots before the equidistant time slot in the first device set, and the second and third devices in the second and third device sets respectively that enable the first device to have the largest transmission rate are connected to the subchannel. The first, second, and third devices connected to the subchannel are removed from the first, second, and third device sets respectively, and the subchannel is removed from the subchannel set.

[0104] In response to determining that the sub-channel set is empty, pairing is completed, and the pairing relationship of the equidistant time slots is obtained.

[0105] The number of sub-channels is less than the number of devices, i.e., S≤M,N,F. The number of devices that can access each time slot is limited. Devices that successfully access the network communicate, while those that do not access the network wait for the next time slot. During the entire flight cycle, each CED has at least one opportunity to access the network, i.e., F≤S×T. Initially, each sub-channel is randomly assigned three devices (one CCD from cell 1, one CCD from cell 2, and one CED). This represents the set of sub-channels that are not occupied in time slot t. and These represent the sets of CCD1, CCD2, and CEDs that are not assigned sub-channels in time slot t, respectively. The pairing method must consider both maximizing the transmission rate of the accessed CEDs and ensuring fairness among all CEDs. (Reference) Figure 3 The pairing methods can also be summarized as follows:

[0106] initialization:

[0107] cycle:

[0108] For all subchannels

[0109] From all CEDs that have not yet been allocated sub-channels, priority is given to selecting the device with the worst overall communication performance in the first t-1 time slots, i.e. And order

[0110]

[0111] from Select the CCD in cell 1 that maximizes the achievable rate of the CED, and access subchannel s, i.e. And order

[0112]

[0113] from Select the CCD in cell 2 that maximizes the achievable rate of the CED, and access subchannel s, i.e. And order

[0114] when The time loop ends.

[0115] In this way, the CED with the lowest total transmission rate among all equidistant time slots before each equidistant time slot is added to the sub-channel, so that the CED can communicate in the equidistant time slot, thereby maximizing the minimum transmission rate of all CEDs.

[0116] As an optional embodiment, the phase shift coefficient matrix of the equidistant time slot is calculated based on the transmission power allocation coefficient, pairing relationship, and UAV location information, including:

[0117] Based on the transmission power allocation coefficient, pairing relationship, and UAV location information of the equidistant time slot, the relationship between the transmission rate of the edge device and the phase shift coefficient matrix in the equidistant time slot is calculated.

[0118] Establish a first relaxation variable to convert the transmission rate of the edge devices in the equidistant time slot into a first lower bound.

[0119] The semidefinite relaxation algorithm is used to calculate the value of the first relaxation variable when the first lower bound is maximized.

[0120] The value of the first relaxation variable is substituted into the relationship between the transmission rate of the edge device and the phase shift coefficient matrix in the equidistant time slot to calculate the phase shift coefficient matrix of the equidistant time slot.

[0121] In this embodiment, the phase shift coefficient matrix is: Here, j is the imaginary unit, therefore the phase shift coefficient matrix is ​​a K×K complex diagonal matrix, where each element on the diagonal is a value related to the angle of the reflecting element. It is difficult to directly calculate the values ​​of each element that maximizes the transmission rate of the edge devices in equidistant time slots. Therefore, in this embodiment, the phase shift coefficient matrix is ​​transformed to obtain a matrix that is easier to solve, and a relationship is established between the transmission rate of the edge devices in equidistant time slots and this matrix. Then, a first relaxation variable is established, transforming the transmission rate of the edge devices in equidistant time slots into a first lower bound, and the value of the first relaxation variable that maximizes the first lower bound is calculated. The matrix is ​​calculated based on the value of the first relaxation variable, and the phase shift coefficient matrix is ​​obtained from this matrix.

[0122] In some embodiments, the phase shift coefficient matrix can be obtained by the following method:

[0123] Let μ(t) = [μ1(t), ..., μ K (t)] H ,in The angle of each reflecting element being between 0 and 2π is equivalent to... make The constraint V(t) ≥ 0 must be satisfied. k，k =1, rank(V(t))=1. By introducing a matrix R that is independent of the phase shift coefficient matrix. i (t) can ultimately be rewritten as the equivalent channel gain. Define the following function

[0124]

[0125]

[0126] in, The optimal solution for the transmission power allocation coefficient between the 1st base station and the mth central device in the t-th equidistant time slot. This is the optimal solution for the transmission power allocation coefficient between the second base station and the nth central device in the t-th equidistant time slot.

[0127] Introducing the first slack variable Each satisfies and Transform the expression for the transmission rate of edge devices in equidistant time slots into its lower bound:

[0128]

[0129] It is still non-convex, therefore a first-order Taylor expansion approximation is used. Linearization, in this context, revolves around satisfying the constraints. It unfolded in the vicinity of [location].

[0130] Furthermore, when optimizing the phase shift coefficient matrix for the t-th equidistant time slot, it is difficult to estimate the channel gain and transmission rate of the subsequent Tt equidistant time slots within the flight cycle. Therefore, obtaining the maximum transmission rate of the edge devices in the equidistant time slot is transformed into maximizing the minimum average transmission rate of all edge devices in the first t equidistant time slots. The constraint can be rewritten accordingly as follows:

[0131]

[0132] Furthermore, since the constraint rank(V(t)) = 1 is non-convex, a semidefinite relaxation method is adopted, introducing an auxiliary variable ζ. The phase shift coefficient matrix optimization problem can then be expressed as follows:

[0133]

[0134] This is a standard positive semidefinite convex optimization problem, which can be solved using existing toolkits (such as CVX). After obtaining the V(t) matrix, the rank-one constraint can be recovered using Gaussian randomization to obtain the optimized phase shift coefficient matrix.

[0135] As an optional embodiment, optimization calculations are performed based on the transmission power allocation coefficient, pairing relationship, and phase shift coefficient matrix of each equidistant time slot to obtain the optimized flight trajectory, including:

[0136] The relationship between the transmission rate of the edge device and the location information of the UAV in each equidistant time slot is calculated based on the transmission power allocation coefficient, pairing relationship and phase shift coefficient matrix of each equidistant time slot.

[0137] A second relaxation variable is established to transform the transmission rate of edge devices in each equidistant time slot into a second lower bound.

[0138] The value of the second slack variable is calculated using the successive convex approximation algorithm to maximize the second lower bound.

[0139] The value of the second relaxation variable is substituted into the relationship between the transmission rate of the edge device and the location information of the UAV in each equidistant time slot to calculate the location information of the UAV in each equidistant time slot.

[0140] The optimized flight trajectory is obtained based on the position information of the UAV in all equidistant time slots.

[0141] In this embodiment, the drone's position within an equidistant time slot is determined by the distance between the drone and the base station and the distance between the drone and the edge device. This position is then combined with the positions of all drones within the equidistant time slots within a flight cycle to obtain the drone's flight trajectory within that flight cycle. The parameter most significantly affecting the drone's position is the small-scale fading information in the channel. Therefore, by calculating the small-scale fading information that maximizes the transmission rate of the edge device within the equidistant time slots, the drone's position corresponding to this small-scale fading information is used to optimize the calculated flight trajectory. However, after converting the transmission rate of the edge device within the equidistant time slots into a relationship related to the small-scale fading information, this relationship is non-convex and difficult to solve. Therefore, this embodiment establishes a second relaxation variable, converting the transmission rate of the edge device within the equidistant time slots into a second lower bound, and calculating the value of the second relaxation variable that maximizes the second lower bound. The small-scale fading information is calculated based on the value of the second relaxation variable, and the drone's position is obtained from this information, thus yielding the drone's flight trajectory.

[0142] In some embodiments, the phase shift coefficient matrix can be obtained by the following method:

[0143] In practical applications, it is difficult to estimate small-scale channel fading before flight. Therefore, the small-scale fading information from the previous iteration is used to obtain an approximate estimate of the optimal flight trajectory for the UAV.

[0144] make

[0145] in, For the small-scale channel fading of the equivalent channel gain between the i-th base station and the f-th edge device in the t-th equidistant time slot, d i,f Let α be the distance between the i-th base station and the f-th edge device, α ≥ 2 be the path loss coefficient, h0 be the path loss per unit distance d0 = 1m, and d r,f (t) represents the distance between the drone and the f-th edge device, d i,r (t) represents the distance between the i-th base station and the drone.

[0146]

[0147] in, This refers to the small-scale channel fading of the channel between the i-th base station and the f-th edge device on the s-th sub-channel in the t-th equidistant time slot. This refers to the small-scale channel fading of the equivalent channel gain between the i-th base station and the UAV in the s-th sub-channel within the t-th equidistant time slot calculated in the previous iteration. This refers to the small-scale channel fading of the equivalent channel gain between the UAV and the f-th edge device in the t-th equidistant time slot calculated in the previous iteration on the s-th sub-channel.

[0148] By substituting into the original formula, we obtain the replaced formula. However, the transmission rate of edge devices in equidistant time slots remains a non-convex function of the UAV's flight trajectory, making it difficult to solve. Therefore, a second slack variable is introduced. And define the following function

[0149]

[0150]

[0151] in And the transmission rate of edge devices in equidistant time slots is rewritten as its lower bound:

[0152]

[0153] The drone path planning problem can be transformed into the following form:

[0154]

[0155] By substituting the UAV's position into the above constraints, it can be expanded into constraints on position information. Then, the convex feasible region can be obtained using a successive convex approximation algorithm, thus obtaining a convex optimization problem. This problem can be solved using a convex optimization toolkit (such as CVX), resulting in the optimized UAV path.

[0156] According to the above embodiments, in some embodiments, a two-layer iterative algorithm is used to solve the optimization problem. In the inner loop, in each time slot t, the optimal value of the power allocation coefficient is obtained using a closed-form solution, then the sub-channel device matching scheme is completed, and finally the RIS phase shift coefficient is optimized. The parameters of each time slot are updated and passed to the next time slot until the entire UAV cycle t=T is completed. In the outer loop, the UAV trajectory is optimized. Finally, the values ​​of the parameters are updated for the next iteration until the algorithm converges. Figure 4 In summary:

[0157] Initialization: x, q, Θ, β, iteration exponent j = 1, maximum number of iterations j max Convergence threshold ξ

[0158] Outer loop:

[0159] Let the UAV flight cycle slot exponent t = 1

[0160] Inner loop:

[0161] Given x (j) (t), Θ (j) (t), q (j)Under the given conditions, the optimal value β for downlink NOMA power factor allocation is obtained using formulas (5) and (6). (j) (t+1);

[0162] Given β (j) (t+1), Θ (j) (t), q (j) Under the given conditions, the sub-channel-device matching result x is obtained. (j) (t+1);

[0163] Given β (j) (t+1), x (j) (t+1), q (j) Under the given conditions, solve the convex optimization problem in (11) to obtain V. (j) (t+1);

[0164] Using Gaussian randomization, from the solved V (j) (t+1) Recover the RIS phase shift coefficient matrix Θ (j) (t+1);

[0165] renew

[0166] t = t + 1;

[0167] When t = T, the loop ends.

[0168] Update β (j+1) x (j+1) Θ (j+1) ;

[0169] Within a given UAV flight cycle T, β (j+1) x (j+1) Θ (j+1) Under the given conditions, solve the convex optimization problem to obtain the UAV trajectory q. (j+1) ;

[0170] renew

[0171] Using a given β (j+1) x (j+1) Θ (j+1) and q (j+1) Calculate the minimum average reachable rate among all edge devices within a flight cycle.

[0172] j = j + 1;

[0173] When j>j max or When the loop ends.

[0174] The effects of the embodiments of this application will be further illustrated by simulation below.

[0175] In the simulation, the noise power was -90dBm, the path loss coefficient was -30dB, and the Rice factor was 3dB. The number of sub-channels was set to 3, and the number of CCD1 (the central device communicating with the first base station), CCD2 (the central device communicating with the second base station), and CED (edge ​​device) were all 4. The number of reflective elements on the reconfigurable smart surface was 60. The distance between the two base stations was 200 meters. The UAV flew at an altitude of 30 meters, with a maximum speed of 50 meters per second. One flight cycle was 60 seconds, divided into 12 equal time slots. To illustrate the technical effects of the proposed solution in this application, the following comparison algorithm is introduced:

[0176] Comparison Algorithm 1: Reconfigurable smart surfaces are deployed in fixed locations

[0177] Comparison Algorithm 2: Orthogonal Multiple Access

[0178] Comparison Algorithm 3: Random Subchannel-Device Matching

[0179] Comparison Algorithm 4: Fixed Power Factor Allocation

[0180] Figure 5 The first simulation results of the communication system parameter optimization method and comparison algorithm provided in the embodiments of this application are shown. Figure 6 The second simulation results of the communication system parameter optimization method and comparison algorithm provided in the embodiments of this application are shown.

[0181] refer to Figure 5 As the number of reflective elements in the Reconfigurable Smart Surface (RIS) increases, the achievable speed of edge devices also increases due to the greater array gain resulting from more reflective elements. Furthermore, regardless of the number of reflective elements, the algorithm proposed in this application consistently outperforms other comparative algorithms, exhibiting the most significant growth rate with increasing reflective element count. This demonstrates that the algorithm proposed in this application can more effectively utilize the array gain provided by the Reconfigurable Smart Surface and the spatial freedom offered by UAVs.

[0182] refer to Figure 6 As the drone's altitude increases, the achievable rate of the CED decreases. This is because the distance of the reflection link associated with the reconfigurable smart surface becomes longer, resulting in worse channel conditions and a reduction in the gain provided by the reconfigurable smart surface. Furthermore, regardless of the drone's flight altitude, the algorithm proposed in this application consistently outperforms other comparative algorithms, demonstrating the effectiveness of the proposed algorithm in improving the performance of edge devices.

[0183] like Figure 7As shown, this application also provides a communication system, including a drone equipped with a reconfigurable smart surface, multiple base stations, multiple communication devices, and multiple sub-channels. The multiple communication devices include multiple first devices, and the distance between each first device and any base station is greater than a distance threshold. The communication system further includes:

[0184] The parameter acquisition module is used to acquire the base station's transmit power allocation coefficient, the pairing relationship between the sub-channel and the communication equipment, the phase shift coefficient matrix of the reconfigurable smart surface, and the UAV's flight trajectory; wherein, one flight cycle of the UAV includes multiple equidistant time slots, and the flight trajectory includes the UAV's position information in each equidistant time slot.

[0185] The transmit power allocation coefficient calculation module is used to calculate the transmit power allocation coefficient for any one of the equidistant time slots based on the pairing relationship of the previous equidistant time slot, the phase shift coefficient matrix, and the position information of the UAV in the equidistant time slot.

[0186] The pairing relationship acquisition module is used to pair the first devices in all equidistant time slots before the equidistant time slot according to their transmission rates, and obtain the pairing relationship of the equidistant time slot.

[0187] The phase shift coefficient matrix calculation module is used to calculate the phase shift coefficient matrix of the equidistant time slot based on the transmission power allocation coefficient, pairing relationship and UAV position information of the equidistant time slot.

[0188] The flight trajectory optimization calculation module is used to perform optimization calculations based on the transmission power allocation coefficient, pairing relationship, and phase shift coefficient matrix of each equidistant time slot to obtain the optimized flight trajectory.

[0189] The communication module is used to communicate based on the transmission power allocation coefficient, pairing relationship, phase shift coefficient matrix of each equidistant time slot, and the optimized flight trajectory.

[0190] As an optional embodiment, the communication system further includes a minimum transmission rate calculation module for the first device, which is used to calculate the transmission rate of each first device in each equidistant time slot based on the transmission power allocation coefficient, pairing relationship, phase shift coefficient matrix and the position information of the UAV in each equidistant time slot; calculate the average transmission rate of each first device in each equidistant time slot to obtain the average transmission rate of each first device in one flight cycle; and take the minimum value among all average transmission rates as the minimum transmission rate of the first device.

[0191] As an optional embodiment, the communication system further includes a pairing relationship optimization module, used to initialize the pairing relationship between sub-channels and communication devices, obtaining a sub-channel set, a first device set, a second device set, and a third device set; for any sub-channel in the sub-channel set, the first device with the smallest total transmission rate among all equidistant time slots before the equidistant time slot in the first device set, and the second and third devices in the second and third device sets respectively that enable the first device to have the largest transmission rate are connected to the sub-channel; the first, second, and third devices connected to the sub-channel are removed from the first, second, and third device sets respectively, and the sub-channel is removed from the sub-channel set; in response to determining that the sub-channel set is an empty set, pairing is completed, and the pairing relationship of the equidistant time slot is obtained.

[0192] As an optional embodiment, the communication system further includes a transmit power allocation coefficient optimization module, which is used to calculate the relationship between the transmission rate of the edge device in the equidistant time slot and the transmit power allocation coefficient based on the pairing relationship of the previous equidistant time slot, the phase shift coefficient matrix, and the location information of the UAV in the equidistant time slot; and based on the threshold of the transmit power allocation coefficient, calculate the value of the transmit power allocation coefficient that maximizes the transmission rate of the edge device in the equidistant time slot, based on the relationship between the transmission rate of the edge device in the equidistant time slot and the transmit power allocation coefficient, and use this value as the transmit power allocation coefficient of the equidistant time slot.

[0193] As an optional embodiment, the communication system further includes a phase shift coefficient matrix optimization module, which is used to calculate the relationship between the transmission rate of the edge device in the equidistant time slot and the phase shift coefficient matrix based on the transmission power allocation coefficient, pairing relationship and UAV location information of the equidistant time slot; establish a first relaxation variable to convert the transmission rate of the edge device in the equidistant time slot into a first lower bound; use a semidefinite relaxation algorithm to calculate the value of the first relaxation variable when the first lower bound is maximized; substitute the value of the first relaxation variable into the relationship between the transmission rate of the edge device in the equidistant time slot and the phase shift coefficient matrix to calculate the phase shift coefficient matrix of the equidistant time slot.

[0194] As an optional embodiment, the communication system parameter optimization device further includes a flight trajectory optimization module, which is used to calculate the relationship between the transmission rate of the edge device in each equidistant time slot and the position information of the UAV based on the transmission power allocation coefficient, pairing relationship and phase shift coefficient matrix of each equidistant time slot; establish a second relaxation variable to transform the transmission rate of the edge device in each equidistant time slot into a second lower bound; use a successive convex approximation algorithm to calculate the value of the second relaxation variable when the second lower bound is maximized; substitute the value of the second relaxation variable into the relationship between the transmission rate of the edge device in each equidistant time slot and the position information of the UAV to calculate the position information of the UAV in each equidistant time slot; and obtain the optimized flight trajectory based on the position information of the UAV in all equidistant time slots.

[0195] This application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the above-described method.

[0196] This application also provides a non-transitory computer-readable storage medium that stores computer instructions for causing a computer to perform the above-described method.

[0197] Based on the same inventive concept, corresponding to the methods of any of the above embodiments, this disclosure also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the signal peak-to-average power ratio suppression method described in any of the above embodiments.

[0198] Figure 8 This embodiment illustrates a more specific server hardware structure, which may include a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, memory 1020, input / output interface 1030, and communication interface 1040 are interconnected internally via the bus 1050.

[0199] The processor 1010 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0200] The memory 1020 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program code is stored in the memory 1020 and is called and executed by the processor 1010.

[0201] The input / output interface 1030 is used to connect input / output modules to realize information input and output. Input / output modules can be configured as components in the server (not shown in the figure) or externally connected to the server to provide corresponding functions. Input devices may include keyboards, mice, touchscreens, microphones, various sensors, etc., while output devices may include displays, speakers, vibrators, indicator lights, etc.

[0202] The communication interface 1040 is used to connect the communication module (not shown in the figure) to enable communication between this server and other devices. The communication module can communicate via wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0203] Bus 1050 includes a pathway for transmitting information between various components of the server, such as processor 1010, memory 1020, input / output interface 1030, and communication interface 1040.

[0204] It should be noted that although the above-described electronic device only shows the processor 1010, memory 1020, input / output interface 1030, communication interface 1040, and bus 1050, in specific implementations, the electronic device may also include other components necessary for normal operation. Furthermore, those skilled in the art will understand that the above-described electronic device may only include the components necessary for implementing the embodiments of this specification, and not necessarily all the components shown in the figures.

[0205] The electronic devices described above are used to implement the corresponding signal peak-to-average power ratio (PAPR) suppression methods in any of the foregoing embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0206] Based on the same inventive concept, corresponding to the methods of any of the above embodiments, this disclosure also provides a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute the signal peak-to-average power ratio suppression method as described in any of the above embodiments.

[0207] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0208] The computer instructions stored in the storage medium of the above embodiments are used to cause the computer to execute the signal peak-to-average power ratio suppression method as described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0209] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of this application (including the claims) is limited to these examples; within the framework of this application, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of the embodiments of this application as described above, which are not provided in the details for the sake of brevity.

[0210] Although this application has been described in conjunction with specific embodiments thereof, many substitutions, modifications and variations of these embodiments will be apparent to those skilled in the art from the foregoing description.

[0211] The embodiments of this application are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the embodiments of this application should be included within the protection scope of this application.

Claims

1. A method for reconfigurable intelligent surface drone cooperative communication, characterized in that, The communication method is applied to a communication system including a UAV provided with a reconfigurable intelligent surface, a plurality of base stations, a plurality of communication devices and a plurality of sub-channels, the plurality of communication devices including a plurality of first devices, the distance between each of the first devices and any one of the base stations being greater than a distance threshold, and the method comprising: obtaining a transmission power allocation coefficient of the base station, a pairing relationship between the sub-channels and the communication devices, a phase shift coefficient matrix of the reconfigurable intelligent surface, and a flight trajectory of the UAV; wherein one flight cycle of the UAV includes a plurality of equidistant time slots, and the flight trajectory includes position information of the UAV in each of the equidistant time slots; for any one of all equidistant time slots, calculating the transmission power allocation coefficient of the equidistant time slot according to the pairing relationship of the previous equidistant time slot, the phase shift coefficient matrix, and the position information of the UAV in the equidistant time slot; pairing the transmission rates of the first devices in all equidistant time slots before the equidistant time slot to obtain the pairing relationship of the equidistant time slot; calculating the phase shift coefficient matrix of the equidistant time slot according to the transmission power allocation coefficient, the pairing relationship, and the position information of the UAV in the equidistant time slot; optimizing the flight trajectory according to the transmission power allocation coefficient, the pairing relationship, and the phase shift coefficient matrix of each of the equidistant time slots; communicating based on the transmission power allocation coefficient, the pairing relationship, the phase shift coefficient matrix, and the optimized flight trajectory of each of the equidistant time slots.

2. The communication method according to claim 1, characterized by, The method further comprises calculating a minimum value of the transmission rate of the first device by: calculating the transmission rate of each of the first devices in each of the equidistant time slots according to the transmission power allocation coefficient, the pairing relationship, the phase shift coefficient matrix, and the position information of the UAV in each of the equidistant time slots; averaging the transmission rate of each of the first devices in each of the equidistant time slots to obtain an average transmission rate of each of the first devices in one flight cycle; taking the minimum value of all the average transmission rates as the minimum value of the transmission rate of the first device.

3. The communication method according to claim 2, wherein, When the plurality of base stations includes a first base station and a second base station, the plurality of communication devices includes a plurality of second devices with a distance to the first base station less than a first distance threshold, and a plurality of third devices with a distance to the second base station less than a second distance threshold, the transmission rate of each of the first devices in each of the equidistant time slots is calculated by the following formula: ； wherein ; ； ； For the first t In the equidistant time slots, the first f The transmission rate of the first device, S The total number of the sub-channels. M To the total number of the second device, N The total number of the third devices. For the first s The sub-channel and the first m The pairing coefficient between the second devices For the first s The sub-channel and the first n The pairing coefficient between the third devices. For the first s The sub-channel and the first f The pairing coefficient between the first devices For the first t In the equidistant time slots, the 1st s On the sub-channel, the first f The signal-to-noise ratio of the first device, at this time the first device s Accessing the first sub-channel m The second device, the first n The third device and the first f The first device. For the first t In the equidistant time slots, the first i Base station and the f Between the first devices s The equivalent channel gain on each of the sub-channels. For the first t In the equidistant time slots, the first i Base station and the f Between the first devices s Channels on the aforementioned sub-channels For the first t The UAV in the equidistant time slots and the first f Between the first devices s Channel gain vectors on each of the sub-channels This is a matrix conjugate transpose transformation. For the first t The phase shift coefficient matrix in the equidistant time slots For computational auxiliary quantities, For the first i The base station's transmission power, For noise power, For the first t In the equidistant time slots, the first base station and the... m The transmission power allocation coefficient between the second devices. For the first t In the equidistant time slots, the second base station and the first n The transmission power allocation coefficient among the third devices. For the first t In the equidistant time slots, the first i The base station and the drone were connected in the first... s Channel gain vectors on each of the sub-channels.

4. The communication method according to claim 3, wherein, the pairing relationship of the equidistant time slot is obtained by pairing the transmission rates of the first devices in all equidistant time slots before the equidistant time slot, comprising: initializing the pairing relationship between the sub-channels and the communication devices to obtain a sub-channel set, a first device set, a second device set, and a third device set; For any one of the sub-channels in the set of sub-channels, the first device set is removed from the set of first devices, the second device set and the third device set are removed from the set of second devices and the set of third devices, and the sub-channel is removed from the set of sub-channels, where the total transmission rate of all equidistant time slots before the equidistant time slot is minimum, and one second device and one third device in the set of second devices and the set of third devices can maximize the transmission rate of the first device in the sub-channel; In response to determining that the set of sub-channels is empty, completing pairing, and obtaining the pairing relationship of the equidistant time slot.

5. The communication method according to claim 1, wherein, According to the pairing relationship of the previous equidistant time slot, the phase shift coefficient matrix, and the position information of the unmanned aerial vehicle in the equidistant time slot, the transmission power allocation coefficient of the equidistant time slot is obtained by calculation, including: According to the pairing relationship of the previous equidistant time slot, the phase shift coefficient matrix, and the position information of the unmanned aerial vehicle in the equidistant time slot, the relationship between the first device transmission rate in the equidistant time slot and the transmission power allocation coefficient is obtained by calculation. Based on the threshold value of the transmission power allocation coefficient, the value of the transmission power allocation coefficient that maximizes the first device transmission rate in the equidistant time slot is obtained by calculating the relationship between the first device transmission rate in the equidistant time slot and the transmission power allocation coefficient, as the transmission power allocation coefficient of the equidistant time slot.

6. The communication method according to claim 1, wherein, The transmission power allocation coefficient of the equidistant time slot is obtained by calculating the relationship between the first device transmission rate in the equidistant time slot and the phase shift coefficient matrix according to the transmission power allocation coefficient of the equidistant time slot, the pairing relationship and the position information of the unmanned aerial vehicle. According to the pairing relationship of the previous equidistant time slot, the phase shift coefficient matrix, and the position information of the unmanned aerial vehicle in the equidistant time slot, the relationship between the first device transmission rate in the equidistant time slot and the phase shift coefficient matrix is obtained by calculation. The first lower bound of the first device transmission rate in the equidistant time slot is obtained by establishing a first slack variable. The value of the first slack variable that maximizes the first lower bound is obtained by using a semi-definite relaxation algorithm. The value of the first slack variable is substituted into the relationship between the first device transmission rate in the equidistant time slot and the phase shift coefficient matrix to obtain the phase shift coefficient matrix of the equidistant time slot.

7. The communication method of claim 1, wherein, The optimized flight trajectory is obtained by optimizing calculation according to the transmission power allocation coefficient, the pairing relationship and the phase shift coefficient matrix of each equidistant time slot, including: The relationship between the first device transmission rate in each equidistant time slot and the position information of the unmanned aerial vehicle is obtained by calculating according to the transmission power allocation coefficient, the pairing relationship and the phase shift coefficient matrix of each equidistant time slot. The second lower bound of the first device transmission rate in each equidistant time slot is obtained by establishing a second slack variable. The value of the second slack variable that maximizes the second lower bound is obtained by using a successive convex approximation algorithm. The value of the second slack variable that maximizes the second lower bound is obtained by using a successive convex approximation algorithm. substituting the value of the second relaxation variable into a relationship between the first device transmission rate in each equidistant time slot and the position information of the UAV to obtain the position information of the UAV in each equidistant time slot; obtaining the optimized flight trajectory according to the position information of the UAV in all equidistant time slots.

8. A communication system, characterized by The system comprises a UAV provided with a reconfigurable intelligent surface, a plurality of base stations, a plurality of communication devices, and a plurality of sub-channels, the plurality of communication devices comprises a plurality of first devices, the distance between the first device and any one of the base stations is greater than a distance threshold, and the system further comprises: a parameter acquisition module configured to acquire a transmission power allocation coefficient of the base station, a pairing relationship between the sub-channels and the communication devices, a phase shift coefficient matrix of the reconfigurable intelligent surface, and flight trajectory information of the UAV; wherein one flight cycle of the UAV comprises a plurality of equidistant time slots, and the flight trajectory comprises position information of the UAV in each equidistant time slot; a transmission power allocation coefficient calculation module configured to, for any one of all equidistant time slots, calculate the transmission power allocation coefficient of the equidistant time slot according to the pairing relationship of a previous equidistant time slot of the equidistant time slot, the phase shift coefficient matrix, and the position information of the UAV in the equidistant time slot; a pairing relationship acquisition module configured to pair the transmission rates of the first devices in all equidistant time slots before the equidistant time slot to obtain the pairing relationship of the equidistant time slot; a phase shift coefficient matrix calculation module configured to calculate the phase shift coefficient matrix of the equidistant time slot according to the transmission power allocation coefficient of the equidistant time slot, the pairing relationship, and the position information of the UAV; a flight trajectory optimization calculation module configured to optimize the flight trajectory according to the transmission power allocation coefficient, the pairing relationship, and the phase shift coefficient matrix of each equidistant time slot; a communication module configured to perform communication based on the transmission power allocation coefficient, the pairing relationship, the phase shift coefficient matrix, and the optimized flight trajectory of each equidistant time slot.

9. An electronic device, comprising: The system comprises a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor implements the method of any one of claims 1 to 7 when executing the program.

10. A non-transitory computer-readable storage medium, comprising: The non-transitory computer readable storage medium stores computer instructions for causing a computer to perform the method of any one of claims 1 to 7.

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