A 6G RIS-assisted full-duplex UAV system energy efficiency optimization method and device

Abstract

The present invention relates to the field of mobile communications technology, and more specifically to a method and apparatus for optimizing the energy efficiency of a 6G RIS-assisted full-duplex UAV system. The method comprises obtaining an optimal three-dimensional trajectory of the full-duplex UAV using a dual-depth Q network algorithm; obtaining the full-duplex UAV's position coordinates based on the obtained optimal three-dimensional trajectory; and performing passive beamforming on the RIS based on the full-duplex UAV's position coordinates and ground user coordinates. This allows the full-duplex UAV to achieve maximum total user throughput while minimizing its energy consumption in 6G high-speed network communications through the RIS, thereby optimizing the energy efficiency of the entire 6G RIS-assisted full-duplex UAV system.

Description

Technical Field

[0001] The present invention relates to the field of mobile communication technology, and in particular to a method and device for optimizing energy efficiency of a 6G RIS-assisted full-duplex UAV system. Background Art

[0002] 6G, the sixth-generation mobile communication standard, is also known as the sixth-generation mobile communication technology. 6G's data transmission rate could reach 50 times that of 5G, with latency reduced to one-tenth of 5G. It significantly outperforms 5G in terms of peak rate, latency, traffic density, connection density, mobility, spectrum efficiency, and positioning capabilities. Reconfigurable Intelligent Surface (RIS) technology intelligently reflects received signals between base stations and ground users, creating a more controllable intelligent wireless communication environment. Unmanned Aerial Vehicles (UAVs) are being used in urban wireless network infrastructure to enhance service quality and eliminate network coverage gaps. However, the line-of-sight (LoS) link between UAVs and ground users can be hindered by ground obstacles. RIS is a plane composed of a large number of low-cost passive reflective elements, each of which independently modulates the amplitude and phase of the incident signal, collaboratively achieving reflective beamforming and optimizing the received signal strength. However, most existing research on RIS-assisted UAV systems has not considered the relationship between full-duplex (FD) UAVs, full-duplex UAV path planning, and propulsion energy consumption under 6G high-speed data transmission. The methods in existing technologies are complex and difficult to solve in a timely manner. Summary of the Invention

[0003] In view of the above-mentioned shortcomings of the prior art, the object of the present invention is to provide a 6G RIS-assisted full-duplex UAV system energy efficiency optimization method and device to solve the problems in the prior art.

[0004] To achieve the above and other related objectives, the present invention provides a 6G RIS-assisted full-duplex UAV system energy efficiency optimization method, the method comprising:

[0005] Obtaining the optimal three-dimensional trajectory of the full-duplex UAV according to a dual-depth Q-network algorithm;

[0006] Obtaining the position coordinates of the full-duplex UAV according to the obtained optimal three-dimensional trajectory;

[0007] Passive beamforming is performed on the RIS according to the full-duplex UAV position coordinates and the ground user coordinates to achieve maximum total user throughput while minimizing the energy consumption of the full-duplex UAV.

[0008] In one embodiment of the present invention, the 6G RIS-assisted full-duplex UAV system includes:

[0009] Ground users, RIS installed on high-rise buildings, and full-duplex drones acting as aerial base stations.

[0010] In one embodiment of the present invention, the full-duplex UAV includes:

[0011] At least one transmitting antenna and at least one receiving antenna.

[0012] In one embodiment of the present invention, the method further includes:

[0013] Given a full-duplex UAV's three-dimensional trajectory, the RIS beamforming matrix is ​​adjusted to achieve signal phase alignment at the ground user, thereby maximizing the average channel gain and data rate of the uplink and downlink communication users.

[0014] In one embodiment of the present invention, the energy efficiency of the 6G RIS-assisted full-duplex UAV system is defined as:

[0015]

[0016] Among them, EE represents system energy efficiency, e k represents the propulsion energy of the full-duplex UAV in the kth time slot, and denote the data transmission rates of the uplink communication user and the downlink communication user in the kth time slot, t k Indicates the duration of each time slot.

[0017] In one embodiment of the present invention, the propulsion energy of the full-duplex UAV in the kth time slot is defined as:

[0018]

[0019] Among them, e k represents the propulsion energy of the full-duplex UAV in the kth time slot, t k represents the duration of each time slot, P0 represents the constant blade power in the hovering state, and They represent the horizontal flight speed and vertical flight speed of the full-duplex UAV in the kth time slot, U tip represents the tip speed of the rotor blade, d0 represents the airframe drag ratio, ρ represents the air density, s represents the rotor solidity, G represents the rotor disk area, P1 represents the induced power in the hovering state, v0 represents the average rotor induced speed of the full-duplex UAV in hovering, and P2 represents the constant descent / ascent power.

[0020] In one embodiment of the present invention, the data transmission rates at the uplink communication user and the downlink communication user in the k-th time slot are respectively defined as:

[0021]

[0022] in, and They represent the data transmission rates of the uplink communication user and the downlink communication user in the kth time slot, respectively, and p U represents the transmit power of the uplink communication user, and represents the average channel gain at the uplink communication user and the downlink communication user, η represents the variance of the independent and identically distributed zero-mean complex Gaussian noise on the full-duplex UAV, σ U 2 and σ D 2 denotes the variance of the independent and identically distributed zero-mean complex Gaussian noise at the uplink communication user and the downlink communication user, p denotes the fixed transmit power of the full-duplex UAV, h UD represents the channels of uplink communication users and downlink communication users, and They represent the channel gains of RIS to uplink communication users and downlink communication users in the kth time slot, Θ k represents the RIS beamforming matrix.

[0023] In one embodiment of the present invention, obtaining the optimal three-dimensional trajectory of the full-duplex UAV according to the dual-depth Q network algorithm specifically includes:

[0024] Initialize the experience buffer, time slot count, number of training rounds, and the parameters of the original Q network and the parameters of the target network, where the parameters of the initialized original Q network are equal to the parameters of the initialized target network;

[0025] In each training round, a deep neural network is used to learn to select and execute the corresponding action from the action space, while updating the corresponding RIS beamforming matrix. The action, state, and corresponding reward are stored in the experience buffer. In the first training round, the initial action is obtained through a random method or a greedy method. The action includes the three-dimensional trajectory and flight time of the full-duplex UAV.

[0026] In each training round, a small batch of samples is randomly selected from the experience buffer to update and train the deep neural network until the end of the round to obtain the optimal three-dimensional trajectory of the full-duplex UAV.

[0027] In one embodiment of the present invention, the reward is defined as:

[0028]

[0029] Among them, r represents reward, e k represents the propulsion energy of the full-duplex UAV in the kth time slot, and denote the data transmission rates of the uplink communication user and the downlink communication user in the kth time slot, t k represents the duration of each time slot, and p0 represents the penalty.

[0030] To achieve the above-mentioned purpose, the present invention further provides a 6G RIS-assisted full-duplex UAV system energy efficiency optimization device, characterized in that the device comprises:

[0031] Deep learning optimization module: obtaining the optimal three-dimensional trajectory of the full-duplex UAV based on a dual deep Q-network algorithm;

[0032] Parameter acquisition module: obtaining the position coordinates of the full-duplex UAV according to the obtained optimal three-dimensional trajectory;

[0033] Problem-solving module: Passive beamforming is performed on the RIS according to the full-duplex UAV position coordinates and the ground user coordinates to achieve maximum total user throughput while minimizing the energy consumption of the full-duplex UAV.

[0034] The present invention provides the following beneficial effects: The 6G RIS-assisted full-duplex UAV system energy efficiency optimization method utilizes a dual-depth Q network algorithm to obtain the optimal three-dimensional trajectory of the full-duplex UAV; determines the full-duplex UAV's position coordinates based on the obtained optimal three-dimensional trajectory; and performs passive beamforming on the RIS based on the full-duplex UAV's position coordinates and ground user coordinates. This allows the full-duplex UAV to achieve maximum total user throughput while minimizing its energy consumption in 6G high-speed network communications through RIS, thus achieving energy efficiency optimization for the entire 6G RIS-assisted full-duplex UAV system.

[0035] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 This is a schematic diagram of the structure of a 6G RIS-assisted full-duplex UAV system model;

[0037] Figure 2 This is a flow chart of a method for optimizing energy efficiency of a 6G RIS-assisted full-duplex UAV system according to an exemplary embodiment of the present invention;

[0038] Figure 3This is a schematic diagram comparing the propulsion energy consumption of UAVs in various schemes according to an exemplary embodiment of the present invention;

[0039] Figure 4 A schematic diagram showing a comparison of total throughput and energy efficiency of various solutions according to an exemplary embodiment of the present invention;

[0040] Figure 5 The block diagram of a 6G RIS-assisted full-duplex UAV system energy efficiency optimization device is shown as an exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0041] The following describes the embodiments of the present invention with reference to the accompanying drawings and preferred embodiments. Those skilled in the art will readily appreciate the other advantages and benefits of the present invention from the disclosure herein. The present invention may also be implemented or applied through various other specific embodiments, and the various details in this specification may be modified or altered based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are intended only to illustrate the present invention and are not intended to limit the scope of protection of the present invention.

[0042] It should be noted that the illustrations provided in the following embodiments are merely schematic illustrations of the basic concept of the present invention. Therefore, the illustrations only show components related to the present invention and are not drawn according to the number, shape, and size of components in actual implementation. In actual implementation, the type, quantity, and proportion of each component may be changed arbitrarily, and the component layout may also be more complex.

[0043] In the following description, numerous details are discussed to provide a more thorough explanation of the embodiments of the present invention. However, it will be apparent to those skilled in the art that the embodiments of the present invention may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring the embodiments of the present invention.

[0044] In the description and claims of the embodiments of the present disclosure, as well as in the accompanying drawings, the terms "first," "second," and the like are used to distinguish similar items and are not necessarily used to describe a particular order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate to describe the embodiments of the present disclosure herein. In addition, the terms "including," "having," and any variations thereof are intended to cover non-exclusive inclusions.

[0045] Unless otherwise stated, the term "plurality" means two or more.

[0046] In the embodiment of the present disclosure, the character " / " indicates that the preceding and following objects are in an "or" relationship. For example, A / B means: A or B.

[0047] The term "and / or" describes an association between objects, indicating that three relationships can exist. For example, A and / or B means: A or B, or A and B.

[0048] First, it's important to note that 6G, the sixth-generation mobile communication standard, is also known as the sixth-generation mobile communication technology. 6G's data transmission rate could reach 50 times that of 5G, with latency reduced to one-tenth of 5G. It significantly outperforms 5G in terms of peak rate, latency, traffic density, connection density, mobility, spectrum efficiency, and positioning capabilities. Reconfigurable Intelligent Surface (RIS) technology intelligently reflects received signals between base stations and ground users, creating a more controllable intelligent wireless communication environment. Unmanned Aerial Vehicles (UAVs) are being used in urban wireless network infrastructure to enhance service quality and eliminate network coverage gaps. However, the line-of-sight (LoS) link between UAVs and ground users can be hindered by ground obstacles. RIS is a planar structure composed of a large number of low-cost passive reflective elements, each of which independently modulates the amplitude and phase of the incident signal, collaboratively achieving reflective beamforming and optimizing the received signal strength. However, most existing research on RIS-assisted UAV systems has not considered the relationship between full-duplex (FD) UAVs, full-duplex UAV path planning, and propulsion energy consumption under 6G high-speed data transmission. The methods in existing technologies are complex and difficult to solve in a timely manner.

[0049] Figure 1 This is a schematic diagram of the structure of a 6G RIS-assisted full-duplex UAV system model;

[0050] like Figure 1 As shown in an exemplary embodiment, a 6G RIS-assisted full-duplex UAV system includes: a ground user G, a RIS installed on a high-rise building, and a full-duplex UAV serving as an aerial base station. The ground users include uplink (UL) and downlink (DL) users. Due to the long distance between the ground user and the UAV, and the presence of obstacles such as buildings during flight, a LoS connection between the two cannot be maintained. The RIS is deployed on the high-rise building to assist communication between the ground user and the UAV. By reflecting millimeter-wave signals, the RIS replaces the NLOS link with two connected LoS links.

[0051] In one exemplary embodiment, a full-duplex UAV includes at least one transmitting antenna and at least one receiving antenna. The full-duplex UAV acts as an aerial base station. The RIS (Receiving Infrared Response) (RIS) is used to reflect signals, reducing the UAV's motion. This allows for tandem virtual line-of-sight communication between the UAV and ground users, while also reducing the UAV's energy consumption. The RIS is deployed on the surface of high-rise buildings to redirect signals and avoid NLoS connections between the UAV and users.

[0052] RIS is a uniform planar array (UPA) formed by L×N passive reflecting units (PRUs). Each column of UPA contains N PRUs with equal spacing, and the distance between them is d. c ; Each row contains L PRUs with equal spacing, and their distance is d r Each PRU can passively change its phase shift via an independent reflection coefficient: where a∈[0,1) is the fixed reflection loss of the RIS element and the phase shift of the (i,j)th PRU is θ i,j ∈[-π,π). Considering that the distance between RIS and users and UAV is much larger than the size of UPA Assuming that the RIS is the far-field array response vector model, the position coordinate of the first element of RIS is R = [x R ,y R ,z R ] T Indicates its position. RIS improves the NLoS link between UAV-G by two LoS ​​links: UAV-RIS and RIS-G. The position coordinates of the uplink user and downlink user are G U =[x U ,y U ] T and G D =[x D ,y D ] T , the horizontal position coordinates of the UAV are expressed as Its flight altitude is Affected by the minimum and maximum values ​​of height h min 、h max constraint.

[0053] In an exemplary embodiment, other parameters and variables of the model are defined as follows:

[0054] parameter:

[0055] λ: carrier wavelength

[0056] ξ: path loss at the reference distance

[0057] d: Euclidean distance from UL user to DL user

[0058] κ: inter-user path loss exponent

[0059] P0: Constant blade power in hovering state

[0060] P1: Induced power in hovering state

[0061] variable:

[0062] The Euclidean distance between the full-duplex UAV and the RIS in the kth time slot is:

[0063]

[0064] The channel gain of the full-duplex UAV-to-RIS link in the kth time slot is:

[0065]

[0066] in, and are the cosine and sine of the horizontal angle of arrival (AoA) of the signal at the RIS, respectively. The sine of the vertical AoA of the signal at the RIS is

[0067] The Euclidean distances from UL users and DL users to RIS are:

[0068]

[0069] in, and They represent the cosine and sine of the angle of departure (AoD) of the signal level at the UL user, and denote the cosine and sine of the signal level deviation angle at the DL user, respectively, and They represent the sine of the vertical AoD of the signal at the UL user and DL user, respectively.

[0070] The channel gains of RIS to UL users and DL users in the kth time slot are and

[0071]

[0072] The reflection channel gains of UL users and DL users are:

[0073]

[0074] The RIS reflection phase matrix is

[0075] in, The elevation angles of the UAV and the UL and DL users in the kth time slot are: and The horizontal distances between the UAV and the UL user and DL user in the kth time slot are: and The probability that a LoS link exists between the UAV and the user can be expressed as:

[0076]

[0077] Among them, a and b are constants related to the environment.

[0078] The average channel gain and data transmission rate (bits / second / Hz) at the UL user and DL user in the kth time slot are:

[0079]

[0080] Among them, the fixed transmission power of full-duplex UAV is p, and the transmission power of UL user is p U , due to the propagation attenuation between the full-duplex UAV and the RIS, the reflected SI is much weaker than the LI. However, the LI can be effectively suppressed according to the existing technology. The channel from the UL user to the DL user can be modeled as a Rayleigh fading channel as Among them, h ～ Denotes the random scattering component modeled by a circularly symmetric complex Gaussian (CSCG) random variable with zero mean and unit variance. It is assumed that any residual noise resulting from interference cancellation is independent and identically distributed complex Gaussian noise on the full-duplex UAVs with zero mean and variance η. U 2 and σ D 2 denote the variance of the independent and identically distributed zero-mean complex Gaussian noise at the UL and DL users, respectively.

[0081] The horizontal and vertical flight speeds of the full-duplex UAV in the kth time slot are: and The full-duplex UAV three-dimensional coordinates The horizontal coordinate is

[0082] The entire system is limited by:

[0083]

[0084] Among them, from top to bottom, the phase shift matrix of RIS is represented in sequence Φ={Θ k, k∈K} constraints, the flight altitude and maximum flight speed constraints of the full-duplex UAV and the duration of each time slot.

[0085] In an exemplary embodiment, the energy efficiency of a 6G RIS-assisted full-duplex UAV system is defined as:

[0086]

[0087] Among them, EE represents system energy efficiency, e k represents the propulsion energy of the full-duplex UAV in the kth time slot, and denote the data transmission rates of the uplink communication user and the downlink communication user in the kth time slot, t k Indicates the duration of each time slot.

[0088] It can be seen from the formula that optimizing the energy efficiency of the full-duplex UAV system is to optimize the product of the data transmission rate at the uplink communication user and the downlink communication user in the entire time slot and the duration of the time slot and maximize the value of the propulsion energy of the full-duplex UAV in the entire time slot.

[0089] In an exemplary embodiment, the propulsion energy of the full-duplex UAV in the kth time slot is defined as:

[0090]

[0091] Among them, e k represents the propulsion energy of the full-duplex UAV in the kth time slot, t k represents the duration of each time slot, P0 represents the constant blade power in the hovering state, and They represent the horizontal flight speed and vertical flight speed of the full-duplex UAV in the kth time slot, U tip represents the tip speed of the rotor blade, d0 represents the airframe drag ratio, ρ represents the air density, s represents the rotor solidity, G represents the rotor disk area, P1 represents the induced power in the hovering state, v0 represents the average rotor induced speed of the full-duplex UAV in hovering, and P2 represents the constant descent / ascent power.

[0092] In an exemplary embodiment, the data transmission rates at the uplink communication user and the downlink communication user in the kth time slot are respectively defined as:

[0093]

[0094] in, and They represent the data transmission rates of the uplink communication user and the downlink communication user in the kth time slot, respectively, and pU represents the transmit power of the uplink communication user, and represents the average channel gain at the uplink communication user and the downlink communication user, η represents the variance of the independent and identically distributed zero-mean complex Gaussian noise on the full-duplex UAV, σ U 2 and σ D 2 denotes the variance of the independent and identically distributed zero-mean complex Gaussian noise at the uplink communication user and the downlink communication user, p denotes the fixed transmit power of the full-duplex UAV, h UD represents the channels of uplink communication users and downlink communication users, and They represent the channel gains of RIS to uplink communication users and downlink communication users in the kth time slot, Θ k represents the RIS beamforming matrix.

[0095] Figure 2 This is a flow chart of a method for optimizing energy efficiency of a 6G RIS-assisted full-duplex UAV system according to an exemplary embodiment of the present invention;

[0096] like Figure 2 As shown, in an exemplary embodiment, the 6G RIS-assisted full-duplex UAV system energy efficiency optimization method includes at least steps S210 to S230, which are described in detail as follows:

[0097] Step S210: Obtain the optimal three-dimensional trajectory of the full-duplex UAV according to a dual-depth Q network algorithm.

[0098] In an exemplary embodiment, an experience buffer, a time slot count, a number of training rounds, and parameters of the original Q network and parameters of the target network are initialized, wherein the parameters of the initialized original Q network are equal to the parameters of the initialized target network;

[0099] In each training round, a deep neural network is used to learn to select and execute the corresponding action from the action space, while updating the corresponding RIS beamforming matrix. The action, state, and corresponding reward are stored in the experience buffer. In the first training round, the initial action is obtained through a random method or a greedy method. The action includes the three-dimensional trajectory and flight time of the full-duplex UAV.

[0100] In each training round, a small batch of samples is randomly selected from the experience buffer to update and train the deep neural network until the end of the round to obtain the optimal three-dimensional trajectory of the full-duplex UAV.

[0101] In an exemplary embodiment, the reward is defined as:

[0102]

[0103] Among them, r represents reward, e k represents the propulsion energy of the full-duplex UAV in the kth time slot, and denote the data transmission rates of the uplink communication user and the downlink communication user in the kth time slot, t k represents the duration of each time slot, and p0 represents the penalty.

[0104] Step S220: obtaining the position coordinates of the full-duplex UAV according to the obtained optimal three-dimensional trajectory.

[0105] Step S230 , passive beamforming is performed on the RIS according to the full-duplex UAV position coordinates and the ground user coordinates, so as to achieve maximum total user throughput while minimizing energy consumption of the full-duplex UAV.

[0106] In an exemplary embodiment, given a full-duplex UAV three-dimensional trajectory, the RIS beamforming matrix is ​​adjusted to achieve signal phase alignment at the ground user, thereby maximizing the average channel gain and data rate of the uplink communication user and the downlink communication user.

[0107] According to the full-duplex UAV position and ground user position in the kth time slot, to ensure that the uplink communication user and the downlink communication user transmit simultaneously, we divide the total L×N PRUs of RIS into M U ×N and M D ×N two parts, corresponding to the (i,j)th UL user, The corresponding phase shift of j∈N PRUs is set as:

[0108]

[0109] Corresponding to the (m,n)th DL user, The corresponding phase shift of n∈N PRUs is set as:

[0110]

[0111] The sum of the reflected channel gains for UL users and DL users can be rewritten as

[0112]

[0113] Get the maximum average channel gain of UL users and DL users

[0114]

[0115] Will Bring in Calculation formula to obtain the maximum achievable data rate for uplink and downlink users

[0116] Figure 3 This is a schematic diagram comparing the propulsion energy consumption of UAVs in various schemes according to an exemplary embodiment of the present invention;

[0117] Figure 4 A schematic diagram showing a comparison of total throughput and energy efficiency of various solutions according to an exemplary embodiment of the present invention;

[0118] Combine Figure 3 and Figure 4 As shown in the figure, the experimental group is the FD RIS UAV system, while the other control groups are the FD Random Phase system, the FD No RIS system, and the TDD RIS UAV system. Both the FD RIS UAV system and the TDD RIS UAV system can adjust the phase shift through the RIS. Therefore, compared to other schemes, the UAV can maintain a high sum rate by simply flying around the RIS, and the UAV does not need to descend to a lower altitude, reducing UAV energy consumption. In contrast, the full-duplex UAV in the FD No RIS case flies as close to the ground user as possible and dives to a lower altitude to establish a communication link with the user, which significantly increases UAV energy consumption. Conversely, the UAV in the FD Random Phase system attempts to approach the RIS to find a trajectory that increases the user's data transmission rate.

[0119] In the present invention, the UAVs in various systems are controlled to have the same flight time and similar energy consumption, and the corresponding total user throughput and UAV energy consumption ratio are compared, such as Figure 4 As shown in the figure, the energy efficiency and system throughput cumulative distribution functions of the 6G RIS-assisted full-duplex UAV system energy efficiency optimization method of the present invention are compared with those of other methods. When the UAVs have the same flight time and similar energy consumption, the 6G RIS-assisted full-duplex UAV system energy efficiency optimization method of the present invention enables the entire system to achieve the maximum total user throughput while minimizing the energy consumption of the full-duplex UAVs.

[0120] Figure 5 The block diagram of a 6G RIS-assisted full-duplex UAV system energy efficiency optimization device is shown as an exemplary embodiment of the present invention.

[0121] like Figure 5 As shown, the exemplary 6G RIS-assisted full-duplex UAV system energy efficiency optimization device includes:

[0122] Deep learning optimization module: obtaining the optimal three-dimensional trajectory of the full-duplex UAV based on a dual deep Q-network algorithm;

[0123] Parameter acquisition module: obtaining the position coordinates of the full-duplex UAV according to the obtained optimal three-dimensional trajectory;

[0124] Problem-solving module: Passive beamforming is performed on the RIS according to the full-duplex UAV position coordinates and the ground user coordinates to achieve maximum total user throughput while minimizing the energy consumption of the full-duplex UAV.

[0125] It should be noted that the 6G RIS-assisted full-duplex UAV system energy efficiency optimization device provided in the above embodiment and the 6G RIS-assisted full-duplex UAV system energy efficiency optimization method provided in the above embodiment are based on the same concept. The specific manner in which each module and unit performs operations has been described in detail in the method embodiment and will not be repeated here. In actual applications, the 6G RIS-assisted full-duplex UAV system energy efficiency optimization device provided in the above embodiment can, as needed, allocate the above functions to different functional modules, that is, divide the internal structure of the device into different functional modules to complete all or part of the functions described above. This is not a limitation herein.

[0126] An embodiment of the present invention also provides an electronic device, comprising: one or more processors; a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the electronic device implements the 6G RIS-assisted full-duplex UAV system energy efficiency optimization method provided in the above-mentioned embodiments.

[0127] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. Among them, each box in the flowchart or block diagram can represent a module, program segment, or part of the code, and the above-mentioned module, program segment, or part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0128] The units involved in the embodiments described in this application may be implemented by software or hardware, and the units described may also be set in a processor. In some cases, the names of these units do not constitute limitations on the units themselves.

[0129] Another aspect of the present application provides a computer-readable storage medium storing a computer program. When executed by a computer processor, the computer program causes the computer to perform the aforementioned hybrid encryption-based wireless network authentication method. The computer-readable storage medium may be included in the electronic device described in the above embodiments, or may exist independently and not be incorporated into the electronic device.

[0130] Another aspect of the present application provides a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the 6G RIS-assisted full-duplex UAV system energy efficiency optimization method provided in each of the above embodiments.

[0131] In the description herein, numerous specific details, such as examples of components and / or methods, are provided to provide a complete understanding of the embodiments of the present invention. However, those skilled in the art will recognize that embodiments of the present invention may be practiced without one or more of the specific details or with other devices, systems, assemblies, methods, components, materials, parts, etc. In other cases, well-known structures, materials, or operations are not specifically shown or described in detail to avoid obscuring aspects of the embodiments of the present invention.

[0132] It should also be understood that one or more of the elements shown in the figures may also be implemented in a more separate or more integrated manner, or even removed because they are inoperable in certain circumstances or provided because they may be useful depending on the application.

[0133] In addition, unless otherwise expressly indicated, any marking arrows in the drawings should be regarded as illustrative only and not limiting. Furthermore, unless otherwise indicated, the term "or" as used herein is generally intended to mean "and / or." Where a term is unclear in providing separation or combination capabilities, the combination of components or steps will also be considered as indicated.

[0134] The above description of the illustrated embodiments of the present invention (including that described in the Abstract) is not intended to be exhaustive or to limit the invention to the precise forms disclosed herein. Although specific embodiments of the present invention and examples of the present invention are described herein for illustrative purposes only, as those skilled in the art will recognize and appreciate, various equivalent modifications are possible within the spirit and scope of the present invention. As noted, modifications may be made to the present invention in light of the above description of the illustrated embodiments of the present invention, and such modifications will be within the spirit and scope of the present invention.

[0135] Systems and methods have been generally described herein in detail to facilitate understanding of the present invention. In addition, various specific details have been given to provide an overall understanding of embodiments of the present invention. However, those skilled in the relevant art will recognize that embodiments of the present invention may be practiced without one or more of these specific details, or with other devices, systems, accessories, methods, components, materials, parts, etc. In other cases, well-known structures, materials, and / or operations are not specifically shown or described in detail to avoid obscuring aspects of embodiments of the present invention.

[0136] Thus, although the invention has been described herein with reference to specific embodiments thereof, freedom of modification, various changes and substitutions are contemplated within the foregoing disclosure, and it should be understood that in some cases, some features of the invention will be employed without the corresponding use of other features without departing from the scope and spirit of the claimed invention. Thus, many modifications may be made to adapt a particular environment or material to the true scope and spirit of the invention. The invention is not intended to be limited to the specific terminology used in the claims below and / or to the specific embodiments disclosed as the best mode contemplated for carrying out the invention, but the invention is intended to include any and all embodiments and equivalents falling within the scope of the appended claims. Thus, the scope of the invention will be determined solely by the appended claims.

Claims

1. A 6G RIS-assisted full-duplex UAV system energy efficiency optimization method, characterized in that: The method comprises: Obtaining the optimal three-dimensional trajectory of the full-duplex UAV according to a dual-depth Q-network algorithm; Obtaining the position coordinates of the full-duplex UAV according to the obtained optimal three-dimensional trajectory; Passive beamforming is performed on the RIS according to the full-duplex UAV position coordinates and the ground user coordinates to achieve maximum total user throughput while minimizing the energy consumption of the full-duplex UAV; The step of performing passive beamforming on the RIS according to the full-duplex UAV position coordinates and the ground user coordinates to achieve maximum total user throughput while minimizing energy consumption of the full-duplex UAV includes: According to the position of the full-duplex UAV and the position of the ground user, the uplink communication user and the downlink communication user are transmitted simultaneously to obtain the corresponding phase shift of the uplink communication user and the corresponding phase shift of the downlink communication user; and according to the corresponding phase shift of the uplink communication user and the corresponding phase shift of the downlink communication user, a RIS beamforming matrix is ​​obtained; Based on the RIS beamforming matrix, the fixed transmit power of the full-duplex UAV, the transmit power of the uplink communication user, the Rayleigh fading channel model, and the statistical model of the circularly symmetric complex Gaussian random variable, the maximum data transmission rate of the uplink communication user and the maximum data transmission rate of the downlink communication user are calculated; The maximum system energy efficiency of the full-duplex UAV system is calculated based on the maximum data transmission rate of the uplink communication user, the maximum data transmission rate of the downlink communication user, the duration corresponding to the maximum data transmission rate of the uplink communication user, the duration corresponding to the maximum data transmission rate of the downlink communication user, and the propulsion energy of the full-duplex UAV corresponding to the duration.

2. The 6G RIS-assisted full-duplex UAV system energy efficiency optimization method according to claim 1 is characterized in that: The 6G RIS-assisted full-duplex UAV system includes: Ground users, RIS installed on high-rise buildings, and full-duplex drones acting as aerial base stations.

3. The 6G RIS-assisted full-duplex UAV system energy efficiency optimization method according to claim 1 is characterized in that: The full-duplex drone includes: At least one transmitting antenna and at least one receiving antenna.

4. The 6G RIS-assisted full-duplex UAV system energy efficiency optimization method according to claim 1, characterized in that: The method further comprises: Given a full-duplex UAV's three-dimensional trajectory, the RIS beamforming matrix is ​​adjusted to achieve signal phase alignment at the ground user, thereby maximizing the average channel gain and data rate of the uplink and downlink communication users.

5. The 6G RIS-assisted full-duplex UAV system energy efficiency optimization method according to claim 1 is characterized in that: The energy efficiency of the 6G RIS-assisted full-duplex UAV system is defined as: Among them, EE represents system energy efficiency, e k represents the propulsion energy of the full-duplex UAV in the kth time slot, and denote the data transmission rates of the uplink communication user and the downlink communication user in the kth time slot, t k Indicates the duration of each time slot.

6. The 6G RIS-assisted full-duplex UAV system energy efficiency optimization method according to claim 5 is characterized in that: The propulsion energy of the full-duplex UAV in the kth time slot is defined as: Among them, e k represents the propulsion energy of the full-duplex UAV in the kth time slot, P0 represents the constant blade power in the hovering state, and They represent the horizontal flight speed and vertical flight speed of the full-duplex UAV in the kth time slot, U tip represents the tip speed of the rotor blade, d0 represents the airframe drag ratio, ρ represents the air density, s represents the rotor solidity, G represents the rotor disk area, P1 represents the induced power in the hovering state, v0 represents the average rotor induced speed of the full-duplex UAV in hovering, and P2 represents the constant descent / ascent power.

7. The 6G RIS-assisted full-duplex UAV system energy efficiency optimization method according to claim 5, characterized in that: The data transmission rates at the uplink communication user and the downlink communication user in the kth time slot are respectively defined as: Among them, p U represents the transmit power of the uplink communication user, and represents the average channel gain at the uplink communication user and the downlink communication user, η represents the variance of the independent and identically distributed zero-mean complex Gaussian noise on the full-duplex UAV, σ U 2 and σ D 2 denotes the variance of the independent and identically distributed zero-mean complex Gaussian noise at the uplink communication user and the downlink communication user, p denotes the fixed transmit power of the full-duplex UAV, h UD represents the channels of uplink communication users and downlink communication users, and They represent the channel gains of RIS to uplink communication users and downlink communication users in the kth time slot, Θ k represents the RIS beamforming matrix.

8. The 6G RIS-assisted full-duplex UAV system energy efficiency optimization method according to claim 1, characterized in that: The obtaining of the optimal three-dimensional trajectory of the full-duplex UAV according to the dual-depth Q network algorithm specifically includes: Initialize the experience buffer, time slot count, number of training rounds, and the parameters of the original Q network and the parameters of the target network, where the parameters of the initialized original Q network are equal to the parameters of the initialized target network; In each training round, a deep neural network is used to learn to select and execute the corresponding action from the action space, while updating the corresponding RIS beamforming matrix. The action, state, and corresponding reward are stored in the experience buffer. In the first training round, the initial action is obtained through a random method or a greedy method. The action includes the three-dimensional trajectory and flight time of the full-duplex UAV. In each training round, a small batch of samples is randomly selected from the experience buffer to update and train the deep neural network until the end of the round to obtain the optimal three-dimensional trajectory of the full-duplex UAV.

9. The 6G RIS-assisted full-duplex UAV system energy efficiency optimization method according to claim 8, characterized in that: The reward is defined as: Among them, r represents reward, e k represents the propulsion energy of the full-duplex UAV in the kth time slot, and denote the data transmission rates of the uplink communication user and the downlink communication user in the kth time slot, t k represents the duration of each time slot, and p0 represents the penalty.

10. A 6G RIS-assisted full-duplex UAV system energy efficiency optimization device, characterized in that: The device comprises: Deep learning optimization module: obtaining the optimal three-dimensional trajectory of the full-duplex UAV based on a dual deep Q-network algorithm; Parameter acquisition module: obtaining the position coordinates of the full-duplex UAV according to the obtained optimal three-dimensional trajectory; Problem-solving module: passive beamforming is performed on the RIS according to the full-duplex UAV position coordinates and the ground user coordinates to achieve maximum total user throughput while minimizing the energy consumption of the full-duplex UAV; The step of performing passive beamforming on the RIS according to the full-duplex UAV position coordinates and the ground user coordinates to achieve maximum total user throughput while minimizing energy consumption of the full-duplex UAV includes: According to the position of the full-duplex UAV and the position of the ground user, the uplink communication user and the downlink communication user are transmitted simultaneously to obtain the corresponding phase shift of the uplink communication user and the corresponding phase shift of the downlink communication user; and according to the corresponding phase shift of the uplink communication user and the corresponding phase shift of the downlink communication user, a RIS beamforming matrix is ​​obtained; Based on the RIS beamforming matrix, the fixed transmit power of the full-duplex UAV, the transmit power of the uplink communication user, the Rayleigh fading channel model, and the statistical model of the circularly symmetric complex Gaussian random variable, the maximum data transmission rate of the uplink communication user and the maximum data transmission rate of the downlink communication user are calculated; The maximum system energy efficiency of the full-duplex UAV system is calculated based on the maximum data transmission rate of the uplink communication user, the maximum data transmission rate of the downlink communication user, the duration corresponding to the maximum data transmission rate of the uplink communication user, the duration corresponding to the maximum data transmission rate of the downlink communication user, and the propulsion energy of the full-duplex UAV corresponding to the duration.

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