Multi-input multi-output communication rate optimization method and system based on unmanned aerial vehicle IRS

Abstract

The invention discloses a multiple-input-multiple-output communication rate optimization method and system based on an unmanned aerial vehicle-mounted IRS, and belongs to the technical field of air-ground cooperative network communication, and the method specifically comprises the steps: constructing an air-to-ground channel model based on a constructed IRS-assisted millimeter wave MIMO communication model; building a joint optimization problem of beam forming, phase shift design and unmanned aerial vehicle deployment based on the obtained air-to-ground channel model; based on the obtained joint optimization problem, adopting an iterative optimization algorithm, and performing iterative solution through a quadratic transformation technology, a Lagrange multiplication and a distributed discrete time convex optimization algorithm to obtain a closed-form solution of an optimal beam forming and phase shift design strategy; unmanned aerial vehicle deployment is optimized based on the obtained optimal beam forming and phase shift design strategy closed-form solution, the achievable weighted data rate sum maximization of MIMO communication based on the unmanned aerial vehicle IRS is achieved, and MIMO communication rate optimization is completed.

Description

Background Art

[0002] With the development of millimeter-wave (mmWave) and massive multiple-input multiple-output (MIMO) technologies, 5G and beyond 5G (B5G) Internet of Things (IoT) networks have achieved significant improvements in network capacity, bandwidth, and connection density. In B5G IoT networks, millimeter-wave technology enables higher-frequency transmission rates, while MIMO technology improves signal transmission efficiency and reliability by using multiple antenna systems. However, in areas where base station deployment faces huge challenges (such as mining areas, mountainous areas, ocean ports, etc.), millimeter-wave signals have a short propagation range and are easily blocked by obstacles, making it difficult for B5G IoT networks to achieve extensive and reliable coverage. Facing the above challenges, intelligent reflecting surface (IRS), as a new type of wireless communication technology, has been introduced into B5G IoT networks. By optimizing the signal propagation path, IRS can achieve low-cost and high-energy-efficient dense deployment in areas with weak signal coverage, significantly improving spectrum efficiency and communication quality. In addition, IRS can reflect signals from remote base stations, and its precise signal reflection control helps reduce signal attenuation and interference. Therefore, the combined use of millimeter-wave MIMO and IRS can further improve the transmission performance of B5G IoT networks and effectively improve the quality of service (QoS) of cellular users (CUEs).

[0003] Although the integration of IRS and MIMO has shown considerable potential in improving the transmission performance of B5G IoT networks, the static deployment of IRS greatly limits its adaptability and flexibility in remote areas with limited infrastructure. To overcome the limitations of fixed IRS, IRS can be installed on drones, leveraging their mobility to achieve flexible signal coverage and dynamic adjustment. The IRS installed on a drone can actively adjust spatial electromagnetic waves to establish stable communication links in different geographical locations and communication scenarios, thus bypassing obstacles such as buildings and trees. In this case, it can effectively alleviate the communication interruption problem caused by obstacles between the base station and cellular users, thereby providing a real-time communication support network for B5G IoT.

[0004] Prior Art One aims to achieve fast, stable, and high-quality signal transmission. Prior Art One combines IRS with millimeter-wave MIMO technology. Specifically, Prior Art One utilizes the sparsity of the millimeter-wave channel in the angular domain and combines the large bandwidth of millimeter waves to study the channel estimation problem of IRS-assisted millimeter-wave MIMO systems. Further, to meet the low-power requirement, a joint active and passive beamforming design method and an optimal deployment scheme are proposed, while optimizing the IRS reflection coefficient and the base station beamforming to achieve the best signal quality and support high-speed data transmission. In addition, this technology further expands the coverage range through intelligent reflection using IRS, reduces interference between users, and improves the spectrum utilization rate.

[0005] Prior Art Two studies the hovering UAV platform equipped with IRS and provides network coverage-related services based on this platform. Specifically, in the single-input single-output (SISO) scenario, Prior Art Two assembles IRS on a hovering UAV, uses the UAV to provide additional aerial coverage to facilitate line-of-sight (LoS) transmission, supports diverse scenarios such as from urban to suburban and from ground to low altitude, meets the needs of vertical fields such as emergency communication and smart cities, and thus solves the problem of limited system propagation range. To maximize the spectral efficiency and energy efficiency of the UAV system equipped with IRS, Prior Art Two designs a joint trajectory planning and beamforming scheme, aiming to improve the spectral efficiency and energy efficiency of the B5G IoT network by optimizing multi-dimensional resources.

[0006] Prior Art Three aims to solve the problems of signal coverage and transmission quality. This technology uses single-IRS-assisted millimeter-wave MIMO technology to support air-ground integrated communication. Specifically, considering the signal transmission problems in more complex real-world scenarios, Prior Art Three jointly applies single IRS and MIMO technology and deploys it on a hovering UAV platform. Through intelligent reflection and spatial resource optimization, single-IRS-assisted millimeter-wave MIMO technology achieves a balance among signal coverage expansion, system capacity improvement, energy efficiency optimization, and dynamic adaptability in the air-ground integrated environment. Its low cost and easy deployment characteristics make it suitable for building an efficient and collaborative air-ground fusion network. Through spatial multiplexing and intelligent reflection, the network capacity can be significantly improved, and the signal quality can be improved, thus effectively meeting the usage requirements of IoT terminals in air-ground scenarios.

[0007] However, the problems existing in the prior art are: First, the existing technology one adopted the combined IRS and millimeter-wave MIMO technology, introduced the millimeter-wave channel, but ignored the performance advantages brought by the integration of air and ground cooperation. The integrated air-ground network can make full use of air and ground resources, significantly expanding the coverage and robustness of the B5G Internet of Things network, which is particularly important for millimeter-wave MIMO communication. As mentioned above, due to the limited penetration ability of millimeter-wave signals, it is extremely easy to be significantly reduced in an environment with obstacles. In this case, it is necessary to consider deploying an IRS installed on a drone to expand the signal propagation range.

[0008] Second, the existing technology two considered introducing an air-ground mechanism and installing an IRS on a hovering drone to enhance the signal coverage, but they only studied the SISO case. Compared with the SISO system, the MIMO system can transmit and receive signals through multiple antennas within the same frequency band, thereby improving the data transmission rate and system capacity. In addition, MIMO can also improve the anti-interference ability and signal quality, thus enhancing the reliability and performance of the B5G Internet of Things network. Therefore, by intelligently controlling the wireless transmission environment, the IRS-assisted MIMO system provides a new degree of freedom for optimizing the B5G Internet of Things network. At the same time, the existing technology two implicitly made an assumption that the deployment of the IRS was at the optimal position without optimizing the deployment of the IRS. In fact, the deployment position of the IRS affects the reflection angle and coverage of the signal.

[0009] Finally, the existing technology three mainly focused on the scenario of a single IRS and did not consider how to comprehensively improve the coverage and performance in a complex multipath propagation environment. Specifically, deploying only a single IRS in the B5G Internet of Things network cannot fully improve the service quality of cellular users, and its application environment is also greatly limited. The cooperation of multiple IRS units improves the transmission performance by utilizing multiple reflection paths, thus providing greater flexibility and control ability. From the above, it is urgent to further explore the resource optimization problem in the scenario of multiple IRSs.

[0010] Difficulties in solving the above technical problems: The core difficulty in solving the above problems lies in the coupling between multi-dimensional resource joint optimization and complex system collaborative control. First, the proposed multi-UAV collaborative IRS air-ground integrated framework needs to optimize beamforming (continuous variable), IRS phase shift (high-dimensional discrete variable), and UAV deployment (three-dimensional space discrete variable) simultaneously. The three are highly coupled and there are non-convex and non-linear constraints, making it difficult to directly solve the global optimization of the optimization objective (the sum of achievable weighted data rates). Second, in the multi-IRS dynamic reflection scenario, the high-dimensional characteristics of the millimeter-wave MIMO channel and the multipath propagation effect are superimposed on each other, making the acquisition of channel state information and the joint design of phase shifts face high computational complexity. In addition, the deployment of UAVs needs to be dynamically adjusted to obtain the optimal position, but the strong correlation between the IRS position and the signal coverage range makes it difficult to balance the real-time performance and optimality of the deployment strategy. Even more complex is that the air-ground integrated network needs to balance the quality of service requirements of air and ground users, and the multi-IRS collaborative reflection may introduce co-channel interference, further exacerbating the resource allocation conflict. Therefore, how to handle the technical challenges brought by the joint optimization of discrete-continuous hybrid variables and construct an efficient and low-complexity optimization scheme on the premise of ensuring that the algorithm obtains the optimal feasible solution is the most challenging technical bottleneck of the present invention. Summary of the Invention

[0011] The technical problem to be solved by the present invention is to provide a method and system for optimizing the multi-input multi-output communication rate based on an unmanned aerial vehicle (UAV)-borne intelligent reflecting surface (IRS) in view of the deficiencies in the above-mentioned prior art. The method transforms the optimization problem of the sum of achievable weighted data rates (AWDRs) into an optimization problem related to beamforming, phase shift design, and multi-UAV deployment optimization, with the goal of maximizing the sum of achievable weighted data rates, and iteratively solves it by combining quadratic variation, constraint relaxation technology, and the distributed discrete-time convex optimization algorithm (Push-Sum Consensus Optimization Algorithm, PSCOA) to solve the technical problem of difficult network communication.

[0012] The present invention adopts the following technical solutions: A method for optimizing the multi-input multi-output communication rate based on an unmanned aerial vehicle (UAV)-borne intelligent reflecting surface (IRS) includes the following steps: Construct an IRS-assisted millimeter-wave MIMO communication model including 1 remote base station, a uniform linear array, and B IRS units; Construct an air-to-ground channel model based on the obtained IRS-assisted millimeter-wave MIMO communication model; Construct a joint optimization problem of beamforming, phase shift design, and UAV deployment based on the obtained air-to-ground channel model; Based on the obtained joint optimization problem, an iterative optimization algorithm is adopted, and iterative solution is carried out through quadratic transformation technology, Lagrange multiplication, and distributed discrete-time convex optimization algorithm to obtain the closed-form solution of the optimal beamforming and phase shift design strategy; Optimize the UAV deployment based on the obtained closed-form solution of the optimal beamforming and phase shift design strategy, maximize the sum of achievable weighted data rates of the UAV-mounted IRS-based MIMO communication, and complete the optimization of the multi-input multi-output communication rate.

[0013] Preferably, in the IRS-assisted millimeter-wave MIMO communication model, the uniform linear array includes U cellular users and A antenna elements, and the sets composed of cellular users, antenna elements, and intelligent reflecting surfaces are defined as , and ; Let the three-dimensional coordinate position of the BS be , the position of the b th IRS unit be , and the position of the u th cellular user be , being fixed values; Let the reference distance between the BS and the IRS unit be , determine the Euclidean distance u between the th cellular user and the IRS unit, each intelligent reflecting surface has C elements, and define .

[0014] Preferably, the Euclidean distance u between the th cellular user and the IRS unit is:

[0015] where are the X-axis, Y-axis, and Z-axis coordinates of the u th cellular user respectively, and are the X-axis, Y-axis, and Z-axis coordinates of the b th IRS unit respectively.

[0016] Preferably, the construction of the air-to-ground channel model is specifically as follows: Considering the line-of-sight and non-line-of-sight propagation conditions, determine the path loss and in the air-to-ground scenario for each channel; Determine the desired signal u of the th cellular user and the total signal u received by the th cellular user, and construct the air-to-ground channel model.

[0017] Preferably, the path loss and are respectively:

[0018]

[0019] wherein, represents the channel between the BS and the b th IRS unit; represents the channel between the b th IRS unit and the u th cellular user; is the path loss exponent from the BS to the IRS; indicates that the path loss increases with the increase of distance; is the path loss exponent per unit distance; and are respectively the Rice channel factors of the two links; and are the LoS components; and are the NLoS components that follow a complex Gaussian distribution with a mean of 0 and a variance of 1; The desired signal of the u th cellular user is:

[0020] wherein, represents the phase shift matrix of the b th IRS, represents the reflection coefficient, represents the phase offset, represents the precoding of the signals sent to different cellular users, represents the BS sending the signal of the u th cellular user after preprocessing; The total signal received by the u th cellular user is:

[0021] wherein, represents the noise, indicates that the output power at the BS shall not exceed a predetermined maximum value, is the maximum transmit power, represents the number of IRSs, represents the b th IRS unit and the uThe channel between cellular users denotes the b phase shift matrix of the th IRS, denotes the channel between the BS and the b th IRS unit, denotes the u th transmit power vector of the cellular users, denotes the BS transmitting the u th preprocessed cellular user signal, denotes the number of cellular users, j denotes the u th cellular user occupying the same channel as the j th cellular user, denotes the j th transmit power vector of the cellular users, denotes the BS transmitting the j th preprocessed cellular user signal, denotes the u th noise of the cellular users.

[0022] Preferably, the joint optimization problem of beamforming, phase shift design, and UAV deployment is specifically: Determine the u th signal-to-interference-plus-noise ratio (SINR) of the cellular users ; the objective function of the joint optimization problem of beamforming, phase shift design, and UAV deployment; and the objective function and optimization conditions of the joint optimization problem of beamforming, phase shift design, and UAV deployment, to construct the joint optimization problem of beamforming, phase shift design, and UAV deployment.

[0023] Preferably, the objective function and optimization conditions of the joint optimization problem of beamforming, phase shift design, and UAV deployment are:

[0024] wherein, is the u th SINR of the cellular users, is is the weight factor, is the auxiliary vector introduced by the Lagrangian dual transformation for the u th cellular user, is the transmit power of the BS, is the maximum transmit power of the BS, is the b th c th element phase of the th IRS, is the continuous phase, is the b th IRS, is the c th element of the IRS, are respectively the b minimum values of the X-axis and Y-axis coordinates of the th IRS unit, b are respectively the X-axis and Y-axis coordinates of the th IRS unit, b are respectively the maximum values of the X-axis and Y-axis coordinates of the

[0025] Preferably, maximizing the sum of achievable weighted data rates for implementing MIMO communication based on an airborne IRS specifically means: Decouple the optimization problem P1 into a beamforming optimization problem P2, a phase shift design optimization problem P3, and a multi-UAV position deployment optimization problem P4. Solve problems P2 and P3 through quadratic variation and constraint relaxation techniques, and solve the multi-UAV position deployment optimization problem P4 through a distributed discrete-time convex optimization algorithm.

[0026] Preferably, for the beamforming optimization problem P2, reset , and transform it into a multi-ratio fractional programming problem. Then, use the quadratic transformation technique to obtain the objective function . Let be a concave differentiable function with respect to . Derive the optimal solution . Obtain the optimal solution through Lagrange multiplication. Under the given power constraint, obtain the optimal solution ; For the phase shift design optimization problem P3, perform phase adjustment and diagonalization of the channel vector on . Introduce the auxiliary vector to obtain the corresponding quadratic transformation formula. According to the Lagrange multi-gradient method, obtain the optimal solution ; Through the constraint relaxation method, obtain a new form of the objective function: . Use the Lagrange dual decomposition method to construct the Lagrangian function and the dual function, and obtain the optimal solution . Transform the problem into a semidefinite programming problem, and use the chain rule to obtain the optimal solution ; For the multi-UAV position deployment optimization problem P4, regard the time as the number of iterations . Initialize the coordinates of the b th IRS unit. Let the coordinates of the u th user be randomly generated , . Define , the sum of the Euclidean distances from all cellular users to the IRS installed on the UAV , calculate its gradient value; update the corresponding coordinates according to the rules; set the squared norm of the error term as , define the iteration threshold as , find the optimal solution and .

[0027] In a second aspect, an embodiment of the present invention provides a multi-input multi-output communication rate optimization system based on an airborne IRS of a UAV, which is characterized by including: A construction module that constructs an IRS-assisted millimeter-wave MIMO communication model including 1 remote base station, a uniform linear array, and B IRS units; A model module that constructs an air-to-ground channel model based on the obtained IRS-assisted millimeter-wave MIMO communication model; An optimization module that constructs a joint optimization problem of beamforming, phase shift design, and UAV deployment based on the obtained air-to-ground channel model; An iteration module that, based on the obtained joint optimization problem, uses an iterative optimization algorithm to perform iterative solution through quadratic transformation technology, Lagrange multiplication, and distributed discrete-time convex optimization algorithm to obtain a closed-form solution of the optimal beamforming and phase shift design strategy; An output module that optimizes the UAV deployment based on the obtained closed-form solution of the optimal beamforming and phase shift design strategy to maximize the sum of the achievable weighted data rates of MIMO communication based on the airborne IRS of the UAV.

[0028] In a third aspect, a computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the above-mentioned multi-input multi-output communication rate optimization method based on the airborne IRS of the UAV.

[0029] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium including a computer program, and when the computer program is executed by a processor, it implements the steps of the above-mentioned multi-input multi-output communication rate optimization method based on the airborne IRS of the UAV.

[0030] In a fifth aspect, a chip includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the above-mentioned multi-input multi-output communication rate optimization method based on the airborne IRS of the UAV.

[0031] In a sixth aspect, an embodiment of the present invention provides an electronic device, including a computer program, which, when executed by the electronic device, implements the steps of the above-mentioned method for optimizing the multi-input multi-output communication rate based on an airborne IRS of a drone.

[0032] Compared with the prior art, the present invention has at least the following beneficial effects: A method for optimizing the multi-input multi-output communication rate based on an airborne IRS of a drone uses the airborne IRS of a drone and multi-input multi-output technology to achieve low-cost and high-energy-efficient dense deployment, aiming to improve the communication quality of cellular users. Then, the problem of maximizing the sum of the weighted data rates achievable by cellular users in the downlink is formulated as a joint optimization problem related to beamforming, phase shift design, and drone deployment; afterwards, iterative solution is carried out through quadratic transformation technology, Lagrange multiplication, and a distributed discrete-time convex optimization algorithm; compared with existing mechanisms, the present invention can maximize the sum of the weighted data rates achievable by cellular users in the downlink.

[0033] Furthermore, by using the airborne IRS of a drone, the communication path can be flexibly adjusted to improve the signal coverage area. Combined with multi-input multi-output (MIMO) technology, through multi-antenna transmission, the data rate and system capacity are significantly improved. In addition, the intelligent reflecting surface can also optimize the spectrum resources, improve the network efficiency, reduce interference, and enhance the communication quality.

[0034] Furthermore, compared with directly calculating the path loss based on distance and frequency, the present invention constructs an air-to-ground channel model considering both line-of-sight and non-line-of-sight propagation conditions, which can more accurately reflect the signal attenuation in the actual communication environment. Under the line-of-sight condition, the signal propagates relatively straight and the loss is small; while under the non-line-of-sight condition, the signal may propagate through reflection, refraction and other paths, and the loss is greater. By distinguishing these two propagation conditions, the path loss can be more accurately estimated, thereby optimizing the network design and improving the signal quality and system performance.

[0035] Furthermore, jointly optimizing beamforming, phase shift design, and UAV deployment aims to maximize the sum of the achievable weighted data rates of cellular users in the downlink. Specifically, first, beamforming can enhance the transmission efficiency of signals and reduce interference by controlling the signal transmission methods of each antenna in the MIMO system. Optimizing beamforming can improve the directivity of signals reaching the target users, thereby effectively increasing the signal strength and data rate. Second, the main function of the IRS is to change the propagation direction of the reflected wave by adjusting the phase of each reflecting element on the reflecting surface. Reasonable phase shift design can improve the interference ratio between the reflected signal and the direct signal, maximize the signal gain, and improve the coverage area, especially significantly improving the communication performance in complex air-to-ground environments. Finally, as the carrier of the IRS, the position and flight altitude of the UAV directly affect the signal coverage and link quality. By optimizing the deployment position of the UAV, the reliability of the communication link and the signal strength can be ensured, thereby enhancing the overall performance of the network.

[0036] Furthermore, setting the goal of maximizing the sum of the achievable weighted data rates of cellular users in the downlink aims to optimize the performance of the MIMO communication network with UAV-mounted IRS, ensure the best balance of the communication rates of each user in the network, and thus improve the overall efficiency and user experience of the system. In a multi-user environment, optimizing the rate of each user individually may result in poor performance for some users. By maximizing the weighted data rate, the rate allocation can be balanced according to the importance or priority (weight) of different users. For example, some users may require higher rates or lower latency, and the weight can reflect these needs, thereby ensuring the fairness and efficiency of the system. In addition, optimizing the sum of the weighted data rates can ensure that the resources of the entire network are fully utilized, while avoiding some users occupying too much bandwidth and affecting the communication quality of other users, thus improving the quality of service of the MIMO communication network with UAV-mounted IRS.

[0037] It can be understood that the beneficial effects of the second to sixth aspects above can be referred to the relevant descriptions in the first aspect above, and will not be elaborated here.

[0038] In summary, the present invention can effectively meet the communication needs of cellular users in the case of insufficient base station coverage. Specifically, the present invention combines the UAV-mounted intelligent reflecting surface (IRS) technology and the multiple input multiple output (MIMO) technology, adopts a low-cost and high-energy-efficient dense deployment method, significantly expands the communication coverage area, and increases the channel capacity. Compared with traditional communication base stations, the present invention can effectively cover the edge area of the base station, making up for the deficiencies of traditional base stations in terms of rapid deployment, flexibility, and coverage area. At the same time, with this innovative solution, the present invention can maximize the sum of the weighted data rates of the cellular user downlink, improve the user experience, and optimize the utilization efficiency of network multi-dimensional resources (beamforming, phase shift design, and UAV deployment).

[0039] The technical solutions of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Description of the Drawings

[0040] To more clearly illustrate the technical solutions of the embodiments of the present application, the accompanying drawings required for use in the embodiments of the present application will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present application, and those of ordinary skill in the art can obtain other accompanying drawings based on these drawings without creative efforts.

[0041] Figure 1 is a flowchart of the method of the present invention; Figure 2 is a schematic diagram of the IRS-assisted millimeter-wave MIMO communication model provided by an embodiment of the present invention; Figure 3 is an iterative optimization framework diagram provided by an embodiment of the present invention; Figure 4 shows the performance comparison of the embodiments of the present invention with Comparative Scheme 1, Comparative Scheme 2, Comparative Scheme 3, and the baseline scheme in terms of the achievable sum of weighted rates under different numbers of cellular users; Figure 5 shows the performance comparison of the embodiments of the present invention with Comparative Scheme 1, Comparative Scheme 2, Comparative Scheme 3, and the baseline scheme in terms of the achievable sum of weighted rates under different numbers of IRS elements; Figure 6 shows the performance comparison of the embodiments of the present invention with Comparative Scheme 1, Comparative Scheme 2, and Comparative Scheme 3 in terms of the achievable sum of weighted rates under different maximum transmission powers.

[0042] Figure 7 is a schematic diagram of a computer device provided by an embodiment of the present invention; Figure 8 is a block diagram of an electronic device provided by an embodiment of the present invention.

[0043] Among them, 60. Computer device; 61. Processor; 62. Memory; 63. Computer program; 600. Electronic device; 610. Processing unit; 620. Storage unit; 6201. Random access storage unit; 6202. Cache storage unit; 6203. Read-only storage unit; 6204. Program / utilities; 6205. Program module; 630. Bus; 640. Display unit; 650. Input / output interface; 660. Network adapter; 700. External device. Detailed Embodiments

[0044] The present invention provides a method for optimizing the communication rate of multi-input multi-output based on an airborne IRS of an unmanned aerial vehicle. First, an IRS-assisted millimeter-wave MIMO communication model is adopted. Then, the optimization problem of maximizing the sum of achievable weighted data rates is formulated as an optimization problem related to beamforming, phase shift design, and the deployment of multiple unmanned aerial vehicle positions. After that, iterative solutions are carried out by combining quadratic variation, constraint relaxation techniques, and distributed discrete-time convex optimization algorithms. Finally, the purpose of maximizing the sum of achievable weighted data rates is achieved, aiming to increase the sum of achievable weighted data rates. To meet the communication requirements of networks with blocked signal coverage, the present invention adopts a multi-unmanned aerial vehicle hovering mode equipped with intelligent reflecting surfaces to enhance the signal strength through the IRS. At the same time, the stability and efficiency of data transmission are improved by enabling the air-ground integrated network architecture of multi-input multi-output, thereby meeting the user's requirements for communication performance.

[0045] Embodiment 1 Please refer to Figure 1 , a method for optimizing the communication rate of multi-input multi-output based on an airborne IRS of an unmanned aerial vehicle according to the present invention includes the following steps: S1. Construct an IRS-assisted millimeter-wave MIMO communication model including 1 remote base station, equipped with U cellular users (CUEs), A antenna elements to form a uniform linear array (ULA), and B IRS units; S101. Consider an IRS-assisted millimeter-wave MIMO communication model including 1 remote base station, equipped with U cellular users, A antenna elements to form a uniform linear array, and B IRS units; Define the sets , and composed of cellular users, antenna elements, and intelligent reflecting surfaces respectively; at the same time, set the three-dimensional coordinate position of the BS, the position b of the th IRS unit, and the position u of the th cellular user, where is a fixed value, each intelligent reflecting surface has C elements, and define ; S102. Set the reference distance between the BS and the IRS unit as , and define the Euclidean distance between the u th cellular user and the IRS unit ;

[0046] Among them, , .

[0047] S103. To improve the signal coverage, install the IRS on a hovering unmanned aerial vehicle and intelligently adjust the phase and amplitude of the signal through a controller, that is, data transmission in the air-to-ground scenario.

[0048] According to steps S101, S102, and S103, the present invention constructs an IRS-assisted millimeter-wave MIMO communication model consisting of 1 remote base station, a uniform linear array equipped with U cellular users, A antenna elements, and B IRS units (installed on a hovering unmanned aerial vehicle).

[0049] S2. Based on the IRS-assisted millimeter-wave MIMO communication model obtained in step S1, construct an air-to-ground (A2G) channel model; S201. Considering two propagation conditions of line-of-sight and non-line-of-sight, define the path losses in the air-to-ground scenario in each channel as follows: (1) and (2) Among them, is a complex matrix, represents the channel between the BS and the b th IRS unit; is a complex vector, represents the channel between the b th IRS unit and the u th cellular user; is the path loss exponent from the BS to the IRS; is the path loss exponent of the IRS to the cellular user, indicating that the path loss increases with the increase of distance; is the path loss exponent per unit distance; and are the Rice channel factors of the two links respectively; and are the LoS components; and are the NLoS components that follow a complex Gaussian distribution (CSCG) with a mean of 0 and a variance of 1, that is , ; S202. Define the desired signal of the u th cellular user as: It is expressed as: (3) where represents the phase shift matrix of the b th IRS, , represents the reflection coefficient; set . In addition, it can be obtained that , ; represents the phase offset; is a complex vector, representing the precoding of the signals sent to different cellular users. The precoding matrix is ; represents the signal of the u th cellular user sent by the BS after preprocessing; For the interference from other users it is expressed as: (4) where j represents the u th cellular user occupying the same channel as the j th cellular user; S203. Define the total signal received by the u th cellular user as It is expressed as: (5) Substitute (3) and (4) into it, and the total signal is rewritten as: (6) where represents the noise, which follows the complex Gaussian distribution ; represents that the output power at the BS shall not exceed a predetermined maximum value, is the maximum transmission power, represents the number of IRSs, represents the channel between the b th IRS unit and the u th cellular user, represents the b th phase shift matrix of the IRS conjugate matrix of, represents the channel between the BS and the b th IRS unit, denotes the transmit power vector of the u th cellular user, denotes the signal of the u th cellular user preprocessed by the BS, denotes the number of cellular users, j denotes the u th cellular user occupying the same channel as the j th cellular user, denotes the transmit power vector of the j th cellular user, denotes the signal of the j th cellular user preprocessed by the BS, denotes the u th cellular user's noise.

[0050] According to step S201, step S202, and step S203, the method of the present invention constructs an air-to-ground channel model.

[0051] S3. Based on the air-to-ground channel model obtained in step S2, construct a joint optimization problem for beamforming, phase shift design, and UAV deployment; S301. The signal-to-interference-plus-noise ratio of the u th cellular user is: (7) wherein, the desired signal power of the u th cellular user is , the interference power of the j th cellular user relative to the u th cellular user is , is the noise power; S302. The objective function of the joint optimization problem for beamforming, phase shift design, and UAV deployment is expressed as: (8) wherein, is the continuous phase, . Since the phase change of the intelligent reflecting surface is limited by the hardware design and control accuracy, it can only take discrete values.

[0052] The present invention defines as the discrete phase of the intelligent reflecting surface, wherein, represents the phase resolution. In addition, is the minimum value of the horizontal and vertical axes of the intelligent reflecting surface installed on the UAV; The maximum values of the horizontal and vertical axes of the intelligent reflecting surface installed on the UAV; 。

[0053] Since the problem of optimizing the sum of the achievable weighted data rates modeled is a non-convex problem and has the characteristics of non-deterministic polynomial (NP-hard). To solve this problem, the objective function is redefined as with the following expression: (9) where, is the auxiliary vector introduced by the Lagrangian dual transformation.

[0054] Given , let be a concave differentiable function with respect to , and the optimal solution is: (10) Based on (10), the objective function can be simplified by eliminating the same terms and changing the base of the logarithm to: (11) Verify that the two objective functions are equivalent, that is: (12) S303. The objective function and optimization conditions for the joint optimization problem of beamforming, phase shift design, and UAV deployment are: (13) where, is the signal-to-interference-plus-noise ratio of the u th cellular user, is is the weight factor used to represent the priority of the u th cellular user, is the auxiliary vector introduced by the Lagrangian dual transformation for the u th cellular user, is the transmit power of the BS, is the maximum transmit power of the BS, is the b th c element of the th IRS, is the continuous phase, , is the b th IRS , is the c th element of the IRS, are respectively the bThe minimum values of the X-axis and Y-axis coordinates of an IRS unit are respectively the b X-axis and Y-axis coordinates of the th IRS unit, b and the maximum values of the X-axis and Y-axis coordinates of the

[0055] th IRS unit respectively.

[0056] S4. Based on the joint optimization problem obtained in step S3, an iterative optimization algorithm is adopted, and iterative solutions are obtained through quadratic transformation technology, Lagrange multiplication, and distributed discrete-time convex optimization algorithm to obtain the closed-form solutions of the optimal beamforming and phase shift design strategies; Please refer to Figure 3 , and through the distributed discrete-time convex optimization algorithm, the coordinates of the optimal solution IRS are iteratively solved using error term analysis.

[0057] S5. Based on the closed-form solutions of the optimal beamforming and phase shift design strategies obtained in step S4, the UAV deployment is optimized to maximize the sum of the achievable weighted data rates of the MIMO communication based on the UAV-mounted IRS.

[0058] S501. To simplify the calculation, the optimization problem P1 is first decoupled into three sub-problems, namely the beamforming optimization problem P2, the phase shift design optimization problem P3, and the multi-UAV position deployment optimization problem P4; The beamforming optimization problem P2 is: (14) The phase shift design optimization problem P3 is: (15) The multi-UAV position deployment optimization problem P4 is: (16) S502. For the beamforming optimization problem P2, the derivation process of the optimal solutions , , is as in steps S5021, S5022, and S5023; for the phase shift design optimization problem P3, the design of the optimal solutions , is as in steps S5024 and S5025; The optimal solutions , , of the beamforming optimization problem P2 are specifically: S5021. Assume that the deployment of the UAV has been pre - given. To simplify the notation, define: (17) We can get: (18) Where, , represents the transmit power vector corresponding to each cellular user.

[0059] By introducing the auxiliary variable and the quadratic transformation technique, we get: (19) Where, is the introduced auxiliary vector, represents taking the real part. Assume is a concave differentiable function with respect to . The optimal solution is derived as: (20) S5202. Given the optimal solution of obtained by Lagrange multiplication, it is expressed as: (21) S5203. Rewrite it as: (22) Where, , and , the value of the transmit power with respect to is: (23) Since is positive definite, its inverse matrix is also positive definite. Furthermore, it can be concluded that is non - negative, which means , indicating that as increases, the value of decreases, that is, it is monotonically decreasing with the increase of . Therefore, we can obtain the optimal under the given power constraint, expressed as: (24) The optimal solutions , of the phase - shift design optimization problem P3 are specifically: S5024. For Perform phase adjustment and diagonalization of the channel vector, and rewrite it as: (25) where , represents the transmit power matrix, , then is rewritten as: (26) For ease of calculation, define: (27) and (28) Calculate to obtain: (29) then is rewritten as: (30) where , ; Introduce the auxiliary vector , and obtain The corresponding quadratic transformation formula is: (31) where . According to the Lagrange multi-gradient method, we have: (32) Perform optimization on to obtain: (33) where and .

[0060] Perform constraint relaxation on and remove the constant term, thereby obtaining a new objective function in the form of: (34) where , and apply the Lagrangian bi-decomposition method to to construct the Lagrangian function as: (35) Then we obtain: (36) where , ; To handle the constraints , the Lagrange multipliers are introduced to construct the dual function: (37) Obtain the optimal solution which is expressed as: (38) (39) where. .

[0061] S5205. Substitute (38) and (39) into (37) to get: (40) where, can be regarded as a semidefinite programming problem. Using the CVX toolbox, solve the following optimization problem: (41) where, is a scalar variable.

[0062] To simplify the derivative calculation of complex functions, the chain rule is adopted, and we can get: (42) After transformation, we get: (43) The optimal can be obtained and is expressed as: (44) Therefore, the solution obtained by the method based on constraint relaxation guarantees that the unit modulus constraint is satisfied.

[0063] S503. For the multi-UAV position deployment optimization problem P4, assume that and have been given in advance, and the distributed discrete-time convex optimization algorithm is used for iterative solution.

[0064] Specifically, the design of initializing variables, iteratively updating variables, and the optimal position coordinates is as in Step S5031, Step S5032, and Step S5033; S5031. In the present invention, time is regarded as the number of iterations , and the optimal solution coordinates are solved using error term analysis. The objective function is the sum of convex functions, that is, cellular users.

[0065] The goal is to obtain the optimal IRS coordinates , that is, to minimize the distance between the IRS and the cellular users , so as to maximize the sum of the achievable weighted data rates. Mathematically, this problem is defined as: (45) where represents the set of coordinates of the IRS is a n -dimensional real vector.

[0066] It should be noted that and are fixed values; subsequently, according to the push-sum framework and combined with the gradient descent method to handle the relevant closed convex set constraints, the coordinates of the b th drone-mounted IRS are initialized. On this basis, the gradient value of the objective function is calculated as follows: (46) where (47) and (48) S5032. In each iteration process, the IRS unit updates its coordinates according to the following rules: (49) and (50) where represents the th iteration, represents the projection operation onto , is the step size; is the Lagrange multiplier, which is updated according to the following rules: (51) where is the step size of the Lagrange multiplier. We define the constraint condition to obey: (52) In formula (52), take the maximum value among these four values (i.e., , , and ). Therefore, only when , will increase. By using Equation (52), we can ensure that the deployment of the drone-mounted IRS unit is within the feasible range.

[0067] S5033. Define the squared norm of the error term as: (53) where, to verify whether the convergence of the algorithm approaches the optimal solution, we define the iteration threshold as . When , it can be considered that the algorithm has converged, and thus the iteration is stopped.

[0068] Those skilled in the art can understand that various aspects of the present invention can be implemented as a system, a method, or a program product. Therefore, various aspects of the present invention can be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or an implementation combining hardware and software aspects, which can be collectively referred to as "circuit", "module", or "platform" here.

[0069] Embodiment 2 The present invention provides a multi-input multi-output communication rate optimization system based on a drone-mounted IRS, which can be used to implement the above multi-input multi-output communication rate optimization method based on a drone-mounted IRS. Specifically, the multi-input multi-output communication rate optimization system based on a drone-mounted IRS includes a construction module, a model module, an optimization module, an iteration module, and an output module.

[0070] Among them, the construction module constructs an IRS-assisted millimeter-wave MIMO communication model including 1 remote base station, a uniform linear array, and B IRS units; The model module constructs an air-to-ground channel model based on the obtained IRS-assisted millimeter-wave MIMO communication model; The optimization module constructs a joint optimization problem of beamforming, phase shift design, and drone deployment based on the obtained air-to-ground channel model; The iteration module, based on the obtained joint optimization problem, adopts an iterative optimization algorithm and performs iterative solution through quadratic transformation technology, Lagrange multiplication, and a distributed discrete-time convex optimization algorithm to obtain a closed-form solution of the optimal beamforming and phase shift design strategy; The output module optimizes the drone deployment based on the obtained closed-form solution of the optimal beamforming and phase shift design strategy, realizes the maximization of the sum of the achievable weighted data rates of MIMO communication based on a drone-mounted IRS, and completes the multi-input multi-output communication rate optimization.

[0071] Embodiment 3 The present invention provides a terminal device, which includes a processor and a memory. The memory is used to store a computer program, and the computer program includes program instructions. The processor is used to execute the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Graphics Processing Unit (GPU), Tensor Processing Unit (TPU), Digital Signal Processor (DSP), Application Specific Integrated Circuit (ASIC), Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, and is suitable for implementing one or more instructions. Specifically, it is suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding function. The processor described in the embodiments of the present invention can be used for the operation of the multi-input multi-output communication rate optimization method based on an airborne IRS of an unmanned aerial vehicle, including: Construct an IRS-assisted millimeter-wave MIMO communication model including 1 remote base station, a uniform linear array, and B IRS units; construct an air-to-ground channel model based on the obtained IRS-assisted millimeter-wave MIMO communication model; construct a joint optimization problem of beamforming, phase shift design, and unmanned aerial vehicle deployment based on the obtained air-to-ground channel model; based on the obtained joint optimization problem, use an iterative optimization algorithm to perform iterative solution through quadratic transformation technology, Lagrange multiplication, and distributed discrete-time convex optimization algorithm to obtain a closed-form solution of the optimal beamforming and phase shift design strategy; optimize the unmanned aerial vehicle deployment based on the obtained closed-form solution of the optimal beamforming and phase shift design strategy to maximize the sum of the achievable weighted data rates of MIMO communication based on an airborne IRS of an unmanned aerial vehicle, and complete the optimization of the multi-input multi-output communication rate.

[0072] Please refer to Figure 7, the terminal device is a computer device. The computer device 60 in this embodiment includes: a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When the computer program 63 is executed by the processor 61, it implements the method for optimizing the multi-input multi-output communication rate based on the airborne IRS of the drone in the embodiment. To avoid repetition, it will not be elaborated here one by one. Alternatively, when the computer program 63 is executed by the processor 61, it implements the functions of each model / unit in the system for optimizing the multi-input multi-output communication rate based on the airborne IRS of the drone in the embodiment. To avoid repetition, it will not be elaborated here one by one.

[0073] The computer device 60 can be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art can understand that Figure 7 This is only an example of the computer device 60 and does not constitute a limitation on the computer device 60. It may include more or fewer components than shown in the figure, or combine some components, or different components. For example, the computer device may also include input / output devices, network access devices, buses, etc.

[0074] The so-called processor 61 may be a central processing unit (CPU), or may also be other general-purpose processors, a graphics processing unit (GPU), a tensor processing unit (TPU), a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0075] The memory 62 may be an internal storage unit of the computer device 60, such as the hard disk or memory of the computer device 60. The memory 62 may also be an external storage device of the computer device 60, such as a plug-in hard disk equipped on the computer device 60, a smart media card (SMC), a secure digital (SD) card, a flash card, etc.

[0076] Further, the memory 62 may also include both internal storage units of the computer device 60 and external storage devices. The memory 62 is used to store computer programs as well as other programs and data required by the computer device. The memory 62 may also be used to temporarily store data that has been output or is to be output.

[0077] Please refer to Figure 8 , the terminal device is the electronic device 600, and the electronic device 600 is presented in the form of a general computing device. The components of the electronic device may include but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including the storage unit 620 and the processing unit 610), a display unit 640, etc.

[0078] Among them, the storage unit stores program codes, and the program codes can be executed by the processing unit 610, so that the processing unit 610 executes the steps according to various exemplary embodiments of the present invention described in the above method part of this specification. For example, the processing unit 610 can execute steps as shown in Figure 1 .

[0079] The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 6201 and / or a cache storage unit 6202, and may further include a read-only storage unit (ROM) 6203.

[0080] The storage unit 620 may also include a program / utilities 6204 having a set (at least one) of program modules 6205. Such program modules 6205 include but are not limited to: an operating system, one or more application programs, other program modules, and program data. The implementation of a network environment may be included in each or some combination of these examples.

[0081] The bus 630 may represent one or more of several types of bus structures, including a memory bus or a memory controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the multiple bus structures.

[0082] The electronic device 600 may also communicate with one or more external devices 700 (such as a keyboard, a pointing device, a Bluetooth device, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 600, and / or may communicate with any device (such as a router, a modem) that enables the electronic device 600 to communicate with one or more other computing devices. Such communication may be carried out through the input / output interface 650. Moreover, the electronic device 600 may also communicate with one or more networks (such as a local area network, a wide area network, and / or a public network, such as the Internet) through the network adapter 660. The network adapter 660 may communicate with other modules of the electronic device 600 through the bus 630. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in combination with the electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms, etc.

[0083] Embodiment 4 The present invention also provides a storage medium, specifically a computer-readable storage medium, which is a memory device in a terminal device and is used to store programs and data. It can be understood that the computer-readable storage medium here may include both the built-in storage medium in the terminal device and, of course, the extended storage medium supported by the terminal device. It may be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, apparatus, or device. The computer-readable storage medium provides a storage space, and this storage space stores the operating system of the terminal. Moreover, in this storage space, one or more instructions suitable for being loaded and executed by a processor are also stored, and these instructions may be one or more computer programs (including program codes). It should be noted that more specific examples of the computer-readable storage medium here include: an electrical connection having one or more wires, a portable disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, an optical fiber, a portable compact disk read-only memory, an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0084] The computer-readable storage medium also includes a data signal propagated in a baseband or as part of a carrier wave, which carries the readable program code. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The readable storage medium can also be any readable medium other than the readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium can be transmitted using any appropriate medium, including but not limited to wireless, wired, optical fiber cable, radio frequency, etc., or any suitable combination of the above.

[0085] The program code for performing the operations of the present invention can be written in any combination of one or more programming languages. The programming languages include object-oriented programming languages such as Java, C++, etc., and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computing device, partially on the user's device, executed as an independent software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user's computing device through any type of network, including a local area network or a wide area network, or can be connected to an external computing device (e.g., by using an Internet service provider to connect through the Internet).

[0086] One or more instructions stored in the computer-readable storage medium can be loaded and executed by a processor to implement the corresponding steps of the method for optimizing the multi-input multi-output communication rate based on an airborne IRS in the above embodiments; one or more instructions in the computer-readable storage medium are loaded and executed by the processor in the following steps: Construct an IRS-assisted millimeter-wave MIMO communication model including 1 remote base station, a uniform linear array, and B IRS units; construct an air-to-ground channel model based on the obtained IRS-assisted millimeter-wave MIMO communication model; construct a joint optimization problem of beamforming, phase shift design, and UAV deployment based on the obtained air-to-ground channel model; based on the obtained joint optimization problem, use an iterative optimization algorithm to perform iterative solution through quadratic transformation technology, Lagrange multiplication, and distributed discrete-time convex optimization algorithm to obtain a closed-form solution of the optimal beamforming and phase shift design strategy; optimize the UAV deployment based on the obtained closed-form solution of the optimal beamforming and phase shift design strategy to maximize the sum of the achievable weighted data rates of the MIMO communication based on the airborne IRS and complete the optimization of the multi-input multi-output communication rate.

[0087] In each of the embodiments provided in the present application, the database involved may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., and is not limited thereto. In each of the embodiments provided in the present application, the processor involved may be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., and is not limited thereto.

[0088] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Usually, the components described and shown in the accompanying drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed present invention, but merely represents selected embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0089] The technical effects of the present invention will be described in detail below in combination with simulations.

[0090] This experiment simulates the method for maximizing the sum of achievable weighted data rates of MIMO communication based on an airborne IRS of an unmanned aerial vehicle and an existing mechanism with the same network parameters to verify the superiority of the method of the present invention. The specific steps are as follows: The same network parameters are: The number of ULA antenna elements is ; The number of IRS units is ; The number of IRS elements is ; The number of users is ; The maximum transmit power is watts; ; ; and ; The users are randomly distributed within the range of ; The noise power is . The simulation results are the average values after 1000 times.

[0091] The performance of the present invention is compared with Comparative Scheme 1 [1], Comparative Scheme 2 [2], and Comparative Scheme 3 [3]. The specific comparison literature is as follows. In addition, the scheme for optimizing beamforming and deployment is used as a benchmark for comparison.

[0092] [1] X.Zhang, H.Zhang, W.Du, K.Long and A.Nallanathan, “IRS empowered drone wireless communication with resource allocation, reflecting design and trajectory optimization,” IEEE Trans.Wireless Commun , vol.21, no.10, pp.7867 - 7880, Oct.2022; [2] Y.Cao, T.Lv and W.Ni, “Intelligent reflecting surface aided multi - user wave communications for coverage enhancement,” Proc.IEEE Int.Symp.Pers.Indoor Mobile Radio Commun.(PIMRC) , pp.1 - 6, London, UK, Oct.2020; [3] Y.Wang, Z.Lian, Y.Wang, Y.Su, B.Jin and Z.Zhang, “Geometry - based UAV - MIMO channel model for intelligent reflecting surface - assisted communication systems,” IEEE Trans.Veh.Technol. , vol.73, no.1, pp.14 - 27, Jan.2024.

[0093] The performance of the present invention is compared with Comparative Scheme 1, Comparative Scheme 2, Comparative Scheme 3 and the Benchmark Scheme, as Figure 4 , Figure 5 , Figure 6 shown.

[0094] Please refer to Figure 4, which shows the curves of the sum of achievable weighted data rates varying with the number of cellular users in different scenarios. We find that the sum of achievable weighted data rates of all scenarios increases significantly with the increase in the number of cellular users. Specifically, compared with Comparative Scheme 1, by increasing the number of IRS units carried by the UAV, the sum of achievable weighted data rates can be increased by 122.3%. Secondly, compared with Comparative Scheme 2, on the premise of the same number of IRSs, by introducing the air-ground integrated cooperation mechanism, the sum of achievable weighted data rates can be increased by 93.5%. Thirdly, for Comparative Scheme 3, optimizing the deployment of multiple UAVs can significantly increase the sum of achievable weighted data rates. Therefore, the proposed scheme demonstrates good transmission performance in both sparse-user and dense-cellular-user scenarios.

[0095] Please refer to Figure 5 , which gives the curves of the sum of achievable weighted data rates varying with the number of IRS elements in different scenarios. From Figure 5 it can be seen that the sum of achievable weighted data rates of all scenarios shows an increasing trend with the increase in the number of IRS elements. This result indicates that increasing the number of IRSs and the number of elements in each IRS can significantly improve the transmission performance of the air-ground integrated network. However, it should be noted that increasing the number of IRS elements will also lead to an increase in algorithm complexity. When the number of IRS elements is large, even if the number of IRS elements is increased, the sum of achievable weighted data rates cannot be significantly improved. In this case, there is a trade-off between the number of IRS elements and algorithm complexity in terms of the sum of achievable weighted data rates. Optimizing the sum of achievable weighted data rates considering both the number of IRS elements and algorithm complexity is an important issue for future research.

[0096] Please refer to Figure 6 , which shows the variation of the sum of achievable weighted data rates with the maximum transmit power. It can be seen from the figure that the sum of achievable weighted data rates of all comparative scenarios increases with the increase in . However, the upward trend of the sum of achievable weighted data rates gradually slows down with the increase in the maximum transmit power. In particular, when the maximum transmit power is higher than 7 watts, even if we increase the maximum transmit power, the transmission performance of the air-ground integrated network cannot be improved. The reason is that the base station optimizes the transmit power during data transmission. In addition, no matter how much the transmit power is increased, the sum of achievable weighted data rates of the proposed scheme is always higher than that of Comparative Scheme 1, Comparative Scheme 2, and Comparative Scheme 3. This result is consistent with the simulation results of Figure 4 and Figure 5 , thus verifying the effectiveness of the proposed scheme from another aspect.

[0097] In summary, a method and system for optimizing the communication rate of multiple-input multiple-output based on an airborne IRS of an unmanned aerial vehicle according to the present invention adopt an air-ground integration and a joint MIMO technology, significantly improving the network communication quality and performance. Then, the optimization problem of the sum of the achievable weighted data rates is transformed into an optimization problem related to beamforming, phase shift design, and multi-unmanned aerial vehicle deployment optimization, with the goal of maximizing the sum of the achievable weighted data rates. By combining quadratic variation, constraint relaxation technology, and a distributed discrete-time convex optimization algorithm for iterative solution, the goal of maximizing the sum of the achievable weighted data rates is achieved.

[0098] The above content is only to illustrate the technical idea of the present invention and cannot be used to limit the protection scope of the present invention. Any modification made on the basis of the technical solution according to the technical idea proposed by the present invention falls within the protection scope of the claims of the present invention.

Claims

1. A multi-input multi-output communication rate optimization method based on unmanned aerial vehicle IRS, characterized in that: The following steps are involved: The construction includes 1 remote base station, uniform linear array, and B An IRS-assisted millimeter-wave MIMO communication model with 1 IRS unit; An air-to-ground channel model is constructed based on the obtained IRS-assisted millimeter-wave MIMO communication model; Based on the obtained air-to-ground channel model, a joint optimization problem of beamforming, phase shift design and UAV deployment is constructed; Based on the obtained joint optimization problem, an iterative optimization algorithm is used to iteratively solve the problem through quadratic transformation technology, Lagrange multiplication and distributed discrete-time convex optimization algorithm to obtain the closed-form solution of the optimal beamforming and phase shift design strategy. The closed-form solution based on the obtained optimal beamforming and phase shift design strategy optimizes the UAV deployment, maximizes the sum of achievable weighted data rates of MIMO communication based on UAV-borne IRS, and completes the multiple-input multiple-output communication rate optimization.

2. The multi-input multi-output communication rate optimization method based on unmanned aerial vehicle IRS according to claim 1 is characterized in that: In the IRS-assisted millimeter-wave MIMO communication model, the uniform linear array includes U Cellular users and A antenna units, defining a collection of cellular users, antenna units, and smart reflective surfaces , and ; Assume the three-dimensional coordinate position of BS , No. b IRS unit locations and u Cellular user locations , is a fixed value; let the reference distance between BS and IRS unit be , determine the u The Euclidean distance between a cellular user and the IRS unit , each smart reflective surface has C elements, define .

3. The multi-input multi-output communication rate optimization method based on unmanned aerial vehicle IRS according to claim 2 is characterized in that: No. u The Euclidean distance between a cellular user and the IRS unit for: in, Respectively u The X-axis, Y-axis and Z-axis coordinates of each cellular user, Respectively b The X, Y, and Z coordinates of each IRS unit.

4. The multi-input multi-output communication rate optimization method based on unmanned aerial vehicle IRS according to claim 1 is characterized in that: The specific steps of constructing the air-to-ground channel model are as follows: Determine the path loss in each channel for air-to-ground scenarios, considering both line-of-sight and non-line-of-sight propagation conditions and ; Determine the u The desired signal of a cellular user and u The total signal received by cellular users , and the air-to-ground channel model is constructed.

5. The multi-input multi-output communication rate optimization method based on unmanned aerial vehicle IRS according to claim 4 is characterized in that: Path loss and They are: in, Indicates BS and b Channels between IRS units; Indicates b IRS unit and u Channels between cellular users; represents the path loss index from BS to IRS; It means that the path loss increases with the distance; Represents the path loss exponent per unit distance; and denote the Ricean channel factors of the two links respectively; and Indicates the LoS component; and represents the NLoS component that follows a complex Gaussian distribution with mean 0 and variance 1; No. u The desired signal of a cellular user for: in, Indicates the b The phase shift matrix of an IRS, represents the reflection coefficient, represents the phase shift, represents the precoding of signals sent to different cellular users, Indicates that the BS sends the pre-processed u The signal of a cellular user; No. u The total signal received by cellular users for: in, represents noise, Indicates that the output power at the BS must not exceed the predetermined maximum value. is the maximum transmit power, represents the number of IRS, Indicates b IRS unit and u The channels between cellular users, Indicates b Phase shift matrix of IRS The conjugate matrix of Indicates BS and b channels between IRS units, Indicates u The transmit power vector of each cellular user is Indicates that the BS sends the pre-processed u Cellular user signals, represents the number of cellular users, j Indicates u Cellular users occupy the same channel j Cellular users, Indicates j The transmit power vector of each cellular user is Indicates that the BS sends the pre-processed j Cellular user signals, Indicates u The noise of a cellular user.

6. The multi-input multi-output communication rate optimization method based on unmanned aerial vehicle IRS according to claim 1 is characterized in that: The joint optimization problem of beamforming, phase shift design and UAV deployment is constructed as follows: Determine u Signal-to-interference-noise ratio for each cellular user ; The objective function of the joint optimization problem of beamforming, phase shift design and UAV deployment; as well as the objective function and optimization conditions of the joint optimization problem of beamforming, phase shift design and UAV deployment, the joint optimization problem of beamforming, phase shift design and UAV deployment is constructed.

7. The multi-input multi-output communication rate optimization method based on unmanned aerial vehicle IRS according to claim 6 is characterized in that: The objective function and optimization conditions of the joint optimization problem of beamforming, phase shift design and UAV deployment are: in, For the u The signal-to-interference-to-noise ratio of a cellular user is is the weight factor, The Lagrangian dual transformation introduced for the u The auxiliary vectors of cellular users, is the transmission power of BS, is the maximum transmit power of the BS, For the b IRS c indivual The phase of the element, is a continuous phase, For the b IRS, For the IRS c elements, Respectively b The minimum X and Y coordinates of an IRS unit. Respectively b The X-axis and Y-axis coordinates of each IRS unit, Respectively b The maximum value of the X and Y coordinates of an IRS unit.

8. The multi-input multi-output communication rate optimization method based on unmanned aerial vehicle IRS according to claim 1 is characterized in that: The maximum sum of achievable weighted data rates for MIMO communication based on UAV-based IRS is: The optimization problem P1 is decoupled into the beamforming optimization problem P2, the phase shift design optimization problem P3, and the multi-UAV position deployment optimization problem P4. Problems P2 and P3 are solved by quadratic changes and constraint relaxation techniques, and the multi-UAV position deployment optimization problem P4 is solved by a distributed discrete-time convex optimization algorithm.

9. The multi-input multi-output communication rate optimization method based on unmanned aerial vehicle IRS according to claim 8 is characterized in that: For the beamforming optimization problem P2, we re-set , and transform it into a multi-ratio fractional programming problem, and then use the quadratic transformation technique to obtain the objective function of the optimization problem ,set up is a The concave differentiable function derives the optimal solution , the optimal solution is obtained by Lagrange multiplication , under a given power constraint, the optimal solution is obtained ; For the phase shift design optimization problem P3, Perform phase adjustment, diagonalization of channel vectors, and introduction of auxiliary vectors The corresponding quadratic transformation formula is obtained, and the optimal solution is obtained according to the Lagrange multi-gradient method. ; Through the constraint relaxation method, a new objective function form is obtained: , using the Lagrangian bidirectional decomposition method, constructing the Lagrangian function and the dual function, and obtaining the optimal solution , convert the problem into a semidefinite programming problem, and use the chain rule to obtain the optimal solution ; For the multi-UAV position deployment optimization problem P4, the time Considered as number of iterations , initialize the b IRS unit coordinates, let the randomly generated u User coordinates , ,definition , the sum of the Euclidean distances from all cellular users to the IRS installed on the drone , calculate its gradient value; update the corresponding coordinates according to the rules; set the square norm of the error term to , define the iteration threshold as , find the optimal solution and .

10. A multi-input multi-output communication rate optimization system based on unmanned aerial vehicle IRS, characterized in that: include: Building blocks, including 1 remote base station, uniform linear array, and B An IRS-assisted millimeter-wave MIMO communication model with 1 IRS unit; A model module constructs an air-to-ground channel model based on the obtained IRS-assisted millimeter-wave MIMO communication model; The optimization module constructs the joint optimization problem of beamforming, phase shift design and UAV deployment based on the obtained air-to-ground channel model; The iterative module uses an iterative optimization algorithm based on the obtained joint optimization problem to iteratively solve the problem through quadratic transformation technology, Lagrange multiplication and distributed discrete-time convex optimization algorithm to obtain the closed-form solution of the optimal beamforming and phase shift design strategy; The output module optimizes the UAV deployment based on the closed-form solution of the optimal beamforming and phase shift design strategy, maximizes the sum of the achievable weighted data rates of MIMO communication based on the UAV-mounted IRS, and completes the multi-input multi-output communication rate optimization.

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