Omnidirectional intelligent metasurface-assisted 5G AeroMACS design method

Abstract

The invention relates to an omni-directional intelligent metasurface assisted 5G AeroMACS design method, belongs to the technical field of airport scene communication, solves the problems of signal shielding and transmission blind areas in the prior art, and comprises the following steps: step S0, establishing an omni-directional intelligent metasurface assisted 5G AeroMACS, including setting a base station, an omni-directional intelligent metasurface and two airport communication terminals; the method comprises the following steps: S1, constructing an omnidirectional intelligent metasurface-assisted 5G AeroMACS model; s2, establishing an optimization problem model and setting constraint conditions by taking the minimization of the total power consumption of the system as an optimization target; s3, converting and solving the optimization problem model after the constraint conditions are set to obtain an optimization scheme for minimizing the total power consumption of the system; and S4, using the optimization scheme for minimizing the total power consumption of the system to set the 5G AeroMACS of the airport, and providing the airport scene mobile communication service.

Description

Technical Field

[0001] The present invention relates to the field of airport surface communication technology, and in particular to a 5G AeroMACS design method assisted by an omnidirectional intelligent metasurface. Background Art

[0002] Traditional airport surface communication systems rely on fixed base stations and antennas. However, in the airport environment, a large number of obstructions such as bridges and terminal buildings will cause signal transmission to be blocked, affecting communication quality and coverage. Faced with the complex and changing airport environment, these fixed facilities cannot flexibly deal with obstruction and interference problems, resulting in unstable communication quality. Therefore, there is an urgent need for a technical means to overcome these physical obstacles and significantly improve communication efficiency.

[0003] The Chinese invention patent application, publication number CN113259946A, invention name "ground-to-air full coverage power control and protocol design method based on centralized array antenna", discloses a ground-to-air full coverage power control and protocol design method, which optimizes the three-dimensional layout and cruising trajectory of two drones, and adjusts the transmission power of ground machine communication equipment at the same time, and combines the spatial data compression process with the energy domain multi-antenna cooperative transmission process for transmission. However, first of all, the application scenario of drones is far from the complex obstruction of conventional large airports. In addition, the disclosure does not involve how to solve the problem of a large number of obstructions in airports causing signal transmission obstruction.

[0004] The Chinese invention patent application, publication number CN105722101A, titled "An energy-saving strip coverage relay station deployment method", discloses a method for deploying relay stations for full coverage of strip areas on airport runways, wherein a long strip area is divided into a number of sub-strip areas, and relay stations with disc coverage areas are used for coverage. However, a large number of relay stations need to be set up in the disclosure, and their mobility is not enough to cope with rapidly changing environmental conditions, and the deployment and maintenance costs of a large number of relay stations are very high.

[0005] Therefore, existing communication systems face the following major problems when facing complex airport environments: (1) Communication blind spots and dead angles caused by signal blocking and multipath effects limit communication coverage, affecting ground services and flight safety; (2) Fixed base stations and antennas are difficult to adjust dynamically and cannot adapt to rapidly changing environments, resulting in fluctuations in communication quality and unstable connections; (3) In scenarios with high-density airport communication terminals, spectrum resources are limited and it is difficult to meet the high-quality communication needs of large-scale airport communication terminals. Summary of the invention

[0006] In order to solve the above problems, an omnidirectional intelligent metasurface-assisted 5G AeroMACS (Aviation 5G Airport Broadband Mobile Communication System) design method is provided according to the embodiment of the present invention to achieve comprehensive optimization of communication data transmission. By intelligently adjusting the phase of the omnidirectional intelligent metasurface, allocating and transmitting input and output signals, it is ensured that the omnidirectional coverage of the signal and the minimization of the total power consumption of the system are achieved while meeting the minimum rate requirements of all airport communication terminals, thereby improving the continuity and stability of communication.

[0007] According to an embodiment of the present invention, a 5G AeroMACS design method assisted by an omnidirectional intelligent metasurface is provided, comprising the following steps: Step S0, establishing an omnidirectional intelligent metasurface-assisted 5G AeroMACS, including setting up a base station, an omnidirectional intelligent metasurface and two airport communication terminals , where the two airport communication terminals A reflective airport communication terminal for receiving reflected signals is included, which is located on both sides of the omnidirectional smart metasurface and receiving the transmission signal of the transmitting airport communication terminal , and the base station is set to have antennas, and each airport communication terminal is equipped with an antenna. The omnidirectional intelligent super surface has Reflection unit; Step S1, constructing an omnidirectional intelligent metasurface-assisted 5G AeroMACS model based on the established omnidirectional intelligent metasurface-assisted 5G AeroMACS; Step S2, taking minimization of the total system power consumption of the constructed omnidirectional intelligent metasurface-assisted 5G AeroMACS model as the optimization goal, establishing an optimization problem model and setting constraints; Step S3, converting and solving the optimization problem model after setting the constraint conditions to obtain an optimization solution for minimizing the total power consumption of the system; Step S4, using the optimization solution for minimizing the total power consumption of the system to set up the 5G AeroMACS at the airport to provide mobile communication services at the airport; Wherein, the step S1 specifically includes: Step S1.1, establish a reflection beamforming matrix of the reflection signal. The wireless signal is incident from the base station to the reflection unit of the omnidirectional intelligent metasurface and is divided into a reflection signal and a transmission signal. The reflection beamforming matrix of the reflection signal is:

[0008] in, represents the reflection beamforming matrix of the reflected signal, represents the first diagonal elements, ,in represents the phase shift of the reflected beam, represents the actual magnitude of the reflection coefficient, is the imaginary unit, The first Reflection unit; Step S1.2, establishing a transmission beamforming matrix for the transmission signal:

[0009] in, represents the transmit beamforming matrix of the transmitted signal, represents the first diagonal elements, ,in represents the phase offset of the transmission beam, represents the actual magnitude of the transmission coefficient; Step S1.3, construct a channel from the base station to the omnidirectional intelligent metasurface and a channel from the omnidirectional intelligent metasurface to the airport communication terminal, let , , They represent the channel from the base station to the omnidirectional smart metasurface, the channel from the omnidirectional smart metasurface to the reflective airport communication terminal, and the channel from the omnidirectional smart metasurface to the transmitting airport communication terminal, and are modeled as Rician fading channels:

[0010] in, From omnidirectional intelligent metasurface to airport communication terminal The channel, , represents the distance between the base station and the omnidirectional smart metasurface, represents the distance between the omnidirectional smart metasurface and the airport communication terminal; represents the path loss index between the base station and the omnidirectional smart metasurface, and represents the path loss index between the omnidirectional smart metasurface and the airport communication terminal; represents the Rice coefficient between the base station and the omnidirectional smart metasurface, and represents the Rice coefficient between the omnidirectional smart metasurface and the airport communication terminal, Indicates the reference distance The path loss parameter when represents the deterministic Line of Sight (LoS) component from the base station to the omnidirectional smart metasurface, From omnidirectional smart metasurfaces to reflective airport communication terminals The LoS component, Represents the non-line of sight (NLoS) component from the base station to the omnidirectional smart metasurface, From omnidirectional smart metasurfaces to reflective airport communication terminals The NLoS component of Step S1.4, constructing an omnidirectional intelligent metasurface-assisted 5G AeroMACS model, an airport communication terminal The received signal is

[0011] in, Indicates the base station for the transmission airport communication terminal The active beamforming vector of Indicates the base station for the reflection airport communication terminal The active beamforming vector of Indicates that the base station transmits to the airport communication terminal The transmission signal, Indicates that the base station reflects the airport communication terminal The transmission signal, For airport communication terminals The additive Gaussian white noise at has a mean of 0 and a variance of , From omnidirectional intelligent metasurface to airport communication terminal The conjugate transposed vector of the channel at , represents the reflection beamforming matrix of the reflected signal or the transmission beamforming matrix of the transmitted signal, Airport communication terminal The communication rate is

[0012] Among them, Indicates reflective airport communication terminal When Indicates transmission airport communication terminal On the contrary, Indicates transmission airport communication terminal When Indicates reflective airport communication terminal , Indicates the airport communication terminal at the base station The active beamforming vector of .

[0013] Optionally, the constraint conditions in step S2 are as follows:

[0014] in, represents the phase shift of the reflected beam or the transmitted beam, The omnidirectional intelligent super surface The actual amplitude of the reflection coefficient or transmission coefficient of each reflection unit, , Indicates the communication rate that meets the minimum service requirements. The omnidirectional intelligent super surface The energy distribution coefficient of the reflected signal for each reflection unit is: The omnidirectional intelligent super surface The energy distribution coefficient of each reflection unit for the transmitted signal.

[0015] Optionally, the step S3 specifically includes: Step S3.1, converting the optimization problem model after setting the constraint conditions, the converted optimization problem model is:

[0016] in, Indicates the base station for the transmission airport communication terminal The Hermitian matrix of the active beamforming vector, Indicates the base station for the reflection airport communication terminal The Hermitian matrix of the active beamforming vector, The Hermitian matrix representing the reflection beamforming vector or the transmission beamforming vector, represents the active beamforming matrix at the base station, represents the channel matrix from the base station to the airport communication terminal, represents the conjugate transposed matrix of the channel matrix from the base station to the airport communication terminal, A vector of diagonal elements in the Hermitian matrix representing the reflected or transmitted beamforming vector, Indicates the minimum signal-to-interference-noise ratio that needs to be met; Step S3.2, processing the non-convex optimization problem in the converted optimization problem model to obtain the processed optimization problem model:

[0017] in, express The nuclear norm of represents the first-order Taylor value of the Hermitian matrix of the reflection beamforming vector or the second-norm of the Hermitian matrix of the transmission beamforming vector at the currently obtained iteration point, represents the penalty factor, Indicates The result in the iterations, The first part of expression (6b) is transformed into the convex difference function, The second part of expression (6b) is transformed into the convex difference function, express The upper bound of express The upper bound of Step S3.3, solving the processed optimization problem model to obtain an optimization solution that minimizes the total power consumption of the system.

[0018] Optionally, the step S3.3 specifically includes: Step S3.3.1, performing an inner loop operation of a penalty-based iterative algorithm on the processed optimization problem model to obtain updated variable values; Step S3.3.2, receiving the updated variable value, performing the outer loop operation of the penalty-based iterative algorithm, and obtaining the optimization solution for minimizing the total power consumption of the system.

[0019] Optionally, the step S3.3 specifically includes: (a) Initialize the processed optimization problem model, including initializing variable values and penalty factor , set the number of iterations ; (b) Inner loop operation, using the current variable value Calculate the optimization problem model after processing to get the updated variable values ,in, Indicates The base station for the airport communication terminal obtained in the iteration The Hermitian matrix of the active beamforming vector, Indicates A vector consisting of the diagonal elements in the Hermitian matrix of the reflection or transmission beamforming vector obtained in the iteration; (c) Determine whether the value of the penalty item is lower than the set threshold , or the number of inner loops Whether the set maximum value is reached , The judgment condition of the penalty value is:

[0020] in, express The spectral norm of If one of the above conditions is met, the inner loop ends and the current new updated variable value is passed to the outer loop as the solution. Otherwise, the number of iterations is increased. , and substitute into step (b) to continue the inner loop; (d) Outer loop operation, receiving the updated variable value from the inner loop and updating the penalty factor ,in , substitute into formula (16) to determine whether the value of the penalty item is lower than the set threshold If the penalty term satisfies equation (16), the outer loop ends and the current updated variable value is used as the optimization solution to minimize the total power consumption of the system. Otherwise, the updated updated variable value and penalty factor are substituted into step (b) to continue the inner loop.

[0021] Compared with the prior art, the 5G AeroMACS design method assisted by an omnidirectional intelligent metasurface provided according to an embodiment of the present invention has at least the following advantages: 1. By modeling the omnidirectional intelligent metasurface-assisted 5G AeroMACS and using mathematical formulas to describe the optimization objectives and constraints of the modeling problem, communication optimization in complex obstruction environments can be achieved, the negative impact of obstructions can be effectively overcome, full coverage of the communication area can be achieved, the signal quality of airport surface communications can be quickly improved, and the continuity and stability of communications can be guaranteed.

[0022] 2. The proposed design method can achieve full coverage of the airport surface communication area by intelligently controlling the phase and reflection characteristics of the omnidirectional smart metasurface.

[0023] 3. Considering the complex and ever-changing environment of the airport, the intelligent metasurface can dynamically adapt to environmental changes, enhance the robustness of the system through intelligent reflection and transmission of signals, and ensure that high-quality communication services can still be provided under various uncertain conditions.

[0024] 4. Considering the high-density environmental conditions, the intelligent metasurface can effectively reduce the interference and attenuation in signal transmission, significantly improve the quality of communication signals and the stability of the system, ensure reliable communication in complex dynamic environments, and minimize the total power consumption of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. The features and advantages of the present invention can be more clearly understood by referring to the drawings. The drawings are schematic and should not be understood as limiting the present invention in any way. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0026] Figure 1A flowchart of a 5G AeroMACS design method assisted by an omnidirectional smart metasurface provided according to an embodiment of the present invention.

[0027] Figure 2 Schematic diagram of an omnidirectional intelligent metasurface-assisted 5G AeroMACS model constructed according to the omnidirectional intelligent metasurface-assisted 5G AeroMACS design method provided in an embodiment of the present invention.

[0028] Figure 3 A flowchart of the post-processing optimization model solving steps of the omnidirectional smart metasurface-assisted 5G AeroMACS design method provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0029] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present invention and the features in the embodiments can be combined with each other without conflict.

[0030] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0031] The following reference Figures 1 to 3 , a 5G AeroMACS design method assisted by an omnidirectional intelligent metasurface provided according to an embodiment of the present invention is described in detail.

[0032] Explanation of symbols: Indicates the total number of antennas of the base station; represents the number of reflective units of the omnidirectional smart metasurface; Reflection airport communication terminal representing receiving reflected signal; represents a transmitting airport communication terminal receiving the transmission signal; Indicates the airport communication terminal, ; A reflection beamforming matrix representing the reflected signal; represents the first diagonal elements; The omnidirectional intelligent super surface The actual amplitude of the reflection coefficient of each reflection unit; The omnidirectional intelligent super surface A reflection unit for the energy distribution coefficient of the reflected signal; Indicates the reflected beam phase offset; represents the imaginary unit; A transmit beamforming matrix representing a transmit signal; represents the first diagonal elements; The omnidirectional intelligent super surface The actual magnitude of the transmission coefficient of each reflection unit; The omnidirectional intelligent super surface The energy distribution coefficient of the reflection unit for the transmission signal; represents the phase offset of the transmission beam; represents the channel from the base station to the omnidirectional smart metasurface; From omnidirectional smart metasurfaces to reflective airport communication terminals Channel; From omnidirectional intelligent metasurface to transmission airport communication terminal Channel; From omnidirectional intelligent metasurface to airport communication terminal Channel; From omnidirectional intelligent metasurface to airport communication terminal The conjugate transposed vector of the channel; represents the distance between the base station and the omnidirectional smart metasurface; Represents omnidirectional intelligent metasurface and airport communication terminal The distance between represents the path loss index between the base station and the omnidirectional smart metasurface; represents the path loss index between the omnidirectional smart metasurface and the airport communication terminal; represents the Rice coefficient between the base station and the omnidirectional smart metasurface; represents the Rice coefficient between the omnidirectional smart metasurface and the airport communication terminal; Indicates the reference distance Path loss parameters when ; Represents the deterministic Line of Sight (LoS) component from the base station to the omnidirectional smart metasurface; From omnidirectional smart metasurfaces to reflective airport communication terminals The LoS component of Represents the non-line of sight (NLoS) component from the base station to the omnidirectional smart metasurface; From omnidirectional smart metasurfaces to reflective airport communication terminals The NLoS component of Indicates airport communication terminal The signal received at Indicates the airport communication terminal at the base station The active beamforming vector of Indicates the base station for the transmission airport communication terminal The active beamforming vector of Indicates the base station for the reflection airport communication terminal The active beamforming vector of Indicates that the base station sends the airport communication terminal The transmission signal; Indicates that the base station transmits to the airport communication terminal The transmission signal; Indicates that the base station reflects the airport communication terminal The transmission signal; Indicates airport communication terminal Additive white Gaussian noise at ; Indicates airport communication terminal The variance of the additive white Gaussian noise at ; A reflection beamforming matrix representing a reflection signal or a transmission beamforming matrix representing a transmission signal; Indicates airport communication terminal The communication rate; Indicates the phase shift of the reflected beam or the transmitted beam; a Hermitian matrix representing a reflection beamforming vector or a Hermitian matrix representing a transmission beamforming vector; represents the transmission coefficient vector and the reflection coefficient vector; Indicates the communication from the base station to the airport terminal The channel matrix between A vector consisting of the diagonal elements of the Hermitian matrix representing the reflected or transmitted beamforming vector; The omnidirectional intelligent super surface A reflection unit for the energy distribution coefficient of the reflected signal or the transmitted signal; The omnidirectional intelligent super surface The actual amplitude of the reflection coefficient or transmission coefficient of each reflection unit; represents the active beamforming matrix at the base station; Indicates the communication rate that meets the minimum service requirements; Indicates the minimum signal-to-interference-noise ratio that needs to be met; Indicates the base station for the transmission airport communication terminal The Hermitian matrix of the active beamforming vector; Indicates the base station for the reflection airport communication terminal The Hermitian matrix of the active beamforming vector; Indicates the communication from the base station to the airport terminal The conjugate transpose matrix of the matrix between; represents the convex difference function into which the first part of constraint (6b) is transformed; represents the convex difference function into which the second part of constraint (6b) is transformed; express The upper bound of express The upper bound of represents the penalty factor; Indicates The product of the beamforming vector of the omnidirectional smart metasurface obtained in the iteration and its conjugate transpose; express The eigenvector corresponding to the maximum eigenvalue of ; Indicates setting threshold; Indicates A vector consisting of the diagonal elements in the Hermitian matrix of the reflection or transmission beamforming vector obtained in the iteration; Indicates The base station for the airport communication terminal obtained in the iteration The Hermitian matrix of the active beamforming vector .

[0033] As an emerging communication auxiliary technology, the omnidirectional intelligent metasurface is composed of a large number of controllable reflection units, which can flexibly adjust the phase and amplitude of the incident electromagnetic wave to realize the intelligent reflection and transmission of the signal. Through reasonable design and deployment, the omnidirectional intelligent metasurface can redirect the blocked signal to the target area, effectively make up for the blind spot of signal coverage, and significantly improve the overall performance of the communication system. Specifically, the omnidirectional intelligent metasurface can divide the incident signal into a transmission signal and a reflection signal, and transmit them to both sides of the space around the surface respectively, to achieve omnidirectional coverage of the signal. At the same time, by intelligently adjusting the phase of the reflection unit, the omnidirectional intelligent metasurface can optimize the signal transmission path, reduce the total power consumption of the system, and improve the continuity and stability of communication while meeting the minimum rate requirements of the airport communication terminal.

[0034] like Figures 1 to 3 As shown, an omnidirectional intelligent metasurface-assisted 5G AeroMACS design method provided according to an embodiment of the present invention includes the following steps.

[0035] Step S0, establish an omnidirectional intelligent metasurface-assisted 5G AeroMACS (aviation 5G airport broadband mobile communication system).

[0036] like Figure 2 As shown in FIG. 1 , the omnidirectional intelligent metasurface-assisted 5G AeroMACS established in this step includes setting up a base station, an omnidirectional intelligent metasurface, and two airport communication terminals. Among them, the reflective airport communication terminal that receives the reflected signal and receiving the transmission signal of the transmitting airport communication terminal They are located on both sides of the omnidirectional intelligent metasurface. The base station is set to have The application scenario of this model is the airport scene, where there are a large number of obstructing buildings. There is no direct path between the airport communication terminal and the base station. The omnidirectional intelligent metasurface is deployed between the unobstructed non-blind area and the obstructed blind area. The deployed omnidirectional intelligent metasurface has A reflective unit.

[0037] Step S1, based on the omnidirectional intelligent metasurface-assisted 5G AeroMACS established in step S0, construct an omnidirectional intelligent metasurface-assisted 5G AeroMACS model.

[0038] Step S1.1, establish a reflection beamforming matrix for the reflection signal. The wireless signal is incident from the base station to the reflection unit of the omnidirectional intelligent metasurface and is divided into a reflection signal and a transmission signal. Relevant data and information can be obtained from the database of the base station. The reflection beamforming matrix for the reflection signal is:

[0039] Among them, the diagonal elements in the reflection beamforming matrix are ,in represents the phase shift of the reflected beam, represents the actual magnitude of the reflection coefficient, The omnidirectional intelligent super surface A reflective unit.

[0040] Step S1.2, establish the transmission beamforming matrix of the transmission signal as follows:

[0041] Among them, the diagonal elements in the transmit beamforming matrix are ,in represents the phase offset of the transmission beam, Represents the actual magnitude of the transmission coefficient.

[0042] In addition, the amplitude adjustment reflected and transmitted by each reflection unit satisfies the law of conservation of energy, that is, .

[0043] Step S1.3, construct the channel from the base station to the omnidirectional intelligent metasurface and the channel from the omnidirectional intelligent metasurface to the airport communication terminal. In this system model, the direct communication link between the base station and the airport communication terminal is blocked by obstacles, so the omnidirectional intelligent metasurface is used as a relay. , , They represent the channel from the base station to the omnidirectional smart metasurface, the channel from the omnidirectional smart metasurface to the reflective airport communication terminal, and the channel from the omnidirectional smart metasurface to the transmitting airport communication terminal, and are modeled as Rician fading channels, which can be expressed as

[0044] in, represents the distance between the base station and the omnidirectional smart metasurface, Represents omnidirectional intelligent metasurface and airport communication terminal The distance between represents the path loss index between the base station and the omnidirectional smart metasurface, represents the path loss index between the omnidirectional smart metasurface and the airport communication terminal; represents the Rice coefficient between the base station and the omnidirectional smart metasurface, represents the Rice coefficient between the omnidirectional smart metasurface and the airport communication terminal; Indicates the reference distance Path loss parameters when ; Represents the deterministic Line of Sight (LoS) component from the base station to the omnidirectional smart metasurface; From omnidirectional smart metasurfaces to reflective airport communication terminals The LoS component of Represents the non-line of sight (NLoS) component from the base station to the omnidirectional smart metasurface; From omnidirectional smart metasurfaces to reflective airport communication terminals The NLoS component of represents the channel from the base station to the omnidirectional smart metasurface; From omnidirectional intelligent metasurface to airport communication terminal The channel, .

[0045] Step S1.4, construct an omnidirectional intelligent metasurface-assisted 5G AeroMACS model. Airport communication terminal The signal received at

[0046] in, Indicates the base station for the transmission airport communication terminal The active beamforming vector of Indicates the base station for the reflection airport communication terminal The active beamforming vector of Indicates that the base station transmits to the airport communication terminal The transmission signal, Indicates that the base station reflects the airport communication terminal The transmission signal, For airport communication terminals The additive Gaussian white noise at has a mean of 0 and a variance of ; From omnidirectional intelligent metasurface to airport communication terminal The conjugate transposed vector of the channel at , represents the conjugate transpose symbol, represents the reflection beamforming matrix of the reflected signal or the transmission beamforming matrix of the transmitted signal. Indicates that the base station sends the airport communication terminal The transmission signal ,and .

[0047] Therefore, the airport communication terminal The communication rate can be expressed as

[0048] Among them, Indicates reflective airport communication terminal When Indicates transmission airport communication terminal On the contrary, Indicates transmission airport communication terminal When Indicates reflective airport communication terminal , Indicates the airport communication terminal at the base station The active beamforming vector of .

[0049] Step S2, taking minimizing the total power consumption of the communication system in the constructed omnidirectional intelligent metasurface-assisted 5G AeroMACS model as the optimization goal, establishing an optimization problem model and setting constraints.

[0050] The main goal of the embodiments of the present invention is to minimize the total power consumption of the base station by jointly optimizing the active beamforming of the base station and the passive transmission beamforming and reflection beamforming of the omnidirectional smart metasurface, while meeting the service quality requirements of the airport communication terminal and the phase shift constraints of the omnidirectional smart metasurface. The optimization problem model is specifically:

[0051] in, represents the phase shift of the reflected beam or the transmitted beam, The omnidirectional intelligent super surface The actual amplitude of the reflection coefficient or transmission coefficient of each reflection unit, Indicates 1 to The number set, , Indicates the communication rate that meets the minimum service requirements. The omnidirectional intelligent super surface The energy distribution coefficient of the reflected signal for each reflection unit is: The omnidirectional intelligent super surface The energy distribution coefficient of the transmission signal for each reflection unit. (5b) represents the service quality requirement of the airport communication terminal, (5c) represents the phase shift constraint of the omnidirectional smart metasurface, and (5d) and (5e) represent the energy distribution constraints of the omnidirectional smart metasurface reflection and transmission. It means that the omnidirectional intelligent super surface has A reflective unit.

[0052] Step S3, converting and solving the optimization problem model after setting the constraint conditions, and obtaining an optimization solution for minimizing the total power consumption of the system.

[0053] Step S3.1, converting the optimization problem model after setting the constraint conditions.

[0054] In order to better solve the optimization problem, (5a) is converted into a more manageable form. First, the transmission coefficient vector and the reflection coefficient vector are defined as

[0055] Thus we can get

[0056] in, , Indicates the communication from the base station to the airport terminal The channel matrix between them.

[0057] Going further, define .thus, The Hermitian matrix representing the reflected or transmitted beamforming vector; Several conditions need to be met , , , , A vector of diagonal elements in the Hermitian matrix representing the reflected or transmitted beamforming vector, The omnidirectional intelligent super surface The reflection unit is the energy distribution coefficient of the reflected signal or the transmitted signal.

[0058] Similarly, define . represents the active beamforming matrix at the base station; Needs to be satisfied , Assumptions ,in, represents the minimum signal to noise ratio that needs to be met, and (4) is combined to simplify (5b) into an addition and subtraction form. Based on the above processing, the optimization problem model can be restated as

[0059] in, represents the channel matrix from the base station to the airport communication terminal, Indicates the communication from the base station to the airport terminal The conjugate transposed matrix of the channel matrix, Indicates the base station for the transmission airport communication terminal The Hermitian matrix of the active beamforming vector, Indicates the base station for the reflection airport communication terminal The Hermitian matrix of the active beamforming vector .

[0060] Step S3.2, process the non-convex optimization problem in the converted optimization problem model to obtain the processed optimization problem model. The non-convex optimization problem in the original model is converted into a convex optimization problem. Since (6b) and (6d), (6e) are all non-convex constraints, the above optimization problem model is still non-convex and cannot be directly used for solving.

[0061] Therefore, we first process the non-convex constraint (6b) to make it convex. According to the derivation, we can get

[0062] From the above, (6b) can be converted to

[0063] in, represents the first part of constraint (6b) transformed into a convex difference function, represents the convex difference function transformed from the second part of constraint (6b), represents the F-norm.

[0064] Next, the non-convex rank-one constraint (6d) is processed using the penalty method, and the constraint can be equivalently converted to

[0065] in ， Representative Matrix The nuclear norm of Representative Matrix The spectral norm of Representative Matrix No. Large singular values, Less than or equal to matrix The number of elements. It is worth noting that for , all satisfied , where if and only if The equality holds when is a rank-one matrix. Therefore, equation (10) only holds for the matrix is a rank-one matrix. Therefore, using the penalty factor , relax the constraints to the penalty term added to the objective function, and get the optimization problem model

[0066] To avoid the penalty factor Too large causes the objective function to be Dominant, in this step first choose to use a smaller initial value to initialize the penalty factor , and then gradually increase in the iterative process To a sufficiently large value, a feasible rank-one matrix is ​​finally obtained. Since the objective function and constraints (11b) and (6e) are all non-convex, the above optimization problem model is still non-convex. This implementation method uses the method of successive convex approximation to obtain the convex upper bound of the penalty term

[0067] in, represents the first-order Taylor value of the Hermitian matrix of the reflection beamforming vector or the second-norm of the Hermitian matrix of the transmission beamforming vector at the currently obtained iteration point, , Indicates The result in the iteration (i.e., the optimal solution obtained in the current iteration, i.e., The beamforming vector of the omnidirectional smart metasurface obtained in the iteration is the product of its conjugate transpose), express The eigenvector corresponding to the maximum eigenvalue of . And among them, as input for the next iteration.

[0068] Similarly, for The result of the given iteration , we can get and The upper bound of (13) (14) in, Indicates The base station for the airport communication terminal obtained in the iteration The Hermitian matrix of the active beamforming vector .

[0069] In order to solve the non-convexity problem of (6e), (6e) is directly ignored by the semi-definite relaxation method. Then, the optimization problem can be reformulated as

[0070] Step S3.3, solve the processed optimization problem model to obtain an optimization solution that minimizes the total power consumption of the system. Figure 3 This step is described in detail.

[0071] See also Figure 3 ,Now, the reformulated optimization problem is a convex semidefinite programming problem, which can be solved using CVX (convex optimization solver).,In this step, a penalty-based iterative algorithm is used to solve the above processed optimization problem model.,The specific process of step S3.3 is as follows.,The penalty-based iterative algorithm used here contains two loops.

[0072] Step S3.3.1, perform inner loop operation of penalty-based iterative algorithm on the processed optimization problem model to obtain updated variable values.

[0073] Step S3.3.2, receiving the updated variable value, performing the outer loop operation of the penalty-based iterative algorithm, and obtaining the optimization solution for minimizing the total power consumption of the system.

[0074] like Figure 3 As shown, alternatively, step S3.3 may specifically include the following steps.

[0075] (a) First, initialize the processed optimization problem model, including initializing the variable values and penalty factor , set the number of iterations .

[0076] (b) Next, perform the inner loop operation, using the current variable value Calculate the optimization problem model after processing to get the updated variable values .in, Indicates The vector consisting of the diagonal elements in the Hermitian matrix of the reflected or transmitted beamforming vector obtained in the iteration.

[0077] (c) Convergence condition judgment: whether the value of the penalty item is lower than the set threshold , or the number of inner loops Whether the set maximum value is reached .

[0078] The judgment condition of the penalty value is:

[0079] in, is a preset threshold, representing a predefined maximum constraint accuracy that violates equation (12).

[0080] If the convergence condition is met, the inner loop ends and the current updated variable value is passed to the outer loop as the solution. Otherwise, the number of iterations is increased. , and substitute it into step (b) to continue the inner loop.

[0081] (d) The outer loop receives updated variable values ​​from the inner loop , , and update the penalty factor ,in , substitute into formula (16).

[0082] If the penalty term satisfies formula (16), the algorithm terminates and obtains the optimization solution, outputting the final variable value As an optimization solution to minimize the total power consumption of the system.

[0083] If the penalty item does not satisfy condition (16), return to step (b) and substitute the updated variable value and penalty factor into the inner loop to continue the inner loop.

[0084] Therefore, with As the value of increases, formula (16) will eventually satisfy the accuracy , and output the final variable value As an optimization scheme to minimize the total power consumption of the system. The objective function is non-increasing in each iteration of the inner loop, and the optimal objective function has a lower bound. Therefore, when When approaching infinity, the iterative algorithm can converge to a stable point and obtain the variable value .

[0085] In the process of solving the processed optimization problem model, the inner loop is first optimized step by step to ensure that the local optimal solution is found under the current penalty factor conditions, and then the outer loop is entered to adjust the parameters and repeat the inner loop steps until global convergence is achieved. Through this combination of inner and outer loops, the optimization results are gradually improved, so that the system can obtain more precise control and optimization effects.

[0086] Step S4, obtain an optimization scheme for minimizing the total power consumption of the system, and use the optimization scheme for the setting of the airport communication system to provide airport surface mobile communication services. The specific operation includes adjusting the equipment configuration, optimizing the network layout and parameter settings according to the optimization scheme to ensure that the system achieves low power consumption while providing wireless communication services. After deployment, comprehensive testing and real-time monitoring are used to verify the system performance and make necessary adjustments. In this way, a 5G AeroMACS (airport surface mobile communication) system with low system total power consumption and improved communication continuity and stability can be obtained.

[0087] As described above, the 5G AeroMACS design method assisted by an omnidirectional intelligent metasurface provided in accordance with an embodiment of the present invention can establish a 5G AeroMACS architecture assisted by an omnidirectional intelligent metasurface. The system architecture utilizes the high flexibility and controllability of the omnidirectional intelligent metasurface to achieve omnidirectional coverage of communication signals through intelligent reflection and transmission of signals. Specifically, the intelligent metasurface can divide the incident signal into a transmission signal and a reflection signal, and transmit them to both sides of the surface space respectively. At the same time, by intelligently adjusting the phase of the reflection unit, the signal transmission path is optimized, the total power consumption of the system is reduced, and the continuity and stability of communication are improved under the premise of meeting the minimum rate requirements of the airport communication terminal. In addition, the omnidirectional intelligent metasurface also has the advantages of flexible deployment and low cost, and can quickly improve the coverage and signal quality of airport field communications without significantly changing the existing infrastructure. The introduction of the intelligent metasurface enables the airport communication system to dynamically adapt to environmental changes, overcome the negative effects of occlusion through intelligent reflection signals, and ensure the continuity and stability of communication. By constructing a minimum system total power consumption optimization model and adopting advanced optimization algorithms (such as successive convex approximation and penalty method), complex constraint problems can be effectively solved, the feasibility and effectiveness of the optimization solution can be ensured, and green communication can be achieved.

[0088] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.

Claims

1. The 5G AeroMACS design method assisted by an omnidirectional intelligent metasurface is characterized by: The following steps are involved: Step S0, establishing an omnidirectional intelligent metasurface-assisted 5G AeroMACS, including setting up a base station, an omnidirectional intelligent metasurface and two airport communication terminals , where the two airport communication terminals A reflective airport communication terminal for receiving reflected signals is included, which is located on both sides of the omnidirectional smart metasurface and receiving the transmission signal of the transmitting airport communication terminal , and the base station is set to have antennas, and each airport communication terminal is equipped with an antenna. The omnidirectional intelligent super surface has Reflection unit; Step S1, constructing an omnidirectional intelligent metasurface-assisted 5G AeroMACS model based on the established omnidirectional intelligent metasurface-assisted 5G AeroMACS; Step S2, taking minimization of the total system power consumption of the constructed omnidirectional intelligent metasurface-assisted 5G AeroMACS model as the optimization goal, establishing an optimization problem model and setting constraints; Step S3, converting and solving the optimization problem model after setting the constraint conditions to obtain an optimization solution for minimizing the total power consumption of the system; Step S4, using the obtained optimization solution for minimizing the total power consumption of the system to set up the 5G AeroMACS at the airport to provide mobile communication services at the airport; Wherein, the step S1 specifically includes: Step S1.1, establish a reflection beamforming matrix of the reflection signal. The wireless signal is incident from the base station to the reflection unit of the omnidirectional intelligent metasurface and is divided into a reflection signal and a transmission signal. The reflection beamforming matrix of the reflection signal is: in, represents the reflection beamforming matrix of the reflected signal, represents the first diagonal elements, ,in represents the phase shift of the reflected beam, represents the actual magnitude of the reflection coefficient, is the imaginary unit, The first Reflection unit; Step S1.2, establishing a transmission beamforming matrix for the transmission signal: in, represents the transmit beamforming matrix of the transmitted signal, represents the first m diagonal elements, ,in represents the phase offset of the transmission beam, represents the actual magnitude of the transmission coefficient; Step S1.3, construct a channel from the base station to the omnidirectional intelligent metasurface and a channel from the omnidirectional intelligent metasurface to the airport communication terminal, let , , They represent the channel from the base station to the omnidirectional smart metasurface, the channel from the omnidirectional smart metasurface to the reflective airport communication terminal, and the channel from the omnidirectional smart metasurface to the transmitting airport communication terminal, and are modeled as Rician fading channels: in, From omnidirectional intelligent metasurface to airport communication terminal The channel, , represents the distance between the base station and the omnidirectional smart metasurface, represents the distance between the omnidirectional smart metasurface and the airport communication terminal; represents the path loss index between the base station and the omnidirectional smart metasurface, and represents the path loss index between the omnidirectional smart metasurface and the airport communication terminal; represents the Rice coefficient between the base station and the omnidirectional smart metasurface, and represents the Rice coefficient between the omnidirectional smart metasurface and the airport communication terminal, Indicates the reference distance The path loss parameter when represents the deterministic line-of-sight component from the base station to the omnidirectional smart metasurface, From omnidirectional smart metasurfaces to reflective airport communication terminals The LoS component, represents the non-line-of-sight component from the base station to the omnidirectional smart metasurface, From omnidirectional smart metasurfaces to reflective airport communication terminals The NLoS component of Step S1.4, constructing an omnidirectional intelligent metasurface-assisted 5G AeroMACS model, an airport communication terminal The received signal is in, Indicates the base station for the transmission airport communication terminal The active beamforming vector of Indicates the base station for the reflection airport communication terminal The active beamforming vector of Indicates that the base station transmits to the airport communication terminal The transmission signal, Indicates that the base station reflects the airport communication terminal The transmission signal, For airport communication terminals The additive Gaussian white noise at has a mean of 0 and a variance of , From omnidirectional intelligent metasurface to airport communication terminal The conjugate transposed vector of the channel at , represents the reflection beamforming matrix of the reflected signal or the transmission beamforming matrix of the transmitted signal, Airport communication terminal The communication rate is Among them, Indicates reflective airport communication terminal When Indicates transmission airport communication terminal On the contrary, Indicates transmission airport communication terminal When Indicates reflective airport communication terminal , Indicates the airport communication terminal at the base station The active beamforming vector of .

2. The omnidirectional intelligent metasurface-assisted 5G AeroMACS design method according to claim 1, characterized in that: The constraints in step S2 are as follows: in, represents the phase shift of the reflected beam or the transmitted beam, The omnidirectional intelligent super surface The actual amplitude of the reflection coefficient or transmission coefficient of each reflection unit, , Indicates the communication rate that meets the minimum service requirements. The omnidirectional intelligent super surface The energy distribution coefficient of the reflected signal for each reflection unit is: The omnidirectional intelligent super surface The energy distribution coefficient of each reflection unit for the transmitted signal.

3. The omnidirectional intelligent metasurface-assisted 5G AeroMACS design method according to claim 2, characterized in that: The step S3 specifically includes: Step S3.1, converting the optimization problem model after setting the constraint conditions, the converted optimization problem model is: in, Indicates the base station for the transmission airport communication terminal The Hermitian matrix of the active beamforming vector, Indicates the base station for the reflection airport communication terminal The Hermitian matrix of the active beamforming vector, The Hermitian matrix representing the reflection beamforming vector or the transmission beamforming vector, represents the active beamforming matrix at the base station, represents the channel matrix from the base station to the airport communication terminal, represents the conjugate transposed matrix of the channel matrix from the base station to the airport communication terminal, A vector of diagonal elements in the Hermitian matrix representing the reflected or transmitted beamforming vector, Indicates the minimum signal-to-interference-noise ratio that needs to be met; Step S3.2, processing the non-convex optimization problem in the converted optimization problem model to obtain the processed optimization problem model: in, express The nuclear norm of represents the first-order Taylor value of the Hermitian matrix of the reflection beamforming vector or the second-norm of the Hermitian matrix of the transmission beamforming vector at the currently obtained iteration point, represents the penalty factor, Indicates The result in the iterations, The first part of expression (6b) is transformed into the convex difference function, The second part of expression (6b) is transformed into the convex difference function, express The upper bound of express The upper bound of Step S3.3, solving the processed optimization problem model to obtain an optimization solution that minimizes the total power consumption of the system.

4. The omnidirectional intelligent metasurface-assisted 5G AeroMACS design method according to claim 3, characterized in that: The step S3.3 specifically includes: Step S3.3.1, performing an inner loop operation of a penalty-based iterative algorithm on the processed optimization problem model to obtain updated variable values; Step S3.3.2, receiving the updated variable value, performing the outer loop operation of the penalty-based iterative algorithm, and obtaining the optimization solution for minimizing the total power consumption of the system.

5. The omnidirectional intelligent metasurface-assisted 5G AeroMACS design method according to claim 3, characterized in that: The step S3.3 specifically includes: (a) Initialize the processed optimization problem model, including initializing variable values and penalty factor , set the number of iterations ; (b) Inner loop operation, using the current variable value Calculate the optimization problem model after processing to get the updated variable values ,in, Indicates The base station for the airport communication terminal obtained in the iteration The Hermitian matrix of the active beamforming vector, Indicates A vector consisting of the diagonal elements in the Hermitian matrix of the reflection or transmission beamforming vector obtained in the iteration; (c) Determine whether the value of the penalty item is lower than the set threshold , or the number of inner loops Whether the set maximum value is reached , The judgment condition of the penalty value is: in, express The spectral norm of If one of the above conditions is met, the inner loop ends and the current new updated variable value is passed to the outer loop as the solution. Otherwise, the number of iterations is increased. , and substitute into step (b) to continue the inner loop; (d) Outer loop operation, receiving the updated variable value from the inner loop and updating the penalty factor ,in , substitute into formula (16) to determine whether the value of the penalty item is lower than the set threshold If the penalty term satisfies equation (16), the outer loop ends and the current updated variable value is used as the optimization solution to minimize the total power consumption of the system. Otherwise, the updated updated variable value and penalty factor are substituted into step (b) to continue the inner loop.

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