Aerial reconfigurable metasurface assisted network mobility parameter configuration method

Abstract

The application provides an over-the-air reconfigurable metasurface assisted network mobility parameter configuration method. In the method, the fixed parameters of an over-the-air intelligent metasurface network are counted and taken as inputs of an average network switching probability estimation algorithm; the total number of RIS units in a cell and the typical user moving speed are considered, a model is established with the aim of minimizing the average switching probability, and a probability estimation method is obtained through reasonable network modeling. By comparing the switching probability estimation values under different RIS unit numbers N and corresponding deployment densities, the optimal configuration parameters are obtained, so as to minimize the network switching times and reduce the switching overhead. The embodiment of the application solves the problem of how to configure the RIS unit number and the deployment density in the deployment process of the over-the-air reconfigurable metasurface assisted network to reduce the influence of frequent switching on the network performance.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and in particular to a method for configuring mobility parameters in an airborne intelligent metasurface-assisted network in sixth-generation mobile communication. Background Technology

[0002] Aerial Intelligent Reflecting Surface (A-RIS) is an evolutionary technology that deploys RIS on aerial platforms such as drones, and is considered a promising key solution for 6G and future three-dimensional full-coverage networks. By achieving software-controllable signal reflection at aerial nodes, A-RIS can dynamically reconstruct the wireless propagation environment between air and ground. Specifically, A-RIS utilizes a large number of passive reflective elements mounted on the flight platform to independently adjust the phase and amplitude of the incident signal, thereby achieving high-gain dynamic beamforming in complex three-dimensional space.

[0003] Traditional Terrestrial Intelligent Reflecting Surfaces (T-RIS) are easily obstructed by complex urban obstacles due to their fixed deployment locations and low altitudes. They cannot dynamically adapt to changes in network topology and cannot guarantee line-of-sight (LoS) links with users, resulting in limitations in handover performance optimization. Compared to traditional terrestrial RIS, A-RIS offers significant advantages such as high mobility, on-demand deployment, and a higher probability of line-of-sight links. It can significantly improve communication quality at cell edges and in obstructed areas by actively modifying air-to-ground channel characteristics. This novel air-assisted architecture provides 6G networks with greater freedom, enabling intelligent and reconfigurable three-dimensional wireless environments. Because of its low power consumption and ease of deployment, A-RIS can enhance network coverage while optimizing terminal access strategies by dynamically adjusting coverage areas.

[0004] In the highly dynamic 6G network environment, the introduction of A-RIS can effectively improve signal quality, thereby smoothing the handover process and reducing unnecessary handovers. However, due to the energy constraints of the air platform and the topology changes brought about by high mobility, there is currently a lack of mature and effective configuration methods for how to coordinate the configuration of RIS unit size and deployment density in A-RIS assisted networks to achieve optimal handover performance and mobility management. Summary of the Invention

[0005] This invention proposes a method for configuring mobility parameters of an airborne reconfigurable metasurface-assisted network. The method first statistically analyzes fixed parameters of the airborne intelligent metasurface network, including base station deployment density, airborne intelligent metasurface deployment density, base station deployment height, airborne intelligent metasurface deployment height, number of reflective elements per airborne intelligent metasurface, airborne intelligent metasurface service distance threshold, path loss factor, carrier frequency, and typical user mobility speed. Then, combining these network parameters, considering the average path gain for line-of-sight and non-line-of-sight sensing and the airborne intelligent metasurface reselection mechanism, a model is constructed using the average network handover probability as a key indicator. Finally, an estimation method for the handover probability is obtained through reasonable network modeling. By comparing the estimated values ​​of the average network handover probability under different parameters, the optimal airborne intelligent metasurface network configuration parameters for handover performance are obtained, namely the optimal deployment height and the corresponding number and density of reflective elements, thereby achieving an airborne intelligent metasurface network configuration that minimizes network handover overhead.

[0006] The method for configuring mobility parameters of an airborne reconfigurable metasurface-assisted network according to the present invention includes the following steps:

[0007] Step 200: Calculate the fixed parameters of the aerial intelligent metasurface network, including the deployment density of base stations. Density of aerial intelligent metasurface deployment Base station deployment height Deployment height of aerial intelligent metasurface Number of each aerial intelligent metasurface reflective unit Service distance threshold of aerial intelligent metasurface Signal loss factor, carrier frequency, and typical average speed of users. .

[0008] In a multi-cell wireless network assisted by an aerial smart metasurface, when estimating user handover probabilities, the ground projections of the base stations and the aerial smart metasurfaces are modeled using pre-defined spatial random distribution models. Each aerial smart metasurface is deployed at a uniform aerial altitude. Each base station is deployed at high altitude Analysis was conducted on typical users selected at random locations within the plane. Each aerial smart metasurface was equipped with... Each reflecting unit, together with the base station, provides transmission to users. Considering a limited service distance threshold... A typical user initially connects to the nearest base station and the nearest airborne smart metasurface. To determine the optimal configuration parameters, the network parameters described above need to be used as input parameters for the average network handover probability estimation algorithm.

[0009] Step 210: Combining network parameters, considering the average path gain of line-of-sight and non-line-of-sight sensing and the airborne intelligent metasurface reselection mechanism, an equivalent base station distance model is established, and the estimation method for the average network handover probability is derived. First, considering the significant impact of airborne deployment altitude on air-to-ground channel characteristics, a line-of-sight probability model for air-to-ground links is established, and its calculation formula is shown in equation (1):

[0010] (1)

[0011] in, Horizontal distance The difference is the vertical height. and This is an environment-related constant. Based on this, the state-average path gain is derived. Its expression is shown in equation (2):

[0012] (2)

[0013] in, Power gain per unit distance and These are the line-of-sight and non-line-of-sight path loss indices, respectively. For the probability of sight distance, It is a non-line-of-sight probability, and satisfies For direct links from the base station to the user, effective long-term path gain is used. Its expression is shown in equation (3):

[0014] (3)

[0015] in, The horizontal distance between a typical user and a base station. For base station height, The effective path loss factor. Secondly, in order to quantify the airborne intelligent metasurface cascade reflection gain and retain the explicit characteristics of the channel state, a first-order moment matching approximation model is defined by matching the cascade channel and direct link gain within the design interval, as shown in equation (4):

[0016] (4)

[0017] in, For line-of-sight or non-line-of-sight channel propagation states, the matching coefficient is... The expression for Gaussian hypergeometric functions is shown in equation (5):

[0018] (5)

[0019] The maximum horizontal distance threshold for the design interval is used to perform first-order moment matching of channel gain. This is then used to determine the effective matching coefficients. As shown in equation (6):

[0020] (6)

[0021] in, The average line-of-sight probability of the base station to the airborne intelligent metasurface link. The average non-line-of-sight probability of the base station to the airborne intelligent metasurface link. and These represent the first-order moment matching coefficients under line-of-sight and non-line-of-sight conditions, respectively. Based on this, an auxiliary reflection gain model for the aerial intelligent metasurface is constructed, including beamforming gain. and random scattering gain The calculation formulas are shown in equations (7) and (8) respectively:

[0022] (7) (8)

[0023] in, The beamforming constant is... For the average random scattering term, The average gain between the airborne intelligent metasurface and the user. The horizontal distance between a typical user and the aerial intelligent metasurface is given. Then, based on the actual gain threshold, the service distance threshold radius is derived. An approximate expression for is shown in equation (9):

[0024] (9)

[0025] To determine whether an aerial smart metasurface can provide an effective cascaded reflection service, a practical channel gain threshold is defined. Additionally, the equivalent base station distance is defined. The model characterizes the reshaping of handover boundaries, equivalent to the distance between adjacent base stations. The expression is shown in equation (10):

[0026] (10)

[0027] in, This is the only random scattering gain of the target base station. This represents the A-RIS gain of the original serving base station. Furthermore, consider a typical user with a displacement length of... The distance relative to the original serving base station and the original aerial smart metasurface after the unit of time of movement. and Satisfying equations (11) and (12) respectively:

[0028] (11) (12)

[0029] in, and These represent the horizontal distances between the user and the serving base station, and the serving aerial smart metasurface, respectively, at the initial moment. The angle between the user's direction of movement and the line connecting the initial serving base station; The angle between the user's movement direction and the line connecting the initial service aerial intelligent metasurface. Define auxiliary variables. Let be the overlapping area of ​​the two disks, and its expression is shown in equation (13):

[0030] (13)

[0031] in, and These are the radii of the two disks involved in calculating the overlapping area. The area of ​​the new candidate region after movement is defined based on the aerial intelligent metasurface reselection mechanism. Its piecewise expression is shown in equation (14):

[0032] (14)

[0033] Based on this, the distance between the user and the new service device after moving can be deduced. generalized probability density function Its expression is shown in equation (15):

[0034] (15)

[0035] in, Let be the Dirac impulse function, representing the probability mass of no reselection event occurring after a user moves. For the handover determination condition, the overlapping area of ​​the service region is defined. Its piecewise expression is shown in equation (16):

[0036] (16)

[0037] in, and These represent the equivalent distances between adjacent base stations at the initial time and after the move, respectively. Using the above formulas, the conditional handover probability given the initial distance is derived. As shown in equation (17):

[0038] (17)

[0039] The average handover probability of the entire network is obtained by averaging the horizontal distance distribution at the initial time. The compact closed expression is shown in equation (18):

[0040] (18)

[0041] Step 220: Compare the deployment altitudes of different aerial smart metasurfaces Number of reflective units and deployment density The network average handover probability estimate is obtained. The parameter combination that minimizes the estimate at typical user speeds is selected to obtain the optimal parameter configuration for handover performance, namely the optimal deployment height, number of reflection units, and deployment density. The optimal over-the-air intelligent metasurface network configuration for handover performance is determined, minimizing the number of network handovers and reducing network handover overhead.

[0042] Beneficial effects

[0043] This invention uses the fixed parameters of an aerial intelligent metasurface network as input parameters for an estimation algorithm; it establishes an equivalent base station distance model for sensing line-of-sight and non-line-of-sight distances to characterize the different received signal strengths between the serving base station and the target base station, transforming the auxiliary handover conditions into an equivalent distance representation; it fully considers the reselection behavior of serving devices during user movement, deriving a generalized probability distribution including reselection events, thereby obtaining an accurate closed-form expression for the average network handover probability; this invention focuses on network handover performance, taking minimizing the handover probability as the optimization objective, obtaining a compact theoretical expression through reasonable network modeling, comparing the estimated values ​​under different network parameters to obtain the optimal parameters, completing the configuration for handover performance, minimizing the number of network handovers and reducing handover overhead. This invention reveals the existence of an optimal deployment height that minimizes the handover probability, and compared with traditional ground architectures and networks without metasurfaces, the aerial intelligent metasurface assisted network optimized using this method can significantly reduce the handover probability and ensure service continuity. Attached Figure Description

[0044] Figure 1 This is a system model diagram of the air-reconfigurable metasurface-assisted network mobility parameter configuration method of the present invention;

[0045] Figure 2 This is a flowchart illustrating the implementation of the mobility parameter configuration algorithm of the present invention;

[0046] Figure 3 This is a schematic diagram illustrating the relationship between the average network handover probability of the present invention and the deployment altitude of the aerial smart metasurface;

[0047] Figure 4 This is a schematic diagram illustrating the relationship between the average network switching probability of the present invention and the deployment altitude of the aerial smart metasurface under different numbers of reflective units;

[0048] Figure 5 This is a schematic diagram illustrating the relationship between the average network handover probability of the present invention and the deployment density of the aerial smart metasurface under different base station densities. Detailed Implementation

[0049] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings. The present invention proposes a method for configuring mobility parameters of an airborne reconfigurable metasurface-assisted network. Figure 1 A system model diagram for a mobility parameter configuration method in an air-reconfigurable metasurface-assisted network is presented. Considering an A-RIS-assisted multi-cell wireless network, when estimating user handover probabilities, the ground projection location of the base station (BS) is modeled as a pre-defined spatial random distribution model with a density of... Each base station is configured and deployed at high altitude. The ground projection location of A-RIS is also modeled as a pre-defined spatial random distribution model with a density of Each A-RIS is deployed at a uniform air altitude. Typical users with initial positions are selected in the plane, and analysis is performed at typical user velocities. Each A-RIS is equipped with Each reflecting unit, together with the base station, provides transmission to users. Considering the local A-RIS service distance threshold... A typical user initially connects to the nearest base station and the nearest A-RIS (if the A-RIS exists). (Within the horizontal distance). After moving, mobile users may reselect the base station that provides the highest average received power and the A-RIS that can provide a better air-to-ground link, which may trigger a handover.

[0050] The algorithm flow for this case is attached. Figure 2 As shown, the specific implementation steps are as follows: Step 300, statistically analyze the fixed parameters of the aerial intelligent metasurface network, including the density of base stations. A-RIS deployment density Base station deployment height A-RIS deployment height Number of reflection units per A-RIS A-RIS service distance threshold , Road loss factor (considering line-of-sight LosS and non-line-of-sight NLoS respectively), carrier frequency, and typical average speed of users. The fixed parameters mentioned above are the input parameters for the network average handover probability estimation algorithm.

[0051] (19)

[0052] Step 310: Combining network parameters, considering the average path gain for perceived line-of-sight / non-line-of-sight (LoS / NLoS) and the A-RIS reselection mechanism, calculate the average network handover probability per unit time for a typical user. The core formula of the estimation algorithm is shown in Equation (19):

[0053] in, Let the horizontal distance between the user and the serving base station be at a given initial time. and service A-RIS horizontal distance The conditional switching probability under the given conditions is calculated as shown in equation (20):

[0054] (20)

[0055] in, The displacement length of a typical user per unit time (i.e.) ), The overlapping area of ​​the equivalent service area is an intermediate variable. This is a generalized probability density function representing the distance from a typical user to the new A-RIS service after moving, taking into account the A-RIS reselection event.

[0056] and These represent the equivalent distances between adjacent base stations at the initial time and after the movement, respectively. Equivalent distance The physical meaning is: when the distance between the user and the serving base station is... And the distance between the A-RIS service and the user is Under the given conditions, the equivalent minimum distance between the user and the target base station during handover is calculated as shown in equation (21):

[0057] (21)

[0058] in, This is the effective path loss factor for direct links to the base station. This is the only random scattering gain of the target base station. The A-RIS gain of the original base station is calculated as shown in equation (22):

[0059] (22)

[0060] Among them, when the user's distance from A-RIS is less than or equal to the service threshold When applying beamforming gain that includes cascaded reflection terms. When the distance is greater than When applying random scattering gain .

[0061] Step 320: Compare different A-RIS deployment heights Different numbers of each A-RIS unit and A-RIS deployment density The estimated average network handover probability is used to select the A-RIS deployment height that minimizes the estimated value at typical user speeds. Based on the corresponding parameters, determine the optimal airborne intelligent metasurface network configuration for handover performance, thereby minimizing network handover overhead.

[0062] Simulation and estimation results are shown in the attached figures. A quantitative analysis of the impact of A-RIS height and cell number allocation on the average network handover probability is presented.

[0063] Figure 3 This demonstrates how the average network handover probability varies with the A-RIS deployment height. The relationship between the changes; the marker points are simulation values; the network switching probability first changes with... The decrease followed by an increase indicates the existence of an optimal deployment height. At an A-RIS density of... At that time, the handover probability was about 0.75 when no RIS was deployed, and it dropped to about 0.15 at the optimal altitude, a significant decrease of about 80%.

[0064] Figure 4 The relationship between the average network handover probability and the A-RIS deployment height is shown under different numbers of reflector units. Simulation results show that the network handover probability first decreases and then increases with the increase of A-RIS height, proving the existence of an optimal deployment height. Furthermore, increasing the number of reflector units can further enhance beamforming gain and reduce the equivalent handover region, thereby significantly reducing the handover probability at all heights.

[0065] Figure 5 The relationship between the average network handover probability and A-RIS deployment density is shown under different base station densities. The average network handover probability decreases significantly with increasing A-RIS deployment density. This is because denser A-RIS deployments increase the probability of finding a nearby serving reflector, thereby reducing the equivalent base station boundary. Furthermore, this performance gain is more pronounced in denser cellular network deployments; for example, when the base station density is... At that time, the A-RIS density was increased from 5 to The handover probability of the A-RIS-assisted network significantly decreased from approximately 0.52 to approximately 0.30, while that of the traditional terrestrial smart metasurface decreased only from approximately 0.54 to 0.51. This fully demonstrates the significant advantages of this invention in enhancing mobility and reducing handover overhead in dense networks.

Claims

1. A method for configuring mobility parameters of an airborne reconfigurable metasurface-assisted network, characterized in that, include: Statistical analysis of fixed parameters and base station deployment density of aerial intelligent metasurface-assisted networks. Density of aerial intelligent metasurface deployment Base station deployment height Deployment height of aerial intelligent metasurface Number of each aerial intelligent metasurface reflective unit Service distance threshold of aerial intelligent metasurface Road damage factor Carrier frequency, typical user mobility speed Using the average network handover probability as the key indicator, an equivalent base station distance model is established. The estimated average network handover probability is compared under different deployment heights, number of units per airborne smart metasurface, and deployment densities. The configuration parameters that minimize the average network handover probability are determined, thereby completing the airborne smart metasurface network configuration that minimizes handover overhead.

2. The method according to claim 1, characterized in that, The formula for calculating the equivalent distance is: in, The horizontal distance between the user and the serving base station. The horizontal distance between the aerial smart metasurface and the user. The random scattering gain of adjacent base stations, For the airborne intelligent metasurface gain of the original serving base station, For base station height, For information road loss factor.

3. The method according to claim 1, characterized in that, The formula for calculating the estimated average handover probability of the network is: in, For base station deployment density, For the deployment density of aerial intelligent metasurfaces, The initial horizontal distance between the user and the serving base station. The initial horizontal distance between the user and the aerial intelligent metasurface. This is an intermediate variable representing the conditional switching probability given an initial distance.

4. The method according to claim 1, characterized in that, The airborne intelligent metasurface gain of the original serving base station The formula for calculation is: in, The horizontal distance between the user and the aerial smart metasurface. For users within the service distance threshold Beamforming gain during operation For those at the service distance threshold Random scattering gain outside of this.

5. The method according to claim 3, characterized in that, The condition switching probability The formula for calculation is: in, The horizontal distance relative to the original aerial smart metasurface after the user moves. For typical users at typical speeds within a unit of time The length of the displacement. The overlapping area of ​​the equivalent service area. To provide a generalized probability density function for the distance of a typical user from the new A-RIS service after moving, taking into account the A-RIS reselection event. and These represent the equivalent distances between adjacent base stations at the initial time and after the movement, respectively. The angle between the typical user's direction of movement and the line connecting the initial serving base station. The angle between the typical user's movement direction and the line connecting the initial service aerial intelligent metasurface. To calculate the distance integral variable of the probability distribution.

6. The method according to claim 5, characterized in that, The overlapping area and the area of ​​candidate regions for reselection of aerial intelligent metasurfaces Both are obtained through the overlapping area function of the two disks. Perform calculations, and The piecewise expression is: in, and The radii of the two disks used to calculate the overlapping area.

7. The method according to claim 1, characterized in that, By comparing different aerial smart metasurface deployment altitudes The network average handover probability estimate is used to select the deployment height where the estimate is smallest. As the optimal deployment height parameter.

8. The method according to claim 1, characterized in that, Jointly optimize the deployment altitude of the aerial intelligent metasurface Number of each aerial intelligent metasurface unit and the corresponding deployment density Determine the number of each airborne intelligent metasurface unit when the average network handover probability is minimized. and the corresponding deployment density To achieve optimal mobility management configuration.

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