A basic theory limit modeling method and system of edge-aware intelligence in low-altitude wireless network

Abstract

The application discloses a kind of low-altitude wireless network edge perception intelligent basic theory limit modeling method and system, it is related to wireless communication and intelligent sensing technical field.For the problems that existing low-altitude network communication, sensing and computing resources are independently modeled, cannot reflect the three strong coupling relationship, the application first establishes the low-altitude channel model containing three-dimensional geometric distance and elevation angle dependent LoS / NLoS mixed path loss;Introduce Doppler shift correction factor to construct effective signal-to-noise ratio model to accurately represent high-speed moving characteristics;Based on the Cramer-Rao lower bound, the sensing accuracy limit is derived, and the computing delay model is constructed combined with edge computing power constraint;With the goal of maximizing communication rate, under the dual constraints of sensing accuracy and computing delay, a three-coupling optimization problem is constructed, and the theoretical limit rate is solved by Lagrange multiplier method or numerical search, and it is extended to network-level resource allocation and maximum access scale analysis under the scene of multiple unmanned airports.

Description

Technical Field

[0001] This invention relates to a fundamental theoretical limit modeling method and system for edge-aware intelligence in low-altitude wireless networks, belonging to the field of wireless communication and intelligent sensing technology. Background Technology

[0002] With the booming development of the low-altitude economy and the innovation of unmanned aerial vehicle technology, low-altitude wireless networks have become a core infrastructure supporting urban air traffic, low-altitude logistics distribution, high-precision geographic mapping, and three-dimensional security monitoring. Unlike traditional two-dimensional terrestrial cellular networks, aircraft nodes in low-altitude networks are characterized by significant three-dimensional distribution, high-speed movement (typically >50km / h), and dynamic changes in air-to-ground channels. To adapt to this complex operating environment, modern low-altitude network technology is rapidly evolving towards the integration of communication, sensing, and computing. This means that base stations not only need to undertake high-bandwidth data backhaul tasks but also need to utilize the same spectrum resources to complete target ranging, velocity measurement, and environmental reconstruction, and perform real-time intelligent processing of the generated massive amounts of sensing data at the edge.

[0003] In the existing technological system, the performance evaluation of communication, sensing, and computing has long been fragmented, following their own independent theoretical limits. In the communication field, channel capacity limits are typically derived based on Shannon's classical information theory, but this often ignores the non-ideal impact of significant Doppler shift introduced by low-altitude, high-speed movement on the actual effective signal-to-noise ratio and inter-symbol crosstalk. In the sensing field, radar equations or Cramer-Rao lower bounds are relied upon to analyze parameter estimation accuracy, typically assuming unlimited transmit power and spectrum resources without considering resource competition with communication functions. In the edge computing field, the focus is on computing power scheduling and task offloading decisions, often abstracting the underlying wireless transmission link as an ideal "wired pipe," ignoring the blocking effect of transmission delay jitter caused by channel quality degradation on computing tasks. This discrete modeling approach implies a technological bias: the assumption that system resources (power, bandwidth, computing power) can be optimized independently, failing to reveal the strong coupling constraints between the three.

[0004] However, in practical low-altitude edge intelligent systems, spectrum, power, and hardware processing capabilities are highly shared and mutually constrained. For example, while increasing bandwidth can simultaneously improve communication rate and sensing resolution, it leads to an exponentially increasing computational load on edge servers; increasing the proportion of sensing power can reduce detection errors, but it directly squeezes the transmit power budget of communication links. Currently, the industry lacks a universal theoretical framework that can uniformly characterize the three-way trade-off between "communication rate, sensing accuracy, and computational latency," making it difficult for network planners to accurately define the performance limits of the system under extreme conditions and to make optimal resource scheduling decisions under strict sensing accuracy requirements and extremely low latency constraints. Therefore, it is necessary to break through the limitations of traditional discrete models and construct a fundamental theoretical limit modeling method and system for edge sensing intelligence in low-altitude wireless networks to solve the technical challenges caused by the strong coupling of the aforementioned multi-dimensional resources.

[0005] To address this, a fundamental theoretical limit modeling method and system for edge-aware intelligence in low-altitude wireless networks is proposed. Summary of the Invention

[0006] In view of this, the present invention provides a fundamental theoretical limit modeling method and system for edge-aware intelligence in low-altitude wireless networks, in order to solve or alleviate the technical problems existing in the prior art, and at least provide a beneficial option.

[0007] The technical solution of this invention is implemented as follows: a fundamental theoretical limit modeling method for edge-aware intelligence in low-altitude wireless networks, comprising the following steps:

[0008] Step S1: Construct a low-altitude 3D scene and hybrid channel model;

[0009] Set base station coordinates With drone coordinates Calculate real-time three-dimensional spatial distance ;

[0010] Based on the elevation angle of the drone relative to the base station

[0011] Calculate the line-of-sight (LoS) transmission probability And according to the formula

[0012] Construct a hybrid path loss model in an urban environment;

[0013] Step S2: Construct a communication model with Doppler frequency shift correction;

[0014] Based on the drone's motion velocity vector carrier wavelength And the Doppler frequency shift calculated by incident angle Derivation of the effective signal-to-noise ratio including the Doppler extension suppression factor:

[0015]

[0016] in, For transmission power, For antenna gain, For noise power spectral density, For system bandwidth, The symbol period is used to calculate the basic communication rate. ;

[0017] Step S3: Construct a perceptual accuracy limit model;

[0018] Total transmission power Decomposed into communication power and sensing power Define the percentage of sensing power

[0019] Distance estimation error derived based on Cramer-Rao lower bound (CRLB) With bandwidth Perceived signal-to-noise ratio Mapping relationship between them:

[0020]

[0021] in The speed of light;

[0022] Step S4: Construct an edge computing latency model;

[0023] Set the edge server computing power to Define the amount of perceived data With system bandwidth Linear proportional relationship ,in The scaling factor is used to derive the edge processing delay. ;

[0024] Step S5: Solve for the limit of the ternary coupling theory;

[0025] Under the preset perception accuracy constraint and edge computing latency constraints The following optimization problem is constructed with the objective of maximizing the achievable communication rate R:

[0026]

[0027] The optimization problem is then solved using the Lagrange multiplier method or numerical search algorithm to obtain the maximum achievable communication rate, i.e., the theoretical limit rate, under the given dual composite constraints. .

[0028] Preferably, the line-of-sight transmission probability mentioned in step S1 A nonlinear function based on urban environment fitting is used:

[0029]

[0030] in and These are empirical parameters related to urban building density.

[0031] Preferably, the Doppler frequency shift described in step S2 The specific calculation formula is as follows:

[0032]

[0033] in For the flight speed of the drone, Let be the angle between the projection of the velocity vector onto the horizontal plane and the line connecting the base station. Angle of elevation .

[0034] Preferably, the optimization problem in step S5 is specifically transformed into a mathematical programming problem with inequality constraints:

[0035]

[0036]

[0037]

[0038] The first constraint is determined by the perception accuracy requirement. The second constraint is derived from an equivalent transformation and is based on the computational delay requirement. It is derived from an equivalent transformation.

[0039] Preferably, the solution to the theoretical limit rate The process includes:

[0040] when When the value is large, the objective function is approximated as a quasi-concave function by utilizing the properties of the logarithmic function, and convex optimization is then performed.

[0041] Alternatively, the bandwidth can be traversed using a one-dimensional grid search. feasible domain And in each fixed Take down minimum boundary value Substitute the values ​​into the rate formula to calculate the maximum value.

[0042] Preferably, it also includes network-level resource scheduling and capacity analysis steps in multi-drone scenarios:

[0043] Set total network bandwidth resources Total power resources and total edge computing power ;

[0044] Establish multi-user resource allocation constraints: ,in Indicates the first A drone;

[0045] Construct the network-level total rate maximization objective function:

[0046]

[0047] Based on the computing power-latency coupling relationship, the maximum number of drones that the system can support is derived:

[0048]

[0049] in The minimum bandwidth threshold required for a single drone to maintain basic perception capabilities. This is for floor function.

[0050] Preferably, a fundamental theoretical limit modeling system for edge-aware intelligence in low-altitude wireless networks includes:

[0051] The scenario and channel modeling module is used to configure low-altitude three-dimensional network parameters, calculate three-dimensional spatial distance and elevation angle based on the parameters, and generate a hybrid LoS / NLoS path loss value based on an elevation angle-dependent probability model.

[0052] The communication performance evaluation module, coupled to the scenario and channel modeling module, is used to calculate the UAV's Doppler frequency shift and generate the effective signal-to-noise ratio after Doppler correction. And calculate the base communication rate when there are no resource constraints;

[0053] The sensing performance evaluation module receives total power and bandwidth information and calculates the percentage of a given sensing power based on the Cramer-Rao lower bound principle. Distance estimation error Generate the perception accuracy boundary;

[0054] The edge computing evaluation module is used to receive bandwidth information and combine it with the computing power of the edge server. Computational sensing data processing latency ;

[0055] The joint optimization engine, coupled to the communication performance evaluation module, the perception performance evaluation module, and the edge computing evaluation module, is used to optimize the input perception accuracy. and delay constraints Under the given conditions, execute any one of the described ternary coupling optimization algorithms and output the theoretical limit rate. and the corresponding optimal bandwidth allocation value and optimal sensing power ratio ;

[0056] The visualization output module is used to visualize the theoretical limit rate. Mapping to about and The three-dimensional limit surface is generated and displayed.

[0057] Preferably, the joint optimization engine integrates a parameter verification unit for verifying the current input before the optimization algorithm solves the problem. and Whether it is within the physically realizable domain, if or If the lower bound of the constraint is greater than 1, it is determined that there is no solution and an alarm message is returned.

[0058] Preferably, the system further includes a multi-link scheduling interface, used in multi-UAV scenarios to allocate resources according to the resource allocation strategy output by the joint optimization engine. It dynamically reallocates bandwidth and power among multiple drones within the network and outputs the maximum network access capacity. .

[0059] The embodiments of the present invention have the following advantages due to the adoption of the above technical solutions:

[0060] I. This invention, by constructing for the first time a three-element coupling theoretical limit framework of "communication-sensing-computing" for low-altitude wireless networks, fundamentally breaks the technical bias of independent modeling and optimization of each system in traditional technology fields. Existing technologies usually isolate the rate limit based on Shannon's formula or the sensing accuracy based on Cramer-Rao's lower bound, ignoring the inherent constraints brought about by strong resource sharing in low-altitude, high-speed scenarios. This invention organically integrates the Doppler effect of the physical layer, the bandwidth allocation of the MAC layer, and the computational latency of the application layer through mathematical modeling, revealing the nonlinear trade-off mechanism among the three. This unified theoretical framework defines the Pareto front for the performance of low-altitude edge intelligent systems, filling the gap in the fundamental theoretical limit research of this interdisciplinary field, and has extremely high academic value and theoretical foresight.

[0061] II. Compared to the general ISAC (Integrated Sensor and Communication) model, this invention provides a refined modeling of the unique three-dimensional spatial characteristics of low-altitude networks, significantly enhancing the engineering reference value of the theoretical limits. Specifically, this invention introduces an elevation angle-dependent LoS / NLoS hybrid path loss model, accurately reproducing the occlusion characteristics of urban built-up environments on low-altitude links. Simultaneously, by compressing the effective signal-to-noise ratio through a Doppler frequency shift correction factor, it realistically reflects the degrading impact of high-speed UAV movement on communication capacity. This high-fidelity modeling ensures that the output theoretical limit rate is no longer an inflated estimate under ideal conditions, but rather an achievable performance boundary that closely reflects actual physical layer damage. This provides network planners with a more scientific, conservative, and reliable basis for decision-making in airspace planning, power budgeting, and site selection, avoiding network congestion or sensing failures caused by theory deviating from reality.

[0062] Third, this invention has significant economic and social benefits at the application level. On the one hand, by explicitly introducing edge computing latency constraints, this invention quantifies the bottleneck effect of edge computing power on system throughput, clarifies the feasibility boundary of "computing power for bandwidth" or "computing power for latency" in the context of increasingly stringent requirements for sensing accuracy, and guides operators to rationally allocate edge computing resources to avoid over-investment or resource shortages. On the other hand, the formula for the maximum number of drones that can be supported in multi-drone scenarios derived by this invention directly solves the capacity planning problem in large-scale low-altitude networking. This model can intuitively show the upper limit of services that the system's physical resources can carry under given sensing accuracy and latency requirements, providing solid theoretical support and quantitative tools for multi-access conflict control of low-altitude monitoring platforms and the formulation of industry standards related to the low-altitude economy.

[0063] The above overview is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features of the invention will become readily apparent from the accompanying drawings and the following detailed description. Attached Figure Description

[0064] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0065] Figure 1 This is a schematic diagram of the system architecture of the present invention;

[0066] Figure 2 This is a schematic diagram of the Doppler-corrected signal-to-noise ratio of the present invention;

[0067] Figure 3 This is a schematic diagram of the communication-sensing-computation three-dimensional limit surface of the present invention. Detailed Implementation

[0068] In the following description, only certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments can be modified in various ways without departing from the spirit or scope of the invention. Therefore, the drawings and description are considered to be exemplary in nature and not restrictive.

[0069] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0070] Example

[0071] like Figure 1-3 As shown, this embodiment of the invention provides a fundamental theoretical limit modeling method for edge-aware intelligence in low-altitude wireless networks, comprising the following steps:

[0072] Step S1: Construct a low-altitude 3D scene and hybrid channel model;

[0073] Set base station coordinates With drone coordinates Calculate real-time three-dimensional spatial distance ;

[0074] Based on the elevation angle of the drone relative to the base station

[0075] Calculate the line-of-sight (LoS) transmission probability And according to the formula

[0076] Construct a hybrid path loss model in an urban environment;

[0077] Step S2: Construct a communication model with Doppler frequency shift correction;

[0078] Based on the drone's motion velocity vector carrier wavelength And the Doppler frequency shift calculated by incident angle Derivation of the effective signal-to-noise ratio including the Doppler extension suppression factor:

[0079]

[0080] in, For transmission power, For antenna gain, For noise power spectral density, For system bandwidth, The symbol period is used to calculate the basic communication rate. ;

[0081] Step S3: Construct a perceptual accuracy limit model;

[0082] Total transmission power Decomposed into communication power and sensing power Define the percentage of sensing power

[0083] Distance estimation error derived based on Cramer-Rao lower bound (CRLB) With bandwidth Perceived signal-to-noise ratio Mapping relationship between them:

[0084]

[0085] in The speed of light;

[0086] Step S4: Construct an edge computing latency model;

[0087] Set the edge server computing power to Define the amount of perceived data With system bandwidth Linear proportional relationship ,in The scaling factor is used to derive the edge processing delay. ;

[0088] Step S5: Solve for the limit of the ternary coupling theory;

[0089] Under the preset perception accuracy constraint and edge computing latency constraints The following optimization problem is constructed with the objective of maximizing the achievable communication rate R:

[0090]

[0091] The optimization problem is then solved using the Lagrange multiplier method or numerical search algorithm to obtain the maximum achievable communication rate, i.e., the theoretical limit rate, under the given dual composite constraints. .

[0092] Specifically, the line-of-sight transmission probability in step S1 A nonlinear function based on urban environment fitting is used:

[0093]

[0094] in and These are empirical parameters related to urban building density.

[0095] Specifically, the Doppler frequency shift in step S2 The specific calculation formula is as follows:

[0096]

[0097] in For the flight speed of the drone, Let be the angle between the projection of the velocity vector onto the horizontal plane and the line connecting the base station. Angle of elevation .

[0098] Specifically, the optimization problem in step S5 is transformed into a mathematical programming problem with inequality constraints:

[0099]

[0100]

[0101]

[0102] The first constraint is determined by the perception accuracy requirement. The second constraint is derived from an equivalent transformation and is based on the computational delay requirement. It is derived from an equivalent transformation.

[0103] Specifically, solving for the theoretical limit rate The process includes:

[0104] when When the value is large, the objective function is approximated as a quasi-concave function by utilizing the properties of the logarithmic function, and convex optimization is then performed.

[0105] Alternatively, the bandwidth can be traversed using a one-dimensional grid search. feasible domain And in each fixed Take down minimum boundary value Substitute the values ​​into the rate formula to calculate the maximum value.

[0106] Specifically, it also includes network-level resource scheduling and capacity analysis steps in multi-drone scenarios:

[0107] Set total network bandwidth resources Total power resources and total edge computing power ;

[0108] Establish multi-user resource allocation constraints: ,in Indicates the first A drone;

[0109] Construct the network-level total rate maximization objective function:

[0110]

[0111] Based on the computing power-latency coupling relationship, the maximum number of drones that the system can support is derived:

[0112]

[0113] in The minimum bandwidth threshold required for a single drone to maintain basic perception capabilities. This is for floor function.

[0114] Specifically, a fundamental theoretical limit modeling system for edge-aware intelligence in low-altitude wireless networks includes:

[0115] The scenario and channel modeling module is used to configure low-altitude three-dimensional network parameters, calculate three-dimensional spatial distance and elevation angle based on the parameters, and generate LoS / NLoS hybrid path loss values ​​based on an elevation angle-dependent probability model.

[0116] The communication performance evaluation module, coupled to the scene and channel modeling module, is used to calculate the UAV's Doppler frequency shift and generate the effective signal-to-noise ratio after Doppler correction. And calculate the base communication rate when there are no resource constraints;

[0117] The sensing performance evaluation module receives total power and bandwidth information and calculates the percentage of a given sensing power based on the Cramer-Rao lower bound principle. Distance estimation error Generate the perception accuracy boundary;

[0118] The edge computing evaluation module is used to receive bandwidth information and combine it with the computing power of the edge server. Computational sensing data processing latency ;

[0119] The joint optimization engine, coupled to the communication performance evaluation module, the perception performance evaluation module, and the edge computing evaluation module, is used to optimize the input perception accuracy. and delay constraints Under the given conditions, execute any one of the ternary coupling optimization algorithms and output the theoretical limit rate. and the corresponding optimal bandwidth allocation value and optimal sensing power ratio ;

[0120] The visualization output module is used to visualize the theoretical limit rate. Mapping to about and The three-dimensional limit surface is generated and displayed.

[0121] Specifically, the joint optimization engine integrates a parameter verification unit to verify the current input before the optimization algorithm solves the problem. and Whether it is within the physically realizable domain, if or If the lower bound of the constraint is greater than 1, it is determined that there is no solution and an alarm message is returned.

[0122] Specifically, the system also includes a multi-link scheduling interface, used in multi-UAV scenarios to allocate resources according to the resource allocation strategy output by the joint optimization engine. It dynamically reallocates bandwidth and power among multiple drones within the network and outputs the maximum network access capacity. .

[0123] This invention, by constructing for the first time a three-element coupling theoretical limit framework of "communication-sensing-computing" for low-altitude wireless networks, fundamentally breaks the technical bias of independent modeling and optimization of each system in traditional technology fields. Existing technologies usually isolate the rate limit based on Shannon's formula or the sensing accuracy based on Cramer-Rao's lower bound, ignoring the inherent constraints brought about by strong resource sharing in low-altitude, high-speed scenarios. This invention organically integrates the Doppler effect of the physical layer, the bandwidth allocation of the MAC layer, and the computational latency of the application layer through mathematical modeling, revealing the nonlinear trade-off mechanism among the three. This unified theoretical framework defines the Pareto front for the performance of low-altitude edge intelligent systems, filling the gap in the fundamental theoretical limit research of this interdisciplinary field, and has extremely high academic value and theoretical foresight. Compared with the general ISAC (Sensing-Sensing Integration) model, this invention provides a refined model for the unique three-dimensional spatial characteristics of low-altitude networks, significantly improving the engineering reference significance of the theoretical limit. Specifically, this invention introduces an elevation-dependent LoS / NLoS hybrid path loss model, accurately reproducing the occlusion characteristics of low-altitude links caused by urban built-up environments. Simultaneously, by compressing the effective signal-to-noise ratio through a Doppler frequency shift correction factor, it realistically reflects the degrading impact of high-speed UAV movement on communication capacity. This high-fidelity modeling ensures that the output theoretical limit rate is no longer an inflated estimate under ideal conditions, but rather an achievable performance boundary that closely reflects actual physical layer damage. This provides network planners with a more scientific and conservative basis for decision-making in airspace planning, power budgeting, and site selection, avoiding network congestion or sensing failures caused by theory deviating from reality. This invention has significant economic and social value at the application level. On the other hand, by explicitly introducing edge computing latency constraints, this invention quantifies the bottleneck limiting effect of edge computing power on system throughput, clarifying the feasibility boundary of "computing power for bandwidth" or "computing power for latency" in the context of increasingly stringent sensing accuracy requirements. This guides operators to rationally allocate edge computing resources, avoiding over-investment or resource shortages. On the other hand, the formula for the maximum number of drones that can be supported in a multi-drone scenario derived in this invention directly solves the capacity planning problem in large-scale low-altitude networking. This model can intuitively demonstrate the upper limit of the services that the system's physical resources can support under given sensing accuracy and latency requirements, providing solid theoretical support and quantitative tools for multi-access conflict control of low-altitude monitoring platforms and the formulation of industry standards related to the low-altitude economy.

[0124] Example 1: Limit Solution of Ternary Coupling in a Single UAV Link

[0125] This embodiment selects a typical urban low-altitude patrol scenario and uses numerical calculations to solve for the maximum communication rate limit under given sensing accuracy and computational delay constraints. .

[0126] Step 1: Initialize system parameter configuration

[0127] The basic physical parameters for setting up a low-altitude wireless network are shown in the table below:

[0128] carrier frequency 3.5 GHz Mainstream 5G frequency bands speed of light <![CDATA[3×10 8 m / s]]> physical constants Base station coordinates (0, 0, 30) m Urban macro base station height drone altitude 100 m Typical low-altitude operation height Flight speed 15 m / s Approximately 54 km / h Transmit power 30 dBm (1 W) - noise spectral density -174 dBm / Hz Additive white Gaussian noise Symbol period 1 μs Corresponding to a subcarrier spacing on the order of 1 MHz Edge computing power 50 GFLOPS Base station side edge server computing power Data coefficients 103 The amount of sensing data generated per MHz bandwidth

[0129] Step 2: Establish a three-dimensional channel and Doppler correction model

[0130] Assume the drone hovers or moves at a constant speed at a horizontal position (100,0):

[0131] Distance Calculation: Three-Dimensional Spatial Distance .

[0132] Elevation angle calculation: .

[0133] LoS probability: based on urban environmental parameters Calculated By combining the difference between LoS and NLoS path losses, the hybrid path loss is derived. .

[0134] Doppler correction: Assuming beam alignment, Doppler shift .

[0135] Effective signal-to-noise ratio: calculated by substituting the correction factor. Compared to static It dropped by approximately (The impact is small at low speeds, but this correction factor is crucial in high-speed scenarios.)

[0136] Step 3: Define business constraints and objective function

[0137] Define the Quality of Service (QoS) requirements for this task:

[0138] Sensing accuracy requirement: distance estimation error .

[0139] Computation latency requirements: Edge processing latency .

[0140] Based on the derivation in the document, construct the optimization problem:

[0141]

[0142] Subject to:

[0143] Perceptual constraints:

[0144] Computational constraints:

[0145] Step 4: Numerical search to find the limiting rate

[0146] Because the bandwidth is strictly limited to within 1MHz by computing power, we are in the range Perform a one-dimensional search within.

[0147] Boundary analysis: In order to satisfy Maximum available bandwidth Theoretically, the maximum bandwidth should be used to obtain the highest rate, but it is necessary to check whether the perception constraint is satisfied at this time.

[0148] Power allocation: When At that time, calculate the minimum required sensing power percentage. Assuming at this time... (100 times the linear value), then .

[0149] Rate calculation: Remaining communication power percentage is Final achievable rate .

[0150] Step 5: Sensitivity Analysis and Phenomenon Verification

[0151] Phenomenon 1 (Impact of Perception Accuracy): If the perception accuracy requirement is increased to... ,but It needs to be increased to 25 times the original (because) ),Right now At this time, the communication rate The power consumption decreased significantly because it was squeezed out by the sensing function.

[0152] Phenomenon 2 (Bottleneck of computing power): If edge computing power drops to 10 GFLOPS, then When the frequency is reduced to 0.2MHz, the upper limit of the system communication rate decreases linearly. This verifies the conclusion mentioned in the document that "insufficient computing power leads to bandwidth limitation".

[0153] Example 2: Capacity Planning for Multi-UAV Networks

[0154] This embodiment demonstrates how to use the method of this patent to guide the access management of multiple drones within the coverage area of ​​a base station.

[0155] Step 1: Configure the overall network resource pool

[0156] Assume the total base station resources are:

[0157] Total bandwidth .

[0158] Total edge computing power .

[0159] Total power .

[0160] Step 2: Derive the maximum number of drones that can be supported

[0161] The single-user parameter requirements from Example 1 are retained (each drone requires a certain amount of time). ,produce (processing delay).

[0162] According to the computing power constraint formula given in the document:

[0163]

[0164] Substitute the values:

[0165]

[0166] Step 3: Resource Scheduling Strategy

[0167] Bandwidth allocation: The system can distribute the total bandwidth of 100 MHz equally among 10 drones, with each drone receiving 10 MHz (far exceeding the minimum bandwidth of 1 MHz required by a single drone, at which point bandwidth is no longer a bottleneck).

[0168] Computing power allocation: The system must ensure that each drone is allocated no less than [amount missing]. Total demand It just exhausted the total computing power.

[0169] Conclusion: In this scenario, edge computing power is the primary bottleneck limiting network capacity. To connect the 11th drone, edge computing power must be upgraded or the perception accuracy requirements for a single user must be reduced (thus reducing...). or reduce need).

[0171] Example 3: System Implementation and Visualization

[0172] The modeling system corresponding to this invention can be implemented using a B / S or C / S architecture, and includes the following specific functional units:

[0173] Parameter input interface: Allows users to input physical parameters such as base station altitude, drone trajectory, carrier frequency, total computing power, and desired parameters. and .

[0174] 3D Limit Surface Generator: The system backend calls the joint optimization engine to traverse... and Calculate the corresponding combinations .

[0175] Visualization: The front-end interface displays the surface relationship between "communication rate - sensing accuracy - computation latency" in the form of a 3D mesh. Users can intuitively see:

[0176] When the mouse moves to a low-latency, high-precision region, the rate surface drops sharply (infeasible region).

[0177] In the high latency and low precision region, the rate surface tends to flatten (saturation region).

[0178] Alarm mechanism: If the combination of user input parameters causes... The system will automatically pop up a "Physically Unrealizable" warning, prompting the user to relax the constraints.

[0179] Through the above specific implementation steps, those skilled in the art can clearly understand how the present invention starts from high-precision low-altitude channel modeling, and through rigorous mathematical derivation, ultimately achieves quantitative calculation of the limit of the ternary coupling theory, providing strong theoretical support for the actual deployment of low-altitude networks.

[0180] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various variations or substitutions within the technical scope disclosed in the present invention, and these should all be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A fundamental theoretical limit modeling method for edge-aware intelligence in low-altitude wireless networks, characterized in that, Includes the following steps: Step S1: Construct a low-altitude 3D scene and hybrid channel model; Set base station coordinates With drone coordinates Calculate real-time three-dimensional spatial distance ; Based on the elevation angle of the drone relative to the base station Calculate the line-of-sight (LoS) transmission probability And according to the formula Construct a hybrid path loss model in an urban environment; Step S2: Construct a communication model with Doppler frequency shift correction; Based on the drone's motion velocity vector carrier wavelength And the Doppler frequency shift calculated by incident angle Derivation of the effective signal-to-noise ratio including the Doppler extension suppression factor: in, For transmission power, For antenna gain, For noise power spectral density, For system bandwidth, The symbol period is used to calculate the basic communication rate. ; Step S3: Construct a perceptual accuracy limit model; Total transmission power Decomposed into communication power and sensing power Define the percentage of sensing power Distance estimation error derived based on Cramer-Rao lower bound (CRLB) With bandwidth Perceived signal-to-noise ratio Mapping relationship between them: in The speed of light; Step S4: Construct an edge computing latency model; Set the edge server computing power to Define the amount of perceived data With system bandwidth Linear proportional relationship ,in The scaling factor is used to derive the edge processing delay. ； Step S5: Solve for the limit of the ternary coupling theory; Under the preset perception accuracy constraint and edge computing latency constraints The following optimization problem is constructed with the objective of maximizing the achievable communication rate R: The optimization problem is then solved using the Lagrange multiplier method or numerical search algorithm to obtain the maximum achievable communication rate, i.e., the theoretical limit rate, under the given dual composite constraints. .

2. The method according to claim 1, characterized in that, The line-of-sight transmission probability mentioned in step S1 A nonlinear function based on urban environment fitting is used: in and These are empirical parameters related to urban building density.

3. The method according to claim 1, characterized in that, The Doppler frequency shift mentioned in step S2 The specific calculation formula is as follows: in For the flight speed of the drone, Let be the angle between the projection of the velocity vector onto the horizontal plane and the line connecting the base station. Angle of elevation .

4. The method according to claim 1, characterized in that, The optimization problem in step S5 is specifically transformed into a mathematical programming problem with inequality constraints: The first constraint is determined by the perception accuracy requirement. The second constraint is derived from an equivalent transformation and is based on the computational delay requirement. It is derived from an equivalent transformation.

5. The method according to claim 4, characterized in that, The solution to the theoretical limit rate The process includes: when When the value is large, the objective function is approximated as a quasi-concave function by utilizing the properties of the logarithmic function, and convex optimization is then performed. Alternatively, the bandwidth can be traversed using a one-dimensional grid search. feasible domain And in each fixed Take down minimum boundary value Substitute the values ​​into the rate formula to calculate the maximum value.

6. The method according to claim 1, characterized in that, It also includes network-level resource scheduling and capacity analysis steps in multi-drone scenarios: Set total network bandwidth resources Total power resources and total edge computing power ; Establish multi-user resource allocation constraints: ,in Indicates the first A drone; Construct the network-level total rate maximization objective function: Based on the computing power-latency coupling relationship, the maximum number of drones that the system can support is derived: in The minimum bandwidth threshold required for a single drone to maintain basic perception capabilities. This is for floor function.

7. A fundamental theoretical limit modeling system for edge-aware intelligence in low-altitude wireless networks, characterized in that, include: The scenario and channel modeling module is used to configure low-altitude three-dimensional network parameters, calculate three-dimensional spatial distance and elevation angle based on the parameters, and generate a hybrid LoS / NLoS path loss value based on an elevation angle-dependent probability model. The communication performance evaluation module, coupled to the scenario and channel modeling module, is used to calculate the UAV's Doppler frequency shift and generate the effective signal-to-noise ratio after Doppler correction. And calculate the base communication rate when there are no resource constraints; The sensing performance evaluation module receives total power and bandwidth information and calculates the percentage of a given sensing power based on the Cramer-Rao lower bound principle. Distance estimation error Generate the perception accuracy boundary; The edge computing evaluation module is used to receive bandwidth information and combine it with the computing power of the edge server. Computational sensing data processing latency ; The joint optimization engine, coupled to the communication performance evaluation module, the perception performance evaluation module, and the edge computing evaluation module, is used to optimize the input perception accuracy. and delay constraints Under the given conditions, the ternary coupling optimization algorithm described in any one of claims 1 to 6 is executed to output the theoretical limit rate. and the corresponding optimal bandwidth allocation value and optimal sensing power ratio ; The visualization output module is used to visualize the theoretical limit rate. Mapping to about and The three-dimensional limit surface is generated and displayed.

8. The system according to claim 7, characterized in that, The joint optimization engine integrates a parameter verification unit, which is used to verify the current input before the optimization algorithm solves the problem. and Whether it is within the physically realizable domain, if or If the lower bound of the constraint is greater than 1, it is determined that there is no solution and an alarm message is returned.

9. The system according to claim 7, characterized in that, The system also includes a multi-link scheduling interface, used in multi-UAV scenarios to allocate resources according to the resource allocation strategy output by the joint optimization engine. It dynamically reallocates bandwidth and power among multiple drones within the network and outputs the maximum network access capacity. .

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