An unmanned aerial vehicle and ground cache node cooperative emergency communication network and QoS guarantee method, system, device and medium

Abstract

This invention relates to the field of wireless emergency communication technology, and discloses an emergency communication network and QoS guarantee method, system, equipment, and medium for the collaboration of UAVs and ground cache nodes. The invention includes: constructing an emergency communication network model, defining node distribution density parameters and backhaul offload coefficients, and proportionally allocating user service types; introducing a statistical delay service quality index based on statistical delay service quality assurance theory, with effective capacity as the objective function; establishing a joint optimization problem with the objective of maximizing average effective capacity, setting constraints, and decomposing the original problem into cache placement subproblems and node deployment subproblems, solving each subproblem separately; and iteratively optimizing the cache placement scheme and node deployment density using the block coordinate descent method until convergence, obtaining the joint optimal configuration. This invention effectively ensures the timeliness and reliability of emergency communication, optimizes joint cache placement and CER deployment, and effectively improves cache hit rate and network throughput.

Description

Technical Field

[0001] This invention relates to the field of wireless emergency communication technology, and in particular to an emergency communication network and QoS guarantee method, system, equipment and medium for collaboration between UAVs and ground buffer nodes. Background Technology

[0002] Natural disasters often damage communication infrastructure, rendering traditional communication networks inoperable. While drones, due to their high flexibility, can serve as temporary base stations, their wireless backhaul capabilities are limited and significantly affected by severe weather conditions, environmental parameters, and distance from nearby available base stations. Furthermore, the sudden nature of natural disasters leads to a rapid increase in communication needs among affected users, placing a heavy burden on drone backhaul links.

[0003] To alleviate the burden of drone data transmission, ground-based rescue personnel carrying portable buffering devices can provide emergency information (such as safe evacuation routes and nearby available services) to disaster-stricken users via Device-to-Device (D2D) communication. The coexistence of drones and Communication Emergency Responders (CERs) in the disaster area not only supports more disaster-stricken users but also avoids long delays caused by excessive data transmission load.

[0004] However, existing research largely focuses on cache placement optimization, lacking a comprehensive consideration of latency-enabled Quality of Service (QoS) and network throughput. To support the effectiveness and timeliness of emergency communications, a comprehensive consideration of latency-enabled QoS requirements and network throughput is necessary. Statistical latency-enabled QoS guarantee theory is widely considered a powerful technique for analyzing network throughput under latency-enabled QoS constraints. Although some research has extended statistical QoS guarantees to cache-enabled networks, cache placement and cache node deployment schemes under latency-enabled QoS constraints have not been fully discussed. Summary of the Invention

[0005] In view of the aforementioned existing problems, this invention is proposed. Therefore, this invention provides an emergency communication network and QoS guarantee method that coordinates UAVs and ground-based cache nodes to solve the problem of maximizing effective capacity under latency QoS constraints by jointly optimizing cache placement and CER deployment density.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides an emergency communication network and QoS guarantee method for collaboration between unmanned aerial vehicles (UAVs) and ground-based buffer nodes, including: Construct an emergency communication network model consisting of drone base stations, communication emergency response nodes, and disaster-affected users; define node distribution density parameters and backhaul offload coefficients; and allocate service types to users proportionally. Based on the aforementioned emergency communication network model, the statistical delay quality of service index is introduced through the statistical delay quality of service assurance theory. With effective capacity as the objective function, the maximum achievable rate under delay constraints is determined. A joint optimization problem is established with the goal of maximizing the average effective capacity. Constraints are set, and the original problem is decomposed into a cache placement subproblem and a node deployment subproblem. The problems are solved separately to obtain the solution results. Based on the solution results, the block coordinate descent method is used to iteratively optimize the cache placement scheme and node deployment density until convergence, thus obtaining the joint optimal configuration.

[0007] As a preferred embodiment of the emergency communication network and QoS guarantee method for UAV-ground cache node collaboration described in this invention, the construction of the emergency communication network model includes: The UAVs are randomly distributed on a plane at a given altitude according to a homogeneous Poisson point process, and the communication emergency response nodes and affected users are randomly distributed on the ground according to independent homogeneous Poisson point processes. Each affected user is served by a communication emergency response node with a probability of the backhaul offloading coefficient, and by a drone with a probability of 1 minus the backhaul offloading coefficient. A Rayleigh block fading channel model is used to determine the small-scale fading coefficients, with a fixed frame length. The total bandwidth of the UAV is divided into multiple orthogonal channels, and the direct communication transmission between communication emergency response nodes shares the orthogonal channels in the underlying mode.

[0008] As a preferred embodiment of the emergency communication network and QoS guarantee method for UAV-ground cache node collaboration described in this invention, the method includes: introducing a statistical delay service quality index, using effective capacity as the objective function, and determining the maximum achievable rate under delay constraints, including: The statistical latency service quality index is used to quantify the latency service quality requirements of disaster-affected users. It is defined as the probability of the queue length exceeding a threshold and the queue length threshold when the queue length converges to infinity. The effective capacity is calculated based on the statistical delay service quality index and the service expectation, wherein the service expectation is the expectation of an exponential function over the service rate process, the exponential function is obtained based on the statistical delay service quality index and the service rate of the current frame, and the service rate is calculated from the sub-channel bandwidth, frame length and signal-to-interference-plus-noise ratio. The beneficial effect of this preferred scheme is that it introduces a delay QoS index, establishes an optimization framework with effective capacity as the objective function, maximizes network throughput under the constraint of delay QoS, and effectively ensures the timeliness and reliability of emergency communication.

[0009] As a preferred embodiment of the emergency communication network and QoS guarantee method for UAV and ground cache node collaboration described in this invention, the joint optimization problem includes: The objective of the joint optimization problem is to maximize the average effective capacity, which is obtained by weighted summation of the effective capacity of each content requested by the user of the emergency response node and the effective capacity of the user of the drone service. The constraints include that the sum of the caching probabilities of all requested content does not exceed the storage capacity of the communication emergency response node, and the deployment density of the communication emergency response node does not exceed the maximum allowed deployment density. The joint optimization problem is decomposed into two sub-problems: a cache placement sub-problem under a given communication emergency response node deployment density, and a node deployment density sub-problem under a given cache placement scheme. After solving the two sub-problems respectively, a joint near-optimal solution is obtained through iteration. The beneficial effects of this preferred solution are that it optimizes the placement of the joint cache and the deployment of CER, obtains the joint optimal solution, and significantly improves the average effective capacity of the network.

[0010] As a preferred embodiment of the emergency communication network and QoS guarantee method for UAV and ground buffer node collaboration described in this invention, obtaining the joint optimal configuration includes: Initialize the iteration count and set the initial cache placement scheme to uniform caching. In each iteration, the current deployment density of the communication emergency response nodes is fixed, the cache placement scheme is updated using the solution to the cache placement subproblem, the updated cache placement scheme is fixed, and the node deployment density is updated using the solution to the node deployment density subproblem. The convergence condition is that the difference between the cache placement scheme of the current round and the previous round is less than the preset tolerance level; After iterative convergence, the final cache placement scheme and communication emergency response node deployment density are output as a joint optimal configuration for ensuring latency service quality and maximizing network throughput for disaster-stricken users in the emergency communication network.

[0011] As a preferred embodiment of the emergency communication network and QoS guarantee method for UAV-ground cache node collaboration described in this invention, the emergency communication network model further includes: The probability of urgent content requests follows a Zipf distribution, and the content category... There are N urgent content items, each of the same size L. The Zipf distribution is used to quantify the request probability of each content item. The communication emergency response nodes adopt a probabilistic caching model, and each communication emergency response node uses a cache placement scheme. , Let i be the probability of caching content i, and the sum of all content caching probabilities of each communication emergency response node shall not exceed the node's storage capacity.

[0012] As a preferred embodiment of the emergency communication network and QoS guarantee method for UAV-ground cache node collaboration described in this invention, the original problem decomposition includes: The cache placement subproblem is solved using the Lagrange multiplier method and the Carlow-Kun-Tucker condition. The node deployment density subproblem is solved by transforming the non-convex part into a convex function through a first-order Taylor expansion.

[0013] Secondly, the present invention provides an emergency communication network and QoS guarantee system for the collaboration of UAVs and ground-based buffer nodes, comprising: The network model building module is used to build an emergency communication network model consisting of drone base stations, communication emergency response nodes, and disaster-affected users. It defines node distribution density parameters and backhaul offload coefficients, and allocates service types to users proportionally. The statistical QoS modeling module is used to determine the maximum achievable rate under latency constraints based on the emergency communication network model, through the statistical latency service quality assurance theory, and with effective capacity as the objective function. The optimization problem construction and decomposition module is used to establish a joint optimization problem with the goal of maximizing the average effective capacity. It sets constraints, decomposes the original problem into a cache placement subproblem and a node deployment subproblem, solves the problem separately, and obtains the solution results. The joint optimization iteration module is used to iteratively optimize the cache placement scheme and node deployment density using the block coordinate descent method based on the solution results until convergence, thereby obtaining the joint optimal configuration.

[0014] Thirdly, the present invention provides a computer device, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the emergency communication network and QoS guarantee method for UAV and ground cache node collaboration are implemented.

[0015] Fourthly, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the emergency communication network and QoS guarantee method for collaboration between a UAV and a ground cache node.

[0016] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention introduces a latency QoS index and establishes an optimization framework with effective capacity as the objective function, maximizing network throughput while satisfying latency QoS constraints, effectively ensuring the timeliness and reliability of emergency communication; it optimizes joint cache placement and CER deployment by decomposing the non-convex problem into cache placement subproblems and CER deployment subproblems, solving them separately, and iteratively optimizing based on the block coordinate descent method to obtain the joint optimal solution, significantly improving the average effective network capacity; it obtains a QoS-aware cache placement scheme by solving the optimal cache probability distribution using a convex optimization method under a given CER deployment density, enabling popular content to have a higher cache probability, effectively improving cache hit rate and network throughput; it achieves QoS-aware CER deployment density optimization by transforming the non-convex problem into a convex problem using a first-order Taylor expansion under a given cache placement scheme, and approximating the optimal CER deployment density by fitting an inverse function, achieving precise optimization of CER deployment density; and it achieves a balance between interference and capacity by optimizing CER deployment density, enhancing network capacity while controlling mutual interference between CERs, avoiding performance degradation due to over-deployment, and achieving overall network performance optimization. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of the overall process of an emergency communication network and QoS guarantee method for collaboration between a drone and a ground cache node, as described in one embodiment of the present invention.

[0019] Figure 2 This is a schematic diagram of an emergency communication network and QoS guarantee method for collaboration between UAVs and ground cache nodes, as described in an embodiment of the present invention, illustrating the network topology of UAVs, CERs, and affected users.

[0020] Figure 3 This invention provides an emergency communication network and QoS guarantee method for collaboration between unmanned aerial vehicles (UAVs) and ground-based buffer nodes, as described in one embodiment. and The inverse function fit tightness verification plot.

[0021] Figure 4 Different Zipf parameters for an emergency communication network and QoS guarantee method for UAV and ground buffer node collaboration, as described in one embodiment of the present invention. The following is a probability distribution diagram of QoS-aware caching.

[0022] Figure 5 This invention relates to a method for emergency communication network and QoS guarantee involving UAV and ground-based cache nodes, as described in one embodiment of the invention, with different cache capacities. The following is a probability distribution diagram of QoS-aware caching.

[0023] Figure 6 Different caching schemes for an emergency communication network and QoS guarantee method involving UAV and ground caching nodes, as described in one embodiment of the present invention, are presented in different ways. The following is a comparison chart of average effective capacity.

[0024] Figure 7 Different caching schemes for an emergency communication network and QoS guarantee method involving UAV and ground caching nodes, as described in one embodiment of the present invention, are presented in different ways. The following is a comparison chart of average effective capacity.

[0025] Figure 8 This invention relates to a different embodiment of an emergency communication network and QoS guarantee method for collaboration between a UAV and a ground-based buffer node. Lower average effective capacity Relationship diagram.

[0026] Figure 9 This invention relates to a different embodiment of an emergency communication network and QoS guarantee method for collaboration between a UAV and a ground-based buffer node. Performance comparison chart of the joint optimization scheme with other schemes. Detailed Implementation

[0027] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0028] Reference Figures 1-3 Tables 1 and 2 illustrate an embodiment of the present invention, providing an emergency communication network and QoS guarantee method for collaboration between UAVs and ground-based buffer nodes, comprising: S101, construct an emergency communication network model consisting of drone base stations, communication emergency response nodes and disaster-stricken users, define node distribution density parameters and backhaul offload coefficients, and allocate user service types proportionally. S102, based on the emergency communication network model, introduces the statistical delay quality of service index through the statistical delay quality of service assurance theory, and uses the effective capacity as the objective function to determine the maximum achievable rate under the delay constraint; S103. Establish a joint optimization problem with the objective of maximizing the average effective capacity. Set constraints and decompose the original problem into a cache placement subproblem and a node deployment subproblem. Solve the problem separately to obtain the solution results. S104. Based on the solution results, the block coordinate descent method is used to iteratively optimize the cache placement scheme and node deployment density until convergence, thus obtaining the joint optimal configuration.

[0029] In a preferred embodiment, constructing an emergency communication network model includes: The drones are randomly distributed on a plane at a given altitude according to a homogeneous Poisson point process, while the communication emergency response nodes and affected users are randomly distributed on the ground according to independent homogeneous Poisson point processes. Each affected user is served by the communication emergency response node with a probability of returning the unloading coefficient, and by the drone with a probability of 1 minus the return unloading coefficient. A Rayleigh block fading channel model is used to determine the small-scale fading coefficients, with a fixed frame length. The total bandwidth of the drone is divided into multiple orthogonal channels, and the direct communication transmission between communication emergency response nodes shares the orthogonal channels in the underlying mode.

[0030] Specifically, an emergency communication network model is constructed, consisting of unmanned aerial vehicle (UAV) base stations, emergency communication response nodes, and affected users. The UAV base stations are constructed using a homogeneous Poisson point process (PPP). Distribution, density is Located at altitude H On the plane; CER according to PPP Distribution, density is Located on the ground; affected users follow the homogeneous Poisson point process. Distribution, density is It is located on the ground.

[0031] Define the return offload coefficient Specify the proportion of disaster-affected users that need to be served by CER, with each disaster-affected user having a probability. Serviced by CER, with probability Serviced by drones.

[0032] In a preferred embodiment, the emergency communication network model further includes: The probability of urgent content requests follows a Zipf distribution, and the content category... There are N urgent content items, each of the same size L. The request probability of each content item is quantified using a Zipf distribution. The request probability of content item i is... Represented as: in, This is a Zipf parameter, representing the skewness of the content popularity distribution; The communication emergency response nodes adopt a probabilistic caching model, and each communication emergency response node uses a cache placement scheme. , Let i be the probability of caching content i, and the sum of all content caching probabilities of each Communication Emergency Response (CER) node shall not exceed the node's storage capacity, i.e., the storage capacity of each CER is limited to 1. M， It must meet the following requirements: In a preferred embodiment, a statistical delay quality of service index is introduced, using effective capacity as the objective function, to determine the maximum achievable rate under delay constraints, including: The statistical latency service quality index is used to quantify the latency service quality requirements of disaster-affected users. It is defined as the probability of the queue length exceeding a threshold and the queue length threshold when the queue length converges to infinity. Effective capacity is calculated based on the statistical delay quality of service index and the expected value. The expected value is the expectation of an exponential function over the service rate process. The exponential function is obtained based on the statistical delay quality of service index and the service rate of the current frame. The service rate is calculated from the sub-channel bandwidth, frame length, and signal-to-interference-plus-noise ratio.

[0033] Specifically, the statistical latency QoS index is adopted. θ Quantifying the latency QoS requirements of affected users, based on the large deviation principle, and queue length process. Converging to random variable ,satisfy: in, This is the boundary of the queue length; θ It is a statistical QoS index, representing the exponential decay rate of the probability of a delay boundary service quality violation; θ A larger value indicates a lower probability of violation, corresponding to stricter QoS requirements; θ The smaller the value, the higher the probability of violation, corresponding to more lenient QoS requirements.

[0034] Based on the statistical QoS guarantee theory, a delay QoS index is introduced. θ The effective capacity is defined as the objective function, used to measure the maximum constant arrival rate while satisfying latency QoS constraints. The data service rate process (discrete-time stationary ergodic process) is denoted as a sequence. ,in, Indicates the duration as The frame index, for a given and , The corresponding effective capacity is denoted as It can be written as: in, Expressing expectations, The service rate is the rate within the k-th time frame.

[0035] Further, SINR analysis and effective capacity calculation are performed, if the affected users... The content i requested by CER has a signal-to-interference-plus-noise ratio (SINR) of: in, The signal-to-interference-plus-noise ratio (SIR) when a disaster-affected user is served by CER and requests content i. For the most recent CER of cached content i, The transmit power for each CER, For service nodes To the disaster victim node Small-scale channel fading coefficient, From node To the node physical distance, This is the path loss index. For noise power, Interference caused by drones To remove Interference caused by other CERs is represented as follows: in, This represents the set of PPP nodes in the spatial distribution of CER. The transmit power of each drone base station, express Except The set of all elements outside of, This represents the set of PPP nodes representing the spatial distribution of drones. For the node To the node u Small-scale channel fading coefficient, From node To the node The physical distance, if the affected users The SINR for services provided by drones is: in, For disaster-stricken users, the signal-to-interference-plus-noise ratio (SIR) when using drone services is given. For the latest drones, For service nodes To the node Small-scale channel fading coefficient, For the node To the node physical distance, Interference caused by CER To remove Interference caused by other drones is represented as: Based on the properties of PPP, the cumulative distribution function of SINR can be derived. The complete derivation of Lemma 1 is as follows: Lemma 1, the cumulative distribution function of SINR for disaster-affected users when served by CER is: : In the formula, For probability operators, τ It is the threshold variable of SINR. The probability of caching content i for CER. For CER deployment density, As an intermediate variable related to CER and drone interference, 2 / α is a simplified parameter derived from the path loss exponent.

[0036] SINR cumulative distribution function for disaster-affected users served by drones for: In the formula, Deployment density of drone base stations; The flight altitude of the drone. For intermediate variables related to drones and CER interference; where: In the formula, For an incomplete gamma function, For gamma function, For the SINR of the affected users when using UAV services, , .

[0037] Proof: Based on (1), we get: In the above equation, it is assumed that the noise power is much smaller than the interference power, and therefore can be ignored. To further calculate the value of the above equation, it is necessary to obtain the interference power of the current reception relative to the CER. Interference power between UAV and the current reception The corresponding Laplace transform. Let , The corresponding Laplace transform can be derived as follows: in, To interfere with the Laplace transform of random variable I, , is a complex variable in the Laplace transform, (9a) stems from the independence between different channels; step (9b) stems from the probability generating function of PPP; step (9c) holds because It follows a Rayleigh distribution. Similarly, we can obtain... The corresponding Laplace transform is: in, In (10), It is the gamma function. It is an incomplete gamma function. Then, ignore... The dependency, (8), can be further written as: in, , yes The probability density function. Therefore, It can be represented as: Similarly, let The three-dimensional distance between the service drone and the disaster-stricken user includes: , Based on (13) The corresponding CDF can be obtained as follows: in, , yes The probability density function of the horizontal projection is given in Lemma 1.

[0038] Then, if u o The service provided by CER requests content i, and its instantaneous service rate It can be represented as: In the formula, The bandwidth of each orthogonal channel. For frame length, ( x () is a logarithm with base 2.

[0039] If u0 is served by a drone and requests content i, its instantaneous service rate It can be represented as: Substitute (15) into the effective capacity The formulas and their corresponding effective capacities are as follows: in, The effective capacity when a user requests content i while serving CER. Effective capacity when serving users of drones As an intermediate variable, yes The probability density function.

[0040] In a preferred embodiment, establishing the joint optimization problem includes: The goal of the joint optimization problem is to maximize the average effective capacity, which is obtained by weighted summing of the effective capacity of each user request from the communication emergency response node and the effective capacity of the user of the drone service. The constraints include that the sum of the caching probabilities of all requested content does not exceed the storage capacity of the communication emergency response node, and the deployment density of the communication emergency response node does not exceed the maximum allowed deployment density. The joint optimization problem is decomposed into two subproblems: the cache placement subproblem under a given communication emergency response node deployment density, and the node deployment density subproblem under a given cache placement scheme. After solving the two subproblems respectively, a joint near-optimal solution is obtained through iteration.

[0041] Specifically, we establish an optimization problem P1 for joint cache placement and CER deployment, with the objective of maximizing average effective capacity. Constraints include CER cache capacity limitations and deployment density limitations. The optimization problem P1 is expressed as: in, Place vectors for caching. The maximum CER deployment density; the constraint indicates that the cache placement scheme must meet the storage capacity, and the deployment density of rescue personnel cannot exceed [a certain value]. Problem P1 is a non-convex problem, making it difficult to solve. Therefore, P1 is decomposed into two subproblems: designing a QoS-aware cache placement scheme and optimizing the deployment density of rescue personnel. Then, a near-optimal solution is obtained using the block coordinate descent method.

[0042] In a preferred embodiment, the original problem decomposition includes: The cache placement subproblem is proven to be a convex optimization problem, and solved using the Lagrange multiplier method and the Carlow-Kun-Tucker condition. The node deployment density problem is solved by transforming the non-convex part into a convex function through a first-order Taylor expansion.

[0043] Specifically, the optimization problem P1 is decomposed into a cache placement subproblem P2 and a CER deployment subproblem P3, which are solved separately. Considering the QoS-aware cache placement design under a given rescue personnel deployment density, and given a CER deployment density... The cache placement probability is then solved using a convex optimization method. To obtain a QoS-aware cache placement scheme, the cache placement subproblem P2 is represented as: in: To solve P2, we need to analyze its convexity. The following is the complete derivation of Lemma 2, proving that P2 is a convex optimization problem. Lemma 2: Function right Since it is a convex function, the cache placement subproblem P2 is a convex optimization problem.

[0044] Proof: Because the constraints of P2 are about It is linear, so if about If the problem is convex, then P2 is a convex problem. Rewritten as ,in, , about The second derivative is denoted as , can be represented as: In (19), about The first and second derivatives are denoted as follows: and Then, the molecule of (19) can be written as: In (20), about The first and second derivatives are respectively: For the sake of simplicity, Written as From (21), we know that the numerator of (20) is greater than or equal to 0, because: Combining (19) and (20), we can see that Since it is a convex function, P2 is a convex problem. Q.E.D.

[0045] Then, construct the Lagrangian function corresponding to P2: in, It is a non-negative Lagrange multiplier. Then, based on The KKT condition corresponding to P2 can be written as: in, about The first derivative is: In the formula, for right The first derivative, for right The first derivative, due to the optimal Always satisfied ,therefore Meanwhile, according to Lemma 2, about It is convex, and Follow It increases and increases. Therefore, given , The maximum and minimum values ​​are respectively and Then, based on , And (29a)~(29d), can be based on different Value will The values ​​are categorized as follows.

[0046] Case I: ( In this case, for any have Therefore, according to (24a), we have .because and If it is non-negative, we can obtain Then, according to (29c), we can obtain... .

[0047] Scenario II: ( In this case, it can be seen from (24c) and (24d) that... and They cannot both be true simultaneously. If and According to (24c) Then, based on (24a), we have ,Should The scope is beyond the discussion range of this situation. Furthermore, if and From (24d), we can obtain Therefore, according to (24a), we have ,Should The scope also exceeds the range of discussion in this case. Therefore, for In such cases, there are and Based on the above analysis, the optimal solution in this scenario is... Always satisfy: However, due to It contains complex integrals, which are difficult to obtain by solving (26). The analytical solution was found. To solve this problem, computer mathematical tools were used to find the solution using multiple sampling points. about The exact fit of the inverse function. Then, Approximately: in, yes about The fitted inverse function.

[0048] Scenario III: ( In this case, for any have Therefore, according to (24a), we can obtain... Similarly, due to and If it is non-negative, we can obtain Then, based on (24c), we have . Lemma 2 is proved. Reference Figure 3 Optimal QoS-aware cache placement scheme It can be obtained through the search method shown in Algorithm 1 in Table 1.

[0049] Table 1: Algorithm 1, QoS-Aware Cache Placement Algorithm

[0050] Furthermore, given a cache placement scheme, the optimal CER deployment density is solved by fitting the inverse function method. The CER deployment subproblem P3 is represented as follows: in The cumulative SINR distribution function for disaster-affected users when served by drones; The following is the complete derivation of Lemma 3, with analysis... and The unevenness: Lemma 3, function right It is a convex function, a function right It is a concave function.

[0051] Proof: Function about It is convex if its second derivative is... , about The second derivative is: in, for right The second-order partial derivative, , and These are the first and second derivatives, respectively. For simplified notation, let's denote them as... , ,but Its first and second derivatives are: The molecule of (28) can be further written as: in, , Formula (30a) holds because the integration domain holds with respect to... The symmetry. Following formula (30a), we have... and From (30b), we can see that the molecule of (28) is greater than or equal to 0, because: therefore, about It is convex.

[0052] on the other hand, Can be rewritten as ,in and All are greater than 0, it is convex, and follows It increases and decreases. Therefore, It is convex, and It is concave, therefore we get about It is concave. Q.E.D.

[0053] From Lemma 3, we know that when When smaller (e.g.) P3 may be nonconvex. To transform P3 into a condition for any... The problem of convexity can be approximated by a first-order Taylor expansion. : in: The above formula is exist The first derivative at point P3 is given; therefore, P3 can be transformed into the following convex problem: in, Let be the objective function of the convex problem P4. about The first derivative is: Based on (35), by solving You can get The outpost, denoted as Due to the existence of complex integrals, An analytical solution is difficult to find. Using computer mathematical tools, a solution can be found... about The exact fit of the inverse function, then, It can be approximated as: in, yes about The inverse function of the fitted function. Based on the above analysis, the near-optimal deployment density of rescue personnel at P4. It can be obtained through Algorithm 2 in Table 2.

[0054] Table 2: Algorithm 2, QoS-Aware CER Deployment Density Optimization Algorithm

[0055] In a preferred embodiment, obtaining the joint optimal configuration includes: Initialize the iteration count and set the initial cache placement scheme to uniform caching. In each iteration, the current deployment density of communication emergency response nodes is fixed, the cache placement scheme is updated using the solution to the cache placement subproblem, the updated cache placement scheme is fixed, and the node deployment density is updated using the solution to the node deployment density subproblem. The convergence condition is that the difference between the cache placement scheme of the current round and the previous round is less than the preset tolerance level; After iterative convergence, the final cache placement scheme and communication emergency response node deployment density are output as a joint optimal configuration for ensuring latency service quality and maximizing network throughput for disaster-stricken users in the emergency communication network.

[0056] Specifically, the cache placement and CER deployment are iteratively optimized based on the block coordinate descent method until convergence, resulting in a joint optimization scheme. The iterative optimization based on the block coordinate descent method includes: Initialization, set the number of iterations. Initialize cache placement scheme CER deployment density Average effective capacity ; The iterative process includes: For a given Algorithm 1 is used to solve the problem. See Table 1; For a given Algorithm 2 is used to solve the problem. See Table 2; renew , , ; Repeat the iterations until the convergence condition is met. Satisfy, among which This represents the tolerance level.

[0057] Output joint optimal solution cache placement scheme Deployment density of communication emergency response nodes .

[0058] It should be noted that, compared with existing emergency communication network technologies, this invention has significant technical advantages and improved effectiveness: It features statistical QoS guarantees, introduces a delay QoS index θ, and establishes an optimization framework with effective capacity as the objective function. This maximizes network throughput while satisfying delay QoS constraints, effectively ensuring the timeliness and reliability of emergency communications. The joint cache placement and CER deployment were optimized by decomposing the non-convex problem into cache placement subproblems and CER deployment subproblems, solving them separately and iteratively optimizing them based on the block coordinate descent method to obtain the joint optimal solution, which significantly improved the average effective capacity of the network. We obtained a QoS-aware cache placement scheme. Given a CER deployment density, we solved the optimal cache probability distribution using a convex optimization method, which enabled popular content to have a higher cache probability, effectively improving cache hit rate and network throughput. It achieves QoS-aware CER deployment density optimization. Under a given cache placement scheme, the non-convex problem is transformed into a convex problem through first-order Taylor expansion, and the optimal CER deployment density is approximated by fitting the inverse function, thus achieving accurate optimization of CER deployment density. It achieves a balance between interference and capacity. By optimizing the CER deployment density, it enhances network capacity while controlling mutual interference between CERs, avoiding performance degradation caused by over-deployment, and achieving overall network performance optimization.

[0059] This invention can be applied to various emergency communication scenarios, including rescue communications after natural disasters and large-scale communication support for temporary events, and has significant practical value and application prospects. The above is an illustrative scheme of an emergency communication network and QoS guarantee method for UAV and ground buffer node collaboration according to this embodiment. It should be noted that the technical solution of this emergency communication network and QoS guarantee system for UAV and ground buffer node collaboration belongs to the same concept as the technical solution of the aforementioned emergency communication network and QoS guarantee method for UAV and ground buffer node collaboration. Details not described in detail in the technical solution of the emergency communication network and QoS guarantee system for UAV and ground buffer node collaboration in this embodiment can be found in the description of the technical solution of the aforementioned emergency communication network and QoS guarantee method for UAV and ground buffer node collaboration.

[0060] This embodiment provides an emergency communication network and QoS guarantee system for the collaboration between UAVs and ground-based cache nodes, including: The network model building module is used to build an emergency communication network model consisting of drone base stations, communication emergency response nodes, and disaster-affected users. It defines node distribution density parameters and backhaul offload coefficients, and allocates service types to users proportionally. The statistical QoS modeling module is used to determine the maximum achievable rate under latency constraints based on the emergency communication network model, through the statistical latency service quality assurance theory, and by introducing the statistical latency service quality index, with effective capacity as the objective function. The optimization problem construction and decomposition module is used to establish a joint optimization problem with the goal of maximizing the average effective capacity. It sets constraints, decomposes the original problem into a cache placement subproblem and a node deployment subproblem, solves the problem separately, and obtains the solution results. The joint optimization iteration module is used to iteratively optimize the cache placement scheme and node deployment density using the block coordinate descent method based on the solution results until convergence, thus obtaining the joint optimal configuration.

[0061] This embodiment also provides a computer device suitable for emergency communication networks and QoS assurance scenarios involving collaboration between UAVs and ground-based cache nodes, including: The system includes a memory and a processor. The memory stores computer-executable instructions, and the processor executes these instructions to implement an emergency communication network and QoS guarantee method for collaboration between a UAV and a ground-based cache node, as proposed in the above embodiments.

[0062] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements an emergency communication network and QoS guarantee method for collaboration between a drone and a ground cache node as proposed in the above embodiments.

[0063] The storage medium proposed in this embodiment belongs to the same inventive concept as the emergency communication network and QoS guarantee method for collaboration between UAV and ground cache node proposed in the above embodiments. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0064] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0065] Reference Figures 4-9 This embodiment also provides an emergency communication network and QoS guarantee method for the collaboration between UAVs and ground cache nodes. In order to verify its beneficial effects, it is scientifically demonstrated through economic benefit calculation and simulation experiments.

[0066] This embodiment verifies the effectiveness of the invention in a typical disaster area scenario, assuming the disaster area area is 10 km². 2 Density of drone base stations CER deployment density User density The communication parameters are set as follows: drone bandwidth Sub-channel number Sub-channel bandwidth ; UAV transmission power CER transmit power ; Path loss index drone altitude ; Total content Cache capacity Zipf parameters ; Return unloading coefficient ; Latency QoS Index to Frame length .

[0067] Furthermore, a QoS-aware cache placement scheme simulation was performed. The CER deployment density was set. Latency QoS Index The optimal cache placement scheme is solved using Algorithm 1 of this invention. Figure 4 Different Zipf parameters are shown. The probability distribution of QoS-aware caching under the following conditions shows that: The probability of caching increases as the content index decreases, meaning that content with higher popularity has a higher probability of being cached. For popular content (such as indexes) ), cache probability varies Increase and increase; For non-popular content (such as indexes) ), cache probability varies Increase and decrease; Indicates that with The increased storage capacity allows for a greater focus on popular content.

[0068] Figure 5 Showing different cache capacities QoS-aware cache probability distribution ( The results show that: The probability of caching each piece of content varies Increase and increase; when As the cache size increases, the cache probability tends to be uniformly distributed.

[0069] Furthermore, a performance comparison of different caching schemes was conducted. Figure 6 It demonstrates different caching schemes in different... Comparison of average effective capacity under the following conditions , , , The comparison schemes include: Uniform Caching (UC): ; Most Popular Caching (MPC): The probability of caching the top M most popular content items is 1, and the probability of caching the rest is 0. The present invention provides a QoS-aware caching scheme.

[0070] The results of comparing the three schemes above show that: The present invention solution in various The maximum average effective capacity is obtained under all conditions; when In this case, the solution of the present invention is equivalent to the UC solution; Average effective capacity varies The increase in content concentration indicates that higher content concentration is beneficial to improving network throughput.

[0071] Figure 7 It demonstrates different caching schemes in different... Comparison of average effective capacity ( ) The results show that: The present invention solution in various The maximum average effective capacity is obtained under all conditions; when In this case, the solution of the present invention is equivalent to the MPC solution; Average effective capacity varies The fact that the cache size increases indicates that increasing the cache size is beneficial to improving network throughput.

[0072] Furthermore, simulations were conducted to optimize the CER deployment density. Figure 8 Showing different Lower average effective capacity The changing relationship, , , The diagram is marked with each The corresponding optimal CER deployment density , , The results showed that: Average effective capacity varies Increase and increase; Optimal CER Deployment Density Follow The increase in size means that more CERs are needed to collaboratively serve users; when At that time, the average effective capacity follows The increase in size indicates that deploying CER can improve network throughput; when At that time, the average effective capacity follows An increase followed by a decrease indicates that excessive CER leads to increased interference and reduced network performance.

[0073] Furthermore, the performance of the joint optimization scheme was verified. Figure 9 Showing different The following is a performance comparison of the joint optimization scheme of this invention with other schemes. , , The comparison schemes include: Drone-only transmission solution ); Fixed maximum CER deployment density scheme ( ); The present invention provides a joint optimization scheme.

[0074] The results show that: Average effective capacity varies Increased QoS requirements lead to decreased network throughput; The joint optimization scheme of this invention is in each The maximum average effective capacity is obtained under all conditions; Compared to drone-only transmission solutions, the present invention not only reduces the burden of drone backhaul but also significantly improves network throughput. Compared to a fixed maximum CER deployment density scheme, the present invention optimizes the CER deployment density to avoid interference caused by excessive CERs, thereby further improving network performance.

[0075] The simulation results above verify the correctness of the theoretical analysis of this invention and prove the effectiveness and superiority of the QoS-aware cache placement scheme, QoS-aware CER deployment density optimization scheme and joint optimization scheme proposed in this invention.

[0076] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. An emergency communication network and QoS guarantee method for collaboration between unmanned aerial vehicles (UAVs) and ground-based buffer nodes, characterized in that, include: Construct an emergency communication network model consisting of drone base stations, communication emergency response nodes, and disaster-affected users; define node distribution density parameters and backhaul offload coefficients; and allocate service types to users proportionally. Based on the aforementioned emergency communication network model, the statistical delay quality of service index is introduced through the statistical delay quality of service assurance theory. With effective capacity as the objective function, the maximum achievable rate under delay constraints is determined. A joint optimization problem is established with the goal of maximizing the average effective capacity. Constraints are set, and the original problem is decomposed into a cache placement subproblem and a node deployment subproblem. The problems are solved separately to obtain the solution results. Based on the solution results, the block coordinate descent method is used to iteratively optimize the cache placement scheme and node deployment density until convergence, thus obtaining the joint optimal configuration.

2. The emergency communication network and QoS guarantee method for collaboration between UAVs and ground buffer nodes as described in claim 1, characterized in that, Constructing the emergency communication network model includes: The UAVs are randomly distributed on a plane at a given height according to a homogeneous Poisson point process, and the communication emergency response nodes and affected users are randomly distributed on the ground according to independent homogeneous Poisson point processes. Each of the affected users is served by a communication emergency response node with a probability of the backhaul offloading coefficient, and by a drone with a probability of 1 minus the backhaul offloading coefficient. The Rayleigh block fading channel model is used to determine the small-scale fading coefficient, with a fixed frame length. The total bandwidth of the UAV is divided into multiple orthogonal channels, and the direct communication transmission between communication emergency response nodes shares the orthogonal channels in the underlying mode.

3. The emergency communication network and QoS guarantee method for collaboration between UAVs and ground buffer nodes as described in claim 1, characterized in that, A statistical delay service quality index is introduced, with effective capacity as the objective function, to determine the maximum achievable rate under delay constraints, including: The statistical latency service quality index is used to quantify the latency service quality requirements of disaster-affected users. It is defined as the probability of the queue length exceeding a threshold and the queue length threshold when the queue length converges to infinity. The effective capacity is calculated based on the statistical latency quality of service index and the service expectation, wherein the service expectation is the expectation of an exponential function over the service rate process, the exponential function is obtained based on the statistical latency quality of service index and the service rate of the current frame, and the service rate is calculated from the sub-channel bandwidth, frame length and signal-to-interference-plus-noise ratio.

4. The emergency communication network and QoS guarantee method for UAV and ground buffer node collaboration as described in claim 3, characterized in that, Establishing joint optimization problems includes: The objective of the joint optimization problem is to maximize the average effective capacity, which is obtained by weighted summation of the effective capacity of each content requested by the user of the emergency response node and the effective capacity of the user of the drone service. The constraints include that the sum of the caching probabilities of all requested content does not exceed the storage capacity of the communication emergency response node, and the deployment density of the communication emergency response node does not exceed the maximum allowed deployment density. The joint optimization problem is decomposed into two sub-problems: a cache placement sub-problem under a given communication emergency response node deployment density, and a node deployment density sub-problem under a given cache placement scheme. After solving the two sub-problems respectively, a joint near-optimal solution is obtained through iteration.

5. The emergency communication network and QoS guarantee method for UAV and ground buffer node collaboration as described in claim 4, characterized in that, Achieving the joint optimal configuration includes: Initialize the iteration count and set the initial cache placement scheme to uniform caching. In each iteration, the current deployment density of the communication emergency response nodes is fixed, the cache placement scheme is updated using the solution to the cache placement subproblem, the updated cache placement scheme is fixed, and the node deployment density is updated using the solution to the node deployment density subproblem. The convergence condition is that the difference between the cache placement scheme of the current round and the previous round is less than the preset tolerance level; After iterative convergence, the final cache placement scheme and communication emergency response node deployment density are output as the joint optimal configuration.

6. The emergency communication network and QoS guarantee method for UAV and ground buffer node collaboration as described in claim 2, characterized in that, The emergency communication network model also includes: The probability of urgent content requests follows a Zipf distribution, and the content category... There are N urgent content items, each of the same size L. The Zipf distribution is used to quantify the request probability of each content item. The communication emergency response nodes adopt a probabilistic caching model, and each communication emergency response node uses a cache placement scheme. , Let i be the probability of caching content i, and the sum of all content caching probabilities of each communication emergency response node shall not exceed the node's storage capacity.

7. The emergency communication network and QoS guarantee method for UAV and ground buffer node collaboration as described in claim 4, characterized in that, The original problem decomposition includes: The cache placement subproblem is solved using the Lagrange multiplier method and the Carlow-Kun-Tucker condition. The node deployment density subproblem is solved by transforming the non-convex part into a convex function through a first-order Taylor expansion.

8. An emergency communication network and QoS guarantee system for UAV and ground buffer nodes in collaboration, employing the emergency communication network and QoS guarantee method for UAV and ground buffer nodes in collaboration as described in any one of claims 1 to 7, characterized in that, include: The network model building module is used to build an emergency communication network model consisting of drone base stations, communication emergency response nodes, and disaster-affected users. It defines node distribution density parameters and backhaul offload coefficients, and allocates service types to users proportionally. The statistical QoS modeling module is used to determine the maximum achievable rate under latency constraints based on the emergency communication network model, through the statistical latency service quality assurance theory, and with effective capacity as the objective function. The optimization problem construction and decomposition module is used to establish a joint optimization problem with the goal of maximizing the average effective capacity. It sets constraints, decomposes the original problem into a cache placement subproblem and a node deployment subproblem, solves the problem separately, and obtains the solution results. The joint optimization iteration module is used to iteratively optimize the cache placement scheme and node deployment density using the block coordinate descent method based on the solution results until convergence, thereby obtaining the joint optimal configuration.

9. A computer device, characterized in that, include: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, they implement the steps of the emergency communication network and QoS guarantee method for collaboration between UAV and ground cache node as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It stores computer-executable instructions, which, when executed by a processor, implement the steps of the emergency communication network and QoS guarantee method for collaboration between a UAV and a ground cache node as described in any one of claims 1 to 7.

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