Unmanned aerial vehicle emergency communication network node deployment method, system, device and storage medium
By optimizing UAV deployment and topology construction, and combining FSO/RF communication modules and algorithms, the cost and reliability issues of UAV emergency communication networks in post-disaster communication have been solved, achieving rapid and low-cost communication recovery.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- GUANGDONG UNIV OF TECH
- Filing Date
- 2024-08-19
- Publication Date
- 2026-07-14
AI Technical Summary
Existing drone emergency communication networks struggle to minimize network costs while ensuring network connectivity and traffic requirements during post-disaster communications, and frequent network topology reconfiguration increases control costs and error rates.
By jointly optimizing UAV deployment and topology construction, utilizing FSO/RF communication modules, and combining the K-Medoids algorithm and the improved Dijkstra algorithm, the optimal position and altitude of the UAV are determined, the network topology is optimized, signal coverage is ensured, and energy consumption is reduced.
It enabled the rapid deployment of UAV communication equipment under complex atmospheric conditions, reducing network costs and energy consumption while ensuring communication quality and reliability, thus meeting the communication needs of disaster areas.
Smart Images
Figure CN119031378B_ABST
Abstract
Description
Technical Field
[0001] This application belongs to the field of free-space optical network technology, and in particular relates to a method, system, device and storage medium for joint optimization of node deployment and topology design of UAV emergency communication network based on FSO / RF. Background Technology
[0002] In today's highly interconnected global society, wireless communication technology has become central to information transmission and exchange. Whether for simple communication in daily life or for transmitting critical information in emergencies, wireless communication plays an irreplaceable role.
[0003] In emergencies, existing ground communication infrastructure may be damaged or rendered inoperable. Effective communication is not only crucial for restoring normal life but also a decisive factor in the success of rescue operations. Traditional wireless communication networks, including cellular networks and fixed wireless access networks, often become inoperable after a disaster due to base station damage, power outages, or communication link overload. Furthermore, these networks are often designed with specific coverage areas and user densities in mind, rather than for handling sudden disasters. Therefore, when ground infrastructure is damaged or when regional communication demand increases dramatically, traditional networks often struggle to meet emergency communication needs. FSO (Fly-Side Array) technology transmits data directly through the air using lasers, offering very high data transmission rates, suitable for transmitting large amounts of emergency information and data, such as video surveillance data, but it is significantly affected by weather and atmospheric conditions. When natural disasters such as earthquakes, tsunamis, or flash floods occur, communication infrastructure is often damaged, urgently requiring the restoration of emergency communication services. In such situations, providing rapid and effective communication services to disaster areas, especially in areas with complex or inaccessible terrain, presents a significant challenge.
[0004] The combination of free-space optical communication (FSO) technology and unmanned aerial vehicles (UAVs) offers an effective solution to this challenge. FSO utilizes lasers as carriers, eliminating the need for physical fiber optic cables and transmitting information through the atmosphere, enabling high-speed and high-capacity data transmission. Meanwhile, the use of UAVs provides operational flexibility, allowing for rapid deployment to designated locations. Equipped with FSO and radio frequency (RF) communication modules, UAVs can connect not only to remote base stations but also directly serve users within disaster areas. This UAV-assisted emergency communication system plays a crucial role in post-disaster emergency communications due to its rapid deployment and efficient transmission capabilities.
[0005] However, current work primarily focuses on network reliability while neglecting network construction costs. First, one of the biggest challenges facing drones is the limitation of battery life, requiring maximizing user coverage under the premise of limited drone transmission power. Moreover, drone deployment and network topology formation are interdependent; therefore, to quickly restore wireless communication in disaster-stricken areas, drone deployment and network topology formation must be jointly optimized. At the same time, higher network connectivity may consume more network resources and increase network costs, and frequent reconfiguration of network topology may increase control costs, bit error rate, and transmission latency. Therefore, it is necessary to balance drone deployment altitude and power to minimize network costs while ensuring network connectivity and traffic requirements.
[0006] Therefore, a joint optimization method for the deployment and topology design of UAV emergency communication network nodes based on FSO / RF is urgently needed. Summary of the Invention
[0007] The purpose of this application is to provide a method, system, device and storage medium for deploying emergency communication network nodes for unmanned aerial vehicles (UAVs), so as to solve at least one technical problem in the prior art.
[0008] The technical solution of this application is:
[0009] A method for deploying emergency communication network nodes for unmanned aerial vehicles (UAVs) includes:
[0010] Considering the geographic location information of any user, at least one cluster center is selected as the initial horizontal position of the drone using the distance matrix and dissimilarity index;
[0011] Based on the initial horizontal position of the UAV and the location of the ground base station, the swarm of UAVs acts as a temporary airborne base station to provide communication services to ground users. The ground base station is responsible for exchanging data with the swarm of UAVs to obtain the optimal altitude and optimal power output of any UAV, ensuring that each UAV can provide sufficient signal coverage within its service area while minimizing total power consumption.
[0012] Iteratively process any traffic demand of the user, eliminate connections with insufficient bandwidth, find the shortest path that satisfies the constraints, update the network topology and bandwidth, and adjust connection weights until all traffic demands are routed to build the network with the fewest links.
[0013] The iterative process handles any traffic demand from the user, excluding connections with insufficient bandwidth, finding the shortest path that satisfies the constraints, updating the network topology and bandwidth, and adjusting connection weights until all traffic demands are routed, including:
[0014] A joint optimization model for node deployment and topology construction of an unmanned aerial vehicle (UAV)-assisted emergency communication network is established, assuming there are K UAVs, denoted as [equation missing]. Equipped with FSO and radio frequency (RF) communication modules, it can not only connect to remote base stations but also directly serve users in disaster areas. The FSO link distance between drones is expressed as... The traffic matrix in the network is represented as ,in and These are the source site and the target site of the requirement. This is the required bandwidth. The average path loss between the drone and the ground user is expressed as... ,in It's the speed of light. It is the carrier frequency. It is the distance between the drone and the ground user. It is the average additional loss during free space propagation.
[0015] By jointly optimizing drone deployment and topology construction, we can serve the maximum number of users with limited available power while using the minimum number of FSO links to reduce deployment costs.
[0016] The FSO channel, which is affected by factors such as channel fading, atmospheric turbulence, and pointing error, adopts on-off keyed modulation, and its bit error rate expression is derived.
[0017] The objective function is modeled as Using the following constraints to ensure that the UAV altitude, transmit power, and channel capacity remain within thresholds, and assuming that there is a path from s to t for each traffic demand in the traffic matrix, and that each FSO link satisfies the bit error rate and capacity limits, then:
[0018]
[0019]
[0020]
[0021]
[0022]
[0023]
[0024] in, It is the deployment altitude of the i-th drone. It is its ground transmission power. It is a binary variable, if and The FSO link between them is included in the topology. 1 if true, 0 otherwise. β is a number between (0, 1) used to represent parameter weights; δ is the end-to-end bit error rate threshold. This is the maximum transmit power of the drone's radio frequency communication module. It is the lowest altitude at which a drone can move downwards. This is the minimum channel capacity required for communication. Assume each cluster has M users. It is the distance between the drone and the ground user g. It is the path loss index. It is the variance of AWGN noise. yes and Bit error rate of the FSO link between them Indicate demand The index is Traffic through the link The bandwidth portion of the transmission, It is a binary variable, when demand The index is Traffic using links When the value is 1, it is 1; otherwise, it is 0.
[0025] Create an empty network topology T to store the final determined network structure; construct a fully connected directed graph G using all FSO sites, where each site is connected to other sites in a bidirectional link.
[0026] Using each traffic demand in the given traffic matrix, the final network design is obtained and stored in topology T.
[0027] The step of obtaining the final network design and storing it in topology T using each traffic demand in the given traffic matrix includes:
[0028] Examine all connections in the directed graph G and mark those where the remaining bandwidth is insufficient to support the current traffic demand d. st The connection is then removed from graph G;
[0029] In the directed graph G, find the shortest path from point s to point t for the current traffic demand, and check whether the end-to-end bit error rate of the shortest path meets the preset threshold requirement.
[0030] After selecting the shortest path in the directed graph G, subtract d from the remaining bandwidth of each connection on the shortest path. st Values are assigned, and these connections and their reverse connections are added to topology T;
[0031] For each connection in topology T, its weight is updated according to the ratio of its remaining bandwidth to its total capacity until all traffic demands are processed, and the final network design is stored in topology T.
[0032] For each connection in topology T, updating its weight based on the ratio of its remaining bandwidth to its total capacity includes:
[0033] For each connection (i, j) in topology T, based on its remaining bandwidth Relative to its total capacity The proportion is updated to a weight of 1 - / .
[0034] The FSO channel affected by factors such as channel fading, atmospheric turbulence, and pointing error includes:
[0035] The state density function of the FSO channel is:
[0036] ;
[0037] in, It is a modified Bessel function of the second kind, order v. It is the standard gamma function. It is the ratio between the equipment beam radius and the standard deviation of misalignment jitter. and The two parameters can be adjusted for various turbulent conditions. In the case of plane wave propagation, they are directly related to the physical parameters and are expressed as follows:
[0038] ;
[0039] ;
[0040] in It is a dimensionless Rytov variance, representing the intensity of turbulence.
[0041] The signal-to-noise ratio of the link between two FSO transceivers is defined as follows:
[0042] ;
[0043] in For light emission power, It refers to the responsivity of the photodetector. It is the variance of additive white Gaussian noise. This is represented by a mathematical model of the FSO channel state.
[0044] The instantaneous end-to-end bit error rate of an FSO link is expressed as:
[0045] ;
[0046] in It is a complementary error function, and its closed-form can be expressed by Meijer's G function as follows: Therefore, the average bit error rate of an FSO link can be expressed as: .
[0047] The step of considering the geographic location information of any user and selecting at least one cluster center as the initial horizontal position of the UAV using a distance matrix and a dissimilarity index includes:
[0048] Divide users into K clusters;
[0049] Obtain the distance matrix D of all N users and the dissimilarity index of any user;
[0050] The users are sorted in ascending order according to the dissimilarity index, and the top K values are selected as the initial cluster centers;
[0051] Assign the M nearest users to any cluster and continuously update the cluster center until the center position is obtained, which is used as the initial horizontal position of the drone;
[0052] The central position minimizes the sum of distances within the cluster.
[0053] A system based on the above-described method for deploying unmanned aerial vehicle (UAV) emergency communication network nodes includes:
[0054] The data acquisition module collects the geographic location information of any user.
[0055] The preprocessing module interacts with the data acquisition module to select at least one cluster center as the initial horizontal position of the UAV based on the geographic location information, using a distance matrix and a dissimilarity index.
[0056] The processing module interacts with the preprocessing module to determine the location of the ground base station based on the initial horizontal position of the UAV, and uses the cluster of UAVs as temporary airborne base stations to provide communication services to ground users. The ground base station is responsible for exchanging data with the cluster of UAVs to obtain the optimal altitude and optimal power of any UAV, ensuring that each UAV can provide sufficient signal coverage in its service area while minimizing total power consumption.
[0057] The network construction module interacts with the processing module to iteratively process any traffic demand, eliminate connections with insufficient bandwidth, find the shortest path that meets the constraints, update the network topology and bandwidth, and adjust the connection weights until all traffic demands are routed, so as to build the network with the fewest links.
[0058] An electronic device for deploying nodes and jointly optimizing the topology of an unmanned aerial vehicle (UAV) emergency communication network includes:
[0059] Storage media, used to store computer programs
[0060] The processing unit exchanges data with the storage medium and executes the computer program to perform the steps of the UAV emergency communication network node deployment method described above when deploying UAV emergency communication network nodes and performing joint topology optimization.
[0061] A computer-readable storage medium:
[0062] The computer-readable storage medium stores a computer program.
[0063] When the computer program is running, it executes the steps of the unmanned aerial vehicle emergency communication network node deployment method described above.
[0064] The beneficial effects of this application include at least the following:
[0065] The UAV emergency communication network node deployment method described in this application considers the geographical location information of any user and selects at least one cluster center as the initial horizontal position of the UAV using a distance matrix and a dissimilarity index. Then, based on the initial horizontal position of the UAV and the location of the ground base station, the swarm of UAVs acts as a temporary airborne base station to provide communication services to ground users. The ground base station is responsible for exchanging data with the swarm of UAVs to obtain the optimal altitude and optimal power output of any UAV, ensuring that each UAV can provide sufficient signal coverage within its service area while minimizing total power consumption. Finally, iteratively processes any traffic demand of the user, excluding connections with insufficient bandwidth, finding the shortest path that meets the constraints, updating the network topology and bandwidth, and adjusting connection weights until all traffic demands are routed to build the network with the fewest links. The method described in this application adopts a cross-layer design, optimizing not only at the network level but also considering physical factors such as atmospheric turbulence, pointing errors, and noise. This achieves comprehensive optimization of the network design, enabling rapid deployment of UAV communication equipment and construction of network topology based on the distribution of ground users, reducing costs and energy consumption while ensuring quality of service (QoS) and reliability. Attached Figure Description
[0066] Figure 1This is a flowchart of the method for deploying unmanned aerial vehicle (UAV) emergency communication network nodes as described in this application;
[0067] Figure 2 This is a network architecture diagram for emergency communications in disaster areas.
[0068] Figure 3 This is a flowchart of the K-Medoids algorithm;
[0069] Figure 4 It is an application Figure 1 A schematic diagram of the emergency post-disaster FSO network construction process described in the method;
[0070] Figure 5 This is a block diagram of the system described in this application. Detailed Implementation
[0071] The present application will now be further described with reference to the accompanying drawings.
[0072] The purpose of this application is to use drones equipped with radio frequency (RF) and free space optical (FSO) communication modules as relay nodes to connect to remote base stations. An unsupervised learning algorithm is used to deploy the optimal drone locations, ensuring coverage while reducing energy consumption. Simultaneously, a shortest path algorithm is used to determine the optimal topology for a given traffic matrix, building the network with the fewest links to reduce deployment costs. Through optimization of network topology and link selection, the network can meet the low bit error rate performance requirements even under poor atmospheric conditions, ensuring communication quality and reliability in disaster areas.
[0073] Specific Implementation Example I:
[0074] like Figure 1 This embodiment provides a method for deploying emergency communication network nodes for unmanned aerial vehicles (UAVs) to achieve rapid network setup and restore communication in disaster-stricken areas. The specific steps are as follows:
[0075] S1: Use the K-Medoids algorithm to group users into K clusters. First, calculate the distance matrix D for all N users, then calculate the dissimilarity index for each user, sort the users in ascending order of dissimilarity index, and select the top K values as the initial cluster centers. Assign the M closest users to each cluster, and continuously update the cluster centers until the center position that minimizes the sum of the distances within the cluster is found;
[0076] S2: Deploy clustering drones based on the results of step S1, and sort the users in each cluster in descending order of their distance from the cluster center. Then solve the optimization problem for each user to determine the altitude and transmission power of each clustering drone, satisfying a series of constraints, including upper and lower limits of altitude and transmission power, minimum capacity requirements to ensure communication quality, and LOS (line-of-sight) connection probability constraints.
[0077] S3: Based on the drone locations obtained in steps S1 and S2, find the connection path for each demand for the given traffic matrix and gradually build the network;
[0078] The specific steps for S3 are as follows:
[0079] S301: Modeling the FSO channel state density function affected by atmospheric turbulence, misalignment fading, and noise:
[0080] , among which It is a modified Bessel function of the second kind, order v. It is the standard gamma function. It is the ratio between the equipment beam radius and the standard deviation of misalignment jitter. and The two parameters can be adjusted for various turbulent conditions. In the case of plane wave propagation, they are directly related to the physical parameters and are expressed as follows:
[0081] ;
[0082] ;
[0083] in, It is a dimensionless Rytov variance, representing the intensity of turbulence.
[0084] The signal-to-noise ratio of the link between two FSO transceivers is defined as follows:
[0085] ;
[0086] The instantaneous BER of an FSO link can be expressed as:
[0087] ;
[0088] Further derivation of the bit error rate expression:
[0089] ;
[0090] S302: Define an evaluation metric—the Network Construction Efficiency Index (NCIE)—to evaluate the cost and QoS performance of network construction. It must satisfy the following constraints: traffic conservation constraints for each demand, capacity limitations for each link, end-to-end bit error rate constraints, etc.
[0091] S303: Construct a directed complete graph G based on drone sites, where each pair of sites has two edges in opposite directions, representing FSO links that can be established. The initial weight of all edges is set to 1.
[0092] S304: According to the given flow matrix F, process each demand one by one, that is, each pair of source and destination s, t and the data transmission demand d between them. st .
[0093] The overall optimization process is as follows:
[0094] Step (I): Initialize the topology T to be empty, indicating that no FSO links are selected;
[0095] Step (II): Delete all elements from graph G that do not meet the current requirements. The edge, meaning its remaining bandwidth is less than the demand. ;
[0096] Step (III): Based on Dijkstra's algorithm, find the shortest path in graph G for the current requirement. This step ensures that each link on the path can meet the end-to-end BER requirement.
[0097] Step (IV): Subtract the requirement d from each edge of the selected path. st Update the topology T to include all links on the path and their reverse links. Simultaneously, update the weight of each edge based on its remaining bandwidth, prioritizing edges already existing in F.
[0098] Step (V): Repeat the above steps until all requirements have been processed.
[0099] Specific Implementation Example II:
[0100] This embodiment provides a specific application scenario for the 3D deployment and topology design of unmanned aerial vehicles (UAVs) in emergency communication scenarios. The method employs the K-Medoids algorithm to optimize the UAV's position, utilizes convex optimization methods to solve for the optimal altitude and power settings, and then determines the optimal topology for a given traffic matrix based on an improved Dijkstra's algorithm to support cooperative communication among UAVs. Through the method described in this embodiment, UAVs can minimize power consumption and the number of FSO links while ensuring service quality, thereby reducing costs. The following detailed description, in conjunction with the accompanying drawings and specific examples, further illustrates this approach:
[0101] Step 1: As Figures 2-3 The K-Medoids clustering algorithm is used to group users to determine the optimal horizontal position for each cluster of drones. This step takes into account the geographic location information of all users and selects K cluster centers as the initial horizontal positions of the drones by calculating the distance matrix and dissimilarity index.
[0102] Step 2: Calculate the optimal altitude and transmission power for each drone using an optimization algorithm. Based on the initial horizontal position of the drones and the location of the ground base station, this step uses the swarm of drones as temporary airborne base stations to provide communication services to ground users. The ground base station is responsible for data exchange with the swarm of drones. A convex optimization method is used to obtain the optimal altitude for any drone, ensuring that each drone can provide sufficient signal coverage within its service area while minimizing total energy consumption.
[0103] Step 3: Based on the improved Dijkstra's algorithm, iteratively process each traffic demand, eliminate connections with insufficient bandwidth, find the shortest path that satisfies the constraints, and then update the network topology and bandwidth, adjusting connection weights. Repeat this process until all traffic demands are routed. The algorithm's final result is that, while considering physical layer effects such as atmospheric turbulence and misalignment fading, it builds the network with the fewest possible links to reduce deployment costs, ensuring network efficiency and reliability.
[0104] This embodiment uses a drone equipped with a radio frequency (RF) communication module and a free space optical (FSO) communication module as an airborne base station. A three-step method is used to solve for the optimal deployment location and network topology of the drone. Furthermore, the unsupervised learning algorithm K-Medoids is introduced to determine the optimal horizontal coordinates of the drones for ground user distribution. By selecting actual data points as cluster centers, the algorithm can better handle outliers and noise in the data, ensuring that the drones are deployed at the center of the user group, thus optimizing coverage and communication efficiency. Simultaneously, this application also considers drone altitude constraints, transmit power constraints, channel capacity constraints, and LOS connection probability constraints, using convex optimization methods to determine the optimal altitude and power settings for the drones. Moreover, the network topology is designed based on the shortest path algorithm Dijkstra, aiming to minimize the total number of FSO links. At each iteration, the end-to-end bit error rate and remaining bandwidth are checked to meet all traffic demands within the area.
[0105] Specific Implementation Example III:
[0106] like Figure 4 The diagram shows a flowchart of an emergency post-disaster FSO network design method based on an improved Dijkstra algorithm, as provided in this application example. Following the flowchart, the method for designing a free-space optical mesh network affected by atmospheric turbulence and misalignment fading is completed, specifically including the following steps:
[0107] Step 1: Model the FSO channel affected by factors such as channel fading, atmospheric turbulence, and pointing error. The mathematical model is as follows: Using Meijer's G function and further simplification, the bit error rate (BER) when employing on-keyed OOK modulation can be expressed as: The instantaneous bit error rate of an FSO link using OOK modulation can be expressed as: ,in It is a complementary error function, and its closed-form can be expressed by Meijer's G function as follows: Therefore, the average bit error rate of an FSO link can be expressed as: .
[0108] Step 2: Model the objective function as The following constraints ensure that the UAV altitude, transmit power, and channel capacity remain within thresholds. For each traffic demand in the traffic matrix, there is a path from s to t, and each FSO link meets the bit error rate and capacity limits. Establish a joint optimization model for node deployment and topology construction of the UAV-assisted emergency communication network:
[0109] Let there be K drones, represented as a set. All drones are equipped with FSO and radio frequency communication modules, enabling them not only to connect to remote base stations but also to directly serve users within disaster areas. The FSO link distance between drones is expressed as... The set of traffic in a network is represented as ,in and These are the source site and the target site of the requirement. This is the required bandwidth. The average path loss between the drone and the ground user is expressed as... Where C is the speed of light. Where d is the carrier frequency, and d is the distance between the drone and the ground user. It is the average additional loss during free space propagation.
[0110] By jointly optimizing drone deployment and topology construction, the goal is to serve the maximum number of users with limited available power while using the minimum number of FSO links to reduce deployment costs. The problem can be expressed as:
[0111] ;
[0112]
[0113]
[0114]
[0115]
[0116]
[0117] ;
[0118] in, It is the deployment altitude of the i-th drone. It is its ground transmission power. It is a binary variable, if and The FSO link between them is included in the topology. It is 1 if it is true, otherwise it is 0. This is the maximum transmit power of the drone's radio frequency communication module. It is the lowest altitude at which a drone can move downwards. This is the minimum channel capacity required for communication. Assume each cluster has M users. It is the distance between the drone and the ground user g. It is the path loss index. It is the variance of AWGN noise. yes and Bit error rate of the FSO link between them Indicate demand The index is Traffic through the link The bandwidth portion of the transmission, It is a binary variable, when demand The index is Traffic using links When the value is 1, it is 1; otherwise, it is 0.
[0119] Step 3: Create an empty network topology T to store the final network structure. Construct a fully connected directed graph G from all FSO sites, where each site is connected to every other site, forming bidirectional links. Initially, all links are assigned a weight of 1.
[0120] Step 4: For each flow demand F(s, t, dst) in the given flow matrix, perform the following operations:
[0121] Step A: Check all connections in graph G. If the remaining bandwidth of a connection is insufficient to support the current traffic demand dst, remove the connection from graph G. This step ensures that the algorithm does not select paths that cannot meet the traffic demand.
[0122] Step B: Use the improved Dijkstra algorithm to find the shortest path from s to t in graph G for the current traffic demand. At each step, check whether the end-to-end bit error rate (BER) of the path meets the preset threshold requirement, so as to ensure that the selected path is not only the shortest, but also ensures the reliability of the network.
[0123] Step C: After selecting a path in graph G, subtract the dst value from the remaining bandwidth of each connection on the path, and add these connections, as well as their reverse connections, to topology T.
[0124] Step D: For each connection (i, j) in topology T, based on its remaining bandwidth... Relative to its total capacity The proportion is updated to a weight of 1 - / This weight update mechanism ensures that connections that have been heavily used and have limited remaining capacity will be given lower priority in subsequent path selection.
[0125] Step E: Repeat the above steps until all traffic requirements have been processed, and the final network design is stored in topology T.
[0126] The method described in this application employs a cross-layer optimization strategy, aiming to maximize resource utilization efficiency, improve service quality, and reduce costs. At the physical layer, by adjusting the UAV's transmit power and flight altitude, energy consumption is reduced while ensuring coverage. The impact of atmospheric turbulence, alignment errors, and signal attenuation on the bit error rate (BER) of the FSO link is also considered. At the network layer, the data packet transmission path is optimized to meet all given traffic demands with the fewest possible links, reducing network construction costs. Furthermore, by comprehensively considering the impact of channel loss, atmospheric turbulence, and pointing errors on the FSO channel, atmospheric channel state information is characterized, and an end-to-end BER formula is derived to calculate the BER of each link in the network, ensuring data transmission quality and network reliability. Simultaneously, the UAV-assisted communication network constructed using this application can be rapidly deployed, providing temporary communication support, significantly improving rescue and response efficiency. Moreover, by optimizing UAV deployment and power, limited resources such as battery power and bandwidth can be effectively utilized, reducing operating costs while improving coverage and service quality.
[0127] In summary, this application proposes a joint optimization scheme for UAV 3D deployment and topology design based on an improved Dijkstra algorithm. Employing a cross-layer design approach, it optimizes not only at the network level but also considers physical factors such as atmospheric turbulence, pointing errors, and noise, achieving comprehensive network design optimization. Initially, the method uses an unsupervised learning algorithm to determine the horizontal positions of the swarm of UAVs and ground base stations, and optimizes the UAV positions and power settings. Then, based on a shortest path algorithm, it determines the optimal topology for a given traffic matrix, building the network with the fewest links to reduce deployment costs. Through optimization of network topology and link selection, it ensures that the network meets low bit error rate performance requirements even under adverse atmospheric conditions, guaranteeing communication quality and reliability. This invention provides a method for rapidly deploying UAVs and designing and optimizing FSO mesh networks in application scenarios such as emergency communication recovery. It demonstrates a network design scheme that considers both cost-effectiveness and adaptability to complex atmospheric conditions, providing valuable reference for the promotion of FSO technology in practical applications.
[0128] Specific Implementation Example IV:
[0129] This application provides one embodiment:
[0130] like Figure 5 A system based on the UAV emergency communication network node deployment method as described in Specific Embodiment I includes: a data acquisition module 100, a preprocessing module 200, a processing module 300, and a network construction module 400; wherein, the data acquisition module 100 is used to collect the geographic location information of any user; the preprocessing module 200 interacts with the data acquisition module 100 to select at least one cluster center as the initial horizontal position of the UAV based on the geographic location information, using a distance matrix and a dissimilarity index; the processing module 300 interacts with the preprocessing module 200 to determine the initial horizontal position of the UAV based on the initial horizontal position of the UAV. The location of the ground base station is determined, and the cluster of drones acts as a temporary airborne base station to provide communication services to ground users. The ground base station is responsible for exchanging data with the cluster of drones to obtain the optimal altitude and optimal power output of any drone, ensuring that each drone can provide sufficient signal coverage within its service area while minimizing total power consumption. The network construction module 400 interacts with the processing module 300 to iteratively process any traffic demand, eliminate connections with insufficient bandwidth, find the shortest path that meets the constraints, update the network topology and bandwidth, and adjust connection weights until all traffic demands are routed, so as to build the network with the fewest links.
[0131] This application also provides an embodiment:
[0132] An electronic device for deploying and jointly optimizing the topology of an unmanned aerial vehicle (UAV) emergency communication network node includes a storage medium and a processing unit. The storage medium stores a computer program, and the processing unit exchanges data with the storage medium. During the deployment and joint optimization of the UAV emergency communication network node, the processing unit executes the computer program to perform the steps of the UAV emergency communication network node deployment method as described in Specific Embodiment I.
[0133] A computer-readable storage medium storing a computer program; when the computer program is run, it executes the steps of the UAV emergency communication network node deployment method as described in Specific Embodiment I.
[0134] In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wireline, optical fiber, RF, etc., or any suitable combination thereof.
[0135] The above disclosures are merely a few specific implementation scenarios of this application; however, this application is not limited thereto, and any variations that can be conceived by those skilled in the art should fall within the protection scope of this application. The above application serial numbers are for descriptive purposes only and do not represent the superiority or inferiority of the implementation scenarios.
Claims
1. A method for deploying emergency communication network nodes for unmanned aerial vehicles (UAVs), characterized in that, include: A joint optimization model for node deployment and topology construction of an UAV-assisted emergency communication network is established based on information collected by UAVs. Considering the geographic location information of ground users, K cluster centers are selected as the initial horizontal position of the UAV based on the K-Medoids algorithm; Based on the initial horizontal position of the UAV and the location of the ground base station, the swarm of UAVs acts as a temporary airborne base station to provide communication services to ground users. The ground base station is responsible for exchanging data with the swarm of UAVs. The optimal altitude of any UAV is obtained using a convex optimization method to ensure that each UAV can provide sufficient signal coverage in its service area while minimizing total energy consumption. Based on Dijkstra's algorithm, iteratively processes all traffic demands within the network. In a given traffic matrix, it finds an FSO connection path for each demand, eliminates links with insufficient bandwidth, finds the shortest path that meets the constraints, updates the network topology and bandwidth, and adjusts the connection weights until all traffic demands are routed. The network is built with the fewest links to reduce deployment costs. The iterative processing of all traffic demands within the network based on Dijkstra's algorithm includes: Create an empty network topology T to store the final determined network structure; construct a fully connected directed graph G using all FSO sites, where each site is connected to other sites in a bidirectional link. Using each traffic demand in the given traffic matrix, obtain the final network design and store it in topology T; Examine all connections in the directed graph G and mark those where the remaining bandwidth is insufficient to support the current traffic demand d. st The connection is then removed from graph G; In the directed graph G, find the shortest path from point s to point t for the current traffic demand, and check whether the end-to-end bit error rate of the shortest path meets the preset threshold requirement. After selecting the shortest path in the directed graph G, subtract d from the remaining bandwidth of each connection on the shortest path. st Values are assigned, and these connections and their reverse connections are added to topology T; For each connection in topology T, update its weight according to the ratio of its remaining bandwidth to its total capacity until all traffic demands have been processed, and store the final network design in topology T. For each connection in topology T, updating its weight based on the ratio of its remaining bandwidth to its total capacity includes: For each connection in topology T Based on its remaining bandwidth Relative to its total capacity The proportion is used to update its weight. .
2. The method for deploying UAV emergency communication network nodes according to claim 1, characterized in that, The joint optimization model for node deployment and topology construction in establishing an unmanned aerial vehicle (UAV)-assisted emergency communication network includes: Let there be K drones, represented as a set. All of them are equipped with FSO and radio frequency communication modules, which can not only connect to remote base stations, but also directly serve users in disaster areas; The FSO link distance between the drones is expressed as: The network traffic demand matrix is represented as follows: ,in and These are the source site and the target site of the requirement. This is the required bandwidth; the average path loss between the UAV and the ground user is expressed as... Where C is the speed of light. Where d is the carrier frequency, and d is the distance between the drone and the ground user. It is the average additional loss during free space propagation; By jointly optimizing drone deployment and topology construction, the goal is to serve the maximum number of users with limited available power while using the minimum number of FSO links to reduce deployment costs. The problem can be expressed as: ； ； ； ； ； ； ； in, It is the deployment altitude of the i-th drone. It is its ground transmission power. It is a binary variable. This is the maximum transmit power of the drone's radio frequency communication module. It is the lowest altitude at which a drone can move downwards. δ is the minimum channel capacity required for communication; β is a number between (0, 1) used to represent parameter weights; δ is the end-to-end bit error rate threshold. Assume that each cluster has M users. It is the distance between the drone and the ground user g. It is the path loss index. It is the variance of AWGN noise. yes and The channel bit error rate of the FSO link between them. Indicate demand The index is Traffic through the link The bandwidth portion of the transmission, It is a binary variable, when demand The index is Traffic using links When the value is 1, the value is taken as 1.
3. The method for deploying UAV emergency communication network nodes according to claim 2, characterized in that, The channel bit error rate includes: The signal-to-noise ratio of the link between two FSO transceivers is expressed as: ,in, For light emission power, It refers to the responsivity of the photodetector. A mathematical model representing the FSO channel state; The instantaneous bit error rate of an FSO link using OOK modulation is expressed as: ,in It is a complementary error function, and its closed-form can be expressed by Meijer's G function as follows: Therefore, the average bit error rate of an FSO link can be expressed as: .
4. The method for deploying UAV emergency communication network nodes according to claim 3, characterized in that, The mathematical model of the FSO channel state includes: The mathematical model of the FSO channel state is expressed as follows: ； in It is the channel loss coefficient. This indicates intensity fluctuations caused by atmospheric turbulence. It is the power fraction collected by the photodetector, which depends on the relative distance between the photodetector and the center of the receiving beam; The system performance under moderate to strong turbulence conditions is determined using the Gamma-Gamma distribution, and the probability density function of the FSO channel state is expressed as: ； in, It is a modified Bessel function of the second kind, order v. It is the standard gamma function. It is the ratio between the equipment beam radius and the standard deviation of misalignment jitter; and The two parameters can be adjusted for various turbulent conditions. In the case of plane wave propagation, they are directly related to the physical parameters and are expressed as follows: ； ； in It is a dimensionless Rytov variance, representing the intensity of turbulence.
5. The method for deploying UAV emergency communication network nodes according to claim 1, characterized in that, The step of considering the geographic location information of ground users and selecting K cluster centers as the initial horizontal position of the UAV based on the K-Medoids algorithm includes: Divide users into K clusters; Obtain the distance matrix D of all N users and the dissimilarity index of any user; The users are sorted in ascending order according to the dissimilarity index, and the top K values are selected as the initial cluster centers; Assign the M nearest users to any cluster and continuously update the cluster center until the center position is obtained, which is used as the initial horizontal position of the drone; The central position minimizes the sum of distances within the cluster.
6. A system based on the UAV emergency communication network node deployment method as described in any one of claims 1-5, characterized in that, include: The data acquisition module collects the geographic location information of any user. The preprocessing module interacts with the data acquisition module to select K cluster centers as the initial horizontal position of the UAV based on the geographic location information, using a distance matrix and a dissimilarity index. The processing module interacts with the preprocessing module to determine the location of the ground base station based on the initial horizontal position of the UAV, and uses the cluster of UAVs as temporary airborne base stations to provide communication services to ground users. The ground base station is responsible for exchanging data with the cluster of UAVs to obtain the optimal altitude and optimal power of any UAV, ensuring that each UAV can provide sufficient signal coverage in its service area while minimizing total power consumption. The network construction module interacts with the processing module to iteratively process any traffic demand, eliminate connections with insufficient bandwidth, find the shortest path that meets the constraints, update the network topology and bandwidth, and adjust the connection weights until all traffic demands are routed, so as to build the network with the fewest links.
7. An electronic device for deploying nodes and jointly optimizing the topology of an unmanned aerial vehicle (UAV) emergency communication network, characterized in that, include: Storage media, used to store computer programs The processing unit exchanges data with the storage medium and is used to execute the computer program during the deployment of UAV emergency communication network nodes and topology joint optimization, performing the steps of the UAV emergency communication network node deployment method as described in any one of claims 1-5.
8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program. When the computer program is run, it executes the steps of the UAV emergency communication network node deployment method as described in any one of claims 1-5.