Method and apparatus for routing and time-frequency allocation based on air-ground integrated ad hoc network

Abstract

The embodiment of the application provides a kind of based on air-ground integration ad hoc network's route and time-frequency allocation method and device, it is related to wireless communication network technical field, including: obtaining the node information in air-ground integration ad hoc network, node information includes available channel and edge in ad hoc network, according to available channel and edge, obtain the route and time-frequency allocation problem model and transmission constraint of ad hoc network, obtain the weight of each edge corresponding to data stream respectively, based on weight and route constraint, obtain the minimum weight route of data stream, according to minimum weight route and time-frequency constraint, the route and time-frequency allocation problem model of ad hoc network is converted into time slot channel allocation optimization problem, and the optimization solution of time slot channel allocation optimization problem is carried out based on continuous convex approximation strategy. By simultaneously optimizing and allocating route and time-frequency in ad hoc network, the resource availability of node can be improved, and the potential contention conflict of candidate route path is reduced, to reduce the end-to-end delay of data stream transmission in ad hoc network.

Description

Technical Field

[0001] This application relates to the field of wireless communication network technology, and in particular to a routing and time-frequency allocation method and apparatus based on an integrated air-ground self-organizing network. Background Technology

[0002] Air-ground integrated ad hoc networks refer to ad hoc networks formed by aerial drones and ground equipment. Due to the high flexibility and high line-of-sight probability of aerial drones, combined with the autonomy, fault tolerance, and spatial reuse characteristics of ad hoc network technology, air-ground integrated ad hoc networks can effectively ensure ubiquitous connectivity in areas without cellular network infrastructure coverage. Therefore, air-ground integrated ad hoc networks have enormous potential application value in fields such as environmental monitoring, agricultural plant protection, industrial internet, logistics and transportation, and the military.

[0003] Air-to-ground integrated ad hoc networks need to support end-to-end low-latency transmission of massive amounts of data packets to adapt to the dynamic nature of air-to-ground networks. Routing and communication resource scheduling are crucial for ensuring end-to-end performance in air-to-ground integrated ad hoc networks; however, current research on air-to-ground integrated ad hoc networks typically only focuses on optimizing routing or resource scheduling.

[0004] In fact, when making routing decisions, it is necessary to consider the resource availability of nodes and the potential contention and conflict of candidate routing paths. Simply optimizing the routing or resource scheduling design cannot meet the requirements of end-to-end low-latency transmission in an integrated air-ground self-organizing network. Summary of the Invention

[0005] This application provides a routing and time-frequency allocation method and apparatus based on an air-ground integrated self-organizing network. By jointly designing routing and time-frequency, the optimal channel allocation in the self-organizing network is obtained, thereby reducing the end-to-end transmission latency in the air-ground integrated self-organizing network.

[0006] In a first aspect, embodiments of this application provide a routing and time-frequency allocation method based on an integrated air-ground ad hoc network, including:

[0007] Obtain node information in an air-ground integrated ad hoc network, the node information including available channels and edges in the ad hoc network, the edges being used to indicate the transmission links of data flows in the ad hoc network;

[0008] Based on the available channels and the edges, obtain the routing and time-frequency allocation problem model of the ad hoc network and the transmission constraints of the routing and time-frequency allocation problem model, wherein the transmission constraints include routing constraints and time-frequency constraints;

[0009] Obtain the weight of each edge corresponding to the data flow, and obtain the minimum weight route of the data flow based on the weight and the routing constraints;

[0010] Based on the minimum weight routing and the time-frequency constraints, the routing and time-frequency allocation problem model of the ad hoc network is transformed into a time-slot-by-time channel allocation optimization problem;

[0011] The time-slot-by-time channel allocation optimization problem is solved by using a continuous convex approximation strategy.

[0012] Optionally, obtaining the routing and time-frequency allocation problem model of the ad hoc network based on the available channels and the edges includes:

[0013] Define variable T f The T f The T is used to indicate the time slot occupied by the data stream f entering the destination node. f Represented as:

[0014]

[0015] Based on the first delay weight of the data stream f and the T f With the objective of minimizing the latency of data flow f, the routing and time-frequency allocation problem model is obtained, which is expressed as:

[0016]

[0017] in, This represents the relationship between data stream f, nodes i and j, channel c, and time slot s. Indicates the destination node of data stream f. This represents the set of available channels in an ad hoc network. express The set of neighboring nodes, W represents the set of data streams. f This represents the first delay weight of the data stream f.

[0018] Optionally, the transmission constraints of the routing and time-frequency allocation problem model include:

[0019] Flow balance constraint: In the ad hoc network, the outgoing data packets of each node are equal to the incoming data packets. The nodes include relay nodes, source nodes, and destination nodes. The flow balance constraint for the relay nodes, source nodes, and destination nodes is expressed as follows:

[0020]

[0021]

[0022] in, For the defined routing variables, Represents the set of neighboring nodes of node u. Represents the set of neighboring nodes of node u. Represents a set of nodes, Represents the source node of data stream f, L f This indicates the number of data packets in data stream f;

[0023] Propagation constraints stipulate that data streams can only be transmitted along a single route. These propagation constraints are expressed as follows:

[0024]

[0025] Where ε represents the set of edges;

[0026] Communication interference constraints stipulate that no link can occupy the same time-frequency resource block within the interference distance range of the receiving node of each link. These communication interference constraints are expressed as follows:

[0027]

[0028] in, This represents the set of time slots within each frame. Represents the set of nodes that are disturbed by node v;

[0029] Half-duplex constraint: Each node operates in half-duplex communication mode. The half-duplex constraint is expressed as follows:

[0030]

[0031] The packet scheduling constraint states that for an edge (u,v) on a potential path of flow f, a necessary condition for packet transmission from u to v in time slot s is that node u has already received a packet in a previous time slot. This packet scheduling constraint is expressed as:

[0032]

[0033] in, This indicates whether the transmitting node u of edge (u,v) has a data packet to be transmitted at the start of time slot s. Indicates whether edge (u,v) transmitted a data packet in time slot s;

[0034] Binary constraints on variables, wherein the binary constraints on variables are expressed as follows:

[0035]

[0036] The routing constraints include flow balancing constraints and propagation constraints, while the time-frequency constraints include communication interference constraints, half-duplex constraints, packet scheduling constraints, and variable binary constraints.

[0037] Optionally, obtaining the weight of each edge corresponding to the data stream includes:

[0038] For the target data stream, the weight of each edge of the target data stream is determined by the following formula:

[0039]

[0040] Where, Δ u and Δ v This represents the number of times a data flow with a known route passes through nodes u and v, where α, β, and γ are the hop count, transmission collision, and channel contention coefficients, respectively. C u,v Represents the set of available channels for edge (u,v).

[0041] Optionally, obtaining the minimum weight route for the data flow based on the weights and the routing constraints includes:

[0042] Based on the routing constraints and the weight of each edge corresponding to the target data flow, a minimum weight routing model for the target data flow is constructed, which is expressed as follows:

[0043]

[0044] Relax the propagation constraints in the minimum weight routing model as follows: The minimum weight routing model is solved using the interior point method to determine the minimum weight route for the data flow.

[0045] Optionally, the step of transforming the routing and time-frequency allocation problem model of the ad hoc network into a time-slot-by-time channel allocation optimization problem based on the minimum weight route and the time-frequency constraint includes:

[0046] The time-frequency allocation model in the minimum weight route is determined based on the minimum weight route of the data stream and the time-frequency constraint. The time-frequency allocation model is expressed as follows:

[0047]

[0048] Among them, f (u,v) This represents the data flow associated with edge (u,v). Let (u,v) and (i,j) be adjacent to each other, and let ε0 be the set of edges formed by adjacent nodes on the minimum weight routing path of the data flow.

[0049] Based on the set of valid edges in each time slot and the time-frequency allocation model, the routing and time-frequency allocation problem of the ad hoc network is transformed into a time-slot-by-time channel allocation optimization problem. Here, the valid edges refer to those that satisfy... The edge.

[0050] Optionally, the step of transforming the routing and time-frequency allocation problem model of the ad hoc network into a time-slot-by-time channel allocation optimization problem based on the set of valid edges in each time slot and the time-frequency allocation model includes:

[0051] The set of valid edges in each time slot is determined according to the following formula:

[0052] and

[0053] Based on the set of valid edges in each time slot and the time-frequency allocation model, the channel allocation model for each time slot is determined, and the channel allocation model is expressed as follows:

[0054]

[0055] in, Indicates the target time slot currently being processed;

[0056] The time-slot-by-time channel allocation optimization problem is obtained based on the second delay weight of data stream f and the channel allocation model; the second delay weight is determined based on the first delay weight.

[0057] Optionally, the step of obtaining the slot-by-slot channel allocation optimization problem based on the second delay weight of the data stream f and the channel allocation model includes:

[0058] The second delay weight is determined according to the following formula:

[0059]

[0060] Based on the second delay weight and the channel allocation model, a time-slot-by-time channel allocation problem is obtained, which is expressed as follows:

[0061]

[0062] The binary constraints in the slot-by-slot channel allocation problem are equivalently transformed to obtain the slot-by-slot channel allocation optimization problem, which is expressed as:

[0063]

[0064] in, It is a Lagrange multiplier.

[0065] Optionally, the optimization solution to the slot-by-slot channel allocation problem based on the continuous convex approximation strategy includes:

[0066] Obtain the set of active edges in the current time slot, and initialize the Lagrange multiplier and the optimal allocation value for the current time slot;

[0067] Update the communication interference constraints and half-duplex constraints of the active edges;

[0068] The continuous convex approximation strategy is used to optimize the time-slot channel allocation problem, and the optimal solution set and optimal value of the current time-slot channel allocation are obtained.

[0069] The current time slot is obtained based on the optimal solution set. The suboptimal value of channel allocation, and the Lagrange multiplier updated according to the optimal solution set, the optimal value and the suboptimal value;

[0070] When the updated Lagrange multipliers and the optimal value converge, the optimal value is taken as the optimal solution to the current time slot channel allocation optimization problem.

[0071] If all data packets of the data stream in the current time slot have been transmitted to the destination node, the above steps for obtaining the optimal solution to the channel allocation optimization problem of the current time slot are repeated in the next time slot until the optimal solution to the channel allocation optimization problem of all time slots is obtained.

[0072] Secondly, embodiments of this application provide a routing and time-frequency allocation device based on an integrated air-ground ad hoc network, comprising:

[0073] The first acquisition module is used to acquire node information in the air-ground integrated self-organizing network. The node information includes available channels and edges in the self-organizing network, and the edges are used to indicate the transmission links of data flow in the self-organizing network.

[0074] The second acquisition module is used to acquire the routing and time-frequency allocation problem model of the ad hoc network and the transmission constraints of the routing and time-frequency allocation problem model based on the available channels and the edges. The transmission constraints include routing constraints and time-frequency constraints.

[0075] The third acquisition module is used to acquire the weight of each edge corresponding to the data flow, and to acquire the minimum weight route of the data flow based on the weight and the routing constraints.

[0076] The transformation module is used to transform the routing and time-frequency allocation problem model of the ad hoc network into a time-slot-by-time channel allocation optimization problem based on the minimum weight routing and the time-frequency constraints.

[0077] The solution module is used to optimize and solve the slot-by-slot channel allocation optimization problem based on a continuous convex approximation strategy.

[0078] Optionally, the routing and time-frequency allocation device based on the air-ground integrated self-organizing network can execute the routing and time-frequency allocation method based on the air-ground integrated self-organizing network described in any of the first aspects.

[0079] Thirdly, this application provides an electronic device, including: a memory and a processor;

[0080] The memory is used to store computer instructions; the processor is used to execute the computer instructions stored in the memory to implement the method of any one of the first aspects.

[0081] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, the computer program being executed by a processor to implement the method of any of the first aspects.

[0082] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the method of any one of the first aspects.

[0083] The routing and time-frequency allocation method and apparatus based on an integrated air-ground ad hoc network provided in this application obtains node information in the integrated air-ground ad hoc network, including available channels and edges. Edges indicate the transmission links of data flows in the ad hoc network. Based on the available channels and edges, a routing and time-frequency allocation problem model and its transmission constraints are obtained. These constraints include routing constraints and time-frequency constraints. The weight of each edge corresponding to the data flow is obtained. Based on the weights and routing constraints, the minimum weight route for the data flow is obtained. The routing and time-frequency allocation problem model of the ad hoc network is transformed into a time-slot-by-time channel allocation optimization problem based on the minimum weight route and time-frequency constraints. The time-slot-by-time channel allocation optimization problem is then optimized and solved using a continuous convex approximation strategy. By simultaneously optimizing the allocation of routes and time-frequency in the ad hoc network, the resource availability of nodes can be improved and potential contention for candidate routing paths can be reduced, thereby reducing end-to-end latency of data flow transmission in the ad hoc network. Attached Figure Description

[0084] Figure 1 This is a schematic diagram of a scenario provided for an embodiment of this application;

[0085] Figure 2 A flowchart illustrating the routing and time-frequency allocation method based on an integrated air-ground ad hoc network provided in this application embodiment. Figure 1 ;

[0086] Figure 3 A flowchart illustrating the routing and time-frequency allocation method based on an integrated air-ground ad hoc network provided in this application embodiment. Figure 2 ;

[0087] Figure 4 A comparative diagram illustrating the change in latency performance with the amount of data streams provided in this application embodiment;

[0088] Figure 5This is a schematic diagram illustrating the comparison of latency performance with the number of data packets provided in the embodiments of this application.

[0089] Figure 6 This is a schematic diagram showing the comparison of latency performance with the number of channels in an embodiment of this application;

[0090] Figure 7 This is a schematic diagram illustrating the comparison of latency performance with the number of aerial drone nodes in an embodiment of this application.

[0091] Figure 8 A schematic diagram of the routing and time-frequency allocation device based on an air-ground integrated self-organizing network provided in this application embodiment;

[0092] Figure 9 This is a schematic diagram of the structure of a routing and time-frequency allocation electronic device based on an air-ground integrated self-organizing network provided in an embodiment of this application. Detailed Implementation

[0093] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of this application, but not all embodiments.

[0094] In the embodiments of this application, the terms "first" and "second" are used to distinguish identical or similar items with essentially the same function and effect, without limiting their order. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and that the terms "first" and "second" do not necessarily imply that they are different.

[0095] It should be noted that, in the embodiments of this application, the words "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design scheme described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0096] Air-ground integrated ad hoc networks refer to ad hoc networks formed by aerial drones and ground equipment. Due to the high flexibility and high line-of-sight probability of aerial drones, combined with the autonomy, fault tolerance, and spatial reuse characteristics of ad hoc network technology, air-ground integrated ad hoc networks can effectively ensure ubiquitous connectivity in areas without cellular network infrastructure coverage. Therefore, air-ground integrated ad hoc networks have enormous potential application value in fields such as environmental monitoring, agricultural plant protection, industrial internet, logistics and transportation, and the military.

[0097] Next-generation wireless networks will support ubiquitous connectivity for massive numbers of devices. It is anticipated that 6G mobile communication systems will integrate air and terrestrial networks, connecting various heterogeneous users and sensors. Therefore, integrated air-terrestrial ad hoc networks will play a crucial role in realizing a seamless, ubiquitous connected world. Future integrated air-terrestrial ad hoc networks need to support low-latency end-to-end transmission of massive data packets to adapt to the dynamic nature of integrated air-terrestrial networks. Routing and communication resource scheduling are of paramount importance for ensuring end-to-end performance in integrated air-terrestrial ad hoc networks; currently, research on integrated air-terrestrial ad hoc networks typically only focuses on optimizing routing or resource scheduling.

[0098] In fact, when making routing decisions, it is necessary to consider the resource availability of nodes and the potential contention and conflict of candidate routing paths. At the same time, different routing schemes will produce different optimal resource scheduling decisions. Simply optimizing the design of routing or resource scheduling cannot guarantee end-to-end low-latency transmission in an integrated air-ground self-organizing network.

[0099] In view of this, embodiments of this application provide a routing and time-frequency allocation method and apparatus based on an air-ground integrated ad hoc network. According to the characteristics of the air-ground integrated ad hoc network, with the goal of minimizing end-to-end latency in the ad hoc network, the routing and time-frequency in the air-ground integrated ad hoc network are jointly designed to obtain the optimal channel allocation in the ad hoc network, so as to reduce the end-to-end transmission latency in the air-ground integrated ad hoc network.

[0100] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be implemented independently or in combination with each other. The same or similar concepts or processes may not be described again in some embodiments.

[0101] Figure 1 This is a schematic diagram illustrating an application scenario of an embodiment of this application, such as... Figure 1 As shown, it includes multiple air nodes and multiple ground nodes, which together constitute the air-ground integrated self-organizing network in this embodiment of the application.

[0102] In this embodiment, a self-organizing network refers to a network combining mobile communication and computer networks. Information exchange in the network adopts the packet switching mechanism found in computer networks. Terminals are portable and mobile devices. Each terminal in the self-organizing network functions as both a router and a host. As a host, the terminal needs to run various user-facing applications, such as editors and browsers; as a router, the terminal needs to run corresponding routing protocols and perform data packet forwarding and route maintenance based on routing policies and routing tables.

[0103] In the air-ground integrated self-organizing network provided in this application embodiment, multiple air nodes are composed of multiple air drones, which can be called air drone nodes, and multiple ground nodes are composed of multiple ground devices that can access the Internet of Things, such as mobile phones, computers, vehicles and base stations, which can be called ground Internet of Things nodes. The air drone nodes and ground Internet of Things nodes together form an air-ground integrated self-organizing network with different transmission power and number of channels.

[0104] In this embodiment of the application, airborne drone nodes and ground-based IoT nodes can transmit messages via three channels: air-to-ground, air-to-air, and ground-to-ground.

[0105] For example, each airborne UAV node and ground IoT node in an air-ground integrated ad hoc network can act as a terminal in the ad hoc network, performing routing and host functions to enable message transmission between two arbitrary nodes. Since the terminal's transmission power and coverage are limited, when a terminal needs to communicate with a terminal outside its coverage area, it needs to use the terminal connected to it as an intermediate node for forwarding.

[0106] Optionally, the air node can also be other nodes with air transmission capabilities, such as airships. This application embodiment does not limit the devices constituting the air node.

[0107] The application scenarios provided in the embodiments of this application have been briefly described above. The routing and time-frequency allocation method based on the air-ground integrated self-organizing network provided in the embodiments of this application will be described in detail below.

[0108] Figure 2 A flowchart illustrating the routing and time-frequency allocation method based on an integrated air-ground ad hoc network provided in this application embodiment. Figure 1 ,like Figure 2 As shown, it includes the following steps:

[0109] S201. Obtain node information in the air-ground integrated self-organizing network. The node information includes available channels and edges in the self-organizing network. Edges are used to indicate the transmission links of data flow in the self-organizing network.

[0110] In this embodiment of the application, the nodes in the air-ground integrated self-organizing network include airborne UAV nodes and ground IoT nodes, and the node information includes information of UAV nodes and information of ground IoT nodes.

[0111] A channel is a communication route between nodes, which is the transmission medium through which a signal or data stream is transmitted from the transmitter to the receiver. An edge is a transmission link formed by nodes through which data is transmitted from the source node to the destination node within the communication distance. The source node is called the tail of the edge, and the destination node is called the tail of the edge.

[0112] For example, the nodes of the air-ground integrated self-organizing network in this application embodiment can be represented as a set. in and The network consists of ground-based IoT nodes and airborne drone nodes. The self-organizing network adopts a hybrid Time Division Multiple Access (TDMA) and Frequency Division Multiple Access (FDMA) access protocol. Each node can select an available channel in each time slot to complete the transmission of a data packet. That is, a time-frequency resource block is the basic unit of data packet transmission.

[0113] In this embodiment of the application, node information in the self-organizing network can be obtained according to the structure of the self-organizing network.

[0114] S202. Based on the available channels and edges, obtain the routing and time-frequency allocation problem model and the transmission constraints of the routing and time-frequency allocation problem model for the ad hoc network. The transmission constraints include routing constraints and time-frequency constraints.

[0115] In this embodiment of the application, based on the nodes and edges in the ad hoc network, the ad hoc network can be modeled as a directed graph of nodes and edges using graph theory. Here, the edge set ε is defined as Nodes u and v are called the tail and head of the edge (u,v). Let be the set of neighboring nodes of node u. The set of neighboring nodes represents the set of nodes located within the transmission distance of node u.

[0116] Define a binary set of routing and time-frequency allocation variables:

[0117]

[0118] This represents the relationship between data stream f, tail u, header v, channel c, and time slot s, where, This represents the set of available channels in an ad hoc network. A set representing the number of data streams. This represents the set of time slots within each frame.

[0119] If edge (u,v) transmits data packets of stream f using channel c in time slot s, then otherwise

[0120] Based on the binary set of routing and time-frequency allocation variables, a model for the routing and time-frequency allocation problem is obtained with the optimization objective of minimizing the data flow transmission delay between nodes in an ad hoc network.

[0121] In this embodiment of the application, in the routing and time-frequency allocation problem model, each node needs to meet certain transmission requirements and the transmission characteristics of the self-organizing network when transmitting data. Based on the above transmission requirements and transmission characteristics, the transmission constraints of the routing and time-frequency allocation problem model can be determined.

[0122] For example, transmission requirements and characteristics include: the number of data packets flowing out of each node is equal to the number of data packets flowing in; a data packet is transmitted on a single route; and all nodes in the ad hoc network operate in half-duplex communication mode. Based on the above transmission requirements and characteristics, the transmission constraints of the corresponding routing and time-frequency allocation problem model can be determined.

[0123] S203. Obtain the weight of each edge corresponding to the data flow, and obtain the minimum weight route of the data flow based on the weight and routing constraints.

[0124] In this embodiment of the application, each data stream has a different priority, that is, different data streams have different requirements for transmission latency. The latency weight of each data stream can be determined according to its different priorities. The higher the priority of the data stream, the lower the required latency.

[0125] In this embodiment of the application, the delay weight of the data flow is determined, and the route of the data flow can be determined according to the delay weight. For any data flow, the weight of each edge of the data flow can be determined according to the number of times the data flow that has been routed passes through nodes u and v, the number of hops of the data flow through each edge, the transmission conflict of the data flow and the channel contention coefficient. The weight of each edge can also be called the adaptive weight of the edge. The transmission conflict and channel contention coefficient can be determined according to empirical values.

[0126] In this embodiment of the application, when the adaptive weight of each edge of the data flow is determined, the minimum weight route of the data flow is obtained according to the routing constraints of the routing and time-frequency allocation problem model.

[0127] For example, by using the adaptive weights and routing constraints of each edge of the data flow, a minimum weight routing problem for the data flow is constructed. The optimal minimum weight route is obtained through the interior point method, thereby determining the edges of the data flow transmission path. That is, the relationship between the data flow and the edges can be determined by obtaining the minimum weight route of the data flow.

[0128] S204. Based on minimum weight routing and time-frequency constraints, the routing and time-frequency allocation problem model of the ad hoc network is transformed into a time-slot-by-time channel allocation optimization problem.

[0129] In this embodiment of the application, after determining the relationship between the data flow and the edges, a directed subgraph about the determined edges and nodes can be constructed based on graph theory according to the routing results. Subgraphs can be determined middle Since the relationship between the data flow and the edges has been determined, we only need to focus on the connection between the edges, channels, and time slots.

[0130] In this embodiment, according to the above definition, the routing and time-frequency allocation problem model can be transformed into a time-frequency allocation problem based on the routing results and the time-frequency constraints of the routing and time-frequency allocation problem model. For each time slot, channels in each time slot are allocated one by one with the objective of maximizing the number of channel allocations. That is, the routing and time-frequency allocation problem model of ad hoc networks is transformed into a time-slot-by-time channel allocation optimization problem through minimum weight routing and time-frequency constraints. This problem can be considered as a linear programming problem and can be solved using the interior-point method.

[0131] S205. An optimization solution is provided for the time-slot-by-time channel allocation optimization problem based on a continuous convex approximation strategy.

[0132] In this embodiment, the continuous convex approximation strategy is a strategy for solving linear programming problems based on the ideas of iteration and continuous approximation.

[0133] In this embodiment of the application, the routing and time-frequency allocation problem in the ad hoc network has been transformed into the problem of channel allocation on a time-slot basis.

[0134] For any given time slot, the optimal channel allocation value in that time slot is solved using a continuous convex approximation strategy. This process is repeated iteratively until the optimal channel allocation value in that time slot converges, thus completing the channel allocation for that time slot.

[0135] Repeat the above steps until the optimal channel allocation value for all time slots is obtained, or all data packets of all data streams in the current time slot have arrived at the destination node.

[0136] The routing and time-frequency allocation method based on an integrated air-ground ad hoc network provided in this application obtains node information in the integrated air-ground ad hoc network, including available channels and edges. Edges indicate the transmission links of data flows in the ad hoc network. Based on the available channels and edges, the method obtains the routing and time-frequency allocation problem model and its transmission constraints, including routing constraints and time-frequency constraints. It then obtains the weight of each edge corresponding to the data flow, and based on the weights and routing constraints, obtains the minimum weight route for the data flow. Based on the minimum weight route and time-frequency constraints, the routing and time-frequency allocation problem model of the ad hoc network is transformed into a time-slot-by-time channel allocation optimization problem. Finally, it optimizes and solves the time-slot-by-time channel allocation optimization problem based on a continuous convex approximation strategy. By simultaneously optimizing the allocation of routes and time-frequency in the ad hoc network, the resource availability of nodes can be improved and the potential contention for candidate routing paths can be reduced, thereby reducing the end-to-end latency of data flow transmission in the ad hoc network.

[0137] Figure 3 A flowchart illustrating the routing and time-frequency allocation method based on an integrated air-ground ad hoc network provided in this application embodiment. Figure 2 ,exist Figure 2 Based on the illustrated embodiments, the routing and time-frequency allocation method based on an integrated air-ground ad hoc network provided in this application will be further described, such as... Figure 3 As shown, it includes the following steps:

[0138] S301. Obtain node information in the air-ground integrated self-organizing network.

[0139] In this embodiment of the application, the network node set in and These are ground-based IoT nodes and aerial drone nodes, respectively.

[0140] The network employs a hybrid TDMA / FDMA access protocol. Nodes can select an available frequency channel in each time slot to transmit a data packet; that is, a time-frequency resource block is the basic unit of data packet transmission. To meet the requirement of low data transmission latency, it is necessary to minimize the end-to-end latency of each data packet within a frame.

[0141] Each frame contains S time slots, and the set can be represented as follows: The power of the ground node and the power of the UAV node are represented as P1 and P2, respectively, and the available channel sets of the ground node and the UAV node can be represented as follows: and For nodes and For a given link (u, v), the available channels of the link are represented as follows: Where i,j∈{1,2}.

[0142] The number of data streams in the current frame is F, and its set representation can be... The number of data packets in data stream f is L f Each data stream has a different priority {W} 1 W 2 ,...,W F}, where each element is a positive integer, and each data stream also has a source node and a destination node, and the sets are represented as follows: and

[0143] In this embodiment of the application, due to the limited transmission power and coverage of the node, its communication distance is also a fixed range. For example, the node... To the node Communication distance It can be calculated using the following formula:

[0144]

[0145] Where c is the speed of light, f c It is the carrier frequency, S RSS It is the received signal strength threshold, α A2A α G2G α A2G These are the path loss coefficients for air-to-air links, ground-to-ground links, and air-to-ground links, respectively. These are the probabilities of line-of-sight links and non-line-of-sight links, respectively, η. LoS η NLoS These are the additional path loss coefficients for line-of-sight links and non-line-of-sight links, respectively. The loss coefficients can be set based on empirical values.

[0146] According to the protocol interference model, if the transmitting node u is within the interference distance of the receiving node v, then node v cannot demodulate the signal. That is, the communication distance between the transmitting node u and the receiving node v is greater than the interference distance, and the receiving node v and the transmitting node u can communicate.

[0147] The method for calculating interference distance is as follows: Where τ is an empirical coefficient.

[0148] Based on the communication distance and interference distance, the set of neighboring nodes of any node u can be obtained. set of neighboring nodes Set of interfering nodes Set of disturbed nodes Among them, the neighbor node set represents the set of nodes located within the transmission distance of node u, the neighbor node set represents the set of nodes located within the transmission distance of nodes in the set, the interfering node set represents the set of nodes located within the interference distance of u, and the interfering node set represents the set of nodes located within the interference distance of nodes in the set.

[0149] S302. Obtain a routing and time-frequency allocation model for ad hoc networks based on available channels and edges.

[0150] In this embodiment of the application, based on the nodes and edges in the ad hoc network, the ad hoc network can be modeled as a directed graph of nodes and edges using graph theory. Here, the edge set ε is defined as Nodes u and v are called the tail and head of the edge (u,v). Let be the set of neighboring nodes of node u. The set of neighboring nodes represents the set of nodes located within the transmission distance of node u.

[0151] Define a binary set of routing and time-frequency allocation variables: Let f represent the relationship between data stream f, tail u, header v, channel c, and time slot s. If edge (u,v) transmits data packets of stream f using channel c in time slot s, then otherwise

[0152] The routing and time-frequency allocation problem model is derived with the optimization objective of minimizing the data flow transmission delay between nodes in an ad hoc network:

[0153] Specifically, define variable T f This is used to indicate the time slot occupied by the data stream f as it is transmitted to the destination node.

[0154]

[0155] Based on the delay weight of data stream f and T f With the goal of minimizing the latency of data flow f, a routing and time-frequency allocation problem model is obtained, which is expressed as:

[0156]

[0157] S303. Obtain the transmission constraints of the routing and time-frequency allocation problem model for self-organizing networks.

[0158] In this embodiment of the application, the transmission constraints include routing constraints and time-frequency constraints. The routing constraints include flow balancing constraints and propagation constraints, while the time-frequency constraints include communication interference constraints, half-duplex constraints, packet scheduling constraints, and variable binary constraints.

[0159] Define route variables Each node needs to satisfy the flow balancing constraint, that is, apart from the data packets it needs or generates, the data packets flowing out of the node are equal to the data packets flowing in. Therefore, the flow balancing constraints of relay nodes, source nodes, and destination nodes can be as follows:

[0160]

[0161] A data packet is transmitted on a single route, so the single-stream, single-path propagation constraint can be expressed as:

[0162]

[0163] Since all nodes operate in half-duplex communication mode, the node half-duplex constraint is expressed as:

[0164]

[0165] The half-duplex constraint also implies that each node can only transmit data packets through one channel in each time slot.

[0166] Within the interference range of each receiving node, no link can occupy the same time-frequency resource block. The communication interference constraint is expressed as:

[0167]

[0168] For an edge (u,v) on a potential path of data flow f, a necessary condition for packet transmission from u to v in time slot s is that node u has already received a packet in a previous time slot. The packet scheduling constraint is expressed as:

[0169]

[0170] Among them, intermediate variables Indicates whether the transmitting node u of the indicator edge (u,v) has a data packet to be transmitted at the start of time slot s, an intermediate variable. Indicates whether edge (u,v) transmitted a data packet in time slot s.

[0171] The intermediate variable can be obtained using the following formula:

[0172]

[0173] in, Indicates an indicator function. This represents the number of data packets received by node u in time slot 1 to s-1. (This condition is met.) The edges are valid edges. An edge is an active edge, meaning that the packet scheduling constraint requires an edge to become an active edge only after it has become a valid edge.

[0174] Binary constraints on variables are expressed as follows

[0175] In this embodiment of the application, the routing and time-frequency allocation problem model and the corresponding transmission constraints of the ad hoc network are determined, that is, the routing and time-frequency allocation problem model under transmission constraints can be obtained, as shown below:

[0176]

[0177] S304. Determine the minimum weight route for the data flow based on the routing and time-frequency allocation problem model under transmission constraints.

[0178] In this embodiment of the application, the solution of the routing and time-frequency allocation problem model under transmission constraints is divided into two steps: routing allocation solution and time-frequency allocation solution.

[0179] Intuitively, fewer hops in a data flow result in lower latency for route allocation. However, half-duplex constraints and communication interference lead to additional collision and contention delays. Therefore, the minimum hop count routing strategy is not optimal, especially when nodes are overcrowded or when there are many flows but few channels (multiple flows pass through the same node). Therefore, route allocation requires consideration of node hop count, communication congestion, and link resource conditions; that is, determining the minimum weighted route for the data flow.

[0180] Routing can be determined flow by flow based on the latency weight of the data stream, specifically as follows:

[0181] For the target data stream, the target stream set is {π} f The first target data stream can be represented as π. 1 =argmax f {W f The subsequent f-th target data stream can be represented as: 2≤f≤F.

[0182] For the current target data stream, f∈{π f The fitness weights of each edge in the target data stream are determined according to the following formula:

[0183]

[0184] Where, Δ u and Δ v This represents the number of times a data flow with a known route passes through nodes u and v, where α, β, and γ are the hop count, transmission collision, and channel contention coefficients, respectively.

[0185] The second term on the right is set because if each node occupied by routing is selected as a relay, it may cause potential conflict delays due to half-duplex constraints. The third term on the right is set because contention delay mainly depends on the number of available channels and resource scheduling. This term can help select links with more channel resources.

[0186] Based on the routing and time-frequency allocation problem model under adaptive weights and transmission constraints, a minimum weight routing problem for the target data flow is constructed to solve the problem.

[0187] Specifically, a minimum weight routing model for the target data flow is constructed based on the routing and time-frequency allocation problem model under adaptive weights and routing constraints, as shown below:

[0188]

[0189] Relax the propagation constraints in the minimum weight routing model of the target data stream to Since the optimal solution to the optimization problem after relaxation is also the solution to the problem before relaxation, the optimal minimum weight route of the target data flow can be obtained directly by using the interior point method.

[0190] according to We can determine all the edges on the path, and thus obtain the set of points on the path. Where H f =|H f |-1 represents the hop count of the current data stream. Repeat the above process until the minimum weight route for all target data streams is determined, that is, if f < F, then f ← f + 1, until the minimum weight route for all target data streams is determined.

[0191] S305. Based on the minimum weight routing of the data flow, the routing and time-frequency allocation problem model under transmission constraints is transformed into a time-frequency allocation model.

[0192] In this embodiment, determining the minimum weight route of the data flow determines the relationship between the data flow and the edges. Therefore, it is only necessary to focus on the connections between edges, channels, and time slots. Based on the minimum weight route result of the data flow, a new directed subgraph can be constructed. The node and edge calculation methods are as follows: and

[0193] Subgraphs can be determined middle Therefore, the binary set of routing and time-frequency allocation variables can be optimized as follows:

[0194]

[0195] Among them, f (u,v) This represents the data flow connected by the edge (u,v).

[0196] Based on the above definition, the routing and time-frequency allocation problem model under transmission constraints can be transformed into a time-frequency allocation model, as shown below:

[0197]

[0198] The time-frequency allocation model constraints are obtained by simplifying the half-duplex constraint, communication interference constraint, packet scheduling constraint, and binary constraint from step two. This indicates that edges (u,v) and (i,j) share a common node, meaning they are adjacent to each other.

[0199] S306. Based on the set of effective edges in each time slot and the time-frequency allocation model, the routing and time-frequency allocation problem of the ad hoc network is transformed into a time-slot-by-time channel allocation optimization problem.

[0200] For any data stream, the following identity is satisfied:

[0201]

[0202] This identity indicates that the number of optimization variables in the optimization space is a constant, and is equal to the sum of the number of packets in each data stream multiplied by the number of hops. Therefore, in order to minimize the total weighted delay, we can maximize the number of active edges in each time slot according to the greedy idea.

[0203] When transmitting data in each time slot, the set of valid edges can be determined according to the following formula:

[0204] and

[0205] According to the above definition, each time slot The channel allocation problem in [the context of the problem] can be modeled as follows:

[0206]

[0207] The three constraints are half-duplex constraint, communication interference constraint, and binary constraint.

[0208] Due to each time slot When solving the channel allocation problem, multiple optimal solutions may be obtained, and different optimal solutions will lead to different sets of effective edges in subsequent time slots. Therefore, it is necessary to obtain the most suitable optimal solution, which can be achieved by considering each time slot. First delay weight in the channel allocation problem Use the second time delay weight Instead, the second time delay weight can be determined according to the following formula:

[0209]

[0210] Therefore, each time slot The channel allocation problem in the context of time-slot allocation, or the time-slot-by-time channel allocation problem, can be expressed as:

[0211]

[0212] By performing an equivalent transformation on the binary constraints in the above slot-by-slot channel allocation problem and then applying Lagrange relaxation, we can obtain the final slot-by-slot channel allocation problem, as shown below.

[0213]

[0214] in, It is a Lagrange multiplier.

[0215] The final determined time-slot channel allocation problem is a linear programming problem, which can be solved directly using the interior-point method.

[0216] S307. An optimization solution is provided for the time-slot-by-time channel allocation optimization problem based on a continuous convex approximation strategy.

[0217] In this embodiment of the application, based on the optimization process of the above-mentioned time-slot-by-time channel allocation optimization problem, it can be solved according to the continuous convex approximation strategy, as shown below:

[0218] a1: For the current time slot Update the set of active edges based on the identified valid edges.

[0219] a2: Initialize the Lagrange multipliers and the optimal value.

[0220] In this embodiment of the application, initializing the Lagrange multiplier and the optimal value means setting an initial optimized Lagrange multiplier and the current time slot based on experience. The optimal value for channel allocation.

[0221] a3: Update the half-duplex constraint and communication interference constraint of the active edge.

[0222] a4: Using a fixed Lagrange multiplier and a continuous convex approximation strategy, the slot-by-slot channel allocation optimization problem is solved to obtain the current slot. optimal solution set for channel allocation And the optimal value UB.

[0223] a5: Set the non-integer solutions in the above optimal solution set to 0 or 1 under the premise of satisfying the half-duplex constraint and the disturbance constraint, and obtain the suboptimal value LB.

[0224] In this embodiment of the application, non-integer solutions in the optimal solution set that satisfy the half-duplex constraint and the interference constraint are classified as 1, and non-integer solutions that do not satisfy the half-duplex constraint and the interference constraint are classified as 0. The suboptimal value LB is obtained based on the newly obtained optimal solution set.

[0225] It is understandable that the suboptimal value LB is the optimal value obtained from the newly obtained optimal solution set.

[0226] a6: Update the Lagrange multipliers according to the following formula:

[0227]

[0228] a7: Determine whether the optimal solution and the Lagrange multipliers converge. If they do not converge, repeat the steps shown in a4. If they converge, you can execute the steps shown in a8.

[0229] In this embodiment of the application, if the optimal value obtained by the solution is the same as the initial optimal value, then the optimal solution is determined to be converged. If the updated Lagrange multiplier is the same as the initial Lagrange multiplier, then the Lagrange multiplier is converged.

[0230] If either the optimal solution or the Lagrange multiplier fails to converge, then perform the steps shown in a4 with the updated Lagrange multiplier, repeating the above loop until the optimal solution and the Lagrange multiplier converge.

[0231] a8: Updates the number of packets on different data nodes after the current time slot data packet is transmitted through the allocated optimal channel.

[0232] a9: Determine if all data packets of all data streams in the current time slot have reached the destination node. If not, proceed to the next time slot. Repeat steps a1 to a9 until all data packets of all data streams in the current time slot have arrived at the destination node. If so, the per-time-slot channel allocation optimization is complete.

[0233] At this point, the routing and time-frequency allocation method based on an integrated air-ground self-organizing network provided in this application embodiment has been completed.

[0234] Figures 4 to 7 The implementation performance diagram of the routing and time-frequency allocation method based on the air-ground integrated ad hoc network provided in the embodiment is shown in the figure:

[0235] The default parameters include 100 ground IoT nodes, 20 aerial UAV nodes, 5 data stream services, each data stream containing 3 data packets, and latency priority set to 1. Ground IoT nodes have 3 orthogonal channels with a transmit power of 24dBm, while aerial UAV nodes have 5 orthogonal channels with a transmit power of 27dBm, randomly distributed at altitudes between 80m and 100m. All nodes are randomly distributed in a 5000m x 5000m area. The path signal coefficients for air-to-air, ground-to-ground, and air-to-ground links are 2, 2.8, and 2, respectively. In the adaptive routing weights, the hop count coefficient, transmission conflict coefficient, and channel contention coefficient are 1, 0.5, and 0.1, respectively.

[0236] Set the carrier frequency to The received signal sensitivity is S R = -80dBm, transmit and receive antenna gain is a unit value of 1.

[0237] The routing and time-frequency allocation method based on an integrated air-ground ad hoc network provided in this application compares four algorithms: "minimum hop count routing," "fixed channel allocation," "time-slot-by-time greedy channel allocation," and "time-slot-by-time branch-bound channel allocation." Figures 4-7As shown. The multi-flow optimal minimum weight routing strategy proposed in this application is superior to "minimum hop count routing," and the proposed continuous convex approximation time-frequency resource scheduling strategy is superior to "fixed channel allocation" and "time-slot greedy channel allocation," and approximates the optimal "time-slot branch-bound channel allocation." However, the branch-bound method has worst-case exponential complexity, which is difficult to meet the real-time requirements of dynamic air-ground integrated ad hoc networks under millisecond frame structures. The proposed strategy has a complexity in polynomial time and its latency performance is close to optimal, making it applicable to dynamic air-ground integrated ad hoc networks.

[0238] Based on the above embodiments of the routing and time-frequency allocation method based on the air-ground integrated self-organizing network, this application also provides a routing and time-frequency allocation device based on the air-ground integrated self-organizing network.

[0239] Figure 8 This is a schematic diagram of the routing and time-frequency allocation device 80 based on an air-ground integrated self-organizing network provided in an embodiment of this application, as shown below. Figure 8 As shown, it includes:

[0240] The first acquisition module 801 is used to acquire node information in the air-ground integrated self-organizing network. The node information includes available channels and edges in the self-organizing network. The edges are used to indicate the transmission links of data flow in the self-organizing network.

[0241] The second acquisition module 802 is used to acquire the routing and time-frequency allocation problem model and the transmission constraints of the routing and time-frequency allocation problem model of the ad hoc network based on the available channels and edges. The transmission constraints include routing constraints and time-frequency constraints.

[0242] The third acquisition module 803 is used to acquire the weight of each edge corresponding to the data stream, and to acquire the minimum weight route of the data stream based on the weight and routing constraints.

[0243] The transformation module 804 is used to transform the routing and time-frequency allocation problem model of the ad hoc network into a time-slot-by-time channel allocation optimization problem based on the minimum weight routing and time-frequency constraints.

[0244] The solver module 805 is used to optimize the time-slot channel allocation problem based on the continuous convex approximation strategy.

[0245] The routing and time-frequency allocation device based on an integrated air-ground ad hoc network provided in this application embodiment can perform... Figure 2 and Figure 3 The technical solution of the routing and time-frequency allocation method embodiment based on the air-ground integrated self-organizing network shown is similar in principle and technical effect, and will not be described again here.

[0246] Figure 9 This is a schematic diagram of the routing and time-frequency allocation electronic device based on an integrated air-ground self-organizing network provided in an embodiment of this application. Figure 9 As shown, the routing and time-frequency allocation electronic device 90 based on an integrated air-ground self-organizing network provided in this embodiment may include:

[0247] Processor 901.

[0248] Memory 902 is used to store executable instructions for the terminal device.

[0249] The processor is configured to execute the above-described routing and time-frequency allocation method embodiment based on air-ground integrated self-organizing network by executing executable instructions. Its implementation principle and technical effect are similar, and will not be repeated here.

[0250] This application also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the technical solution of the above-described embodiment of the routing and time-frequency allocation method based on an integrated air-ground self-organizing network. Its implementation principle and technical effects are similar and will not be repeated here.

[0251] In one possible implementation, a computer-readable medium may include random access memory (RAM), read-only memory (ROM), compact discread-only memory (CD-ROM) or other optical disc storage, disk storage or other magnetic storage devices, or any other medium targeted to carry or to store the required program code in the form of instructions or data structures, and accessible by a computer. Furthermore, any connection is appropriately referred to as a computer-readable medium. For example, if software is transmitted from a website, server, or other remote source using coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave, then coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of medium. As used herein, disks and optical discs include optical discs, laser discs, optical discs, Digital Versatile Discs (DVDs), floppy disks, and Blu-ray discs, where disks typically reproduce data magnetically, while optical discs optically reproduce data using lasers. The above combinations should also be included within the scope of computer-readable media.

[0252] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the technical solution of the above-described embodiment of the routing and time-frequency allocation method based on an integrated air-ground self-organizing network. Its implementation principle and technical effects are similar and will not be repeated here.

[0253] In the specific implementation of the aforementioned terminal device or server, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in the embodiments of this application can be directly manifested as execution by a hardware processor, or execution by a combination of hardware and software modules within the processor.

[0254] Those skilled in the art will understand that all or part of the steps in any of the above method embodiments can be implemented by hardware associated with program instructions. The aforementioned program can be stored in a computer-readable storage medium, and when the program is executed, all or part of the steps in the above method embodiments are performed.

[0255] If the technical solution of this application is implemented in software form and sold or used as a product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the technical solution of this application can be embodied in the form of a software product, which is stored in a storage medium and includes a computer program or several instructions. This computer software product causes a computer device (which may be a personal computer, server, network device, or similar electronic device) to execute all or part of the steps of the method described in the embodiments of this application.

[0256] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A routing and time-frequency allocation method based on an integrated air-ground ad hoc network, characterized in that, include: Obtain node information in an air-ground integrated ad hoc network, the node information including available channels and edges in the ad hoc network, the edges being used to indicate the transmission links of data flows in the ad hoc network; Based on the available channels and the edges, obtain the routing and time-frequency allocation problem model of the ad hoc network and the transmission constraints of the routing and time-frequency allocation problem model, wherein the transmission constraints include routing constraints and time-frequency constraints; Obtain the weight of each edge corresponding to the data flow, and obtain the minimum weight route of the data flow based on the weight and the routing constraints; Based on the minimum weight routing and the time-frequency constraints, the routing and time-frequency allocation problem model of the ad hoc network is transformed into a time-slot-by-time channel allocation optimization problem; The time-slot-by-time channel allocation optimization problem is solved by an optimization strategy based on continuous convex approximation. The step of obtaining the routing and time-frequency allocation problem model of the ad hoc network based on the available channels and the edges includes: Define a binary set of routing and time-frequency allocation variables: This represents the relationship between data stream f, node u, node v, channel c, and time slot s, where, This represents the set of available channels for the link formed by nodes u and v. A collection representing data streams. Represents the set of time slots within each frame; The edge set ε is defined as in, For a set of nodes, Let u be the set of neighboring nodes; If edge (u,v) transmits data packets of stream f using channel c in time slot s, then otherwise Define variable T f The T f The T is used to indicate the time slot occupied by the data stream f entering the destination node. f Represented as: Based on the first delay weight of the data stream f and the T f With the goal of minimizing the latency of data flow f, the routing and time-frequency allocation problem model is obtained, expressed as: in, Indicates the destination node of data stream f. express The set of neighboring nodes, W f This represents the first delay weight of the data stream f; The transmission constraints of the routing and time-frequency allocation problem model include: Flow balance constraint: In the ad hoc network, the outgoing data packets of each node are equal to the incoming data packets. The nodes include relay nodes, source nodes, and destination nodes. The flow balance constraint for the relay nodes, source nodes, and destination nodes is expressed as follows: in, For the defined routing variables, Represents the set of neighboring nodes of node u. Represents the source node of data stream f, L f This indicates the number of data packets in data stream f; Propagation constraints stipulate that data streams can only be transmitted along a single route. These propagation constraints are expressed as follows: Communication interference constraints stipulate that no link can occupy the same time-frequency resource block within the interference distance range of the receiving node of each link. These communication interference constraints are expressed as follows: in, This represents the set of nodes that are disturbed by node v. Represents the set of neighboring nodes of node i; Half-duplex constraint: Each node operates in half-duplex communication mode. The half-duplex constraint is expressed as follows: The packet scheduling constraint states that for an edge (u,v) on a potential path of flow f, a necessary condition for packet transmission from u to v in time slot s is that node u has already received a packet in a previous time slot. This packet scheduling constraint is expressed as: in, This indicates whether the transmitting node u of edge (u,v) has a data packet to be transmitted at the start of time slot s. Indicates whether edge (u,v) transmitted a data packet in time slot s; Binary constraints on variables, wherein the binary constraints on variables are expressed as follows: The routing constraints include flow balancing constraints and propagation constraints, and the time-frequency constraints include communication interference constraints, half-duplex constraints, packet scheduling constraints, and variable binary constraints. The step of obtaining the minimum weight route for the data stream based on the weights and the routing constraints includes: Based on the routing constraints and the weight of each edge corresponding to the target data flow, a minimum weight routing model for the target data flow is constructed, expressed as: Where, m (u,v) This represents the weight of each edge in the target data stream; Relax the propagation constraints in the minimum weight routing model as follows: The minimum weight routing model is solved using the interior point method to determine the minimum weight route for the data flow. The process of transforming the routing and time-frequency allocation problem model of the ad hoc network into a time-slot-by-time channel allocation optimization problem based on the minimum weight routing and the time-frequency constraints includes: The time-frequency allocation model in the minimum weight routing is determined based on the minimum weight routing of the data stream and the time-frequency constraint, and is expressed as follows: Among them, f (u,v) This represents the data flow associated with edge (u,v). Let (u,v) and (i,j) be adjacent to each other, and let ε0 be the set of edges formed by adjacent nodes on the minimum weight routing path of the data flow. Based on the set of valid edges in each time slot and the time-frequency allocation model, the routing and time-frequency allocation problem of the ad hoc network is transformed into a time-slot-by-time channel allocation optimization problem. Here, the valid edges refer to those that satisfy... The edge.

2. The method according to claim 1, characterized in that, The step of obtaining the weight of each edge corresponding to the data stream includes: For the target data stream, the weight of each edge of the target data stream is determined by the following formula: Where, Δ u and Δ v This represents the number of times a data flow with a known route passes through nodes u and v, where α, β, and γ are the hop count, transmission collision, and channel contention coefficients, respectively.

3. The method according to claim 2, characterized in that, The step of transforming the routing and time-frequency allocation problem of the ad hoc network into a time-slot-by-time channel allocation optimization problem based on the set of valid edges in each time slot and the time-frequency allocation model includes: The set of valid edges in each time slot is determined according to the following formula: and Based on the set of valid edges in each time slot and the time-frequency allocation model, the channel allocation model for each time slot is determined, and the channel allocation model is expressed as follows: in, Indicates the target time slot currently being processed; The time-slot-by-time channel allocation optimization problem is obtained based on the second delay weight of data stream f and the channel allocation model; the second delay weight is determined based on the first delay weight.

4. The method according to claim 3, characterized in that, The step of obtaining the slot-by-slot channel allocation optimization problem based on the second delay weight of data stream f and the channel allocation model includes: The second delay weight is determined according to the following formula: Based on the second delay weight and the channel allocation model, a time-slot-by-time channel allocation problem is obtained, which is expressed as follows: The binary constraints in the slot-by-slot channel allocation problem are equivalently transformed to obtain the slot-by-slot channel allocation optimization problem, which is expressed as: in, It is a Lagrange multiplier.

5. The method according to claim 4, characterized in that, The optimization solution to the slot-by-slot channel allocation problem based on the continuous convex approximation strategy includes: Obtain the set of active edges in the current time slot, and initialize the Lagrange multiplier and the optimal allocation value for the current time slot; Update the communication interference constraints and half-duplex constraints of the active edges; The continuous convex approximation strategy is used to optimize the time-slot channel allocation problem, and the optimal solution set and optimal value of the current time-slot channel allocation are obtained. The current time slot is obtained based on the optimal solution set. The suboptimal value of channel allocation, and the Lagrange multiplier updated according to the optimal solution set, the optimal value and the suboptimal value; When the updated Lagrange multipliers and the optimal value converge, the optimal value is taken as the optimal solution to the current time slot channel allocation optimization problem. If all data packets of the data stream in the current time slot have been transmitted to the destination node, the above steps for obtaining the optimal solution to the channel allocation optimization problem of the current time slot are repeated in the next time slot until the optimal solution to the channel allocation optimization problem of all time slots is obtained.

6. A routing and time-frequency allocation device based on an integrated air-ground self-organizing network, characterized in that, include: The first acquisition module is used to acquire node information in the air-ground integrated self-organizing network. The node information includes available channels and edges in the self-organizing network, and the edges are used to indicate the transmission links of data flow in the self-organizing network. The second acquisition module is used to acquire the routing and time-frequency allocation problem model of the ad hoc network and the transmission constraints of the routing and time-frequency allocation problem model based on the available channels and the edges. The transmission constraints include routing constraints and time-frequency constraints. The third acquisition module is used to acquire the weight of each edge corresponding to the data flow, and to acquire the minimum weight route of the data flow based on the weight and the routing constraints. The transformation module is used to transform the routing and time-frequency allocation problem model of the ad hoc network into a time-slot-by-time channel allocation optimization problem based on the minimum weight routing and the time-frequency constraints. The solution module is used to optimize and solve the slot-by-slot channel allocation optimization problem based on a continuous convex approximation strategy. The step of obtaining the routing and time-frequency allocation problem model of the ad hoc network based on the available channels and the edges includes: Define a binary set of routing and time-frequency allocation variables: This represents the relationship between data stream f, node u, node v, channel c, and time slot s, where, This represents the set of available channels for the link formed by nodes u and v. A collection representing data streams. Represents the set of time slots within each frame; The edge set ε is defined as in, For a set of nodes, Let u be the set of neighboring nodes; If edge (u,v) transmits data packets of stream f using channel c in time slot s, then otherwise Define variable T f The T f The T is used to indicate the time slot occupied by the data stream f entering the destination node. f Represented as: Based on the first delay weight of the data stream f and the T f With the goal of minimizing the latency of data flow f, the routing and time-frequency allocation problem model is obtained, expressed as: in, Indicates the destination node of data stream f. express The set of neighboring nodes, W f This represents the first delay weight of the data stream f; The transmission constraints of the routing and time-frequency allocation problem model include: Flow balance constraint: In the ad hoc network, the outgoing data packets of each node are equal to the incoming data packets. The nodes include relay nodes, source nodes, and destination nodes. The flow balance constraint for the relay nodes, source nodes, and destination nodes is expressed as follows: in, For the defined routing variables, Represents the set of neighboring nodes of node u. Represents the source node of data stream f, L f This indicates the number of data packets in data stream f; Propagation constraints stipulate that data streams can only be transmitted along a single route. These propagation constraints are expressed as follows: Communication interference constraints stipulate that no link can occupy the same time-frequency resource block within the interference distance range of the receiving node of each link. These communication interference constraints are expressed as follows: in, This represents the set of nodes that are disturbed by node v. Represents the set of neighboring nodes of node i; Half-duplex constraint: Each node operates in half-duplex communication mode. The half-duplex constraint is expressed as follows: The packet scheduling constraint states that for an edge (u,v) on a potential path of flow f, a necessary condition for packet transmission from u to v in time slot s is that node u has already received a packet in a previous time slot. This packet scheduling constraint is expressed as: in, This indicates whether the transmitting node u of edge (u,v) has a data packet to be transmitted at the start of time slot s. Indicates whether edge (u,v) transmitted a data packet in time slot s; Binary constraints on variables, wherein the binary constraints on variables are expressed as follows: The routing constraints include flow balancing constraints and propagation constraints, and the time-frequency constraints include communication interference constraints, half-duplex constraints, packet scheduling constraints, and variable binary constraints. The step of obtaining the minimum weight route for the data stream based on the weights and the routing constraints includes: Based on the routing constraints and the weight of each edge corresponding to the target data flow, a minimum weight routing model for the target data flow is constructed, expressed as: Where, m (u,v) This represents the weight of each edge in the target data stream; Relax the propagation constraints in the minimum weight routing model as follows: The minimum weight routing model is solved using the interior point method to determine the minimum weight route for the data flow. The process of transforming the routing and time-frequency allocation problem model of the ad hoc network into a time-slot-by-time channel allocation optimization problem based on the minimum weight routing and the time-frequency constraints includes: The time-frequency allocation model in the minimum weight routing is determined based on the minimum weight routing of the data stream and the time-frequency constraint, and is expressed as follows: Among them, f (u,v) This represents the data flow associated with edge (u,v). Let (u,v) and (i,j) be mutually adjacent, and let ε0 represent the set of edges formed by adjacent nodes on the minimum weight routing path of the data flow. Based on the set of effective edges in each time slot and the time-frequency allocation model, the routing and time-frequency allocation problem model of the ad hoc network is transformed into a time-slot-by-time channel allocation optimization problem. Here, the effective edges are those that satisfy... The edge.

Images