Construction method of complex communication network information transmission model
Through three-layer network modeling and heterogeneous parameter expansion, the problem of collaborative propagation of multiple packets in complex communication networks is solved, and wider applicability and computing efficiency are achieved.
Patent Information
- Application Number
- CN202510885704.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-30
- Publication Date
- 2025-08-12
AI Technical Summary
The existing communication network model is difficult to adapt to the diverse needs of data packet collaborative propagation. Especially in complex heterogeneous network environments, traditional models are limited to the transmission of a single type of data packet, and the existing models have problems of inconservation and high computational complexity in information propagation dynamic modeling.
Three-layer network modeling is adopted to characterize the diversity structure within and between the network layer through heterogeneous parameters, expand the single packet model to multi-packet collaborative propagation, and simplify path calculations through randomization processing to reduce the computational complexity.
The collaborative propagation modeling of multiple data packets in multi-layer, heterogeneous complex communication networks is realized, which reduces the computational complexity, makes the model suitable for a wider range of network environments and application scenarios, and provides a basis for rapid simulation and optimization.
Smart Images
Figure CN120474936A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the technical field of wireless network modeling and analysis, and in particular relates to a method for constructing an information transmission model of a complex communication network. Background Art
[0002] The communication process in wireless communication networks is not only complex but also highly dynamic. Due to differences in transmission protocols, hardware implementations, network resource scheduling algorithms, and other factors, the specific processes of information propagation within networks exhibit diverse characteristics, posing significant challenges to modeling and analyzing the overall properties of communication networks. Modeling and analysis methods based on specific protocols or hardware models are generally applicable only to specific scenarios and are difficult to generalize to other network structures or application scenarios. To address this, researchers have constructed various theoretical models based on general propagation laws in communication networks that are inherent and universal, independent of specific protocols, hardware, or algorithms. Identifying and understanding fundamental information propagation laws through communication network models helps apply research findings to a wider range of network environments, providing important guidance for analyzing communication systems with diverse protocols and algorithms.
[0003] In current research on communication network models, the coordinated propagation of multiple data packets is a core research area in communication networks. This technology has important theoretical value and practical application significance, especially for optimizing information propagation in complex heterogeneous network environments. Currently, research on communication network models mainly focuses on optimizing the transmission of a single type of data packet. However, with the continuous expansion of communication network application scenarios, the need for coordinated propagation of different types of data packets in the same network is becoming increasingly prominent. The types of data transmitted in modern communication networks are becoming increasingly diverse, including video streams, control signaling, sensor data, etc., which have differentiated requirements for indicators such as latency, reliability, and priority during transmission.
[0004] Current approaches to modeling the dynamics of information propagation in communication networks suffer from two key shortcomings. First, the most widely used general models in the field of information propagation originate from the epidemiological model proposed by Kermack-McKendrick and a series of derived propagation models, such as the SIS. After nearly a century of development, these models have become important theoretical tools for studying disease transmission, information diffusion, and computer virus propagation. However, models like the SIS depict information propagation behavior in a probabilistic manner, and information (or data packets) are not conserved in the models, thus limiting their ability to accurately model specific problems. Second, while several packet-based communication modeling methods exist, most focus on the transmission of a single type of packet. For example, the packet transmission model proposed by Alex Arenas, while possessing properties such as data conservation and path tracing, is limited to tree-like networks and difficult to extend to more complex network structures. Furthermore, it only supports the modeling and analysis of a single type of packet. Summary of the Invention
[0005] To better characterize the fundamental laws governing the coordinated propagation of multiple data types within complex communication networks, this paper proposes a method for constructing an information transmission model for complex communication networks. This method implements modeling of complex space-ground-air communication networks through three-layer network modeling. Heterogeneous parameters are used to characterize the diverse structures within and between network layers. By extending the single-packet model to the coordinated propagation of multiple packets, this method significantly expands the scope of modeling the dynamics of information propagation in traditional communication networks, making it more suitable for current research and application needs.
[0006] The technical solution adopted in the present invention is:
[0007] A method for constructing a complex communication network information transmission model comprises the following steps:
[0008] Step 1: According to the actual properties of the communication nodes in different layers, appropriate parameters are selected to establish heterogeneous random networks layer by layer;
[0009] Step 2: According to the actual nature of the inter-layer communication process, appropriate parameters are selected to establish heterogeneous correlations between the heterogeneous random networks at different layers.
[0010] Step 3: According to the requirements of the problem to be studied, the number of data packet types in the network and their respective propagation properties are set, and the matching relationship between network nodes and data packet processing capabilities is set;
[0011] Step 4: According to the requirements of the problem to be studied, set the generation and reception rate of data packets in the network, and set the matching relationship between the generation and reception capabilities of network nodes for different data packets.
[0012] Furthermore, each layer of the heterogeneous random network in step 1 is controlled by its own heterogeneity parameter p, which is used to characterize different network properties; when p = 0, the corresponding network layer is an ER random network, when p = 1, the corresponding network layer is a BA scale-free network, and when p is between 0 and 1, the corresponding network layer is in a heterogeneous state between the ER and BA networks.
[0013] Furthermore, the specific process of step 2 is:
[0014] On the basis of a multi-layer heterogeneous random network, inter-layer connections are established through regular random connections. The connection rules between any two layers are regulated by the heterogeneity parameter q. The inter-layer connections adopt a uniform random connection method with probability q and a degree-preferred connection method with probability 1-q.
[0015] Furthermore, the specific process of step 3 is:
[0016] Select the number of data packets M, and the state of each communication node in the network is described by a high-dimensional vector S with M information quantities. i =(n i1 ,n i2 ,…,n iM ), where n im Indicates the number of m-th type of information on node i;
[0017] At any time t, the data packet containing the mth information is sent to all nodes with probability p. k The probability that a data packet located at node i is delivered to node j on its path depends on the communication quality between the two nodes, that is, the probability Normalize the propagation probabilities calculated independently for different information to obtain the probability in is the ability of nodes α and β to propagate information m, specifically expressed as is the maximum communication capacity of the corresponding communication channel, f(n αm ) is the node information processing capability that depends on the current α nodes and m types of data packets, where α and β represent nodes i and j respectively;
[0018] Assume that at every moment t, a communication channel is established between node i and its connected nodes, and at most one data packet is sent. The probability of information sending is p i Depends on the sum of probabilities calculated independently for all packets where q ih It is the basic parameter for not issuing data packets, N i is the number of neighbor nodes connected to the i-th node;
[0019] At time t, for node i, with the probability of issuing i Decide whether to release the data packet and use the normalized probability Determine the type m of data packet transmission and the transmission node j.
[0020] Furthermore, the specific process of step 4 is as follows:
[0021] At time t, node i generates the probability vector according to the given By probability Randomly generate a new data packet of the mth information so that n im (t) = n im (t-Δt)+1; where n im ((t) is the number of the mth type of data packets at the i-th node at time t, n im (t-Δt) is the number of type m packets at the i-th node at time t-Δt;
[0022] At the same time, at time t, node i is eliminated according to the given probability vector By probability Eliminate an existing data packet of type m information, so that n im (t) = n im (t-Δt)-1; elimination probability is the total number of information packets in the current communication network, and N is the number of nodes in the network.
[0023] Compared with the prior art, the present invention has the following advantages:
[0024] 1. The present invention extends the existing single-layer heterogeneity controllable network model to a multi-layer network and adds a heterogeneity modeling method for inter-layer associations.
[0025] 2. The present invention extends the existing single data packet propagation model to a multi-data packet cooperative propagation model, and adds a collaborative mechanism modeling method for shared channels.
[0026] 3. The method of the present invention simplifies the original data packet path calculation based on complex networks into a random generation and reception process, overcoming the problem of non-unique paths in complex networks and greatly reducing the computational complexity, making large-scale and rapid calculation of the model possible, and facilitating large-scale modeling in subsequent analysis and optimization problems. BRIEF DESCRIPTION OF THE DRAWINGS
[0027] Figure 1 It is a principle block diagram of an embodiment of the present invention. DETAILED DESCRIPTION
[0028] A method for constructing an information transmission model for a complex communication network is based on a modeling approach for complex networks with controllable heterogeneity. First, a multi-layer network is established that can characterize heterogeneity within and between layers, used to characterize the underlying structure of a complex communication network involving air-space-ground collaboration. Second, based on the data packet propagation model, a collaborative propagation model capable of characterizing multiple data packets is established. A collaborative mechanism is introduced based on a shared communication channel, and by randomizing the propagation process, the non-uniqueness difficulties introduced by the complex network structure are overcome. This significantly reduces computational complexity, enabling large-scale rapid simulations and laying the foundation for subsequent application in practical problems such as analysis and optimization.
[0029] Reference Figure 1 , the specific steps of this method are as follows:
[0030] Step 1: According to the actual properties of the communication nodes in different layers, appropriate parameters are selected to establish heterogeneous random networks layer by layer;
[0031] In step 1 of this embodiment, three-layer heterogeneous random network models are established to characterize the three-layer communication network of air, space, and ground, which can be expanded to multiple layers. Each layer of heterogeneous random network is controlled by its own heterogeneity parameter p to characterize different network properties. When p = 0, the corresponding network layer is an ER random network. When p = 1, the corresponding network layer is a BA scale-free network. When p is between 0 and 1, the corresponding network layer is in a heterogeneous state between ER and BA networks. The details are as follows:
[0032] For each layer of the network, its node list N and heterogeneity parameter p are selected according to actual needs; then, two different network generation mechanisms are used to model each layer of the network, and the heterogeneity parameter p is used to adjust it. First, a fully connected network with m nodes is established, and a network composed of Nm nodes that are not connected to each other is used as the initial point. Then, a scattered point is randomly selected and the network is connected with probability p. –Rényi random network, where connections are established between any two nodes with the same probability, forming a network structure with binomial degree distribution, thus forming a random network with uniform connection probability. At the same time, connections are established in the manner of Barabási–Albert random network with probability 1-p, where the probability of establishing edges between nodes is proportional to the degree of the nodes, thus forming a network structure with power-law degree distribution. During the modeling process, the average degree of the entire network is indirectly adjusted by controlling the number of connections established to each node.
[0033] Step 2: According to the actual nature of the inter-layer communication process, appropriate parameters are selected to establish heterogeneous correlations between the heterogeneous random networks at different layers.
[0034] In step 2, based on the multi-layer heterogeneous random network, inter-layer connections are established by regular random connections. The connection rules between any two layers are regulated by the heterogeneity parameter q. The inter-layer connections are connected in a uniform random manner with probability q and in a degree-preferred manner with probability 1-q. The details are as follows:
[0035] Taking into account the different physical foundations of the communication nodes on which each layer of the network is based, a heterogeneous random network with different parameters P is established for each layer to characterize the different levels of network heterogeneity that may exist on the ground, in the air, and in space. The connections between layers also need to have adjustable heterogeneous associations. To this end, the heterogeneity control method in step 1 is extended to the layers of different networks to construct a multi-layer heterogeneous network with adjustable preference. Specifically, according to the needs of the specific problem, the preference index q is selected between networks at different levels, and when establishing connections between layers, the probability q is used to determine the preference index q. -Rényi random networks establish random connections, selecting nodes randomly in both passes, and simultaneously establish degree-biased connections with probability 1-q in the Barabási–Albert random network, where the probability of selecting nodes in both layers is proportional to their degrees within the layer. Thus, when q = 0, the connections between nodes at different layers are completely random, and as q increases, more and more cross-layer connections will be established between central nodes.
[0036] Therefore, by combining steps 1 and 2, the modeling of complex communication networks is realized, and the intra-layer heterogeneity index p and the inter-layer heterogeneity index q are used to characterize the heterogeneous connections of the network to meet the needs of different practical problems.
[0037] Step 3: According to the requirements of the problem to be studied, set the number of data packet types in the network and their respective propagation properties, and set the matching relationship between network nodes and data packet processing capabilities; the specific process is as follows:
[0038] In order to describe the propagation process of data packets in the communication network, the present invention extends the Arenas information propagation model; the number of data packets M is selected, and the state of each communication node in the network is described by a high-dimensional vector S of M information quantities. i =(n i1 ,n i2 ,…,n iM ), where n im Indicates the number of m-th type of information on node i;
[0039] At any time t, the data packet containing the mth information is sent to all nodes with probability p. kThe probability that a data packet located at node i is delivered to node j on its path depends on the communication quality between the two nodes, that is, the probability Normalize the propagation probabilities calculated independently for different information to obtain the probability in is the ability of nodes α and β to propagate information m, specifically expressed as is the maximum communication capacity of the corresponding communication channel, f(n αm ) is the node information processing capability that depends on the current α node and m types of data packets, α and β represent nodes i and j respectively; among them, for the function f(n) of information processing capability, it is assumed that f(n)=(1+n γ ) -1 , thus, including all cases with and without data packets, the function f(n) describes the information processing capacity of the current node, which depends on the current amount of information n, and as the amount of information n increases, the remaining information processing capacity f(n) decreases. In the function design, a controllable factor γ is used to characterize the speed at which f(n) decreases as n increases; for practical problems, the parameter and p k All can be adjusted independently according to the actual problem settings;
[0040] Since there are multiple kinds of information at the same time, and the communication channel resources between nodes are limited, all information shares communication resources and needs to be independently calculated in the propagation probability. Normalization and other operations are performed; therefore, suppose that at each time t, a communication channel is established between node i and its connected nodes, and at most one data packet is sent. The probability of information sending is p i Depends on the sum of probabilities calculated independently for all packets where q ih It is the basic parameter for not issuing data packets, N i is the number of neighbor nodes connected to the i-th node;
[0041] At time t, for node i, with the probability of issuing i Decide whether to release the data packet and use the normalized probability Determine the type m of data packet transmission and the transmission node j.
[0042] Step 4: According to the requirements of the problem to be studied, set the generation and reception rate of data packets in the network, and set the matching relationship between the generation and reception capabilities of network nodes for different data packets.
[0043] In step 4, when simulating the propagation of multi-dimensional data packets in a multi-layer communication network, in addition to the transmission of information in each dimension between nodes, it is also necessary to further model the process of generating new information and eliminating old information. These two scenarios correspond to the actual communication process in which each communicating entity (corresponding to each node in the communication network) sends new information and receives information but does not pass it on to other nodes.
[0044] Specifically, at time t, node i generates the probability vector By probability Randomly generate a new data packet of the mth information, so that n im (t) = n im (t-Δt)+1,n im (t) is the number of the mth type of data packets at the i-th node at time t, n im (t-Δt) is the number of the mth type of data packets at the i-th node at time t-Δt. At the same time, at time t, node i also eliminates the given probability vector By probability Eliminate an existing data packet of the mth type of information, so that n im (t) = n im (t-Δt)-1. Elimination probability The total number of information packets in the current communication network is multiplied by the basic elimination probability It is used to express the influence of the behavior of each node tending to clear information packets due to overload when there are too many data packets in the communication network; where N is the number of nodes in the network.
[0045] Based on the generation and elimination probabilities, this paper employs a mean field approximation to solve the problem of calculating and selecting different propagation paths in complex networks. This approach treats packet reception as a random event, meaning that at a given time t, any packet, after being delivered to a new node, will be received by that new node with a fixed probability. During packet propagation, for node α, all connected nodes β can be considered as the next node for information propagation. This significantly simplifies path selection and calculation, while also reflecting the influence of the overall network topology on information propagation.
[0046] This invention can model the coordinated propagation of multiple data packets in multi-layered, heterogeneous, and complex communication networks, and provides a randomized, rapid simulation method. Compared to traditional single-packet propagation models, this method is more suitable for analyzing and optimizing complex communication processes. Compared to refined models designed for specific protocols, the modeling method provided by this invention adheres more closely to the first principles of information propagation and exhibits greater robustness.
[0047] The modeling method provided by this invention includes numerous adjustable parameters, including intra-layer and inter-layer heterogeneity of the network, the number of transmitted data packets, and the upper limit of the channel capacity between different nodes. These parameters can be adjusted based on practical problems, significantly enhancing the model's ability to characterize real-world problems and the parameter space available for analysis and optimization using this invention.
Claims
1. A method for constructing a complex communication network information transmission model, characterized in that: The following steps are involved: Step 1: According to the actual properties of the communication nodes in different layers, appropriate parameters are selected to establish heterogeneous random networks layer by layer; Step 2: According to the actual nature of the inter-layer communication process, appropriate parameters are selected to establish heterogeneous correlations between the heterogeneous random networks at different layers. Step 3: According to the requirements of the problem to be studied, the number of data packet types in the network and their respective propagation properties are set, and the matching relationship between network nodes and data packet processing capabilities is set; Step 4: According to the requirements of the problem to be studied, set the generation and reception rate of data packets in the network, and set the matching relationship between the generation and reception capabilities of network nodes for different data packets.
2. The method for constructing a complex communication network information transmission model according to claim 1, characterized in that: In step 1, each layer of heterogeneous random network is controlled by its own heterogeneity parameter p, which is used to characterize different network properties; when p = 0, the corresponding network layer is an ER random network, when p = 1, the corresponding network layer is a BA scale-free network, and when p is between 0 and 1, the corresponding network layer is in a heterogeneous state between ER and BA networks.
3. The method for constructing a complex communication network information transmission model according to claim 1, characterized in that: The specific process of step 2 is: On the basis of a multi-layer heterogeneous random network, inter-layer connections are established through regular random connections. The connection rules between any two layers are regulated by the heterogeneity parameter q. The inter-layer connections adopt a uniform random connection method with probability q and a degree-preferred connection method with probability 1-q.
4. The method for constructing a complex communication network information transmission model according to claim 1, characterized in that: The specific process of step 3 is as follows: Select the number of data packets M, and the state of each communication node in the network is described by a high-dimensional vector S with M information quantities. i =(n i1 ,n i2 ,…,n iM ), where n im Indicates the number of m-th type of information on node i; At any time t, the data packet containing the mth information is sent to all nodes with probability p. k The probability that a data packet located at node i is delivered to node j on its path depends on the communication quality between the two nodes, that is, the probability Normalize the propagation probabilities calculated independently for different information to obtain the probability in is the ability of nodes α and β to propagate information m, specifically expressed as is the maximum communication capacity of the corresponding communication channel, f(n αm ) is the node information processing capability that depends on the current α nodes and m types of data packets, where α and β represent nodes i and j respectively; Assume that at every moment t, a communication channel is established between node i and its connected nodes, and at most one data packet is sent. The probability of information sending is p i Depends on the sum of probabilities calculated independently for all packets where q ih It is the basic parameter for not issuing data packets, N i is the number of neighboring nodes connected to the i-th node; At time t, for node i, with the probability of issuing i Decide whether to release the data packet and use the normalized probability Determine the type m of data packet transmission and the transmission node j.
5. The method for constructing a complex communication network information transmission model according to claim 4, characterized in that: The specific process of step 4 is as follows: At time t, node i generates the probability vector according to the given By probability Randomly generate a new data packet of the mth information so that n im (t) = n im (t-Δt)+1; where n im (t) is the number of the mth type of data packets at the i-th node at time t, n im (t-Δt) is the number of type m packets at the i-th node at time t-Δt; At the same time, at time t, node i is eliminated according to the given probability vector By probability Eliminate an existing data packet of type m information, so that n im (t) = n im (t-Δt)-1; elimination probability is the total number of information packets in the current communication network, and N is the number of nodes in the network.