Multi-hop multi-frequency wireless mesh network adaptive routing method based on channel modeling

Abstract

The invention discloses a multi-hop multi-frequency wireless mesh network adaptive routing method based on channel modeling, which comprises the following steps of: S1, carrying out routing discovery and networking, and establishing a mesh network; s2, carrying out channel modeling by utilizing an interpretable neural network to predict channel information, judging a link deterioration condition and a node movement condition, carrying out rapid convergence by utilizing a routing protocol, and carrying out routing maintenance on current data transmission by utilizing a potential path; and S3, broadcasting and collecting state information of each node by using a network controller, introducing a route selection process according to network state dynamics and business particularity, and selecting an optimal route and a secondary route by using node states and business characteristics. By constructing the self-learning channel model, self-adaptive modeling and prediction can be carried out based on historical channel data, the channel state is updated in real time, the accuracy and the intelligent level of routing maintenance are improved, meanwhile, the channel quality can be comprehensively analyzed, and the network performance is optimized.

Description

Technical Field

[0001] The present invention relates to the field of mobile communication technology, and in particular to a multi-hop multi-frequency wireless mesh network adaptive routing method, device and storage medium based on channel modeling. Background Art

[0002] With the rapid development of the Internet and wireless communication technologies in the digital age, the scale and complexity of networks continue to increase. Especially in application scenarios such as the Internet of Things, smart cities, and mobile ad hoc networks, the requirements for network performance are becoming more stringent.

[0003] Traditional network architectures often find it difficult to adapt quickly to dynamically changing environments, resulting in inefficient resource allocation and insufficient network reliability. Therefore, software-defined networking (SDN) is often used in existing technologies as an innovative network architecture. By separating the control plane from the data plane, it achieves higher flexibility and programmability, and can effectively support dynamic resource allocation and automated management.

[0004] However, due to the mobility of nodes and the frequent changes of links, it is often difficult to meet the required requirements for network management and performance monitoring. Therefore, based on this problem, the prior art further proposes a BATMAN protocol, which can effectively reflect the dynamic behavior of nodes in the network and the link quality fluctuations by quantifying the link status using the transmission quality (TQ) value.

[0005] However, even if the existing technology uses the transmission quality (TQ) value to quantify the link status, it cannot well predict and preset the path selection of the node within the existing network performance evaluation model framework. When a link failure occurs, it is not convenient to seamlessly complete the route switching and ensure the stability of the network.

[0006] In addition, although traditional deep learning models such as deep neural networks (DNN), convolutional neural networks (CNN) and recurrent neural networks (RNN) perform well in processing complex tasks, they are often regarded as "black boxes" because their internal decision-making processes are difficult for humans to intuitively understand. Therefore, a neural network model that improves the transparency and understandability of the model's decision-making process has been proposed in the prior art, which is usually called an interpretable neural network model (XNN).

[0007] At the same time, with the development of new technologies and applications in 5G and beyond, a new trend has emerged in wireless channels, including higher frequencies, larger antenna arrays, and more complex wireless communication schemes. At this time, the wireless channels in the channel modeling process have brought huge challenges to traditional channel modeling. Therefore, there is an urgent need for an effective and accurate channel modeling method to discover basic channel characteristics.

[0008] To this end, the present application proposes a multi-hop multi-frequency wireless mesh network adaptive routing method based on channel modeling to solve the above technical problems. Summary of the invention

[0009] The main purpose of the present invention is to provide a multi-hop multi-frequency wireless mesh network adaptive routing method based on channel modeling. In view of the complexity of the multi-hop multi-frequency wireless mesh network, the optimal routing path and frequency combination are dynamically selected to maximize the satisfaction of business needs. At the same time, by combining multi-frequency switching based on Wifi6 with a new routing mechanism, the adaptability and performance of the network in a high-interference environment are improved to solve the technical problems raised in the background technology.

[0010] The present invention adopts the following technical solutions to solve the above technical problems:

[0011] A multi-hop multi-frequency wireless mesh network adaptive routing method based on channel modeling comprises the following steps:

[0012] S1. Use Batman protocol for routing discovery and networking to build a mesh network;

[0013] S2. Use interpretable neural networks to model channels, predict channel information, and determine link deterioration and node mobility. Use routing protocols for rapid convergence and use potential paths to maintain routing for current data transmission.

[0014] S3. Using the SDN network controller, the node reports the service type and transmission request to the SDN network controller. The SDN network controller broadcasts and collects the status information of each node, introduces the routing selection process according to the dynamic nature of the network status and the particularity of the service, and uses the node status and service characteristics to select the best route and secondary route.

[0015] Preferably, the specific operation process of step S1 includes:

[0016] S11. All nodes periodically generate and send OGM messages, which contain key information including the unique identifier of the sending node, sequence number, hop count, and lifetime. The purpose of the OGM message is to let other nodes in the network know the existence of the source node and provide a basis for the route discovery process. The OGM message is sent to surrounding nodes in a broadcast manner, and the propagation range is controlled by the lifetime TTL field. When the TTL is reduced to 0, the OGM message will be discarded, which limits its maximum propagation range and avoids network overload.

[0017] S12. After receiving the OGM message, the neighbor node checks the validity of the message. First, the validity of the message is checked, especially the sequence number of the OGM is used to determine whether it is a duplicate message. If it is a non-duplicate message, that is, the sequence number is received for the first time, it indicates that the message is new. The node forwards it to other neighbor nodes and increments it according to the hop count field in the message to indicate that the number of hops on the path has increased.

[0018] S13. All nodes gradually build and update their own routing tables based on the OGM messages received from neighboring nodes.

[0019] Preferably, the specific operation process in step S2 includes:

[0020] S21. Receive the channel parameter information of the node through the SDN network controller as a channel quality indicator;

[0021] S22. Use an interpretable neural network for self-learning channel modeling, build a channel environment model by analyzing channel parameters and node positions, and input channel parameter historical data into the trained channel environment model to perform channel prediction;

[0022] S23. An interpretable neural network model is used to predict the channel changes and network connectivity status between nodes through location information and channel information. On this basis, path presetting is completed according to different business types and using the WD algorithm to achieve seamless switching of links when a link fails, and to achieve rapid signal routing and seamless signal connection.

[0023] Preferably, the specific construction process of the channel environment model in step S22 includes:

[0024] S221. Preprocess the channel training data through MinMaxScaler normalization, transform the original channel data to the same dimension and map it to the given [0,1];

[0025] S222. Initialize the model weights and parameters, use an interpretable neural network to train the preprocessed data, define the loss function as the mean square error during the training process, and add L1 penalty to control the model complexity and reduce overfitting. At the same time, the adaptive learning rate is determined by the Adam optimizer, and the training is iterated until the specified number of iterations is reached or the specified threshold error is met;

[0026] Set the parameters that need to be learned and updated in the XNN model, including: {α, β j ,h j ,w j},j=1,…,k, where k is the update threshold, α is the initial learning rate of the model, and β j is the update decay rate, hj is a subnetwork, w j is the network weight;

[0027] The loss function (loss LOSS) of the XNN model is defined as the root mean square error, and L1 penalty is added to control model complexity and reduce overfitting:

[0028]

[0029] where n is the mini-batch size, k is the number of subnetworks, λ1, λ2 are hyperparameters that control the strength of the regularization penalty, and is the predicted value of the XNN model, y i is the true value, ω j is the projection weight vector and β is the subnetwork weight vector.

[0030] In addition to w j , the remaining parameters are updated by gradient descent θ, and the adaptive learning rate of the gradient descent update iteration is determined by Adam Optimizer. For each iteration t+1=t, we have:

[0031]

[0032] Among them θ represents the loss gradient of parameter θ, and η t Represents a learning rate that can be automatically adjusted using the ADAM optimizer.

[0033] The validation error is defined as the mean square error between the predicted value and the true value in the validation set. The training is iterated until the maximum number of iterations is reached or the threshold error (for example, the threshold error ε = 5dB) is met. After fixing the projection layer and projection matrix, let λ1 = λ2 = 0, and use the Adam optimizer to fine-tune the remaining network;

[0034] S223. After the training is completed, a channel environment model based on an interpretable neural network is obtained.

[0035] Preferably, a noise source module is further provided in the channel prediction process in step S22 to simulate errors caused by the environment or an imperfect measurement system, and the final prediction value of the channel prediction is: where ε i is the Gaussian noise error, is the true value, is the predicted value.

[0036] Preferably, the specific operation process of step S23 includes:

[0037] S231. According to the channel prediction results of the channel environment model, a utility matrix is ​​constructed for the influencing factors of different business types, and the WD routing algorithm is used to realize path prediction and complete path presetting;

[0038] S232. Set the channel quality threshold according to business needs to determine whether the channel quality meets the threshold requirements. If the current path channel quality drops below the threshold requirements, the path switching is triggered to switch to the preset path;

[0039] S233. After switching, the performance of the new path is verified in real time according to the set channel quality threshold. After the path channel is stable, the network status continues to be monitored. If the path channel is unstable, it will fall back to the previous path or continue to look for a new path;

[0040] S234. Summarize and record the log information of the path switching and feed it back to the channel environment model as training data.

[0041] Preferably, during the execution of step S2, the batman protocol is used to perform routing maintenance, and the specific maintenance process includes:

[0042] Each node periodically sends source announcement data to neighboring nodes according to the Batman protocol and updates the node routing table according to the network status. If a neighboring node loses connection or does not receive source announcement data, the neighbor is removed from the node routing table to prevent the link status from deteriorating and affecting data transmission.

[0043] Preferably, the specific operation process of step S3 includes:

[0044] S31 business connection node, request transmission, the node reports the business type and transmission request to the SDN network controller;

[0045] S32. After sending the broadcast, the SDN network controller uses batctl and iwinfo commands to obtain the underlying parameters of the batman protocol, collects multiple parameter information of each node in the 5.8G and 2.4G frequency bands, including RSSI, throughput, and TQ value, and uploads them. It also constructs the maximum channel capacity matrix, throughput matrix, and packet loss rate matrix, respectively. The constructed matrices are multiplied by the influencing factors of each parameter and added together. For different service requests, each influencing factor can be adjusted, and finally a channel matrix with information of each node is constructed;

[0046] (1) Voice services have high requirements for stability. The impact factor of packet loss rate can be increased to give this service transmission priority.

[0047] (2) Video services have higher throughput requirements. The throughput impact factor can be increased to meet the higher throughput requirements of this service.

[0048] S33. Compare each element in the channel information matrix of 2.4G and 5.8G to obtain the channel matrix, and select the optimal transmission frequency band according to the value of the matrix element. The channel matrix uses the WD algorithm to find the optimal path and secondary path;

[0049] S34. The network controller sends the routing table to each node and updates the routing table of each node.

[0050] Preferably, the information collection process in step S32 includes:

[0051] S321. Use the iw dev mesh0 station dump command to obtain the mesh node network data of the mesh network;

[0052] S322. Observe the received signal strength RSSI, the transmit bit rate tx_bitrate, the receive bit rate rx_bitrate and the signal noise strength N0, and calculate the throughput parameter T x , the calculation formula is:

[0053] S323. Calculate the maximum channel capacity C, the calculation formula is: Where B is the channel bandwidth;

[0054] S324. Use the batctlo command to obtain the TQ value parameter. The larger the TQ value, the lower the packet loss rate of the link.

[0055] S325. Normalization processing parameters C, T x and TQ value, and obtain the processed parameter C r , T r and P r , the calculation formula is:

[0056]

[0057] Among them, C max It is expressed as the theoretical maximum channel capacity of the channel, T max It is expressed as the theoretical maximum value of the channel throughput parameter.

[0058] Preferably, in the step S33, the WD algorithm (wide & deep algorithm) is run through the SDN network controller to obtain the shortest path and the second shortest path from the source node to the destination node, and the corresponding IP addresses are collected. According to the source IP, destination IP, port number, frequency band selection, and frequency point selection, a strategy is issued to the nodes on each path, wherein the WD algorithm steps used include:

[0059] S331 creates a graph representation represented by a channel information matrix, sets an initial distance of 0 for the start node, and sets the initial distances of all other nodes to infinity;

[0060] S332. Create a minimum heap priority queue to store each node and its distance to the starting node, initially containing only the starting node;

[0061] S333. Use the D algorithm (Deep Learning) to traverse and calculate the shortest path from the source node to the destination node. After finding the shortest path, reset the distance information of all nodes, set the distance of the starting node to 0, and set the distance of other nodes to infinity, run the Dijkstra algorithm again, and ensure that the edges of the shortest path are not repeated.

[0062] S334. Repeat the D algorithm traversal process, add a check in the D algorithm traversal process to ensure that the same edge as the shortest path found is not selected, and stop the algorithm when a path to the target node is found and the path is not the shortest path.

[0063] S335 outputs the shortest path and the second shortest path from the start node to the target node. If there is no second shortest path, the shortest path and None are output.

[0064] Preferably, the transmission communication between the service connection node and the network controller in the step S3 is realized through the RYU network controller on a server and the OpenvSwitch on the node, and the information interaction is realized by using the Openflow protocol.

[0065] Preferably, when the nodes are in the routing maintenance phase, each node uploads channel information and location information to the SDN network controller every 2 seconds, which can ensure the stability of the network.

[0066] On the other hand, the present invention further discloses a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the steps of the above method.

[0067] On the other hand, the present invention further discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the above method.

[0068] It can be seen from the above technical solution that the present invention provides a multi-hop multi-frequency wireless mesh network adaptive routing method based on channel modeling. Compared with the prior art, the present invention has the following advantages:

[0069] 1. The present invention considers the maximum channel capacity, packet loss rate and throughput as new network performance evaluation parameters, and utilizes the TQ value of the BATMAN protocol to construct a more comprehensive and accurate network performance evaluation model, providing an important basis for improving network reliability, efficiency and intelligent management.

[0070] 2. The present invention introduces a channel modeling and prediction mechanism based on an interpretable neural network on the basis of traditional network routing maintenance. By integrating network performance evaluation parameters and combining the learning ability of the interpretable neural network, the system can predict and preset the path selection of nodes to build a more comprehensive and intelligent network performance evaluation model, improve the adaptability and intelligence level of the network, and seamlessly complete route switching when a link failure occurs to ensure the stability of the network.

[0071] 3. In the routing maintenance step of the present invention, when a node in the network fails or disappears from the network, the routing protocol converges quickly to maintain the connectivity of the network, and uses channel prediction and presetting to ensure the stability of the network, which is easy to use.

[0072] 4. The present invention adopts a software-defined approach, where the controller uniformly allocates routes, realizes the function of on-demand routing according to business types, and also runs at the data link layer, transmitting routing information through Ethernet frames, reducing data packet processing delays. Compared with the routing strategy based on the shortest path in the prior art, it considers multiple channel parameters and adaptively selects routes and frequencies.

[0073] 5. In the process of forming a mesh network, the present invention gradually builds and updates the routing table of the node by receiving OGM messages, self-checking and updating, and forwarding incrementally, so that the OGM can traverse the entire network, and each node only needs to know the neighboring nodes directly connected to it, and does not need to understand the topology of the entire network, which facilitates the rapid component processing of network data.

[0074] 6. The present invention uses a preset path mechanism to address the path switching problem caused by node movement or channel deterioration. It relies on different business change influencing factors, builds a utility matrix based on the output predicted channel information and location information, uses the WD algorithm to find the preset route, and introduces a channel quality threshold in the path switching judgment to avoid excessive switching. This mechanism can flexibly adjust the path selection according to different traffic types, ensure network load balancing and reduce network fluctuations caused by switching.

[0075] 7. The present invention constructs a self-learning channel model, performs adaptive modeling and prediction based on historical channel data, can update the channel status in real time, improves the accuracy and intelligence level of routing maintenance, and dynamically adjusts the model parameter selection according to different business needs. Combined with factors such as throughput, packet loss rate, signal strength, etc., it can comprehensively analyze the channel quality and optimize network performance.

[0076] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become easy to understand through the following description. Of course, it is not necessary to achieve all of the advantages described above simultaneously for any product implementing the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] The drawings constituting a part of the present application are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0078] Figure 1 It is a schematic diagram of the use effect of the model of the method of the present invention;

[0079] Figure 2 A schematic diagram of the protocol routing discovery process of the present invention;

[0080] Figure 3 It is a schematic diagram of the protocol routing maintenance process of the present invention;

[0081] Figure 4 A schematic diagram of the routing process of the present invention;

[0082] Figure 5 Schematic diagram of the experimental comparison result curve of the method of the present invention. DETAILED DESCRIPTION

[0083] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. In the absence of conflict, the embodiments in this application and the features in the embodiments can be combined with each other. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0084] In the embodiment, see Figures 1 to 5 .

[0085] The multi-hop multi-frequency wireless mesh network adaptive routing method based on channel modeling proposed in the embodiment of the present invention adds an SDN network controller and a neural network model to the traditional Mesh network based on the Batman protocol based on an interpretable neural network.

[0086] The SDN network controller collects channel information between nodes, dynamically adjusts routing strategies based on the maximum channel capacity, packet loss rate, and throughput between nodes according to different business needs, and realizes the search for the required optimal routes; the effect diagram is shown in the figure Figure 1 As shown, the improved routing algorithm of the present invention adds secondary routing search, routing prediction and routing preset, so as to better guarantee the data transmission of services in the routing maintenance stage, realize the combination of network dynamics and service dynamics, and ensure the stability of mobile network and the transmission requirements of services.

[0087] The multi-hop multi-frequency wireless mesh network adaptive routing method consists of a routing discovery process, a routing maintenance process and a routing use process, wherein:

[0088] (1) The route discovery process is as follows Figure 2 As shown, the following steps are included:

[0089] Step S1: route discovery step, using batman protocol to build a mesh network. The steps to build a mesh network are as follows:

[0090] Step S1.1: Each node periodically generates and sends OGM (Originator Message), which contains key information such as the unique identifier of the issuing node, sequence number, hop count, and time-to-live (TTL). The purpose of the OGM message is to let other nodes in the network know the existence of the source node and provide a basis for the route discovery process. The OGM message is sent to the surrounding nodes by broadcasting, and the propagation range is controlled by the TTL field. When the TTL is reduced to 0, the OGM message will be discarded, limiting its maximum propagation range and avoiding network overload.

[0091] Step S1.2: After receiving the OGM message, the neighbor node first checks the validity of the message, especially judging whether it is a duplicate message by the sequence number of the OGM. If the message is new (i.e., the sequence number is received for the first time), the node forwards it to other neighbor nodes and appropriately increments the hop count field in the message to indicate that the hop count of the path has increased.

[0092] Step S1.3: Each node gradually builds and updates its own routing table based on the OGM messages received from neighboring nodes. This incremental forwarding method of messages enables OGM to traverse the entire network, and each node only needs to know the neighboring nodes directly connected to it, and does not need to understand the topology of the entire network.

[0093] (2) The routing maintenance process is as follows Figure 3 As shown, the following steps are included:

[0094] Step S2: Routing maintenance step, autonomously determine whether the link is deteriorating, whether the node is moving, use the routing protocol for rapid convergence, and use potential paths to maintain current data transmission. The specific steps are as follows:

[0095] Step S2.1: First, the routing maintenance process relies on the BATMAN protocol itself. The BATMAN protocol will periodically send "source announcements" and update the routing table according to the network conditions. If a neighbor node loses connection (for example, the source announcement is not received on time), the neighbor will be removed from the routing table. This is to prevent the deterioration of the link state from affecting data transmission. In addition, in order to prevent the movement of nodes in the mobile ad hoc network from affecting the overall network conditions and to reduce the frequency of uploading data too fast to cause data redundancy, a logarithmic distance path loss model is added to construct an expression of signal strength RSSI and the distance between nodes. When the node reaches the mobility threshold, it is uploaded to the SDN network controller and the channel information is collected again to achieve the optimal path planning.

[0096] Step S2.2: The SDN network controller collects channel information of each node, uses XNN (interpretable neural network) for self-learning channel modeling, builds a channel environment model by analyzing the maximum channel capacity, throughput, packet loss rate channel parameters and node location, and inputs the channel parameters (throughput, packet loss rate, channel maximum transmission rate, node location) into the trained XNN model for channel prediction.

[0097] Step S2.3: According to the prediction results of XNN, analyze and construct a utility matrix for the influencing factors of different business types, and use the WD routing algorithm to realize path prediction and complete path presetting.

[0098] Step S2.4: Determine whether the current path meets the service requirements. Set a threshold according to the requirements. If the channel quality decreases (such as high packet loss rate, low throughput), trigger path switching and switch to the preset path.

[0099] Step S2.5: After switching, verify the performance of the new path in real time to check whether it meets the requirements of throughput, delay, packet loss rate, etc. If it is stable, continue to monitor the network status; if it is unstable, fall back to the previous path or continue to look for a new path.

[0100] Step S2.6: Summarize and record the log information of path switching and feed it back to the XNN model as training data to further improve the accuracy of the model.

[0101] At this time, new network performance evaluation parameters are considered. In the network performance evaluation, the maximum channel capacity, packet loss rate and throughput are the key parameters that affect the network quality: the maximum channel capacity determines the theoretical transmission upper limit of the network, the packet loss rate reflects the reliability of data transmission, and the throughput represents the actual data transmission efficiency. By comprehensively considering these parameters and using the TQ value of the BATMAN protocol, a more comprehensive and accurate network performance evaluation model can be constructed, providing an important basis for improving network reliability, efficiency and intelligent management.

[0102] At the same time, in order to further improve the adaptability and intelligence level of the network, this application also innovatively introduces a channel modeling and prediction mechanism based on an interpretable neural network (XNN) on the basis of traditional network routing maintenance. By integrating these parameters and combining the learning ability of XNN, the system can predict and preset the path selection of nodes to build a more comprehensive and intelligent network performance evaluation model. In the event of a link failure, routing switching can be completed seamlessly to ensure the stability of the network.

[0103] (3) Routing usage process Figure 4 As shown, using the SDN network controller, broadcasting requires collecting and reporting node status information, introducing the routing selection process according to the dynamic nature of the network status and the particularity of the service, and selecting the best route and secondary route according to the node status and service characteristics, specifically including the following steps:

[0104] Step A1: The service connects to the node and requests transmission. The node reports the service type and transmission request to the SDN controller.

[0105] Step A1.1: The RYU network controller on a host and the OpenvSwitch on the node realize communication between the controller and the node, and the Openflow protocol is used to realize information exchange. After receiving the transmission request, the RYU network controller will broadcast the collection information and collect the status information of all nodes.

[0106] Step A1.2: When the node is in the routing maintenance phase, it sends channel information irregularly. Only when the routing link deteriorates or the node moves, the channel information is re-uploaded to the router, which can effectively reduce the redundant data transmission burden.

[0107] Step A2: After SDN sends the broadcast, it collects and uploads the maximum channel capacity, throughput, and packet loss rate information of each node in the 5.8G and 2.4G frequency bands, and builds the maximum channel capacity matrix, throughput matrix, and packet loss rate matrix respectively, and weights them by the impact factor. Different business requests can adjust each impact factor, and finally build a channel matrix with information of each node. The specific steps are as follows:

[0108] Step A2.1: In a mesh network built with the Batman protocol, you can use the iwdev mesh0 stationdump command to obtain the network data of the mesh nodes in the network structure. The main observations are the received signal strength RSSI, the transmit bit rate tx_bitrate, the receive bit rate rx_bitrate, and the signal noise strength N0, and calculate the throughput parameter T x , the calculation formula is:

[0109] The maximum channel capacity C is calculated by Shannon's formula, and the calculation formula is: Where B is the channel bandwidth;

[0110] The batctlo command can be used to obtain the TQ value parameter. The larger the TQ value, the lower the packet loss rate of the link.

[0111] Normalization processing parameters C, T x and TQ value, and obtain the processed parameter C r 、T r and P r , the calculation formula is:

[0112]

[0113] Among them, C max It is expressed as the theoretical maximum channel capacity of the channel, T max It is expressed as the theoretical maximum value of the channel throughput parameter.

[0114] Step A2.2: Construct matrices based on the information obtained Multiply the influence factors of each parameter and add them together. Then compare each element in the 2.4G and 5.8G channel information matrices, and finally obtain the channel matrix The matrix can effectively display the channel information between each node, and select the frequency band according to the size of the element value to achieve the optimal transmission frequency band. Voice services have high requirements for stability, and the impact factor of packet loss rate can be increased to give this service transmission priority; video services have high requirements for throughput, and the impact factor of throughput can be increased to meet the high throughput requirements of this service.

[0115] Step A3: The channel matrix uses the improved WD algorithm to select the optimal path and find the secondary path. Then the SDN controller sends the routing table to each node and updates the routing table of each node. The specific steps are as follows:

[0116] A3.1: Using the WD algorithm, create a representation of the graph, represented by a channel information matrix. Set the initial distance to 0 for the starting node and the initial distance to infinity for all other nodes. Create a priority queue (minimum heap) to store each node and its distance to the starting node, initially containing only the starting node. Use the D algorithm to traverse and calculate the shortest path from the source node to the destination node. After finding the shortest path, reset the distance information of all nodes, set the distance of the starting node to 0, and other nodes to infinity, and run the Dijkstra algorithm again, but this time make sure that the edges of the shortest path are not repeated. Repeat the process of the D algorithm, but add a check to ensure that the same edges as the shortest path already found are not selected. When a path to the target node is found and the path is not the shortest path, stop the algorithm. Output the shortest path and the second shortest path from the starting node to the target node. If the second shortest path cannot be found, output the shortest path and None.

[0117] In summary, this application adopts a software-defined approach, where the controller uniformly allocates routes, and implements the function of on-demand routing based on business types; in addition, channel modeling based on an interpretable neural network model (XNN) is used to achieve channel quality prediction and path presetting, and seamless routing switching is completed when a failure occurs. By collecting network status information, node weights are adaptively changed, and appropriate routes are selected based on node performance conditions; this study runs at the data link layer and transmits routing information through Ethernet frames, reducing data packet processing delays. Compared with routing strategies based on the shortest path, this study considers multiple channel parameters and adaptively selects routes and frequencies.

[0118] Based on the above method, the present application conducted an experimental comparison and found that the throughput and packet loss rate were significantly improved based on the algorithm of the present invention. The experimental data are as follows:

[0119]

[0120] The experiment uses 3 or 4 nodes for transmission test. The experimental scenarios are divided into L-type (NLOS) and linear (LOS). UDP is used to test the improvement of packet loss rate, and TCP is used to test the improvement of throughput. Figure 5By observing the route selection and whether it is different from the routing decision of the traditional batman, it can be used to demonstrate the effectiveness of the present application in wireless communication solutions to improve the adaptability and performance of the network in high interference environments, and significantly improve the throughput and packet loss rate.

[0121] On the other hand, the present invention further discloses a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the steps of the above method.

[0122] On the other hand, the present invention further discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the above method.

[0123] In another embodiment provided by the present application, a computer program product including instructions is also provided, which, when executed on a computer, enables the computer to execute any of the multi-hop multi-frequency wireless mesh network adaptive routing methods based on channel modeling in the above embodiments.

[0124] It is understandable that the system provided by the embodiment of the present invention corresponds to the method provided by the embodiment of the present invention, and the explanation, examples and beneficial effects of the relevant contents can refer to the corresponding parts in the above method.

[0125] The embodiment of the present application also provides an electronic device, including a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus.

[0126] Memory, used to store computer programs;

[0127] The processor is used to implement the multi-hop multi-frequency wireless mesh network adaptive routing method based on channel modeling when executing the program stored in the memory.

[0128] The communication bus mentioned in the above electronic device may be a Peripheral Component Interconnect (PCI) bus or an Extended Industrial Standard Architecture (EISA) bus, etc. The communication bus may be divided into an address bus, a data bus, a control bus, etc.

[0129] The communication interface is used for communication between the above electronic device and other devices.

[0130] The memory may include a random access memory (RAM) or a non-volatile memory (NVM), such as at least one disk memory. Optionally, the memory may also be at least one storage device located away from the aforementioned processor.

[0131] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0132] It should also be noted that electronic devices also include terminal devices, which can also be called terminals, user equipment, mobile stations, mobile terminals, etc. Terminal devices can be mobile phones, smart TVs, wearable devices, tablet computers, computers with wireless transceiver functions, virtual reality terminal devices, augmented reality terminal devices, wireless terminals in industrial control, wireless terminals in unmanned driving, wireless terminals in remote surgery, wireless terminals in smart grids, wireless terminals in transportation safety, wireless terminals in smart cities, wireless terminals in smart homes, etc. The embodiments of this application do not limit the specific technology and specific device form used by the terminal devices.

[0133] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integration. The available medium can be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium, or a semiconductor medium (e.g., a solid-state hard disk Solid State Disk), etc.

[0134] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

[0135] In addition, it should be noted that if the embodiments of the present invention involve directional indications (such as up, down, left, right, front, back, etc.), the directional indications are only used to explain the relative position relationship, movement status, etc. between the components in a certain specific posture. If the specific posture changes, the directional indication will also change accordingly.

[0136] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present invention, the descriptions of "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the meaning of "and / or" appearing in the full text includes three parallel schemes. Taking "A and / or B" as an example, it includes scheme A, or scheme B, or a scheme that satisfies both A and B. In addition, the technical solutions between the various embodiments can be combined with each other, but it must be based on the ability of ordinary technicians in the field to implement. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

Claims

1. A multi-hop multi-frequency wireless mesh network adaptive routing method based on channel modeling, characterized in that: The following steps are involved: S1. Use Batman protocol for routing discovery and networking to build a mesh network; S2. Use interpretable neural networks to model channels, predict channel information, and determine link deterioration and node mobility. Use routing protocols for rapid convergence and use potential paths to maintain routing for current data transmission. S3. The node reports the service type and transmission request to the network controller. The network controller broadcasts and collects the status information of each node, introduces the routing selection process according to the dynamic nature of the network status and the particularity of the service, and selects the best route and secondary route based on the node status and service characteristics.

2. The multi-hop multi-frequency wireless mesh network adaptive routing method based on channel modeling as claimed in claim 1, characterized in that: The specific operation process of step S1 includes: S11. All nodes generate and send OGM messages regularly; S12. After receiving the OGM message, the neighbor node checks the validity of the message to determine whether it is a duplicate message. If it is a non-duplicate message, it is forwarded to other neighbor nodes and incremented according to the hop count field in the message to indicate that the number of hops on the path has increased; S13. All nodes gradually build and update their own routing tables based on the OGM messages received from neighboring nodes.

3. The multi-hop multi-frequency wireless mesh network adaptive routing method based on channel modeling as claimed in claim 1, characterized in that: The specific operation process in the S2 step includes: S21. Receive the channel parameter information of the node through the network controller as a channel quality indicator; S22. Use an interpretable neural network for self-learning channel modeling, build a channel environment model by analyzing channel parameters and node positions, and input channel parameter historical data into the trained channel environment model to perform channel prediction; S23. Presetting paths according to different service types and using the WD algorithm is performed to achieve seamless switching of links when a link failure occurs.

4. The multi-hop multi-frequency wireless mesh network adaptive routing method based on channel modeling as claimed in claim 3, characterized in that: The specific construction process of the channel environment model in step S22 includes: S221. Preprocess the channel training data by normalization, transform the original channel data to the same dimension and map it to the given [0,1]; S222. Initialize the model weights and parameters, use an interpretable neural network to train the preprocessed data, define the loss function as the mean square error during the training process, and add L1 penalty to control the model complexity and reduce overfitting. At the same time, the adaptive learning rate is determined by the Adam optimizer, and the training is iterated until the specified number of iterations is reached or the specified threshold error is met; S223. After the training is completed, a channel environment model based on an interpretable neural network is obtained.

5. The multi-hop multi-frequency wireless mesh network adaptive routing method based on channel modeling as claimed in claim 3, characterized in that: The specific operation process of step S23 includes: S231. According to the channel prediction results of the channel environment model, a utility matrix is ​​constructed for the influencing factors of different business types, and the WD routing algorithm is used to realize path prediction and complete path presetting; S232. Set the channel quality threshold according to business needs to determine whether the channel quality meets the threshold requirements. If the current path channel quality drops below the threshold requirements, the path switching is triggered to switch to the preset path; S233. After switching, the performance of the new path is verified in real time according to the set channel quality threshold. After the path channel is stable, the network status continues to be monitored. If the path channel is unstable, it will fall back to the previous path or continue to look for a new path; S234. Summarize and record the log information of the path switching and feed it back to the channel environment model as training data.

6. The multi-hop multi-frequency wireless mesh network adaptive routing method based on channel modeling as claimed in claim 5, characterized in that: During the execution of step S2, the batman protocol is used to perform routing maintenance, and the specific maintenance process includes: Each node periodically sends source announcement data to neighboring nodes according to the Batman protocol and updates the node routing table according to the network status. If a neighboring node loses connection or does not receive source announcement data, the neighbor is removed from the node routing table.

7. The multi-hop multi-frequency wireless mesh network adaptive routing method based on channel modeling as claimed in claim 5, characterized in that: The specific operation process of the S3 step includes: S31 business connection node, request transmission, the node reports the business type and transmission request to the network controller; S32. After sending the broadcast, the network controller uses batctl and iwinfo commands to obtain the underlying parameters of the batman protocol, collects multiple parameter information RSSI, throughput, and TQ values ​​of the 5.8G and 2.4G frequency bands of each node and uploads them, and constructs the maximum channel capacity matrix, throughput matrix, and packet loss rate matrix respectively. The constructed matrices are multiplied by the influencing factors of each parameter and added; S33. Compare each element in the channel information matrix of 2.4G and 5.8G to obtain the channel matrix, and select the optimal transmission frequency band according to the value of the matrix element. The channel matrix uses the WD algorithm to find the optimal path and secondary path; S34. The network controller sends the routing table to each node and updates the routing table of each node.

8. The multi-hop multi-frequency wireless mesh network adaptive routing method based on channel modeling as claimed in claim 7, characterized in that: The information collection process in step S32 includes: S321. Obtain mesh node network data of the mesh network; S322. Observe the received signal strength RSSI, the transmit bit rate tx_bitrate, the receive bit rate rx_bitrate and the signal noise strength N0, and calculate the throughput parameter T x , the calculation formula is: S323. Calculate the maximum channel capacity C, the calculation formula is: Where B is the channel bandwidth; S324. Use the batctlo command to obtain the TQ value parameter. The larger the TQ value, the lower the packet loss rate of the link. S325. Normalization processing parameters C, T x and TQ value, and obtain the processed parameter C r , T r and P r , the calculation formula is: Among them, C max It is expressed as the theoretical maximum channel capacity of the channel, T max It is expressed as the theoretical maximum value of the channel throughput parameter.

9. The multi-hop multi-frequency wireless mesh network adaptive routing method based on channel modeling as claimed in claim 7, characterized in that: In the step S33, the WD algorithm is run through the SDN network controller to obtain the shortest path and the second shortest path from the source node to the destination node, and the corresponding IP addresses are collected. According to the source IP, destination IP, port number, frequency band selection, and frequency point selection, a strategy is issued to the nodes on each path. The WD algorithm steps used include: S331 creates a graph representation represented by a channel information matrix, sets an initial distance of 0 for the start node, and sets the initial distances of all other nodes to infinity; S332. Create a minimum heap priority queue to store each node and its distance to the starting node, initially containing only the starting node; S333. Use the D algorithm to traverse and calculate the shortest path from the source node to the destination node. After finding the shortest path, reset the distance information of all nodes, set the distance of the starting node to 0, and set the distance of other nodes to infinity, run the Dijkstra algorithm again, and ensure that the edges of the shortest path are not repeated. S334. Repeat the D algorithm traversal process, add a check in the D algorithm traversal process to ensure that the same edge as the shortest path found is not selected, and stop the algorithm when a path to the target node is found and the path is not the shortest path. S335 outputs the shortest path and the second shortest path from the start node to the target node. If there is no second shortest path, the shortest path and None are output.

10. The multi-hop multi-frequency wireless mesh network adaptive routing method based on channel modeling as claimed in claim 1, characterized in that: In the step S3, the transmission communication between the service connection node and the network controller is realized through the RYU network controller on a server and the OpenvSwitch on the node, and the Openflow protocol is used to realize information interaction.

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