Wireless network automatic channel planning method and device in networking scene and medium

Abstract

The invention discloses a wireless network automatic channel planning method and device in a networking scene and a medium, and relates to the technical field of wireless networking. The method comprises the following steps: acquiring an MAC address and an RSSI value of a neighbor AP received by each AP node, and constructing an interference graph; screening strong interference edges of which the RSSI values are greater than a second threshold value in the interference graph, and then dividing the interference graph into a plurality of conflict sub-graphs; in each conflict sub-graph, extracting a plurality of nodes with minimum interference as representative nodes, only performing channel allocation on the representative nodes, and marking other nodes as exclusion states; and sequentially selecting the channel with the minimum interference cost from the channel candidate list for each representative node, and issuing configuration. According to the automatic channel planning method and device for the wireless network in the networking scene and the medium, the optimal channel can be selected through the interference graph according to the network environment and the interference condition, channel distribution is automatically optimized, and the interference isolation degree of the whole network is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wireless networking, in particular to a wireless network automatic channel planning method, device and medium in a networking scenario. BACKGROUND

[0002] With the development of wireless communication technology, especially in high-density device deployment scenarios such as FTTR-H and FTTR-B, the interference problem between wireless access points (APs) becomes more and more serious. Traditional channel planning methods usually rely on manual configuration or static channel allocation, and do not take into account the dynamic changes of devices and the changes of interference sources, so they cannot effectively avoid channel interference, leading to network performance degradation and even disconnection. In FTTR-H and FTTR-B environments, due to the high density of devices and the large number of APs, the channel overlap and interference problem of adjacent APs is particularly prominent.

[0003] The existing channel planning method has the following shortcomings: 1. Static channel allocation: most existing methods rely on static channel allocation, which cannot adapt to the dynamic changes of network environment. 2. Ignoring dynamic interference: unable to monitor and handle interference between devices in real time, leading to unreasonable channel selection and affecting network performance. 3. Lack of adaptability: existing technology cannot effectively optimize channel planning in high-density device environments, leading to interference and conflict problems that cannot be solved in time. In summary, the existing channel selection method lacks flexibility and cannot respond to network environment changes in real time, leading to channel allocation conflicts and affecting the stability and data transmission efficiency of wireless networks. SUMMARY

[0004] The technical problem to be solved by the present application is to provide a wireless network automatic channel planning method, device and medium in a networking scenario, which can select the optimal channel according to the network environment and interference situation through the interference graph, automatically optimize channel allocation, improve the interference isolation degree of the entire network, and optimize channel usage efficiency.

[0005] In a first aspect, the present application provides a wireless network automatic channel planning method in a networking scenario, comprising: Interference graph construction process: obtain the MAC address and RSSI value of each AP node received by the neighbor AP and construct an interference graph, each node in the interference graph represents an AP, if the AP i can scan the signal of the AP j , and the RSSI ij is greater than or equal to the first threshold value, a directed explicit edge is added from i to j in the interference graph; if the AP i cannot scan the signal of the AP j , an implicit edge is added from i to j in the interference graph, and the RSSI ij is assigned a specified value; The conflict subgraph division process: screening strong interference edges in the interference graph whose RSSI value is greater than a second threshold value, the second threshold value being greater than the first threshold value, and then dividing the interference graph into several conflict subgraphs; The representative node extraction process: in each conflict subgraph, extracting several nodes with the minimum interference as representative nodes, and only performing channel allocation on the representative nodes, and marking the remaining nodes as excluded states; The channel allocation process: sequentially selecting the channel with the minimum interference cost for each representative node in the channel candidate list.

[0006] Further, the method for extracting representative nodes in each conflict subgraph is: performing greedy selection based on the interference cost score of the nodes to obtain one or more representative nodes.

[0007] Further, the calculation formula of the interference cost score is: wherein i represents the AP node of the current channel to be allocated, N(i) represents a set of all AP nodes having an adjacent relationship with the node i, RSSI ij represents the signal strength received from the neighbor node j, and max(,·) represents that when the interference is less than n, the interference does not contribute to the total cost.

[0008] Further, the interference graph is divided into several conflict subgraphs by using a depth-first search or a union-find set algorithm.

[0009] In a second aspect, the present application provides a wireless network automatic channel planning device in a networking scenario, comprising: An interference graph construction module is configured to obtain the MAC address and RSSI value of each neighbor AP received by each AP node and construct an interference graph, each node in the interference graph representing an AP, if the AP i can scan the signal of the AP j , and the RSSI ij is greater than or equal to a first threshold value, a directed explicit edge is added from i to j in the interference graph; if the AP i cannot scan the signal of the AP j , an implicit edge is added from i to j in the interference graph, and the RSSI ij is assigned a specified value; A conflict subgraph division module is configured to screen strong interference edges in the interference graph whose RSSI value is greater than a second threshold value, the second threshold value being greater than the first threshold value, and then divide the interference graph into several conflict subgraphs; A representative node extraction module is configured to extract several nodes with the minimum interference as representative nodes in each conflict subgraph, and only perform channel allocation on the representative nodes, and mark the remaining nodes as excluded states; ​The channel allocation module is configured to select, for each representative node, a channel with the minimum interference cost in the channel candidate list in sequence.

[0010] Further, in the conflict subgraph division module, the method for extracting the representative node in each conflict subgraph is: based on the interference cost score of the node, a greedy selection is performed to obtain one or more representative nodes.

[0011] Further, the calculation formula of the interference cost score is: Wherein, i represents the AP node of the current channel to be allocated, N(i) represents a set of all AP nodes having an adjacent relationship with the node i, RSSI ij represents the signal strength received from the neighbor node j, and max(,·) represents that when the interference is less than n, the interference does not contribute to the total cost.

[0012] Further, in the conflict subgraph division module, a depth-first search or a union-find set algorithm is used to divide the interference graph into several conflict subgraphs.

[0013] In a third aspect, the present application provides a computer readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method of the first aspect.

[0014] The technical solutions provided in the embodiments of the present application have at least the following technical effects: 1. By constructing an interference graph containing signal strength weights, the interference relationship between APs is comprehensively reflected, and the channel conflict recognition accuracy is improved; then, based on the RSSI threshold, the network is divided into multiple conflict subgraphs, the interference strength and channel conflict are comprehensively evaluated in each conflict subgraph based on the interference cost score, the representative node is extracted, and the channel with the minimum interference is preferentially selected, thereby significantly reducing the same-channel interference.

[0015] 2. The method supports fast adaptive channel reconstruction under dynamic network structure changes, supports real-time channel adjustment under dynamic scenarios such as device online and offline and movement, guarantees network stability, and has good real-time performance and engineering deployment feasibility.

[0016] 3. The method can be implemented in a software manner at the controller or AP end, without the need to modify the terminal, and is applicable to various deployment environments such as FTTR-H and FTTR-B.

[0017] The above description is only a summary of the technical solutions of the present application, in order to more clearly understand the technical means of the present application, the content of the specification can be implemented, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application are described as follows. BRIEF DESCRIPTION OF DRAWINGS

[0018] ​The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0019] Figure 1 This is a flowchart of the overall method in Embodiment 1 of the present invention; Figure 2 This is an execution flowchart of Embodiment 1 of the present invention; Figure 3 This is a schematic diagram of the device in Embodiment 2 of the present invention; Figure 4 This is a schematic diagram of the structure of the medium in Embodiment 3 of the present invention. Detailed Implementation

[0020] This invention provides a method, apparatus, and medium for automatic channel planning in a network scenario. Based on the network environment and interference conditions, it can select the optimal channel through an interference map, automatically optimize channel allocation, improve the interference isolation of the entire network, and optimize channel utilization efficiency.

[0021] The overall concept of the technical solutions in the embodiments of the present invention is as follows: To improve the interference isolation of the entire network and optimize channel utilization efficiency, a sparse selection and channel allocation algorithm based on interference graphs is proposed.

[0022] The controller or master device periodically collects wireless scanning reports from each AP node in the network and establishes interference relationships based on the scanned neighbor information (including MAC addresses and RSSI). By collecting neighbor scanning information from each wireless access point, an interference graph containing explicit and implicit interference edges is constructed to accurately reflect the interference relationships between devices. Subsequently, the network is divided into multiple conflict subgraphs based on RSSI thresholds. In each conflict subgraph, a scoring and greedy strategy is used to extract sparse representative nodes to reduce local conflict density. Then, candidate channels are evaluated based on interference cost scores (such as calculating ∑max(0,n+RSSI) for neighbor channel interference intensity) and the channel with the least interference is selected for allocation. Finally, the results are sent to the APs for configuration and application. This method is suitable for multi-AP dense deployment scenarios such as FTTR-H and FTTR-B.

[0023] The automatic channel planning method for wireless networks in this invention can solve the following technical problems: 1. Dynamically adapting to network changes: adjusting channel allocation according to real-time environmental changes. 2. Optimizing channel selection: selecting the optimal channel through interference maps to reduce channel conflicts. 3. Improving system adaptability: providing more stable network connections and improving performance in complex environments.

[0024] This embodiment provides an automatic channel planning method for wireless networks in a networking scenario, such as... Figure 1 As shown, it includes: S1. Interference Graph Construction Process: Obtain the MAC address and RSSI value of the neighboring APs received by each AP node and construct an interference graph. Each node in the interference graph represents an AP. If AP i AP can be scanned j The signal, and RSSI ij If the value is greater than or equal to the first threshold (e.g., -90dBm), then add a directed explicit edge from i to j in the interference graph; if AP i AP cannot be scanned j If the signal is positive, then an implicit edge is added from i to j in the interference graph, and RSSI is set to... ij The value is assigned to a specified value. The purpose of constructing implicit edges is: for example, A and B can scan each other, B and C can scan each other, but A and C cannot see each other. In this case, an implicit edge will be constructed for A and C, and its RSSI will be assigned a value of -120. In this way, the cost of the implicit edge is at least 1, which is used to avoid the problem that A selects channel 1, B selects channel 6, and C should select channel 11 but ultimately selects channel 1.

[0025] The controller or master device can periodically collect wireless scan reports from each AP node in the network and establish interference relationships based on the scanned neighbor information (including MAC addresses and RSSI values). The interference graph, which includes explicit and implicit interference edges, can accurately reflect the interference relationships between devices.

[0026] S2. Conflict subgraph partitioning process: Filter out strong interference edges in the interference graph whose RSSI value is greater than the second threshold (e.g., -65dBm). The second threshold is greater than the first threshold. Then, divide the interference graph into several conflict subgraphs.

[0027] Specifically, depth-first search or disjoint-set data structure algorithms can be used to divide the interference graph into several conflict subgraphs.

[0028] S3. Representative node extraction process: In each conflict subgraph, extract several nodes with the least interference as representative nodes, allocate channels only to the representative nodes, and mark the remaining nodes as excluded.

[0029] In one possible implementation, the method for extracting representative nodes in each conflict subgraph is as follows: a greedy selection is performed based on the interference cost score of the nodes to obtain one or more representative nodes. The greedy algorithm first finds the representative node with the lowest cost, removes its strongly adjacent nodes, and then continues to find the next representative node with the lowest cost among the remaining nodes, removing its strongly adjacent nodes, until all nodes have been selected.

[0030] Preferably, the formula for calculating the interference cost score is: ; Where i represents the AP node currently awaiting channel allocation, N(i) represents the set of all AP nodes adjacent to node i, and RSSI ij The value represents the signal strength received from neighbor node j, and max(,·) indicates that when the interference is less than n, the interference does not contribute to the total cost.

[0031] This formula considers both neighbor channel usage and interference intensity, exhibiting excellent sensing capabilities. It avoids the traditional conflict estimation method based solely on the number of neighbors, thus improving the accuracy and effectiveness of channel allocation. Employing a scoring and greedy strategy to extract sparse representative nodes effectively reduces local conflict density.

[0032] In one possible implementation, the formula for calculating the interference cost score is: ; When the interference is weak (RSSI below -150 dBm), the interference does not contribute to the total cost.

[0033] In another possible implementation, the formula for calculating the interference cost score is: RSSI ij When the interference of a single AP is ≥ -40 dBm, the cost is: ; -40 dBm > RSSI ij When the interference of a single AP is ≥ -60 dBm, the cost is: ; -60 dBm > RSSI ij When the interference of a single AP is ≥ -75 dBm, the cost is: ; RSSI ij When < -75 dBm, the cost of single-AP interference is: ; C = ΣC1 + ΣC2 + ΣC3 + ΣC4 The cost function described above is used to distinguish between weak and strong interference. Strong interference has a higher cost, while weak interference has a lower cost. Nodes with more weak interference should have a lower cost than nodes with fewer strong interference.

[0034] S4. Channel Allocation Process: For each representative node, the channel with the lowest interference cost is selected from the channel candidate list (e.g., 1 / 6 / 11 for 2.4G or 44 / 60 / 153 for 5G). Candidate channels can be evaluated based on a cost function (e.g., calculating ∑max(0,150+RSSI) based on the interference intensity of neighboring channels), and the channel with the lowest interference is selected to complete the allocation. Finally, the result is sent to the AP for configuration and application.

[0035] If a channel is already in use by a neighbor and its RSSI is strong (e.g., ≥ -60 dBm), then the channel will incur a high cost; if the channel is not in use by a neighbor, or the neighbor's signal is extremely weak (e.g., below -90 dBm), then the channel has a lower cost and is suitable as a candidate channel; traverse all candidate channels and select the channel with the lowest total cost for allocation.

[0036] In one specific embodiment, the above method is implemented on the controller side, such as... Figure 2 As shown, the core algorithm steps are as follows: 1. Interference Graph Construction: The controller treats all AP nodes as nodes in the graph. If two APs have mutually perceptible signals (RSSI ≥ -90 dBm), a directed edge is established, and the weight of the edge is the RSSI strength. If two APs do not have mutually perceptible signals, an implicit edge is established in the interference graph, and the weight of the edge is set to -120 dBm (RSSI strength).

[0037] 2. Conflict group identification: Apply a threshold (e.g., -65 dBm) to all edges in the graph to filter out strong interfering edges, and use a depth-first search (DFS) algorithm or a disjoint-set data structure algorithm to divide the interfering graph into multiple conflict subgraphs (i.e., conflict groups).

[0038] 3. Sparse node extraction within each group: In order to avoid concentrated interference, a greedy scoring algorithm is used to select the group of nodes with the least interference as the reserved nodes, and the remaining nodes are marked as excluded and do not participate in channel allocation.

[0039] 4. Channel allocation process: The controller sequentially selects the channel with the lowest interference cost from the channel candidate list (e.g., 1 / 6 / 11 for 2.4G or 44 / 60 / 153 for 5G) for each reserved node to complete the allocation.

[0040] 5. Finally, the results are sent to the AP for configuration and application.

[0041] In other embodiments, the above method can also be implemented on a distributed wireless embedded device.

[0042] Based on the same inventive concept, this application also provides an apparatus corresponding to the method in Embodiment 1, as detailed in Embodiment 2.

[0043] Example 2 This example provides an automatic channel planning device for wireless networks in a networking scenario, such as Figure 3 As shown, it includes: The interference graph construction module is used to obtain the MAC addresses and RSSI values ​​of neighboring APs received by each AP node and construct an interference graph. Each node in the interference graph represents an AP. If AP i AP can be scanned jThe signal, and RSSI ij If the value is greater than or equal to the first threshold, then add a directed explicit edge from i to j in the interference graph; if AP i AP cannot be scanned j If the signal is positive, then an implicit edge is added from i to j in the interference graph, and RSSI is set to... ij Assign a value to the specified value; The conflict subgraph partitioning module is used to filter out strong interference edges in the interference graph whose RSSI value is greater than the second threshold. The second threshold is greater than the first threshold, and then the interference graph is divided into several conflict subgraphs. The representative node extraction module is used to extract several nodes with the least interference as representative nodes in each conflict subgraph, and only channel allocation is performed on the representative nodes, while the remaining nodes are marked as excluded. The channel allocation module is used to select the channel with the lowest interference cost from the channel candidate list for each representative node in turn.

[0044] In one possible implementation, the method for extracting representative nodes in each conflict subgraph in the conflict subgraph partitioning module is as follows: a greedy selection is performed based on the interference cost score of the node to obtain one or more representative nodes.

[0045] In one possible implementation, the formula for calculating the interference cost score is: ; Where i represents the AP node currently awaiting channel allocation, N(i) represents the set of all AP nodes adjacent to node i, and RSSI ij The value represents the signal strength received from neighbor node j, and max(,·) indicates that when the interference is less than n, the interference does not contribute to the total cost.

[0046] In one possible implementation, the conflict subgraph partitioning module uses a depth-first search or disjoint-set data structure algorithm to partition the interference graph into several conflict subgraphs.

[0047] Since the apparatus described in Embodiment 2 of the present invention is an apparatus used to implement the method of Embodiment 1 of the present invention, those skilled in the art can understand the specific structure and variations of the apparatus based on the method described in Embodiment 1 of the present invention, and therefore will not be described again here. All apparatuses used in the method of Embodiment 1 of the present invention fall within the scope of protection of the present invention.

[0048] Based on the same inventive concept, this application provides a storage medium corresponding to Embodiment 1, as detailed in Embodiment 3.

[0049] Example 3

[0050] This embodiment provides a computer-readable storage medium, such as... Figure 4As shown, a computer program is stored thereon, which, when executed by a processor, can implement any of the embodiments in Example 1.

[0051] Since the computer-readable storage medium described in this embodiment is the same computer-readable storage medium used to implement the method in Embodiment 1 of this application, those skilled in the art can understand the specific implementation methods and various variations of the computer-readable storage medium in this embodiment based on the method described in Embodiment 1 of this application. Therefore, how this computer-readable storage medium implements the method in the embodiments of this application will not be described in detail here. Any computer-readable storage medium used by those skilled in the art to implement the method in the embodiments of this application falls within the scope of protection of this application.

[0052] Compared with existing static channel configurations or simple neighbor collision detection methods, the embodiments of the present invention have at least the following technical advantages: 1. By constructing an interference graph that includes signal strength weights, the interference relationship between APs is fully reflected, improving the accuracy of channel conflict identification. Then, based on the RSSI threshold, the network is divided into multiple conflict subgraphs. In each conflict subgraph, the interference intensity and channel conflict are comprehensively evaluated based on the interference cost score. Representative nodes are extracted, and the channel with the least interference is selected first, significantly reducing co-channel interference.

[0053] 2. It supports rapid adaptive channel reconstruction under dynamic network structure changes, and supports real-time channel adjustment in dynamic scenarios such as device offline / offline and mobile operations, ensuring network stability and possessing good real-time performance and engineering deployment feasibility.

[0054] 3. It can be implemented in software on the controller or AP side without terminal modification, and is suitable for various deployment environments such as FTTR-H and FTTR-B.

[0055] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0056] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0057] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0058] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0059] While specific embodiments of the present invention have been described above, those skilled in the art should understand that the specific embodiments described are merely illustrative and not intended to limit the scope of the invention. Equivalent modifications and variations made by those skilled in the art in accordance with the spirit of the invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for automatic channel planning in a wireless network under a networking scenario, characterized in that, include: Interference graph construction process: Obtain the MAC address and RSSI value of the neighboring APs received by each AP node and construct an interference graph. Each node in the interference graph represents an AP. If AP i AP can be scanned j The signal, and RSSI ij If the value is greater than or equal to the first threshold, then add a directed explicit edge from i to j in the interference graph; if AP i AP cannot be scanned j If the signal is positive, then an implicit edge is added from i to j in the interference graph, and RSSI is set to... ij Assign a value to the specified value; Conflict subgraph partitioning process: Filter out strong interference edges in the interference graph whose RSSI value is greater than the second threshold, the second threshold is greater than the first threshold, and then divide the interference graph into several conflict subgraphs; Representative node extraction process: In each conflict subgraph, extract several nodes with the least interference as representative nodes, allocate channels only to the representative nodes, and mark the remaining nodes as excluded. Channel allocation process: For each representative node, the channel with the lowest interference cost is selected from the channel candidate list and the configuration is issued.

2. The method according to claim 1, characterized in that: The method for extracting representative nodes in each conflict subgraph is as follows: a greedy selection is performed based on the interference cost score of the node to obtain one or more representative nodes.

3. The method according to claim 2, characterized in that: The formula for calculating the interference cost score is: ; Where i represents the AP node currently awaiting channel allocation, N(i) represents the set of all AP nodes adjacent to node i, and RSSI ij The value represents the signal strength received from neighbor node j, and max(,·) indicates that when the interference is less than n, the interference does not contribute to the total cost.

4. The method according to claim 1, characterized in that: The interference graph is divided into several conflict subgraphs using a depth-first search or disjoint-set data structure algorithm.

5. An automatic channel planning device for wireless networks in a networking scenario, characterized in that, include: The interference graph construction module is used to obtain the MAC addresses and RSSI values ​​of neighboring APs received by each AP node and construct an interference graph. Each node in the interference graph represents an AP. If AP i AP can be scanned j The signal, and RSSI ij If the value is greater than or equal to the first threshold, then add a directed explicit edge from i to j in the interference graph; if AP i AP cannot be scanned j If the signal is positive, then an implicit edge is added from i to j in the interference graph, and RSSI is set to... ij Assign a value to the specified value; The conflict subgraph partitioning module is used to filter out strong interference edges in the interference graph whose RSSI value is greater than the second threshold. The second threshold is greater than the first threshold, and then the interference graph is divided into several conflict subgraphs. The representative node extraction module is used to extract several nodes with the least interference as representative nodes in each conflict subgraph, and only channel allocation is performed on the representative nodes, while the remaining nodes are marked as excluded. The channel allocation module is used to select the channel with the lowest interference cost from the channel candidate list for each representative node in turn and issue the configuration.

6. The apparatus according to claim 5, characterized in that: In the conflict subgraph partitioning module, the method for extracting representative nodes in each conflict subgraph is as follows: a greedy selection is performed based on the interference cost score of the node to obtain one or more representative nodes.

7. The apparatus according to claim 6, characterized in that: The formula for calculating the interference cost score is: ; Where i represents the AP node currently awaiting channel allocation, N(i) represents the set of all AP nodes adjacent to node i, and RSSI ij The value represents the signal strength received from neighbor node j, and max(,·) indicates that when the interference is less than n, the interference does not contribute to the total cost.

8. The apparatus according to claim 5, characterized in that: In the conflict subgraph partitioning module, a depth-first search or disjoint-set data structure algorithm is used to partition the interference graph into several conflict subgraphs.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1 to 4.

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