Wireless Mesh adaptive channel selection method and system

Abstract

The invention provides a wireless Mesh self-adaptive channel selection method and system, and relates to the technical field of channel selection and optimizing.The wireless Mesh self-adaptive channel selection method comprises the steps that firstly, according to a Mesh network topological structure, a data transmission path and a corresponding channel are marked, and performance parameters such as delay packet loss rate, transmission rate bandwidth signal intensity and noise intensity are collected at fixed time intervals; the method comprises the following steps: calculating a delay packet loss rate transmission rate by detecting a data packet, obtaining bandwidth signal intensity and noise intensity by means of a tool, then extracting the delay packet loss rate transmission rate and bandwidth data, constructing a channel quality model, identifying a service type by using a deep packet inspection engine nDPI, and dynamically adjusting a weight according to a bandwidth proportion. Meanwhile, a channel signal interference quantization model is constructed according to the signal intensity and the noise intensity, finally, a signal interference quantization threshold value is set, the channel meeting the condition is screened out, the channel with the maximum channel quality result is selected as the optimal channel and switched, and the reasonability and communication performance of wireless Mesh network channel selection are effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of channel selection and optimization, and specifically to a wireless Mesh adaptive channel selection method and system. Background Art

[0002] At present, most of the existing wireless Mesh network channel selection methods only focus on single or a few channel performance indicators, such as only considering signal strength or bandwidth. However, the actual wireless communication environment is complex and changeable, and the performance of channels is affected by a variety of factors, including delay, packet loss rate, transmission rate, etc. Only considering a single indicator may result in a selected channel that performs well in some aspects but has serious deficiencies in other aspects and cannot meet diverse service requirements. For example, a channel with high signal strength may have a high packet loss rate and large delay due to strong interference, thus affecting the quality and efficiency of data transmission; Moreover, there are significant differences in the requirements for channel performance among different service types. The same set of fixed channel parameter ratios and selection strategies cannot meet the most core and urgent needs of all services respectively. Most of the channel selection methods are also static or semi-static. After a channel allocation is performed at the initial stage of network deployment, it is difficult to adjust the channel selection strategy in a timely manner according to the real-time changes in the channel environment.

[0003] The above information disclosed in the background art section is only used to enhance the understanding of the background of the present disclosure, and thus it may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0004] The purpose of the present invention is to provide a wireless Mesh adaptive channel selection method and system to solve the problems raised in the above background art.

[0005] To achieve the above purpose, the present invention provides the following technical solutions: A wireless Mesh adaptive channel selection method, the specific steps include: Step 1: Assign a unique identification code to each node according to the network topology structure, record the data transmission path from the data sending node to the receiving node and the corresponding channel allocation, collect the performance parameters of each channel on each data transmission path at fixed time intervals, and construct a performance parameter table, where the performance parameters include delay, packet loss rate, transmission rate, bandwidth, signal strength, and noise strength; Step 2: Extract the delay, packet loss rate, and transmission rate data of the performance parameter table, construct a channel quality model for each data transmission path through weighted summation in combination with the bandwidth and output the channel quality result, identify the service type with the highest current bandwidth occupancy through a traffic analysis tool, and dynamically adjust the weights; Step 3: Based on the signal strength and noise strength, construct a channel signal-to-interference quantization model, set a signal-to-interference quantization threshold, and select channels with signal-to-interference quantization results higher than the signal-to-interference quantization threshold as available channels; Step 4: Among the selected available channels, select the channel with the highest channel quality result as the optimal channel and perform a channel switching operation.

[0006] Furthermore, the method for assigning a unique identification code to each node according to the network topology structure and recording the data transmission path from the data sending node to the receiving node and the corresponding channel allocation is as follows: According to the topology structure of the Mesh network, assign a unique node to each node, record the data transmission path from each data sending node to the data receiving node and the corresponding channel allocation situation. Among them, mark the data transmission path as , , represents the total number of data transmission paths, and the corresponding channel is marked as .

[0007] Furthermore, the method for collecting the performance parameters of each channel on each path at fixed time intervals is as follows: At each fixed time interval , the data sending node sends a probe packet to the next node, records the round-trip time of the packet from the sending end to the next node and then back to the sending end , and calculates the delay of each data transmission path: ; In the formula, represents the delay of the channel of the th data transmission path; Calculate the packet loss rate: ; In the formula, represents the packet loss rate of the channel of the th data transmission path, represents the total number of packets sent by the data sending node, represents the total number of received packets; Calculate the transmission rate: ; In the formula, represents the transmission rate of the channel of the th data transmission path, represents the fixed time interval within which the The data volume of each data transmission path; Through the router management interface, obtain the bandwidth of each data transmission path and record it as , through the R&S CMX500 wireless signal tester, obtain the signal strength of different channels of each data transmission path and record it as , through the FSW spectrum analyzer, obtain the noise strength of different channels of each data transmission path and record it as .

[0008] Furthermore, the method for constructing the channel quality model of each data transmission path by weighted summation in combination with the bandwidth and outputting the channel quality result is as follows: Implement the extraction of delay, packet loss rate, transmission rate, and bandwidth data, and construct a channel quality model for the channels of each data transmission path: ; In the formula, represents the channel quality of the channel of the th data transmission path, represents the transmission rate of the channel of the th data transmission path, represents the minimum historical delay of the channel of the th data transmission path, represents the maximum historical packet loss rate, represents the minimum historical packet loss rate, represents the weight coefficient, and .

[0009] Furthermore, the method for identifying the service type with the highest current bandwidth occupancy ratio through a traffic analysis tool and dynamically adjusting the weights is as follows: Adopt the deep packet inspection engine nDPI to classify the service types into real-time video, interactive games, file transfer, IoT data, and web browsing, and statistically record the actual bandwidth of different service types of each data transmission path in each time interval, denoted as , and calculate the bandwidth occupancy ratio of each service type based on the bandwidth of each data transmission path and the actual bandwidth of different service types: ; In the formula, represents the bandwidth occupancy ratio of each service type of the th data transmission path; When the bandwidth occupancy ratio of interactive games is the highest, the weights of the channel quality model are respectively ; When the bandwidth occupancy ratio of real-time video is the highest, the weights of the channel quality model are respectively ; When the bandwidth ratio of file transmission is the highest, the weights of the channel quality model are respectively ; When the bandwidth ratio of IoT data is the highest, the weights of the channel quality model are respectively ; When the bandwidth ratio of web browsing is the highest, the weights of the channel quality model are respectively .

[0010] Furthermore, the method for constructing a channel signal-to-interference quantization model based on signal strength and noise strength is as follows: Extract the signal strength of the channel of the th data transmission path , and extract the noise strength of the channel of the th data transmission path , and construct a channel signal-to-interference quantization model: ; In the formula, represents the signal-to-interference quantization result of the channel of the th data transmission path .

[0011] Furthermore, the method for screening channels with signal-to-interference quantization results higher than the signal-to-interference quantization threshold as available channels is as follows: First, set a signal-to-interference quantization threshold , and for each channel in each data transmission path, calculate its signal-to-interference quantization result in real time and compare it with the signal-to-interference quantization threshold , and screen out all channels with signal-to-interference quantization results greater than or equal to the signal-to-interference quantization threshold as available channels.

[0012] The present invention further provides a wireless Mesh adaptive channel selection system, which is used to execute the above-mentioned wireless Mesh adaptive channel selection method, including: A performance parameter collection module, which is used to assign a unique identification code to each node according to the network topology structure, record the data transmission path from the data sending node to the receiving node and the corresponding channel allocation, collect the performance parameters of each channel on each data transmission path at fixed time intervals, and construct a performance parameter table, where the performance parameters include delay, packet loss rate, transmission rate, bandwidth, signal strength and noise strength; The channel quality assessment module is used to extract the delay, packet loss rate, and transmission rate data from the performance parameter table, construct a channel quality model for each data transmission path through weighted summation in combination with the bandwidth, and output the channel quality result. It identifies the service type with the highest current bandwidth occupancy through a traffic analysis tool and dynamically adjusts the weights. The signal-to-interference quantization calculation module is used to construct a channel signal-to-interference quantization model based on the signal strength and noise strength, set a signal-to-interference quantization threshold, and screen out the channels with signal-to-interference quantization results higher than the signal-to-interference quantization threshold as available channels. The optimal channel switching module is used to select the channel with the highest channel quality result as the optimal channel from the screened available channels and perform the channel switching operation.

[0013] Compared with the prior art, the beneficial effects of the present invention are as follows: Based on multiple performance parameters such as comprehensive delay, packet loss rate, transmission rate, bandwidth, signal strength, and noise strength, the present invention constructs a channel quality model and a channel signal-to-interference quantization model, which more comprehensively cover all key aspects affecting channel performance. It dynamically adjusts the weights of the channel quality model by identifying the service type, and real-time screens out the available channels that meet the conditions through the channel signal-to-interference quantization model, so that the channel quality assessment better fits the actual needs of current services, realizes the selection and switching of the optimal channel, and solves the problem that the existing methods are difficult to adapt to the dynamically changing channel environment. Description of the Drawings

[0014] Figure 1 It is a schematic diagram of the overall method flow of the present invention; Figure 2 It is an optimized effect diagram of the interactive game of the present invention; Figure 3 It is an optimized effect diagram of the file transmission of the present invention; Figure 4 It is an optimized effect diagram of the real-time video of the present invention; Figure 5 It is a comparison diagram of the channel quality between one and six of the present invention; Figure 6 It is a comparison diagram of the channel quality between eleven and thirty-six of the present invention; Figure 7 It is a schematic diagram of the overall system module of the present invention. Detailed Embodiments

[0015] To make the objectives, technical solutions, and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below in conjunction with specific embodiments.

[0016] It should be noted that unless otherwise defined, the technical terms or scientific terms used in the present invention should have the ordinary meanings understood by those with ordinary skills in the field to which the present invention belongs. The "first", "second" and similar terms used in the present invention do not denote any order, quantity or importance, but are only used to distinguish different components. Words such as "comprising" or "including" mean that the elements or objects appearing before this word cover the elements or objects listed after this word and their equivalents, without excluding other elements or objects. Words such as "connected" or "linked" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right", etc. are only used to represent relative position relationships. When the absolute position of the object being described changes, the relative position relationship may also change accordingly.

[0017] Embodiment: Please refer to Figures 1 to 6 , the present invention provides a technical solution: A wireless Mesh adaptive channel selection method, the specific steps include: Step 1: Assign a unique identification code to each node according to the network topology structure, record the data transmission path from the data sending node to the data receiving node and the corresponding channel allocation, collect the performance parameters of each channel on each data transmission path at fixed time intervals, and construct a performance parameter table. The performance parameters include delay, packet loss rate, transmission rate, bandwidth, signal strength and noise strength; According to the topology structure of the Mesh network, traverse each node in the network and assign a unique node to it, making it unique in the entire network. For example, adopt a globally unique coding strategy to assign identifiers to network nodes, and generate unique IDs in an increasing natural number sequence according to the order in which the nodes access the network, ensuring that the IDs of any two nodes are not repeated, which is convenient for accurately identifying and locating nodes in the network; After that, adopt a depth-first search algorithm to search for the path from the data sending node to the data receiving node that is directly connected and has no relay nodes, mark each path obtained by the search as a data transmission path, determine the occupied channel through the spectrum resource allocation table, and record the data transmission path from each data sending node to the data receiving node and the corresponding channel allocation situation. Among them, mark the data transmission path as , , represents the total number of data transmission paths, and the corresponding channel is marked as , for example, in a simple 4-node Mesh network, there may be 3 data transmission paths without relay nodes from node A to node D, which are respectively marked as 1, 2, and 3. Path 1 uses channel 1, path 2 uses channel 3, etc.

[0018] Delay, packet loss rate, and transmission rate are key metrics for measuring network performance. By regularly sending probe packets and calculating the delay, the transmission speed and response ability of the network can be understood in real time. The transmission rate directly reflects the amount of data that the network can transmit per unit time, and the packet loss rate directly affects the integrity and reliability of the data and is an important indicator reflecting network stability; Therefore, at each fixed time interval calculate the delay of different channels of each data transmission path. The data sending node sends probe packets to the next node, and the time interval ranges from 0.1 - 60 s. Record the round-trip time of the packet from the sending end to the next node and back to the sending end and calculate the delay of each data transmission path: ; where represents the delay of the channel of the th data transmission path; For example, take the time interval , and the measured RTT is 200 ms for a certain measurement. Substitute to get: ; That is, the delay of the channel of the th data transmission path is 100 ms; For the th data transmission path of the channel , the data sending node sent packets, and the total number of packets received by the data receiving node is packets. Calculate the packet loss rate: ; That is, the packet loss rate of the channel of the th data transmission path is ; During the time interval , the amount of data passing through the channel of the th data transmission path is , calculate the transmission rate: ; That is, the transmission rate of the channel of the th data transmission path is ; ​​​Construct a performance parameter table based on the delay, packet loss rate, and transmission rate data of each channel on each path. Bandwidth is a key indicator for measuring the data transmission capacity of a network, which determines the amount of data that the network can transmit per unit time. For example, in an enterprise, the sales department may require a large amount of bandwidth for video conferencing and rapid transmission of customer information; the R & D department also relies on high bandwidth for big data file sharing and remote code debugging. By calculating the bandwidth, we can clearly understand the carrying capacity of each data transmission path and determine whether it meets the business requirements. The noise intensity directly affects the communication quality of the wireless channel. A lower noise intensity means less interference to the channel and more stable data transmission. By calculating the noise intensity of different channels, we can select the channel with the lowest noise intensity for data transmission to avoid signal distortion and increased packet loss rate, etc.

[0019] Therefore, through the router management interface, obtain the bandwidth of each data transmission path and record it as , with the unit of , for example, the bandwidth corresponding to the th data transmission path is 100 Mbps, and record it as ; through the R&S CMX500 wireless signal tester, obtain the signal strength of different channels of each data transmission path and record it as ; through the FSW spectrum analyzer, obtain the noise intensity of different channels of each data transmission path and record it as , for example, after starting the scan, the noise intensity of channel 6 of data transmission path 1 is , and record it as . Table 1 shows 40 groups of data including performance parameters such as round-trip time, delay, and packet loss rate under different paths, which can intuitively reflect the transmission performance of the wireless Mesh network. By analyzing these data, we can evaluate the stability and reliability of the network. For example, a higher packet loss rate may mean problems such as interference, insufficient bandwidth, or node failure in the network; while a longer round-trip time and delay may affect real-time services such as video calls and online games, etc. in terms of experience quality, which helps to calculate which channels have better quality in the follow-up, so as to adjust the channel allocation.

[0020]

[0021] Step 2: Extract the delay, packet loss rate, and transmission rate data from the performance parameter table, and construct a channel quality model for each data transmission path through weighted summation in combination with the bandwidth, and output the channel quality result. Identify the service type with the highest current bandwidth occupancy through a traffic analysis tool, and dynamically adjust the weights; Within a fixed time interval , implement the extraction of the collected delay, packet loss rate, transmission rate, and bandwidth data, and construct a channel quality model for the channels of each data transmission path: ; In the formula, represents the channel quality of the th data transmission path, represents the delay of the channel of the th data transmission path, represents the packet loss rate of the channel of the th data transmission path, represents the transmission rate of the channel of the th data transmission path, represents the minimum historical delay of the channel of the th data transmission path, represents the maximum historical packet loss rate, represents the minimum historical packet loss rate, represents the weight coefficient, and , where, For this term, the unit is eliminated by reducing the time dimension of milliseconds in the numerator and denominator, and its output range is [0, 1]. For this term, both the numerator and denominator are packet loss rates in percentage, and after reduction, it becomes a dimensionless ratio with a range of [0, 1]. For this term, both the numerator and denominator are in the unit of bit / s, and after reduction, it becomes a dimensionless ratio with a range of [0, 1].

[0022] The minimum historical delay is obtained by continuously recording the delay of data packets in each time interval for each path and each channel since the mesh network is set up and running, and the minimum historical delay is updated in real time. The maximum historical packet loss rate is obtained by continuously recording the packet loss rate of data packets in each time interval for each path and each channel since the mesh network is set up and running, and the maximum historical packet loss rate is updated in real time. The minimum historical packet loss rate is obtained by continuously recording the packet loss rate of data packets in each time interval for each path and each channel since the mesh network is set up and running, and the minimum historical packet loss rate is updated in real time.

[0023] Among them, by weighted integration of three key indicators: delay, packet loss rate, and transmission rate, a quantified channel quality value is obtained. The larger the value, the better the channel The better the comprehensive performance under this path, that is, the smaller the latency, the lower the packet loss rate, and the higher the transmission rate, which provides a quantitative basis for the selection and optimization of data transmission paths. By comprehensively evaluating the channel quality, channels with high channel quality values can be preferentially selected, thereby ensuring the efficiency, reliability, and stability of data transmission, and reducing the impact of problems such as latency and packet loss on services. Latency directly affects the timeliness of data transmission. The greater the latency, the longer it takes for the data to arrive, which may cause service lags, so it has a negative impact on channel quality; packet loss will lead to data retransmission, reducing transmission efficiency and reliability. The higher the packet loss rate, the worse the data integrity, which has a negative impact on channel quality. The transmission rate determines the amount of data transmitted per unit time. The higher the rate, the higher the transmission efficiency, which has a positive impact on channel quality. and The numerical ranges of all three are within For this item, 200 represents the upper limit of latency tolerance, which is the widely adopted end-to-end latency upper limit in the industry standard system. This item has the effect of quantifying the remaining latency margin. Delays exceeding this threshold will significantly affect the user experience. When the channel of the data transmission path has an increasing latency, the value becomes smaller, and its channel quality is negatively correlated with latency. For this item, when the channel of the data transmission path has an increasing packet loss rate, the value becomes smaller, and its channel quality is negatively correlated with the packet loss rate. For this item, when the

[0024] Adopt the deep packet inspection engine nDPI, which has a large number of protocol rules and feature libraries predefined internally, covering the protocols used in common real-time video, interactive games, file transfer, IoT data, and web browsing services. For example, for protocols such as RTMP and HLS commonly used in real-time video services, nDPI can identify and classify based on the characteristics of these protocols. When network data packets flow through nDPI, it will automatically match the data packets with the built-in rules and features to determine the service type, which is real-time video, interactive games, file transfer, IoT data, and web browsing. For example, when running interactive games, it will identify protocols such as Blizzard Battle.net protocol and Unity / Unreal engine private protocols, monitor the unique data packet structure and game data of the game protocol, analyze traffic patterns such as short packets with high-frequency interactions and high UDP ratios, and finally match based on the IP-based server cluster, such as the CDN node IP library of game manufacturers, to identify the service type at this time as running interactive games. Real-time statistics for each time interval within, the actual bandwidth of different service types on each data transmission path, denoted as According to the bandwidth of each data transmission path and the actual bandwidth of different service types, calculate the bandwidth ratio of each service type: ; In the formula, represents the bandwidth ratio of each service type on the th data transmission path; When the bandwidth ratio of interactive games is the highest, although players have high requirements for operation response time and a small amount of packet loss can be compensated by the retransmission mechanism, too large a packet loss rate will cause screen tearing or data errors, affecting the experience. The weights of the channel quality model are respectively ; When the bandwidth ratio of real-time video is the highest, video stream decoding requires continuous data, and scenarios such as live streaming have extremely high requirements for real-time performance. To avoid packet loss leading to video frame loss, The weights of the channel quality model are respectively ; When the bandwidth ratio of file transfer is the highest, large file transfer can tolerate hundreds of milliseconds of delay and has the support of the retransmission mechanism. Tilting the weight towards the transmission rate has a better effect. The weights of the channel quality model are respectively ; When the bandwidth ratio of IoT data is the highest, the sensor data acquisition period is usually at the second level, but a delay less than 100ms can meet the requirements. And data loss of IoT devices may lead to production accidents. Therefore, the weight is the highest. Due to the characteristic of small single-device data volume, the transmission is relatively stable and the bandwidth requirement is not high. The weights of the channel quality model are respectively ; When the bandwidth occupancy ratio of web page browsing is the highest, based on the requirement of quickly loading page resources, the transmission rate weight should be the highest. Based on the requirements of reducing TCP connection latency and first-byte time, and avoiding resource loading failures or retransmission problems caused by packet loss, certain latency and packet loss rate weights are required. The weights of the channel quality model are respectively 。

[0025] Then switch to the channel corresponding to the highest value of the channel quality result, so that the actual usage experience better meets the requirements of the main service types in the current network. Table 2 shows 40 sets of data for channel quality assessment in a wireless Mesh network, which are used to display the bandwidth occupancy, weight allocation strategy, and channel quality model calculation results under different service types. Under each data transmission path, sort according to the channel quality results to improve the usage experience.

[0026]

[0027] Such as Figures 2 - 4 shown, they are respectively the statistical charts of latency and packet loss rate before and after optimization for interactive games, file transfer, and live video. It can be directly seen from the figure that the latency after optimization for interactive games and live video is generally lower than that before optimization. For example, for live video sample number 32, the optimization algorithm dynamically adjusts the weight and forces the selection of the path with the lowest latency to ensure the continuity and synchronization of the video stream. The latency before optimization is high, and it is significantly reduced after optimization; for interactive game sample number 8, the optimization algorithm preferentially selects low-latency paths and combines with a predictive channel switching mechanism, so that the latency before optimization is high and it is significantly decreased after optimization, which reflects the improvement of optimization on the latency of interactive games and live video; for file transfer sample number 16, aiming at the high dependence of file transfer on bandwidth, the algorithm preferentially allocates high-throughput channels. The rate before optimization is low and it is greatly improved after optimization, reflecting the effect of file transfer rate optimization. The targeted optimization method effectively improves the user experience.

[0028] Step 3: Construct a channel signal-to-interference quantization model based on the signal strength and noise strength, set the signal-to-interference quantization threshold, and select the channels with signal-to-interference quantization results higher than the signal-to-interference quantization threshold as available channels; The signal strength represents the strength of the useful information expected to be received, and the noise strength represents the strength of the useless signals that interfere with the normal transmission and reception of signals. By combining these two data, the degree of signal interference can be quantitatively described. A decrease in the signal-to-noise ratio will directly lead to an increase in the bit error rate at the physical layer, trigger the ARQ transmission mechanism at the link layer, and ultimately cause an increase in the transmission layer delay and the packet loss rate. In actual situations, for a channel with a very low signal-to-noise ratio, the indicators such as delay and packet loss rate may not have obvious changes at this time. However, due to severe interference, the likelihood of these problems occurring in the future is extremely high. Therefore, taking it as a separate discrimination condition can enable measures to be taken before the problems actually occur, switch to a more suitable channel in advance, and avoid causing serious impacts on communication. Extract the channel of the th data transmission path for its signal strength, extract the channel of the th data transmission path for its noise strength, and construct a channel signal-to-noise quantization model: ; In the formula, represents the signal-to-noise quantization result of the channel of the th data transmission path. The value of directly reflects the quality status of the channel. In essence, it is a standardized expression of the signal-to-noise ratio at the physical layer, reflecting the relative relationship between the power of the useful signal and the power of the noise in the wireless channel. When the signal strength is much greater than the noise strength, the closer the value is to 1, the higher the proportion of the useful signal in the channel, the better the channel quality, and the higher the reliability and accuracy of signal transmission. When the noise strength is relatively large and the value is closer to 0, it indicates that the signal is severely interfered, the channel quality is poor, which may lead to an increase in the signal transmission error rate, data loss and other problems.

[0029] Set a signal-to-noise quantization threshold . Generally, it is set to . The reason is as follows: Shannon's theorem states that the channel capacity is closely related to the signal-to-noise ratio. Under certain bandwidth conditions, to achieve reliable information transmission, a certain signal-to-noise ratio needs to be ensured. Generally speaking, a certain signal-to-noise ratio is required to ensure the accuracy and effectiveness of information transmission. The value of 0.8 is the basic signal-to-noise ratio level that can ensure that the channel has a certain capacity to support common data transmission rates and quality requirements in many theoretical models and practical experiences, that is, when the signal-to-noise ratio SNR reaches 6 dB, it supports the basic modulation method and can retain more available channels in the interference environment. If high real-time requirements and high sensitivity to signal quality are required, the signal-to-noise quantization threshold can be appropriately adjusted ; In channels with low signal-to-interference ratio, due to signal being interfered by noise, in order to ensure reliable data transmission, more complex coding methods and error correction mechanisms are often required, which will reduce the actual data transmission rate. Therefore, for each channel in each data transmission path, by calculating its signal-to-interference quantization result in real time and comparing it with the signal-to-interference quantization threshold to screen out all channels whose signal-to-interference quantization results are greater than or equal to the signal-to-interference quantization threshold to avoid communication interruption or data transmission errors and affect the actual experience effect.

[0030] Step 4: Among the screened available channels, select the channel with the highest channel quality result as the optimal channel and perform the channel switching operation; Calculate the channel quality results of each channel in each data transmission path respectively. For example, within a certain time interval, when calculating the service type of the th data transmission path, the bandwidth occupancy ratio of the interactive game is the highest, and all signal-to-interference quantization results of the channels in this data transmission path are greater than the signal-to-interference quantization threshold , then the channel quality result is: ; Select the channel with the largest value in the channel quality results in each data transmission path as the optimal channel and perform the channel switching operation. Table 3 shows the channel quality results and signal-to-interference quantization results of 4 channels in the same path over time.

[0031]

[0032] As Figures 5 - 6 shown, it shows the channel quality results and signal-to-interference quantization results of channels 1, 6, 11, and 36 at different times. Among them, the left vertical axis uniformly corresponds to the channel quality results, and the right vertical axis uniformly corresponds to the signal-to-interference quantization results. In Figure 5 , the gray star marks correspond to the channel quality result data of channel 1, the red star marks correspond to the signal-to-interference quantization result data of channel 1, the gray dot marks correspond to the channel quality result data of channel 6, and the red dot marks correspond to the signal-to-interference quantization result data of channel 6; in Figure 6 , the gray triangle marks correspond to the channel quality result data of channel 11, the red triangle marks correspond to the signal-to-interference quantization result data of channel 11, the gray square marks correspond to the channel quality result data of channel 36, and the red square marks correspond to the signal-to-interference quantization result data of channel 36. Through the analysis of the signal-to-interference quantization results, channels higher than the signal-to-interference quantization threshold Available channels can avoid communication interruptions that affect the usage experience by avoiding channels with relatively high noise intensity in advance. The signal-to-interference quantization result of channel 36 in the 5GHz frequency band is generally higher than 0.8, and there is less interference in this frequency band. Channels 1 and 11 in this frequency band have excessive interference, and their channel quality results are also poor. Therefore, channel 36 is the preferred channel for the wireless Mesh network to improve network stability.

[0033] Please refer to Figure 7 , the present invention further provides a wireless Mesh adaptive channel selection system, which is used to execute the above-mentioned wireless Mesh adaptive channel selection method, including: A performance parameter collection module is used to assign a unique identification code to each node according to the network topology structure, record the data transmission path from the data sending node to the receiving node and the corresponding channel allocation, collect the performance parameters of each channel on each data transmission path at fixed time intervals, and construct a performance parameter table. The performance parameters include delay, packet loss rate, transmission rate, bandwidth, signal strength, and noise intensity; A channel quality evaluation module is used to extract the delay, packet loss rate, and transmission rate data from the performance parameter table, combine the bandwidth, construct a channel quality model for each data transmission path through weighted summation and output the channel quality result, identify the service type with the highest current bandwidth occupancy ratio through a traffic analysis tool, and dynamically adjust the weights; A signal-to-interference quantization calculation module is used to construct a channel signal-to-interference quantization model based on the signal strength and noise intensity, set a signal-to-interference quantization threshold, and screen out channels with signal-to-interference quantization results higher than the signal-to-interference quantization threshold as available channels; An optimal channel switching module is used to select the channel with the highest channel quality result as the optimal channel among the screened available channels and perform channel switching operations.

[0034] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain a formula that is closest to the actual situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0035] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those skilled in the art can realize that the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or by a combination of computer software and electronic hardware. Whether these functions are executed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0036] The unit described as a separation component may or may not be physically separated. The component shown as a unit may or may not be a physical unit, and it may be located in one place or distributed across multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0037] As described above, the specific implementation manners of the present application are only provided, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered by the protection scope of the present application.

Claims

1. A wireless Mesh adaptive channel selection method, characterized in that The specific steps include: Step 1: Assign a unique identification code to each node according to the network topology structure, record the data transmission path from the data sending node to the receiving node and the corresponding channel allocation, collect the performance parameters of each channel on each data transmission path at fixed time intervals, and construct a performance parameter table. The performance parameters include delay, packet loss rate, transmission rate, bandwidth, signal strength, and noise strength. Step 2: Extract the delay, packet loss rate, and transmission rate data from the performance parameter table, construct a channel quality model for each data transmission path through weighted summation in combination with the bandwidth, and output the channel quality result. Identify the service type with the highest current bandwidth occupancy ratio through a traffic analysis tool and dynamically adjust the weights. Step 3: Construct a channel signal-to-interference quantization model based on the signal strength and noise strength, set a signal-to-interference quantization threshold, and screen out the channels with signal-to-interference quantization results higher than the signal-to-interference quantization threshold as available channels. Step 4: Among the screened available channels, select the channel with the highest channel quality result as the optimal channel and perform a channel switching operation.

2. The wireless Mesh adaptive channel selection method according to claim 1, wherein: The method for assigning a unique identification code to each node according to the network topology structure and recording the data transmission path from the data sending node to the receiving node and the corresponding channel allocation is as follows: According to the topological structure of the Mesh network, assign a unique node to each node , record the data transmission path from each data sending node to the data receiving node and the corresponding channel allocation situation, where the data transmission path is marked as , , represents the total number of data transmission paths, and the corresponding channel is marked as .

3. The wireless Mesh adaptive channel selection method according to claim 2, characterized in that: The method for collecting the performance parameters of each channel on each path at fixed time intervals is as follows: At each fixed time interval the data sending node sends a probe data packet to the next node, records the round-trip time of the data packet from the sending end to the next node and back to the sending end and calculates the delay of each data transmission path: ； In the formula, represents the delay of the channel of the th data transmission path; Calculate the packet loss rate: ； In the formula, represents the packet loss rate of the channel of the th data transmission path, represents the total number of data packets sent by the data sending node, represents the total number of received data packets; Calculate the transmission rate: ； In the formula, represents the channel of the n-th data transmission path, represents the fixed time interval and the amount of data passing through the n-th data transmission path within this time interval; Obtain the bandwidth of each data transmission path through the router management interface and record it as , obtain the signal strength of different channels of each data transmission path through the R&S CMX500 wireless signal tester and record it as , obtain the noise strength of different channels of each data transmission path through the FSW spectrum analyzer and record it as .

4. The wireless Mesh adaptive channel selection method according to claim 3, characterized in that: The method for constructing a channel quality model for each data transmission path through weighted summation in combination with the bandwidth and outputting the channel quality result is as follows: Implement the extraction of delay, packet loss rate, transmission rate, and bandwidth data, and construct a channel quality model for the channels of each data transmission path: ； Wherein, represents the channel quality of the th data transmission path, represents the transmission rate of the channel of the th data transmission path, represents the minimum historical delay of the channel of the th data transmission path, represents the maximum historical packet loss rate, represents the minimum historical packet loss rate, represents the weight coefficient, and .

5. A wireless Mesh adaptive channel selection method according to claim 4, characterized in that: The method for identifying the service type with the highest current bandwidth occupancy ratio through a traffic analysis tool and dynamically adjusting the weights is as follows: Using the deep packet inspection engine nDPI, the service types are divided into real-time video, interactive games, file transfer, IoT data, and web browsing, and the actual bandwidth of different service types on each data transmission path is statistically calculated in real time for each time interval, denoted as , and according to the bandwidth of each data transmission path and the actual bandwidth of different service types, calculate the bandwidth occupancy ratio of each service type: ； In the formula, represents the bandwidth occupancy ratio of each service type of the th data transmission path; When the bandwidth occupancy ratio of the interactive game is the highest, the weights of the channel quality model are respectively ; When the bandwidth ratio of the live video is the highest, the weights of the channel quality model are respectively ; When the bandwidth occupancy ratio of file transmission is the highest, the weights of the channel quality model are respectively ; When the bandwidth ratio of IoT data is the highest, the weights of the channel quality model are respectively ; When the bandwidth occupancy ratio of web browsing is the highest, the weights of the channel quality model are respectively .

6. A wireless Mesh adaptive channel selection method according to claim 3, characterized in that: The method for constructing a channel signal-to-interference quantization model based on the signal strength and noise strength is as follows: Extract the channel of the th data transmission path signal strength, and extract the channel of the th data transmission path noise strength, and construct a channel signal-to-interference quantization model: ； In the formula, represents the channel of the jamming quantization result of the th data transmission path.

7. A wireless Mesh adaptive channel selection method according to claim 1, characterized in that: The method for screening out the channels with signal-to-interference quantization results higher than the signal-to-interference quantization threshold as available channels is as follows: First, set a signal-to-interference quantization threshold , for each channel in each data transmission path, calculate its signal-to-interference quantization result in real time, and compare it with the signal-to-interference quantization threshold . Screen out all channels whose signal-to-interference quantization results are greater than or equal to the signal-to-interference quantization threshold as available channels.

8. A wireless Mesh adaptive channel selection system, characterized in that: The system is used to execute a wireless Mesh adaptive channel selection method according to any one of claims 1-7, and includes: A performance parameter collection module, which is used to assign a unique identification code to each node according to the network topology structure, record the data transmission path from the data sending node to the receiving node and the corresponding channel allocation, collect the performance parameters of each channel on each data transmission path at fixed time intervals, and construct a performance parameter table. The performance parameters include delay, packet loss rate, transmission rate, bandwidth, signal strength, and noise strength. A channel quality evaluation module, which is used to extract the delay, packet loss rate, and transmission rate data from the performance parameter table, construct a channel quality model for each data transmission path through weighted summation in combination with the bandwidth, and output the channel quality result. Identify the service type with the highest current bandwidth occupancy ratio through a traffic analysis tool and dynamically adjust the weights. A signal-to-interference quantization calculation module, which is used to construct a channel signal-to-interference quantization model based on the signal strength and noise strength, set a signal-to-interference quantization threshold, and screen out the channels with signal-to-interference quantization results higher than the signal-to-interference quantization threshold as available channels. An optimal channel switching module, which is used to select the channel with the highest channel quality result as the optimal channel among the screened available channels and perform a channel switching operation.

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