A wireless mesh adaptive channel selection method and system

Abstract

The present invention provides a wireless Mesh adaptive channel selection method and system, which relates to the field of channel selection and optimization technology. The present invention first marks the data transmission path and the corresponding channel based on the Mesh network topology, and collects performance parameters such as delay packet loss rate, transmission rate, bandwidth, signal strength, and noise strength at fixed time intervals. The delay packet loss rate and transmission rate are calculated by detecting data packets, and the bandwidth signal strength and noise strength are obtained with the help of tools. Then, the delay packet loss rate, transmission rate, and bandwidth data are extracted to construct a channel quality model. The deep packet inspection engine nDPI is used to identify the service type, and the weight is dynamically adjusted according to the bandwidth ratio. At the same time, a channel signal-to-interference quantization model is constructed based on the signal strength and noise strength. Finally, the signal-to-interference quantization threshold is set to screen out qualified channels, select the channel with the largest channel quality result as the optimal channel and switch to it, effectively improving the rationality of wireless Mesh network channel selection and communication performance.

Description

Technical Field

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

[0002] Currently, most existing wireless mesh network channel selection methods focus on a single or a few channel performance indicators, such as signal strength or bandwidth. However, the actual wireless communication environment is complex and changeable, and channel performance is affected by a combination of factors, including latency, packet loss rate, and transmission rate. Considering only a single indicator may result in the selected channel performing well in some aspects, but having serious deficiencies in other aspects, unable to meet diverse business needs. For example, a channel with high signal strength may have a high packet loss rate and high latency due to high interference, thus affecting the quality and efficiency of data transmission.

[0003] In addition, different business types have significantly different requirements for channel performance. The same fixed set of channel parameter ratios and selection strategies cannot meet the most core and urgent needs of all businesses. Channel selection methods are mostly static or semi-static. After channel allocation is performed once in the initial stage of network deployment, it is difficult to adjust the channel selection strategy in a timely manner according to real-time changes in the channel environment.

[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0005] The object 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 technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A wireless mesh adaptive channel selection method, specifically comprising the following steps:

[0008] Step 1: Assign a unique identification code to each node based on the network topology, record the data transmission path and corresponding channel allocation from the data sending node to the data receiving node, 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;

[0009] Step 2: Extract the latency, packet loss rate, and transmission rate data from the performance parameter table. Combined with the bandwidth, the data is weighted and summed to construct a channel quality model for each data transmission path. The channel quality results are output. Traffic analysis tools are used to identify the service type with the highest bandwidth usage and dynamically adjust the weight.

[0010] Step 3: Build a channel signal-to-interference quantization model based on signal strength and noise strength, 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;

[0011] Step 4: Among the available channels that have been screened, the channel with the highest channel quality result is selected as the optimal channel, and a channel switching operation is performed.

[0012] Furthermore, a unique identification code is assigned to each node according to the network topology structure, and a method for recording the data transmission path from the data sending node to the receiving node and the corresponding channel allocation is as follows:

[0013] Assign a unique node to each node according to the topology of the Mesh network , record the data transmission path and corresponding channel allocation of each data sending node to the data receiving node, where the data transmission path is marked as , , Represents the total number of data transmission paths, and the corresponding channels are marked as .

[0014] Furthermore, the method for collecting the performance parameters of each channel on each path at a fixed time interval is:

[0015] At each fixed time interval In this case, the data sending node sends a detection data packet to the next node and records the round-trip time of the data packet from the sender to the next node and then back to the sender. , calculate the delay of each data transmission path:

[0016] ;

[0017] Where, Indicates the Channels for data transmission paths Delays;

[0018] Calculate the packet loss rate:

[0019] ;

[0020] Where, Indicates the Channels for data transmission paths The packet loss rate, Indicates the total number of data packets sent by the data sending node. Indicates the total number of packets received;

[0021] Calculate the transfer rate:

[0022] ;

[0023] Where, Indicates the Channels for data transmission paths The transmission rate, Indicates a fixed time interval Through the The amount of data transmitted through the data transmission path;

[0024] Through the router management interface, obtain the bandwidth of each data transmission path and record it as ,The signal strength of different channels of each data transmission path is obtained by R&S CMX500 wireless signal tester and recorded as ,The noise intensity of different channels of each data transmission path is obtained by FSW spectrum analyzer and recorded as .

[0025] Furthermore, a method for constructing a channel quality model for each data transmission path by weighted summation in combination with bandwidth and outputting a channel quality result is as follows:

[0026] Implementation Extraction of latency, packet loss rate, transmission rate, and bandwidth data to build a channel quality model for each data transmission path channel:

[0027] ;

[0028] Where, Indicates the Channels for data transmission paths The channel quality, Indicates the Channels for data transmission paths The transmission rate, Indicates the Channels for data transmission paths The historical minimum delay of Indicates the maximum historical packet loss rate. Indicates the minimum historical packet loss rate. represents the weight coefficient, and .

[0029] Furthermore, the traffic analysis tool is used to identify the service type with the highest bandwidth usage and dynamically adjust the weight as follows:

[0030] 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 in each data transmission path in each time interval is counted in real time, which is recorded as , based on the bandwidth of each data transmission path and the actual bandwidth of different business types, calculate the bandwidth share of each business type:

[0031] ;

[0032] Where, Indicates the The bandwidth ratio of each service type in the data transmission path;

[0033] When the bandwidth of interactive games accounts for the highest proportion, the weights of the channel quality model are ;

[0034] When the bandwidth of real-time video accounts for the highest proportion, the weights of the channel quality model are ;

[0035] When the bandwidth of file transfer accounts for the highest proportion, the weights of the channel quality model are ;

[0036] When IoT data has the highest bandwidth share, the weights of the channel quality model are ;

[0037] When web browsing accounts for the highest bandwidth, the weights of the channel quality model are .

[0038] Furthermore, the method for constructing a channel signal-to-interference quantization model based on signal strength and noise strength is:

[0039] Extract Channels for data transmission paths Signal strength , extract the Channels for data transmission paths Noise intensity , build a channel signal-interference quantization model:

[0040] ;

[0041] Where, Indicates the Channels for data transmission paths The signal-to-noise quantification results.

[0042] Furthermore, a method for screening out channels whose signal-to-interference quantization results are higher than the signal-to-interference quantization threshold as available channels is as follows:

[0043] 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 Compare and filter out all signal-to-interference quantization results that are greater than or equal to the signal-to-interference quantization threshold The channel is used as the available channel.

[0044] 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:

[0045] A performance parameter collection module is used to assign a unique identification code to each node based on the network topology, record the data transmission path and corresponding channel allocation from the data sending node to the receiving node, 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;

[0046] The channel quality assessment module extracts latency, packet loss rate, and transmission rate data from the performance parameter table, combines bandwidth with weighted summation to construct a channel quality model for each data transmission path, and outputs the channel quality results. It uses traffic analysis tools to identify the service type with the highest bandwidth usage and dynamically adjusts the weights.

[0047] The signal-to-noise quantization calculation module is used to build a channel signal-to-noise quantization model based on signal strength and noise strength, set a signal-to-noise quantization threshold, and select channels with signal-to-noise quantization results higher than the signal-to-noise quantization threshold as available channels;

[0048] The optimal channel switching module is used to select the channel with the highest channel quality result as the optimal channel among the available channels screened out, and perform a channel switching operation.

[0049] Compared with the prior art, the present invention has the following beneficial effects:

[0050] The present invention constructs a channel quality model and a channel signal-to-interference quantification model based on multiple performance parameters such as comprehensive delay, packet loss rate, transmission rate, bandwidth, signal strength and noise intensity, which more comprehensively covers all key aspects affecting channel performance. The weight of the channel quality model is dynamically adjusted by identifying the service type, and available channels that meet the conditions are screened out in real time through the channel signal-to-interference quantification model, so that the channel quality assessment is more in line with the actual needs of the current business, and the selection and switching of the optimal channel is realized, which solves the problem that the existing methods are difficult to adapt to the dynamically changing channel environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 Schematic diagram of the overall method flow of the present invention;

[0052] Figure 2 Optimizing the renderings for the interactive game of the present invention;

[0053] Figure 3 This is a diagram showing the file transfer optimization effect of the present invention;

[0054] Figure 4 This is the real-time video optimization effect diagram of the present invention;

[0055] Figure 5 This is a comparison chart of the quality of channels one and six of the present invention;

[0056] Figure 6 This is a comparison chart of the quality of the eleventh and thirty-sixth channels of the present invention;

[0057] Figure 7 It is a schematic diagram of the overall system module of the present invention. DETAILED DESCRIPTION

[0058] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to specific embodiments.

[0059] It should be noted that, unless otherwise defined, the technical or scientific terms used in the present invention should have the usual meanings understood by people with ordinary skills in the field to which the present invention belongs. The "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative position relationships. When the absolute position of the object being described changes, the relative position relationship may also change accordingly.

[0060] Example:

[0061] See also Figures 1 to 6 , the present invention provides a technical solution:

[0062] A wireless mesh adaptive channel selection method, specifically comprising the following steps:

[0063] Step 1: Assign a unique identification code to each node based on the network topology, record the data transmission path and corresponding channel allocation from the data sending node to the data receiving node, 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;

[0064] According to the topology of the Mesh network, traverse each node in the network and assign a unique node to each node. , making it unique in the entire network. For example, a globally unique coding strategy is used to assign identifiers to network nodes. Unique IDs are generated in an increasing natural number sequence according to the order in which the nodes are connected to the network. This ensures that the IDs of any two nodes are unique, making it easier to accurately identify and locate nodes in the network.

[0065] Afterwards, a depth-first search algorithm is used to search for paths from the data sending node to the data receiving node that are directly connected and have no transit nodes. Each path obtained by the search is marked as a data transmission path, and the channel it occupies is determined through the spectrum resource allocation table. The data transmission path and the corresponding channel allocation status from each data sending node to the data receiving node are recorded, where the data transmission path is marked as , , Represents the total number of data transmission paths, and the corresponding channels are marked as For example, in a simple 4-node Mesh network, there may be 3 data transmission paths from node A to node D without transit nodes, marked as 1, 2, and 3 respectively. Path 1 uses channel 1, path 2 uses channel 3, and so on.

[0066] Latency, packet loss rate, and transmission rate are key indicators for measuring network performance. By regularly sending probe packets and calculating latency, we can understand the network's transmission speed and responsiveness in real time. The transmission rate directly reflects the amount of data the network can transmit per unit time. The packet loss rate directly affects data integrity and reliability and is an important indicator of network stability.

[0067] Therefore, at each fixed time interval Under this condition, the delay of different channels of each data transmission path is calculated. The data sending node sends a detection data packet to the next node, and the time interval is The value is between 0.1-60s, recording the round-trip time of the data packet from the sender to the next node and then back to the sender , calculate the delay of each data transmission path:

[0068] ;

[0069] Where, Indicates the Channels for data transmission paths Delays;

[0070] For example, take the time interval , the RTT obtained in a certain measurement is 200ms, Substituting in:

[0071] ;

[0072] That is Channels for data transmission paths The delay is 100ms;

[0073] No. Channels for data transmission paths , the data sending node sends The total number of data packets received by the data receiving node is Calculate the packet loss rate:

[0074] ;

[0075] That is Channels for data transmission paths The packet loss rate is ;

[0076] Time interval Inside, through Channels for data transmission paths The amount of data is ,

[0077] Calculate the transfer rate:

[0078] ;

[0079] That is Channels for data transmission paths The transmission rate is ;

[0080] Performance parameter tables are constructed based on the latency, packet loss rate, and transmission rate data for each channel along each path. Bandwidth is a key metric for measuring a network's data transmission capacity, determining the amount of data the network can transmit per unit time. For example, a sales department within a company may require significant bandwidth for video conferencing and the rapid transfer of customer information. R&D departments also rely on high bandwidth for large data file sharing and remote code debugging. By calculating bandwidth, we can clearly understand the carrying capacity of each data transmission path and determine whether it meets business needs. Noise intensity directly affects the communication quality of wireless channels. Lower noise intensity means less interference on 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, avoiding signal distortion and increased packet loss.

[0081] Therefore, through the router management interface, obtain the bandwidth of each data transmission path and record it as , the unit is , for example, The bandwidth corresponding to the data transmission path is 100Mbps, which is recorded as ; Use the R&S CMX500 wireless signal tester to obtain the signal strength of different channels of each data transmission path and record it as ,The noise intensity of different channels of each data transmission path is obtained by FSW spectrum analyzer and recorded as For example, after starting the scan, the noise intensity of channel 6 of data transmission path 1 is , recorded as Table 1 shows 40 sets of data including performance parameters such as round-trip time, latency, and packet loss rate under different paths. These data can intuitively reflect the transmission performance of the wireless mesh network. By analyzing these data, the stability and reliability of the network can be evaluated. For example, a high packet loss rate may mean that there are problems such as interference, insufficient bandwidth, or node failure in the network; while long round-trip time and latency may affect the experience quality of real-time services such as video calls and online games. This helps to calculate which channels have better quality in the future and adjust channel allocation.

[0082]

[0083] Step 2: Extract the latency, packet loss rate, and transmission rate data from the performance parameter table. Combined with the bandwidth, the data is weighted and summed to construct a channel quality model for each data transmission path. The channel quality results are output. Traffic analysis tools are used to identify the service type with the highest bandwidth usage and dynamically adjust the weight.

[0084] At fixed time intervals The collected delay, packet loss rate, transmission rate and bandwidth data are extracted and collected to build a channel quality model for the channel of each data transmission path:

[0085] ;

[0086] Where, Indicates the Channels for data transmission paths The channel quality, Indicates the Channels for data transmission paths The delay, Indicates the Channels for data transmission paths The packet loss rate, Indicates the Channels for data transmission paths The transmission rate, Indicates the Channels for data transmission paths The historical minimum delay of Indicates the maximum historical packet loss rate. Indicates the minimum historical packet loss rate. represents the weight coefficient, and ,in, This term eliminates the unit by simplifying the numerator and denominator time dimensions to milliseconds, so that the output range is [0, 1]. The numerator and denominator of this item are both percentage packet loss rates, which are simplified to dimensionless ratios in the range of [0, 1]. The numerator and denominator of this term are both in bit / s, and after simplification, they become dimensionless ratios in the range [0,1].

[0087] The historical minimum delay is the value obtained by continuously recording each path and channel at each time interval since the mesh network was established and operated. The delay of the data packet within the network is obtained, and the historical minimum delay value and the historical maximum packet loss rate are updated in real time. After the mesh network is established and operated, the delay of each path and channel is continuously recorded at each time interval. The packet loss rate of the data packet in the network is obtained, and the maximum historical packet loss rate is updated in real time. The minimum historical packet loss rate is set by continuously recording the time interval of each path and channel since the mesh network was established and operated. The packet loss rate of the data packet in the packet is obtained, and the historical minimum packet loss rate is updated in real time.

[0088] Among them, by weighted integration of the three key indicators of delay, packet loss rate and transmission rate, a quantitative channel quality value is obtained. The larger the value, the better the channel quality. The better the overall performance under this path, that is, the smaller the delay, the lower the packet loss rate, and the higher the transmission rate, the more quantitative basis is provided for the selection and optimization of the data transmission path. By comprehensively evaluating the channel quality, channels with high channel quality values can be given priority, thereby ensuring the efficiency, reliability and stability of data transmission, and reducing the impact of delays, packet loss and other problems on the business. Delay directly affects the timeliness of data transmission. The greater the delay, the longer the data arrival time, which may cause business jams, and thus have a negative impact on channel quality; packet loss will cause 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 three values are in the range , to avoid the problem of large differences in parameter influence. This item, 200, represents the upper limit of delay tolerance, which is the end-to-end delay upper limit widely adopted in the industry standard system. This item has the effect of quantifying the remaining delay margin. Delay exceeding this threshold will significantly affect the user experience. Channels for data transmission paths The delay increases, The value becomes smaller, The value becomes smaller, and the channel quality is negatively correlated with the delay. This item is the first Channels for data transmission paths When the packet loss rate increases, The value becomes smaller, The value becomes smaller, and the channel quality is negatively correlated with the packet loss rate. This one, when Channels for data transmission paths When the transmission rate increases, As the value increases, the transmission efficiency is positively correlated with the channel quality.

[0089] It uses a deep packet inspection engine nDPI, which has a large number of predefined protocol rules and feature libraries, covering the protocols used by common real-time video, interactive games, file transfers, 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 them based on the characteristics of these protocols. When network data packets flow through nDPI, it automatically matches the data packets with built-in rules and features to determine the service type, which is real-time video, interactive games, file transfers, IoT data, and web browsing. For example, when running interactive games, it will identify the Blizzard Battle.net protocol, Unity / Unreal engine private protocols, etc., monitor the packet structure and game data unique to the game protocol, analyze traffic patterns such as short packets with high frequency interactions and high UDP proportions, and finally match the server cluster based on IP, such as the game manufacturer's CDN node IP library, to identify the current service type as running interactive games. Real-time statistics for each time interval The actual bandwidth of each data transmission path for different service types is recorded as , based on the bandwidth of each data transmission path and the actual bandwidth of different business types, calculate the bandwidth share of each business type:

[0090] ;

[0091] Where, Indicates the The bandwidth ratio of each service type in the data transmission path;

[0092] When interactive games occupy the highest bandwidth, although players have high requirements for operation response time, a small amount of packet loss can be compensated by the retransmission mechanism, but a large packet loss rate will cause screen tearing or data errors that affect the experience. The weights of the channel quality model are ;

[0093] When real-time video bandwidth accounts for the highest proportion, video stream decoding requires continuous data. Live streaming and other scenarios have extremely high real-time requirements. In order to avoid packet loss that may cause video frame loss, , the weights of the channel quality model are ;

[0094] When the bandwidth of file transfer accounts for the highest proportion, large file transfer can tolerate hundreds of milliseconds of delay and has a retransmission mechanism. The weight is tilted towards the transmission rate for better results. The weights of the channel quality model are ;

[0095] When IoT data has the highest bandwidth share, the sensor data collection cycle is usually in seconds, but the delay is less than 100ms to meet the demand. The loss of IoT device data may cause production accidents, so the weight is the highest. The data volume of a single device is small, the transmission is relatively stable, and the bandwidth demand is not high. The weights of the channel quality model are ;

[0096] When web browsing accounts for the highest bandwidth, based on the need for fast page loading resources, the transmission rate weight should be the highest. Based on the need to reduce TCP connection delay and first byte time, to avoid resource loading failure or retransmission problems caused by packet loss, a certain delay and packet loss rate weight is required. The weights of the channel quality model are .

[0097] Then, the channel corresponding to the highest channel quality result is switched to ensure that the actual user experience better meets the needs of the main business types in the current network. Table 2 shows 40 sets of data for channel quality evaluation in a wireless mesh network. It is used to demonstrate the bandwidth occupancy, weight allocation strategy, and channel quality model calculation results for different business types. Under each data transmission path, the channel quality results are sorted to improve the user experience.

[0098]

[0099] like Figure 2-Figure 4 As shown in the figure, there are statistical graphs of delay and packet loss rate before and after optimization for interactive games, file transfer and real-time video. It can be seen intuitively from the figure that the delay of interactive games and real-time videos after optimization is generally lower than before optimization. For example, for real-time video sample No. 32, the optimization algorithm dynamically adjusts the weights to force the selection of the path with the lowest delay to ensure the continuity and synchronization of the video stream. The delay was high before optimization, but it was significantly reduced after optimization; for interactive game sample No. 8, the optimization algorithm gives priority to low-latency paths and combines them with a predictive channel switching mechanism, which makes the delay high before optimization, but it is significantly reduced after optimization, reflecting the improvement of the delay of interactive games and real-time video by optimization; for file transfer sample No. 16, in view of the high dependence of file transfer on bandwidth, the algorithm gives priority to allocating high-throughput channels. The rate was low before optimization, but it was greatly improved after optimization, reflecting the effect of file transfer rate optimization. Targeted optimization effectively improves user experience.

[0100] Step 3: Build a channel signal-to-interference quantization model based on signal strength and noise strength, 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;

[0101] Signal strength indicates the strength of useful information expected to be received, while noise intensity indicates the strength of useless signals that interfere with normal transmission and reception. Combining these two data points quantifies the degree of signal interference. A decrease in the signal-to-interference ratio directly leads to an increase in the physical layer bit error rate, triggering the link layer ARQ transmission mechanism, ultimately causing increased transport layer latency and packet loss rate. In practice, a channel with a very low signal-to-interference ratio may not show significant changes in metrics such as latency and packet loss rate. However, due to severe interference, these problems are highly likely to occur in the future. Therefore, using this as a separate criterion allows for measures to be taken before a problem actually occurs, enabling early switching to a more appropriate channel to avoid serious impacts on communications.

[0102] Extract Channels for data transmission paths Signal strength , extract the Channels for data transmission paths Noise intensity , build a channel signal-interference quantization model:

[0103] ;

[0104] Where, Indicates the Channels for data transmission paths The signal-to-noise quantization result is The value directly reflects the quality of the channel. It is essentially a standardized expression of the physical layer signal-to-noise ratio, reflecting the relative relationship between the useful signal power and the noise power in the wireless channel. The signal strength is much greater than the noise strength. The closer the value is to 1, the higher the proportion of useful signals in the channel, the better the channel quality, and the higher the reliability and accuracy of signal transmission. The noise strength is relatively large, and the closer the value is to 0, the more serious the signal interference is and the poor the channel quality is, which may lead to problems such as increased signal transmission error rate and data loss.

[0105] Set a signal-to-noise quantization threshold , usually set to The reason is: Shannon's theorem points out that channel capacity is closely related to the signal-to-interference ratio. Under certain bandwidth conditions, to achieve reliable information transmission, a certain signal-to-interference ratio must be guaranteed. Generally speaking, the accuracy and effectiveness of information transmission can only be ensured when the signal-to-interference ratio reaches a certain level. In many theoretical models and practical experience, the value of 0.8 is the basic signal-to-interference ratio level that can ensure that the channel has a certain capacity to support common data transmission rates and quality requirements. That is, the signal-to-interference ratio SNR reaches 6dB, supports basic modulation methods, and can retain more available channels in an interference environment. If there are high real-time requirements and sensitivity to signal quality, the signal-to-interference quantization threshold can be appropriately adjusted. ;

[0106] In channels with low signal-to-interference ratio, since the signal is 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, Calculate the signal-to-interference quantization result in real time and compare it with the signal-to-interference quantization threshold Compare and filter out all signal-to-interference quantization results that are greater than or equal to the signal-to-interference quantization threshold channels to avoid communication interruptions or data transmission errors that affect the actual experience.

[0107] Step 4: Among the available channels that have been screened, the channel with the highest channel quality result is selected as the optimal channel, and a channel switching operation is performed;

[0108] Calculate the channel quality results of each channel of each data transmission path respectively. For example, in a certain time interval, calculate the channel quality of the first When the service type of the data transmission path is 1, the bandwidth of interactive games accounts for the highest proportion, and the signal-to-interference quantization results of all channels of this data transmission path are greater than the signal-to-interference quantization threshold. , then the channel quality result is:

[0109] ;

[0110] Among the data transmission paths, the channel with the largest value in the channel quality results is selected as the optimal channel, and the channel switching operation is performed. Table 3 shows the channel quality results and channel signal-to-interference quantization results of the four channels under the same path over time.

[0111]

[0112] like Figure 5-Figure 6 As shown, the channel quality results and channel signal-to-interference quantization results of channels 1, 6, 11 and 36 at different times are shown, where the left vertical axis corresponds to the channel quality results and the right vertical axis corresponds to the channel signal-to-interference quantization results. Figure 5 The gray star mark corresponds to the channel quality result data of channel 1, the red star mark corresponds to the channel signal-to-interference quantization result data of channel 1, the gray dot mark corresponds to the channel quality result data of channel 6, and the red dot mark corresponds to the channel signal-to-interference quantization result data of channel 6; Figure 6 The gray triangle mark corresponds to the channel quality result data of channel 11, the red triangle mark corresponds to the channel signal-to-interference quantization result data of channel 11, the gray square mark corresponds to the channel quality result data of channel 36, and the red square mark corresponds to the channel signal-to-interference quantization result data of channel 36. By analyzing the channel signal-to-interference quantization results, it is possible to identify channels that are higher than the signal-to-interference quantization threshold. By avoiding channels with relatively high noise intensity in advance, communication interruptions that affect the user experience are avoided. The signal-to-interference quantization results of channel 36 in the 5GHz band are generally higher than 0.8, indicating less interference. Channels 1 and 11 have excessive interference and their channel quality results are also poor. Therefore, channel 36 is the preferred channel for wireless mesh networks to improve network stability.

[0113] See also 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:

[0114] A performance parameter collection module is used to assign a unique identification code to each node based on the network topology, record the data transmission path and corresponding channel allocation from the data sending node to the receiving node, 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;

[0115] The channel quality assessment module extracts latency, packet loss rate, and transmission rate data from the performance parameter table, combines bandwidth with weighted summation to construct a channel quality model for each data transmission path, and outputs the channel quality results. It uses traffic analysis tools to identify the service type with the highest bandwidth usage and dynamically adjusts the weights.

[0116] The signal-to-noise quantization calculation module is used to build a channel signal-to-noise quantization model based on signal strength and noise strength, set a signal-to-noise quantization threshold, and select channels with signal-to-noise quantization results higher than the signal-to-noise quantization threshold as available channels;

[0117] The optimal channel switching module is used to select the channel with the highest channel quality result as the optimal channel among the available channels screened out, and perform a channel switching operation.

[0118] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0119] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination thereof. 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 will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software depends on the specific application and design constraints of the technical solution.

[0120] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, and may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment as needed.

[0121] The above is only a specific implementation method of the present application, but the scope of protection of the present application is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed in this application, which should be covered by the scope of protection 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 based on the network topology, record the data transmission path and corresponding channel allocation from the data sending node to the data receiving node, 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; Step 2: Extract the latency, packet loss rate, and transmission rate data from the performance parameter table. Combined with the bandwidth, the data is weighted and summed to construct a channel quality model for each data transmission path. The channel quality results are output. Traffic analysis tools are used to identify the service type with the highest bandwidth usage and dynamically adjust the weight. Step 3: Build a channel signal-to-interference quantization model based on signal strength and noise strength, 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 available channels that have been screened, the channel with the highest channel quality result is selected as the optimal channel, and a channel switching operation is performed; Implementation Extraction of latency, packet loss rate, transmission rate, and bandwidth data to build a channel quality model for each data transmission path channel: Where Q i,j represents the channel quality of channel j of the i-th data transmission path, v i,j represents the transmission rate of channel j of the i-th data transmission path, T i,j,min represents the minimum historical delay of channel j on the i-th data transmission path, P loss，i，j represents the packet loss rate of channel j in the i-th data transmission path, P loss，max Indicates the maximum historical packet loss rate, P loss，min Indicates the minimum historical packet loss rate, T delay，i，j represents the delay of channel j of the i-th data transmission path, ω1, ω2, ω3 represent weight coefficients, and ω1+ω2+ω3=1, ω1>0, ω2>0, ω3>0.

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 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 of the Mesh network, a unique node ID is assigned to each node, and the data transmission path and corresponding channel allocation from each data sending node to the data receiving node are recorded. The data transmission path is marked as i, i = 1, 2, ..., n, n represents the total number of data transmission paths, and the corresponding channel is marked as j.

3. The wireless mesh adaptive channel selection method according to claim 2, wherein: The method for collecting the performance parameters of each channel on each path at fixed time intervals is: At each fixed time interval t c In this case, the data sending node sends a probe data packet to the next node, records the round-trip time (RTT) of the data packet from the sender to the next node and then back to the sender, and calculates the delay of each data transmission path: Where, T delay，i，j represents the delay of channel j of the i-th data transmission path; Calculate the packet loss rate: Where, P loss,i,j represents the packet loss rate of channel j in the i-th data transmission path, N send Indicates the total number of data packets sent by the data sending node, N recv Indicates the total number of packets received; Calculate the transfer rate: Where, v i，j represents the transmission rate of channel j of the i-th data transmission path, and M represents the fixed time interval t c The amount of data passing through the i-th data transmission path; Through the router management interface, obtain the bandwidth of each data transmission path and record it as B i ,Using R&S CMX500 wireless signal tester, the signal strength of different channels of each data transmission path is obtained and recorded as S i,j , the noise intensity of different channels of each data transmission path is obtained by FSW spectrum analyzer and recorded as N i,j .

4. The wireless mesh adaptive channel selection method according to claim 1, wherein: Use traffic analysis tools to identify the service type with the highest bandwidth usage and dynamically adjust its weight: 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 in each data transmission path in each time interval is counted in real time, recorded as BW i , based on the bandwidth of each data transmission path and the actual bandwidth of different business types, calculate the bandwidth share of each business type: Where B type，i represents the bandwidth share of each service type on the i-th data transmission path; When the bandwidth of interactive games accounts for the highest proportion, the weights of the channel quality model are ω1 = 0.5, ω2 = 0.3, and ω3 = 0.2; When the bandwidth of real-time video accounts for the highest proportion, the weights of the channel quality model are ω1 = 0.7, ω2 = 0.2, and ω3 = 0.1; When the bandwidth share of file transfer is the highest, the weights of the channel quality model are ω1 = 0.1, ω2 = 0.1, and ω3 = 0.8; When IoT data has the highest bandwidth share, the weights of the channel quality model are ω1 = 0.3, ω2 = 0.5, and ω3 = 0.

2. When web browsing accounts for the highest bandwidth, the weights of the channel quality model are ω1 = 0.2, ω2 = 0.2, and ω3 = 0.

6.

5. The wireless mesh adaptive channel selection method according to claim 3, wherein: The method for constructing a channel signal-to-interference quantization model based on signal strength and noise strength is: Extract the signal strength S of channel j of the i-th data transmission path i，j , extract the noise intensity N of channel j of the i-th data transmission path i，j , build a channel signal-interference quantization model: Where, E i，j Represents the signal-to-interference quantization result of channel j of the i-th data transmission path.

6. The wireless mesh adaptive channel selection method according to claim 1, wherein: The method for screening out channels whose signal-to-interference quantization results are higher than the signal-to-interference quantization threshold as available channels is: First, a signal-to-interference quantization threshold K is set. For each channel in each data transmission path, the signal-to-interference quantization result is calculated in real time and compared with the signal-to-interference quantization threshold K. All channels with signal-to-interference quantization results greater than or equal to the signal-to-interference quantization threshold K are screened out as available channels.

7. A wireless mesh adaptive channel selection system, characterized by: The system is used to execute the wireless mesh adaptive channel selection method according to any one of claims 1 to 6, comprising: A performance parameter collection module is used to assign a unique identification code to each node based on the network topology, record the data transmission path and corresponding channel allocation from the data sending node to the receiving node, 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; The channel quality assessment module extracts latency, packet loss rate, and transmission rate data from the performance parameter table, combines bandwidth with weighted summation to construct a channel quality model for each data transmission path, and outputs the channel quality results. It uses traffic analysis tools to identify the service type with the highest bandwidth usage and dynamically adjusts the weights. The signal-to-noise quantization calculation module is used to build a channel signal-to-noise quantization model based on signal strength and noise strength, set a signal-to-noise quantization threshold, and select channels with signal-to-noise quantization results higher than the signal-to-noise 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 among the available channels screened out, and perform a channel switching operation.

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