Modulation coding schemes and spatial stream number prediction methods, systems, equipment and media

Abstract

This invention relates to a modulation and coding scheme and a method, system, device, and medium for predicting spatial stream counts. The method includes: acquiring received signal strength indication data and preset threshold information between at least two access points and a site; determining the synchronous / asynchronous communication status between each access point based on the dynamic relationship between the received signal strength indication data and the preset threshold information; classifying signals transmitted from adjacent access points to the site as interference signals and signals transmitted from adjacent sites as ambient noise based on the synchronous / asynchronous communication status; calculating the signal-to-noise ratio (SNR) of the site based on the signal classification results; inputting the SNR, synchronous / asynchronous communication status, and access point transmit power into a gradient boosting decision tree model; and outputting the modulation and coding scheme and spatial stream count prediction results of the target access point through the gradient boosting decision tree model. This method significantly improves the MCS / NSS prediction accuracy of high-density WLANs.

Description

Technical Field

[0001] This invention relates to the field of wireless communication and machine learning technologies, specifically to a modulation and coding scheme and a method, system, device and medium for predicting spatial stream numbers. Background Technology

[0002] With the widespread adoption of high-density wireless local area networks (WLANs), especially the demand for multi-access point (AP) deployments in the next-generation Wi-Fi 7 standard, the problem of inaccurate throughput prediction due to signal interference and channel contention is becoming increasingly prominent. In dense AP deployment scenarios, overlapping signal coverage between adjacent APs, increased interference between stations (STAs), and dynamic changes in channel conditions lead to the following shortcomings in traditional throughput prediction:

[0003] 1. Existing methods rely on large-scale channel information (such as mean and peak values) such as Received Signal Strength Indication (RSSI) between nodes, but RSSI cannot accurately reflect the rapidly changing channel quality;

[0004] 2. Failure to distinguish the type of interference source (such as the noise difference between adjacent APs and STAs) and confuses the ambient noise with the interference from neighboring cells, resulting in a distortion of the signal-to-noise ratio (SINR) calculation.

[0005] 3. It ignores the dynamic impact of synchronous / asynchronous communication states between APs (such as synchronous transmission, asynchronous transmission, and mixed transmission) on interference modes, and cannot adapt to the complex competition mechanisms in high-density environments.

[0006] The aforementioned defects directly result in insufficient prediction accuracy of MCS (modulation and coding scheme) and NSS (spatial stream number). As the core parameters of physical layer transmission efficiency, the prediction deviation of MCS / NSS will significantly reduce the reliability of throughput assessment, thereby hindering network optimization decisions (such as channel allocation and power control). Summary of the Invention

[0007] This invention provides a method, system, device, and medium for predicting modulation and coding schemes (MCS) and spatial stream numbers (NSS) in high-density wireless local area networks. The purpose is to solve the problem that the prediction accuracy of MCS and NSS is insufficient due to signal interference and dynamic channel changes, which in turn affects the accurate prediction of throughput.

[0008] To achieve the above objectives, the first aspect of the present invention provides a modulation and coding scheme and a method for predicting spatial stream numbers, comprising the following steps:

[0009] Acquire received signal strength indication data and preset threshold information between at least two access points and the site;

[0010] Based on the dynamic relationship between the received signal strength indication data and the preset threshold information, the synchronous / asynchronous communication status between each access point is determined.

[0011] Based on the aforementioned synchronous / asynchronous communication status, signals sent from adjacent access points to the station are classified as interference signals, and signals sent from adjacent stations are classified as environmental noise.

[0012] The signal-to-noise ratio of the station is calculated based on the signal classification results;

[0013] The signal-to-noise ratio, synchronous / asynchronous communication status, and access point transmit power are input into the gradient boosting decision tree model;

[0014] The gradient boosting decision tree model outputs the modulation and coding scheme and spatial stream number prediction results for the target access point.

[0015] Furthermore, methods for determining the synchronous / asynchronous communication status between access points include:

[0016] For the downlink data signal of each access point, extract the maximum value and average value of its received signal strength indicator data;

[0017] When the maximum value of the received signal strength indication data of the downlink data signal of the first access point and the downlink data signal of the second access point does not reach the packet detection threshold and the average value of the received signal strength indication data does not reach the network allocation vector threshold, it is determined that the two access points are in a synchronous communication state.

[0018] When only one of the downlink data signals from the first access point and the second access point reaches the maximum value of the received signal strength indicator data or the average value of the received signal strength indicator data reaches the network allocation vector threshold, it is determined that the two access points are in an asynchronous communication state.

[0019] When the maximum value of the received signal strength indication data of the downlink data signal of the first access point and the downlink data signal of the second access point both reach the packet detection threshold or the average value of the received signal strength indication data both reach the network allocation vector threshold, it is determined that the two access points are in a mixed communication state.

[0020] Furthermore, the signal classification rules are as follows:

[0021] Interference signals are defined as signals originating from non-associated access points;

[0022] Ambient noise floor is defined as the signal originating from a non-target site.

[0023] Furthermore, the signal-to-noise ratio of the station is calculated:

[0024] When APs are in a synchronized state, the signal-to-noise ratio calculation ignores the ambient noise level.

[0025] When APs are in an asynchronous state, the signal-to-noise ratio calculation ignores the strength of interference signals;

[0026] When APs are in a mixed synchronous and asynchronous state, the linear power values ​​of the aggregated environmental noise floor and interference signals are calculated.

[0027] Furthermore, methods for aggregating the linear power values ​​of ambient noise floor and interference signals include:

[0028] Convert all signal strength values ​​to be aggregated from dBm power units to mW units;

[0029] Sum the converted mW unit values;

[0030] Convert the summation result back to dBm units.

[0031] Furthermore, training methods for gradient boosting decision tree models include:

[0032] Using the synchronous / asynchronous communication status, access point transmit power, and signal-to-noise ratio as input features, and the modulation and coding scheme and spatial stream number combination after convergence of the adaptive modulation algorithm as output labels, a training dataset is constructed.

[0033] The gradient boosting decision tree algorithm is used to train the training dataset with 5-fold cross-validation, and the model parameters are optimized by minimizing the mean square error between the predicted value and the output label.

[0034] Furthermore, during the training process of the gradient boosting decision tree model:

[0035] When there are two access points, the signal-to-noise ratio is used as the dominant feature.

[0036] When the number of access points is three or more, increase the weighting factor for the interference signal strength.

[0037] To achieve the above objectives, a second aspect of the present invention provides a modulation and coding scheme and a spatial stream number prediction system, comprising the following modules:

[0038] The data acquisition module is used to acquire received signal strength indication data and preset threshold information between at least two access points and the site;

[0039] The status determination module is used to determine the synchronous / asynchronous communication status between each access point based on the dynamic relationship between the received signal strength indication data and the preset threshold information;

[0040] The signal classification module is used to classify signals sent from adjacent access points to the station as interference signals and signals sent from adjacent stations as environmental noise based on the synchronous / asynchronous communication status.

[0041] The signal-to-noise ratio (SNR) calculation module is used to calculate the SNR of a station based on the signal classification results.

[0042] The model input module is used to input the signal-to-noise ratio, synchronous / asynchronous communication status, and access point transmit power into the gradient boosting decision tree model;

[0043] The prediction output module is used to output the modulation and coding scheme and spatial stream number prediction results of the target access point through the gradient boosting decision tree model.

[0044] To achieve the above objectives, a third aspect of the present invention provides an electronic device including a memory and a processor, the memory being used to store a program that supports the processor in executing the modulation and coding scheme and the spatial stream number prediction method, and the processor being configured to execute the program stored in the memory.

[0045] To achieve the above objectives, a fourth aspect of the present invention provides a computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, performs the steps of the modulation and coding scheme and the spatial stream number prediction method.

[0046] The beneficial effects of this invention are:

[0047] Compared with existing technologies, the present invention provides a modulation and coding scheme and a method, system, device, and medium for predicting spatial stream numbers. By constructing a "dynamic state discrimination - precise interference separation - scenario-based SINR modeling - multi-feature intelligent prediction" system, it significantly improves the MCS / NSS prediction accuracy of high-density WLANs. First, based on the real-time relationship between RSSI and dynamic thresholds (PD / ED / NAV), it accurately determines the synchronous, asynchronous, or mixed transmission states between APs, solving the problem of dynamic adaptation of the competition mechanism. Second, it distinguishes the types of interference sources—classifying adjacent AP signals as directional interference and defining adjacent STA signals as environmental noise floor, overcoming the problem of SINR distortion caused by interference mixing. Furthermore, it dynamically selects the SINR calculation model according to the communication state (only noise floor is counted in synchronous state, only interference is counted in asynchronous state, and multi-source interference is aggregated through the power superposition formula in mixed state), breaking through the limitations of large-scale RSSI information. Finally, it integrates features such as SINR, synchronous / asynchronous state, and AP transmit power, and outputs the prediction results through a gradient boosting decision tree model. Attached Figure Description

[0048] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below.

[0049] Figure 1 This is a flowchart of a modulation coding scheme and a spatial stream number prediction method disclosed in an embodiment of the present invention.

[0050] Figure 2 This is a logic diagram of a modulation coding scheme and spatial stream number prediction method disclosed in an embodiment of the present invention.

[0051] Figure 3 This is a diagram of a 2AP sending downlink data to an associated STA, as disclosed in an embodiment of the present invention.

[0052] Figure 4 This is a flowchart of a synchronous / asynchronous state discrimination method for 2AP disclosed in an embodiment of the present invention.

[0053] Figure 5 This is a classification diagram of 3AP interference signals and environmental noise floor disclosed in an embodiment of the present invention.

[0054] Figure 6 This is a flowchart of a synchronous / asynchronous state discrimination process for 3AP disclosed in an embodiment of the present invention.

[0055] Figure 7 This is a synchronous / asynchronous state diagram between two APs in an embodiment of the present invention.

[0056] Figure 8 This is a classification diagram of interference signals and environmental noise with two access points (APs) disclosed in an embodiment of the present invention.

[0057] Figure 9 This is a synchronous / asynchronous state diagram between any two APs in a set of three APs disclosed in an embodiment of the present invention.

[0058] Figure 10 This is a diagram illustrating a calculation formula for modifying the signal-to-noise ratio when there are two access points (APs) as disclosed in an embodiment of the present invention.

[0059] Figure 11 This is a schematic diagram of gradient boosting tree training disclosed in an embodiment of the present invention.

[0060] Figure 12 This is a prediction accuracy analysis chart for an AP quantity of 2 disclosed in an embodiment of the present invention.

[0061] Figure 13 This is an accuracy prediction chart of MCS and NSS with 2 APs disclosed in an embodiment of the present invention.

[0062] Figure 14 This is an importance ranking chart for APs with a quantity of 2, as disclosed in an embodiment of the present invention.

[0063] Figure 15 This is a prediction accuracy map with 3 APs disclosed in an embodiment of the present invention.

[0064] Figure 16This is an accuracy prediction chart of MCS and NSS with 3 APs disclosed in an embodiment of the present invention.

[0065] Figure 17 This is an importance ranking chart for APs with a quantity of 3, as disclosed in an embodiment of the present invention. Detailed Implementation

[0066] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0067] According to embodiments of the present invention, it should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the following manufacturing method, in some cases the steps shown or described may be performed in a different order than that shown here.

[0068] like Figure 1 , Figure 2 As shown, this invention provides a modulation and coding scheme and a method for predicting spatial stream numbers, comprising the following steps:

[0069] Step S100: Obtain received signal strength indication data and preset threshold information between at least two access points and the site;

[0070] This step involves acquiring physical layer data in a high-density WLAN environment through actual measurements. Specifically, the data collected includes Received Signal Strength Indication (RSSI) data and preset threshold information between at least two Access Points (APs) and their associated Stations (STAs) within the deployment area. The RSSI data includes measured values ​​of signal strength (sum, max, and mean) recorded during AP-STA communication. The preset threshold information includes dynamically configured parameters such as Packet Detection Threshold (PD), Energy Detection Threshold (ED), and Network Allocation Vector Threshold (NAV).

[0071] Step S200: Determine the synchronous / asynchronous communication status between each access point based on the dynamic relationship between the received signal strength indication data and the preset threshold information;

[0072] Each AP sends downlink data only to its associated specific STA (e.g., AP0→STA0, AP1→STA1), such as Figure 3As shown, in a 2AP scenario, four directed communication links (including uplink and downlink) are formed, corresponding to four sets of RSSI data:

[0073] Downlink data sent from AP0 to STA0;

[0074] Uplink data sent from STA0 to AP0;

[0075] Downlink data sent from AP1 to STA1;

[0076] Uplink data sent from STA1 to AP1.

[0077] like Figure 4 As shown, in a wireless communication system containing two access points (APs), the core criterion for determining their communication status (synchronous or asynchronous) is to analyze the downlink data sent by each AP to its associated station (STA) (such as...). )and( The measured value of Received Signal Strength Indication (RSSI).

[0078] The experimentally measured signal strength includes three key indicators: sum, max, and mean. When determining the communication status between two access points (AP0 and AP1), these are based on their downlink data. )and The determination is made based on whether the RSSI value meets the threshold condition:

[0079] like and If the measured signal strengths do not meet the conditions (i.e., the max value is not in the interval [PD,ED] and the mean value is not in the interval [NAV,ED]), then it is determined to be a synchronous transmission state.

[0080] If there is only one downlink signal or If the condition is met (i.e., the max value is in [PD, ED] or the mean value is in [NAV, ED]), then it is determined to be an asynchronous transmission state;

[0081] If both downlink signals meet the conditions (i.e., both max values ​​are located in [PD, ED] or both mean values ​​are located in [NAV, ED]), then it is determined to be a synchronous-asynchronous mixed state.

[0082] By dynamically comparing the peak intensity (max) and overall level (mean) of the downlink signal with respect to preset thresholds (PD, ED, NAV), the communication status classification between the two APs is finally output.

[0083] In 3AP scenarios ( Figure 5 This was expanded into 6 directed links (bidirectional interaction between AP0-STA0, AP1-STA1, and AP2-STA2), using... Figure 5 The six directed edges (solid lines) represent the number of access points (APs). For the case with three access points (APs), two APs are selected from the three, and the synchronous / asynchronous mixed state is determined using the analysis method for two APs, as detailed below. Figure 6 As shown, for a communication scenario involving three access points, the methods for determining the synchronous / asynchronous communication status between the access points include:

[0084] First, label the three access points as the first access point, the second access point, and the third access point;

[0085] Two access points are selected sequentially to form access point pairs, including: a first access point and a second access point, a first access point and a third access point, and a second access point and a third access point. For each access point pair, the following discrimination operation is performed:

[0086] Synchronous transmission status determination: When the API's downlink data signal Downlink data signal with APJ When the received signal strength indication values ​​do not meet any of the following conditions:

[0087] 1. The maximum signal strength is located within the interval formed by the packet detection threshold and the energy detection threshold;

[0088] 2. If the average signal strength is within the range formed by the network allocation vector threshold and the energy detection threshold, then API and APJ are determined to be in synchronous transmission state.

[0089] Asynchronous transmission status determination: When only one of the downlink data signals of API and APJ satisfies any of the following conditions:

[0090] 1. The maximum signal strength meets the packet detection threshold requirement;

[0091] 2. The average signal strength meets the network allocation vector threshold requirement;

[0092] 3. Then it is determined that the API and APJ are in an asynchronous transmission state.

[0093] Hybrid transmission status determination: When the received signal strength indication values ​​of both the API downlink data signal and the APJ downlink data signal meet any of the following conditions:

[0094] 1. The maximum signal strength meets the packet detection threshold requirement;

[0095] 2. The average signal strength meets the network allocation vector threshold requirement;

[0096] 3. Then it is determined that the API and APJ are in a state of synchronous and asynchronous mixed transmission.

[0097] Finally, by combining the communication status categories of all access point pairs, an overall network communication status determination result is generated, including the three access points, such as... Figure 7 As shown.

[0098] Step S300: Based on the synchronous / asynchronous communication status, classify the signals sent from adjacent access points to the station as interference signals, and classify the signals sent from adjacent stations as environmental noise.

[0099] When classifying the scenarios with two access points (APs), we first analyze the characteristics of synchronous transmission, asynchronous transmission, and mixed synchronous / asynchronous transmission. In synchronous transmission, APs listen to each other in real time, and most of the interference when a STA receives data comes from ambient noise. In asynchronous transmission, APs do not listen to each other at all, and the STA ignores ambient noise, receiving only interference signals. In mixed synchronous / asynchronous transmission, APs alternate between real-time and non-listening to each other, and the STA receives both ambient noise and interference signals. Based on data analysis and communication principles, for a given STA node, signals from neighboring AP nodes are interference signals, and signals from other neighboring STA sites are ambient noise, such as... Figure 8 As shown.

[0100] For the case with 3 access points (APs), the classification of interference signals and ambient noise is performed using the same method as for the case with 2 APs, thus obtaining the classification of interference signals and ambient noise. (See...) Figure 9 As shown.

[0101] Step S400: Calculate the signal-to-noise ratio of the station based on the signal classification results;

[0102] For the case where there are two access points (APs), based on the classification of interference signals and ambient noise, the signal-to-noise ratio (SINR) calculation model of the station (STA) is dynamically adjusted according to the synchronous / asynchronous communication status between access points. The SINR calculation method is as follows:

[0103] SINR = P signal -(P noise +P inter )

[0104] Among them, P signal Indicates the useful signal (corresponding to) Figure 8 (The black solid line in the middle), P noise Indicates ambient noise level (corresponding to) Figure 8 (green dashed line in the middle), P inter Indicates interference signal (corresponding to) Figure 8(The red dashed line in the diagram). Based on the three scenarios of synchronous / asynchronous states between the two APs, the signal-to-noise ratio calculation formula is modified as follows: Figure 10 .

[0105] Synchronous Communication Status: When two access points (AP0 and AP1) are transmitting synchronously, the signal-to-noise ratio (SNR) calculation for a station (STA) only considers the ambient noise floor power (P). noise The formula is: SINR = P Signal -P noise .

[0106] Asynchronous communication state: When two access points are in asynchronous transmission, the signal-to-noise ratio calculation only considers the interference signal power (P). inter The formula is: SINR = P Signal -P inter .

[0107] Hybrid Communication State: When two access points are in a hybrid synchronous-asynchronous transmission state, the linear power values ​​(in mW) of the ambient noise floor and interference signals need to be aggregated simultaneously. The formula is: SINR = P Signal -P noise -P inter .

[0108] A network topology with 3 access points (APs) is essentially a directed complete graph consisting of 6 nodes and 30 directed edges. Therefore, the difference in SINR calculation compared to a network with 2 APs lies in the aggregation process of multiple signals.

[0109] Taking a scenario with 3 APs (Analog and Access Points) interacting in a hybrid synchronous and asynchronous manner as an example, when summing the signals in the following formula, it is necessary to first separate each sub-signal (P, expressed in dBm) into its corresponding sub-signal. inter or P noise The power is converted to linear power in mW, then the linear power of all interfering signals is summed, and finally converted back to dBm. Therefore, the formula for calculating the cumulative strength of n signals is:

[0110]

[0111] Among them, P total_n This represents the cumulative intensity of n signals, P1...P... n This represents the signal strength of each of the n signals. Figure 9 Taking station STA0 as an example, the signal-to-noise ratio calculation formula for this station at a certain moment is:

[0112]

[0113] in, Indicates the effective signal strength transmitted from AP0 to STA0 (solid black line), Penv1 The ambient noise level (red dashed line) from the STA1 site is represented by P. inter1 This indicates the interference signal from AP1 (green dashed line), P inter2 The green dashed line indicates interference signals from AP2.

[0114] Step S500: Input the signal-to-noise ratio, synchronous / asynchronous communication status, and access point transmit power into the gradient boosting decision tree model;

[0115] Based on prediction models with 2 and 3 access points (APs), the calculated signal-to-noise ratio (SNR) of the target area (STA) is combined with some basic test information as input to obtain the output (MSC, NSS). Figure 11 The gradient boosting decision tree model training process is shown. The model input layer consists of three types of features: site signal-to-noise ratio features (env_SINR: signal-to-noise ratio calculated based on ambient noise floor power, ferr_SINR: signal-to-noise ratio calculated based on interference signal power), access point status features (categ_0, categ_1, categ_2: three-dimensional classification variables representing the synchronous / asynchronous communication status between APs), and test environment basic features (RSSI: measured value of received signal strength indication, eirp: access point transmit power, nav: network allocation vector threshold value, loc_id: test location identifier, ap_id: access point device identifier).

[0116] Step S600: Output the modulation and coding scheme and spatial stream number prediction results of the target access point through the gradient boosting decision tree model.

[0117] The model output layer contains the target prediction term:

[0118] Modulation and coding scheme (MCS): The most commonly used modulation and coding scheme after the predictive AP adaptive adjustment stabilizes;

[0119] Space Flow Count (NSS): The number of space flows most frequently used after the predicted AP adaptive adjustment stabilizes.

[0120] according to Figure 11 The gradient boosting decision tree algorithm model was trained to predict (MSC, NSS) combinations with 2 environments and 3 environments, respectively.

[0121] For five training datasets with two APs each, 5-fold cross-validation was used to fully train the model and also to verify its effectiveness.

[0122] exist Figure 12In the diagram, the horizontal axis represents the actual NSS or MOS values, and the vertical axis represents the model's predicted values. When a node is located on the diagonal line passing through the origin, it indicates that the prediction is accurate. The color intensity of the node indicates the number of training data points at that point; darker colors on the diagonal indicate higher prediction accuracy. Therefore, it is intuitive to see that the model has high accuracy.

[0123] Secondly, regarding the prediction accuracy of the statistical model, out of 390 training data points, 336 data points accurately predicted the MCS, achieving an accuracy rate of 86.15%. Figure 13 (a)), MSE is 0.6518; 376 NSSs were accurately predicted, with an accuracy of 96.41%. Figure 13 (b) The MSE is 0.5128. Furthermore, the trained gradient boosting decision tree algorithm model can provide a ranking of the importance of influences (MOSNSS).

[0124] Finally, through Figure 14 Based on the ranking of importance of (a) and (b), the following conclusions can be drawn:

[0125] The signal-to-noise ratio (SNR) of the STA has the greatest impact on (MOS, NSS). Reason: The SINR calculated from the ambient noise floor ("env_SINR") and the SINR calculated from the interference signal ("ferr SINR") rank first and third respectively, indicating that the SNR of the STA has the greatest impact on (MCS, NSS).

[0126] In the composition of signal-to-noise ratio (SNR), noise from other STAs is more important than noise from other APs. Reason: In the importance ranking of factors affecting MCS, ambient noise floor increases the impact of interference noise by approximately 50%; while in the importance ranking of factors affecting NSS, ambient noise floor increases the impact of interference noise by approximately 160%. AP transmit power has a relatively significant impact on (MCS, NSS). Reason: In the importance ranking of factors affecting both MCS and NSS, AP transmit power "eirp" ranks second. Threshold information, and the synchronous / asynchronous state information determined by the threshold, have a relatively small impact on (MCS, NSS). Reason: The influence of changes in the NAV threshold and synchronous / asynchronous state information (represented by category_0, category_1, category_2) is ranked low.

[0127] By implementing a 5-fold cross-validation strategy on eight training_sets (each with three APs) of the loc_nav.csv dataset, 858 training data points can be generated. This method not only ensures that the model is adequately trained and covers data diversity, but also rigorously evaluates the model's performance on different subsets through cross-validation, thereby verifying the model's effectiveness and generalization ability.

[0128] from Figure 15 The distribution of the displayed data points cleverly uses varying shades of color to reflect their density and importance, while their positions directly correspond to the relationship between the model's predicted (MCS, NSS) values ​​and the actual measured values. Most data points are closely clustered around the actual measured values ​​and are evenly distributed, indicating consistency between the model's predictions and the measured data. Therefore, it can be concluded that the model demonstrates high accuracy in predicting (MCS, NSS).

[0129] Secondly, regarding the prediction accuracy of the statistical model, out of 858 training data points, 830 correctly predicted NSS, achieving an accuracy rate of 96.74%. Figure 16 (b) The MSE is 0.04315; 559 data points accurately predict the MCS, with an accuracy of 65.16%. Figure 16 (a) The MSE is 1.8949. Although the prediction accuracy is not high when the number of APs is 3, the low MSE indicates that although there is a small bias in the prediction, the prediction result is still relatively accurate. Figure 16 As shown. Furthermore, the trained gradient boosting decision tree algorithm model can provide an importance ranking of the influence (MCS, NSS) in three environments with AP, such as... Figure 17 As shown in (a) and (b).

[0130] Based on the importance ranking of the three influences (MCS, NSS) for APs, the following conclusions can be drawn:

[0131] The signal-to-noise ratio (SNR) of the STA has the greatest impact on (MOS, NSS). Reason: The SINR calculated from the ambient noise floor, "env_SINR," and the SINR calculated from the interference signal, "ferrSINR," rank first and second respectively, indicating that the SNR of the STA has the greatest impact on (MCS, NSS).

[0132] In an environment with 3 access points (APs), the importance of interference noise increases compared to an environment with 2 APs. The reason is that, in the ranking of the importance of MCS and NSS, the importance values ​​of ambient noise and interference noise are basically the same, with ambient noise being slightly more important than interference noise.

[0133] The AP's transmit power has a significant impact on (MCS, NSS). Reason: In the ranking of factors affecting MOS and NSS, AP transmit power "eirp" ranks fourth and third, respectively. Threshold information, and the synchronous / asynchronous state information determined by the threshold, have a relatively small impact on (MCS, NSS). Reason: Changes in the NAV threshold and synchronous / asynchronous state information (represented by category_0, category_1, category2) have a lower impact.

[0134] Using the gradient boosting decision tree algorithm for training, and with 5-fold cross-validation, the following results were obtained: When the number of APs is 2, the accuracy of NSS prediction is 96.4%, and the accuracy of MOS prediction is 86.2%; when the number of APs is 3, the accuracy of NSS prediction is 96.7%, and the accuracy of MOS prediction is 65.2%. Model validation shows that for the prediction of (MOS, NSS), the accuracy with 2 APs is higher than that with 3 APs. Although the accuracy of prediction with 3 APs is not high, the mean squared error (MSE) is only 1.89, which is relatively small. Most of the incorrectly predicted MCSs are not significantly different from the true MCSs, indicating that the prediction model is effective.

[0135] The gradient boosting decision tree algorithm prediction model trained using the training set predicts the (MCS, NSS) results of the test set, as shown in Tables 1 and 2.

[0136] Table 1: Prediction results (MCS, NSS) when the number of APs is 2

[0137]

[0138]

[0139]

[0140] Table 2: Prediction results (MCS, NSS) when the number of APs is 3

[0141]

[0142]

[0143] According to another aspect of the embodiments of this application, a modulation coding scheme and a spatial stream number prediction system are also provided, including the following modules:

[0144] The data acquisition module is used to acquire received signal strength indication data and preset threshold information between at least two access points and the site;

[0145] The status determination module is used to determine the synchronous / asynchronous communication status between each access point based on the dynamic relationship between the received signal strength indication data and the preset threshold information;

[0146] The signal classification module is used to classify signals sent from adjacent access points to the station as interference signals and signals sent from adjacent stations as environmental noise based on the synchronous / asynchronous communication status.

[0147] The signal-to-noise ratio (SNR) calculation module is used to calculate the SNR of a station based on the signal classification results.

[0148] The model input module is used to input the signal-to-noise ratio, synchronous / asynchronous communication status, and access point transmit power into the gradient boosting decision tree model;

[0149] The prediction output module is used to output the modulation and coding scheme and spatial stream number prediction results of the target access point through the gradient boosting decision tree model.

[0150] According to another aspect of the embodiments of this application, an electronic device is also provided, including a processor and a memory, wherein the processor is configured to implement the steps of the method when executing a computer program stored in the memory.

[0151] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0152] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0153] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0154] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0155] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A modulation coding scheme and a method for predicting spatial stream numbers, characterized in that, Includes the following steps: Acquire received signal strength indication data and preset threshold information between at least two access points and the site; Based on the dynamic relationship between the received signal strength indication data and the preset threshold information, the synchronous / asynchronous communication status between each access point is determined. Based on the aforementioned synchronous / asynchronous communication status, signals sent from adjacent access points to the station are classified as interference signals, and signals sent from adjacent stations are classified as environmental noise. The signal-to-noise ratio of the station is calculated based on the signal classification results; The signal-to-noise ratio, synchronous / asynchronous communication status, and access point transmit power are input into the gradient boosting decision tree model; The gradient boosting decision tree model outputs the modulation and coding scheme and spatial stream number prediction results for the target access point.

2. The modulation coding scheme and spatial stream number prediction method as described in claim 1, characterized in that, Methods for determining the synchronous / asynchronous communication status between access points include: For the downlink data signal of each access point, extract the maximum value and average value of its received signal strength indicator data; When the maximum value of the received signal strength indication data of the downlink data signal of the first access point and the downlink data signal of the second access point does not reach the packet detection threshold and the average value of the received signal strength indication data does not reach the network allocation vector threshold, it is determined that the two access points are in a synchronous communication state. When only one of the downlink data signals from the first access point and the second access point reaches the maximum value of the received signal strength indicator data or the average value of the received signal strength indicator data reaches the network allocation vector threshold, it is determined that the two access points are in an asynchronous communication state. When the maximum value of the received signal strength indication data of the downlink data signal of the first access point and the downlink data signal of the second access point both reach the packet detection threshold or the average value of the received signal strength indication data both reach the network allocation vector threshold, it is determined that the two access points are in a mixed communication state.

3. The modulation coding scheme and spatial stream number prediction method as described in claim 1, characterized in that, The signal classification rules are as follows: Interference signals are defined as signals originating from non-associated access points; Ambient noise floor is defined as the signal originating from a non-target site.

4. The modulation coding scheme and spatial stream number prediction method as described in claim 1, characterized in that, Calculate the signal-to-noise ratio (SNR) of the station: When APs are in a synchronized state, the signal-to-noise ratio calculation ignores the ambient noise level. When APs are in an asynchronous state, the signal-to-noise ratio calculation ignores the strength of interference signals; When APs are in a mixed synchronous and asynchronous state, the linear power values ​​of the aggregated environmental noise floor and interference signals are calculated.

5. The modulation coding scheme and spatial stream number prediction method as described in claim 4, characterized in that, Methods for aggregating the linear power values ​​of ambient noise floor and interference signals include: Convert all signal strength values ​​to be aggregated from dBm power units to mW units; Sum the converted mW unit values; Convert the summation result back to dBm units.

6. The modulation coding scheme and spatial stream number prediction method as described in claim 1, characterized in that, Training methods for gradient boosting decision tree models include: Using the synchronous / asynchronous communication status, access point transmit power, and signal-to-noise ratio as input features, and the modulation and coding scheme and spatial stream number combination after convergence of the adaptive modulation algorithm as output labels, a training dataset is constructed. The gradient boosting decision tree algorithm is used to train the training dataset with 5-fold cross-validation, and the model parameters are optimized by minimizing the mean square error between the predicted value and the output label.

7. The modulation coding scheme and spatial stream number prediction method as described in claim 6, characterized in that, During the training of a gradient boosting decision tree model: When there are two access points, the signal-to-noise ratio is used as the dominant feature. When the number of access points is three or more, increase the weighting factor for the interference signal strength.

8. A modulation coding scheme and a spatial stream number prediction system, characterized in that, Includes the following modules: The data acquisition module is used to acquire received signal strength indication data and preset threshold information between at least two access points and the site; The status determination module is used to determine the synchronous / asynchronous communication status between each access point based on the dynamic relationship between the received signal strength indication data and the preset threshold information; The signal classification module is used to classify signals sent from adjacent access points to the station as interference signals and signals sent from adjacent stations as environmental noise based on the synchronous / asynchronous communication status. The signal-to-noise ratio (SNR) calculation module is used to calculate the SNR of a station based on the signal classification results. The model input module is used to input the signal-to-noise ratio, synchronous / asynchronous communication status, and access point transmit power into the gradient boosting decision tree model; The prediction output module is used to output the modulation and coding scheme and spatial stream number prediction results of the target access point through the gradient boosting decision tree model.

9. An electronic device, comprising a memory and a processor, characterized in that, The memory is used to store programs that support the processor in executing any of the modulation and coding schemes and spatial stream number prediction methods described in claims 1-7, and the processor is configured to execute the programs stored in the memory.

10. A computer-readable storage medium storing a computer program thereon, characterized in that, When the computer program is run by the processor, it performs the steps of the modulation and coding scheme and spatial stream number prediction method according to any one of claims 1-7.

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