Prediction method, system, equipment and medium for modulation coding scheme and spatial flow number

Abstract

The invention relates to a method, a system and equipment for predicting a modulation and coding scheme and a spatial flow number, and a medium. The method comprises the following steps: acquiring received signal strength indication data and preset threshold information between at least two access points and a station; according to a dynamic relationship between the received signal strength indication data and preset threshold information, judging a synchronous / asynchronous communication state between the access points; based on the synchronous / asynchronous communication state, signals sent by the adjacent access points to the stations are classified as interference signals, and signals sent by the adjacent stations are classified as environment bottom noise; calculating a signal-to-noise ratio of the site based on a signal classification result; inputting the signal-to-noise ratio, the synchronous / asynchronous communication state and the transmitting power of the access point into a gradient boosting decision tree model; and outputting the modulation coding scheme and the spatial flow number prediction result of the target access point through the gradient boosting decision tree model. According to the method, the MCS / NSS prediction precision of the high-density WLAN is remarkably improved.

Description

Technical Field

[0001] The present invention relates to the interdisciplinary field of wireless communications and machine learning, and in particular to a method, system, device, and medium for predicting a modulation and coding scheme and the number of spatial streams. Background Art

[0002] With the increasing popularity of high-density wireless local area networks (WLANs), especially the next-generation Wi-Fi 7 standard's requirement for multiple access point (AP) deployments, throughput prediction inaccuracies caused by signal interference and channel contention are becoming increasingly prominent. In dense AP deployments, overlapping signal coverage between adjacent APs, increased interference between STAs (stations), and dynamic channel conditions lead to the following drawbacks in traditional throughput prediction:

[0003] 1. Existing methods rely on large-scale channel information (e.g., mean and peak) such as the received signal strength indicator (RSSI) between nodes. However, RSSI cannot accurately reflect the rapidly changing channel quality.

[0004] 2. Failure to distinguish interference source types (such as the difference in noise from adjacent APs and STAs) confuses ambient noise with neighboring cell interference, resulting in distorted signal-to-noise ratio (SINR) calculations.

[0005] 3. Ignoring the dynamic impact of the synchronous / asynchronous communication status between APs (such as synchronous transmission, asynchronous transmission and mixed transmission) on the interference pattern, it is unable to adapt to the complex competition mechanism in high-density environments.

[0006] These deficiencies directly lead to insufficient prediction accuracy for MCS (Modulation and Coding Scheme) and NSS (Number of Spatial Streams). As core parameters for physical layer transmission efficiency, MCS / NSS prediction errors significantly reduce the reliability of throughput assessments, hindering network optimization decisions (such as channel allocation and power control). Summary of the Invention

[0007] The present invention provides a method, system, device, and medium for predicting the modulation and coding scheme and number of spatial streams. The purpose of the present invention is to solve the problem of insufficient prediction accuracy of the modulation and coding scheme (MCS) and number of spatial streams (NSS) in densely deployed wireless local area networks due to signal interference and dynamic channel changes, which in turn affects accurate throughput prediction.

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

[0009] Obtaining received signal strength indication data and preset threshold information between at least two access points and a station;

[0010] Determining the synchronous / asynchronous communication state between the access points according to the dynamic relationship between the received signal strength indication data and the preset threshold information;

[0011] Based on the synchronous / asynchronous communication state, classify the signal sent by the adjacent access point to the station as an interference signal, and classify the signal sent by the adjacent station as environmental noise;

[0012] Calculate the signal-to-noise ratio of the site based on the signal classification results;

[0013] Inputting the signal-to-noise ratio, synchronous / asynchronous communication state and access point transmit power into a gradient boosting decision tree model;

[0014] The modulation and coding scheme of the target access point and the prediction result of the number of spatial streams are output through the gradient boosting decision tree model.

[0015] Furthermore, the method for determining the synchronous / asynchronous communication state between access points includes:

[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 values ​​of the received signal strength indicator data of the downlink data signal of the first access point and the downlink data signal of the second access point do not reach the packet detection threshold and the average values ​​of the received signal strength indicator data do not reach the network allocation vector threshold, it is determined that the two access points are in a synchronous communication state;

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

[0019] When the maximum values ​​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 values ​​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:

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

[0022] The ambient noise floor is defined as the signal from non-target sites.

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

[0024] When APs are synchronized, the signal-to-noise ratio calculation ignores the ambient noise floor intensity.

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

[0026] When APs are in a mixed synchronous and asynchronous state, this value aggregates the linear power of the ambient noise floor and the interference signal.

[0027] Furthermore, the method of aggregating the linear power values ​​of the ambient noise floor and the interference signal includes:

[0028] Convert all signal strength values ​​to be aggregated from dBm to mW.

[0029] Sum the converted mW unit values;

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

[0031] Furthermore, the training method of the gradient boosting decision tree model includes:

[0032] Constructing a training data set using the synchronous / asynchronous communication state, access point transmit power, and signal-to-noise ratio as input features and a combination of a modulation and coding scheme and a number of spatial streams after convergence of an adaptive modulation algorithm as output labels;

[0033] The gradient boosting decision tree algorithm was used to perform 5-fold cross validation training on the training data set, and the model parameters were optimized by minimizing the mean square error between the predicted value and the output label.

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

[0035] When the number of access points is two, the signal-to-noise ratio is used as the dominant feature;

[0036] When there are three or more access points, increase the weight factor of the interference signal strength.

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

[0038] A data acquisition module, configured to acquire received signal strength indication data and preset threshold information between at least two access points and a station;

[0039] A state determination module, configured to determine the synchronous / asynchronous communication state between the access points based on a dynamic relationship between the received signal strength indication data and the preset threshold information;

[0040] a signal classification module, configured to classify a signal sent by an adjacent access point to a station as an interference signal and a signal sent by an adjacent station as environmental noise based on the synchronous / asynchronous communication state;

[0041] A signal-to-noise ratio calculation module, used to calculate the signal-to-noise ratio of a site based on the signal classification result;

[0042] A model input module, configured to input the signal-to-noise ratio, synchronous / asynchronous communication state, and access point transmit power into a 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-mentioned objectives, the third aspect of the present invention provides an electronic device, comprising a memory and a processor, wherein the memory is used to store a program that supports the processor to execute the modulation and coding scheme and the spatial stream number prediction method, and the processor is configured to execute the program stored in the memory.

[0045] To achieve the above-mentioned objectives, the fourth aspect of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method for predicting the modulation and coding scheme and the number of spatial streams are executed.

[0046] Beneficial effects of the present invention:

[0047] Compared with the existing technology, the present invention provides a modulation and coding scheme and a prediction method, system, device and medium for the number of spatial streams. By constructing "dynamic state discrimination - precise interference separation - scenario-based SINR modeling - multi-feature intelligent prediction", the MCS / NSS prediction accuracy of high-density WLAN is significantly improved: First, based on the real-time relationship between RSSI and dynamic thresholds (PD / ED / NAV), the synchronous, asynchronous or mixed transmission state between APs is accurately judged to solve the dynamic adaptation problem of the competition mechanism; secondly, the interference source type is distinguished - adjacent AP signals are classified as directional interference, and adjacent STA signals are defined as environmental noise floor, overcoming the SINR distortion problem caused by interference confusion; then, the SINR calculation model is dynamically selected according to the communication state (synchronous state only considers noise floor, asynchronous state only considers interference, and mixed state aggregates multi-source interference through the power superposition formula), breaking through the large-scale information limitations of RSSI; finally, the SINR, synchronous / asynchronous state and AP transmit power are integrated, and the prediction result is output through a gradient boosting decision tree model. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments.

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

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

[0052] Figure 4 This is a flowchart of determining synchronous / asynchronous states when using 2APs, as disclosed in an embodiment of the present invention.

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

[0054] Figure 6 This is a flowchart of determining synchronous / asynchronous states of 3APs disclosed in an embodiment of the present invention.

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

[0056] Figure 8 This is a classification diagram of interference signals and environmental noise floor when there are two APs, as disclosed in an embodiment of the present invention.

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

[0058] Figure 10 This is a diagram of a calculation formula for modifying the signal-to-noise ratio when the number of APs is 2, disclosed in an embodiment of the present invention.

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

[0060] Figure 12 This is a prediction accuracy analysis diagram for a case where the number of APs is 2, as disclosed in an embodiment of the present invention.

[0061] Figure 13 This is a graph of the accuracy of MCS and NSS prediction when the number of APs is 2, disclosed in an embodiment of the present invention.

[0062] Figure 14 This is an importance ranking diagram when the number of APs is 2, disclosed in an embodiment of the present invention.

[0063] Figure 15 This is a prediction accuracy diagram when the number of APs is 3, as disclosed in an embodiment of the present invention.

[0064] Figure 16This is a graph of the accuracy prediction of MCS and NSS when the number of APs is 3, as disclosed in an embodiment of the present invention.

[0065] Figure 17 This is an importance ranking diagram when the number of APs is 3, as disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0066] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0067] According to an embodiment of the present invention, it should be noted that the steps shown in the flowchart 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 production method, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0068] like Figure 1 、 Figure 2 As shown, the present invention provides a modulation and coding scheme and a method for predicting the number of spatial streams, comprising the following steps:

[0069] Step S100: obtaining received signal strength indication data and preset threshold information between at least two access points and a station;

[0070] This step acquires physical layer data in a high-density WLAN environment through actual measurement. Specifically, the data collected is the received signal strength indicator data and preset threshold information between at least two access points (APs) and their associated stations (STAs) within the deployment area. Received signal strength indicator (RSSI) data includes the measured signal strength values ​​(including sum, max, and mean statistics) recorded when the AP and STA communicate. Preset threshold information includes dynamic configuration parameters such as the packet detection threshold (PD), energy detection threshold (ED), and network allocation vector threshold (NAV).

[0071] Step S200: determining the synchronous / asynchronous communication state between the access points based on the dynamic relationship between the received signal strength indication data and the preset threshold information;

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

[0073] Downlink data sent by AP0 to STA0;

[0074] Uplink data sent by STA0 to AP0;

[0075] Downlink data sent by AP1 to STA1;

[0076] Uplink data sent by STA1 to AP1.

[0077] like Figure 4 As shown in FIG, in a wireless communication system including two access points (APs), the core basis for determining their communication status (synchronous or asynchronous) is to analyze the downlink data sent by each AP to its associated station (STA) (e.g. )and( )'s received signal strength indicator (RSSI) measured value.

[0078] The signal strength measured in the experiment includes three key indicators: sum (total), max (maximum) and mean (average). When judging the communication status between two access points (AP0 and AP1), the downlink data is used to determine the signal strength between the two access points. )and The RSSI value meets the threshold condition to determine:

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

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

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

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

[0083] In the 3AP scenario ( Figure 5 ), expanded to 6 directed links (bidirectional interaction between AP0-STA0, AP1-STA1, and AP2-STA2), with Figure 5 For the case where there are 3 APs, we select 2 APs from the 3 APs and determine the synchronous / asynchronous mixed state according to the analysis method for 2 APs. Figure 6 As shown, for a communication scenario including three access points, the method for determining the synchronous / asynchronous communication state between the access points includes:

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

[0085] Sequentially select two access points to form access point pairs, including: a first access point and a second access point combination, a first access point and a third access point combination, and a second access point and a third access point combination; and perform the following determination operations on each access point pair:

[0086] Synchronous transmission status determination: When the API downlink data signal Downlink data signal with APJ When the received signal strength indicator value does not meet any of the following conditions:

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

[0088] 2. If the average signal strength value is within the interval formed by the network allocation vector threshold and the energy detection threshold, it is determined that API and APJ are in a synchronous transmission state.

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

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

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

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

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

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

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

[0096] 3. It is determined that API and APJ are in a synchronous and asynchronous mixed transmission state.

[0097] Finally, the communication status categories of all access point pairs are combined to generate the overall judgment result of the network communication status including three access points, such as Figure 7 shown.

[0098] Step S300: Based on the synchronous / asynchronous communication state, classify the signal sent by the adjacent access point to the station as an interference signal, and classify the signal sent by the adjacent station as environmental noise.

[0099] For the case where there are 2 APs, when classifying, we first analyze the characteristics of the three states: synchronous transmission, asynchronous transmission, and synchronous / asynchronous mixed transmission. In synchronous transmission, APs listen to each other in real time, and most of the interference when STA receives data comes from the environmental noise floor. In asynchronous transmission, APs do not listen to each other at all, and STA ignores the environmental noise floor and only receives interference signals. In synchronous / asynchronous mixed transmission, APs listen to each other in real time and do not listen to each other alternately, and STA receives environmental noise floor and interference signals. Based on data analysis and knowledge of communication principles, for a certain STA node, the signal from the adjacent AP node is the interference signal, and the signal from other adjacent STA sites is the environmental noise floor, such as Figure 8 shown.

[0100] For the case where there are 3 APs, the classification of interference signals and ambient noise is based on the same method as for the case where there are 2 APs, thus obtaining the classification of interference signals and ambient noise. Figure 9 shown.

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

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

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

[0104] Among them, P signal Represents the useful signal (corresponding to Figure 8 The black solid line in the figure), P noise Represents the ambient noise (corresponding to Figure 8 Green dashed line in the figure), P inter Indicates the interference signal (corresponding to Figure 8According to the three cases of synchronous / asynchronous states between two APs, the calculation formula of signal-to-noise ratio is modified as follows: Figure 10 .

[0105] Synchronous communication state: When two access points (AP0 and AP1) are in synchronous transmission, the signal-to-noise ratio calculation of the station (STA) only considers the ambient noise power (P noise ), the formula is: SINR = P Signal -P noise .

[0106] Asynchronous communication state: When the 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] Mixed communication state: When two access points are in synchronous and asynchronous mixed transmission, it is necessary to aggregate the linear power value (in mW) of the ambient noise floor and the interference signal at the same time. The formula is: SINR = P Signal -P noise -P inter .

[0108] The network topology with three APs is essentially a directed complete graph consisting of six nodes, with a total of 30 directed edges. Therefore, the difference in SINR calculation compared to the one with two APs lies in the aggregation process of multiple signals.

[0109] Taking the example of 3 APs interacting synchronously and asynchronously, when summing the signals in the following formula, it is necessary to first sum each sub-signal (P in dBm) inter or P noise ) is converted into linear power in mW, then the linear power of all interference signals is added together 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 Indicates the cumulative value of the intensity of n signals, P1....P n Indicates the signal strength of each of the n signals. Figure 9 Taking the station STA0 in the example, the signal-to-noise ratio calculation formula of the station at a certain moment is:

[0112]

[0113] in, represents the effective signal strength sent from AP0 to STA0 (black solid line), Penv1 represents the ambient noise from STA1 (red dashed line), P inter1 represents the interference signal from AP1 (green dashed line), P inter2 Represents the interference signal from AP2 (green dashed line).

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

[0115] Based on the prediction model with 2 APs and 3 APs, the calculated STA signal-to-noise ratio value 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 shown in the figure, where the model input layer is composed of three types of features: site signal-to-noise ratio features (env_SINR: signal-to-noise ratio value calculated based on environmental noise floor power, ferr_SINR: signal-to-noise ratio value calculated based on interference signal power), access point status features (categ_0, categ_1, categ_2: three-dimensional classification variables characterizing the synchronous / asynchronous communication status between APs), and test environment basic features (RSSI: measured value of received signal strength indicator, 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 result of the target access point through the gradient boosting decision tree model.

[0117] The model output layer is the target prediction item:

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

[0119] Number of Spatial Streams (NSS): Predicts the number of spatial streams most frequently used after AP adaptive adjustment stabilizes.

[0120] according to Figure 11 The gradient boosting decision tree algorithm model is trained by the process to predict the (MSC, NSS) combination of two environments with two APs and three APs respectively.

[0121] For the five training datasets with an AP number of 2, a 5-fold cross validation was used to fully train the model and also verify the effectiveness of the model.

[0122] exist Figure 12In the graph, the horizontal axis represents the actual NSS or MOS value, and the vertical axis represents the model's predicted value. When a node is located on a diagonal line passing through the origin, the prediction is accurate. The color of the node indicates the number of training data points at that location. Darker points on the diagonal line indicate a high prediction accuracy. Therefore, it is intuitively clear that the model is highly accurate.

[0123] Secondly, the prediction accuracy of the statistical model is 86.15% ( Figure 13 (a)), MSE is 0.6518; 376 NSS are accurately predicted, with an accuracy of 96.41% ( Figure 13 (b)), the MSE is 0.5128. In addition, the trained gradient boosting decision tree algorithm model can give the importance ranking of influence (MOSNSS).

[0124] Finally, through Figure 14 According to the importance ranking of (a) and (b), we can draw the following conclusions:

[0125] The STA's signal-to-noise ratio has the greatest impact on (MOS, NSS). Reason: The environmental noise floor SINR (env_SINR) and the interference signal SINR (ferr SINR) ranked first and third, respectively, indicating that the STA's signal-to-noise ratio has the greatest impact on (MCS, NSS).

[0126] In the signal-to-noise ratio (SNR) component, noise from other STAs is more important than noise from other APs. Reason: When ranking the factors influencing MCS, the environmental noise floor increases the impact of interference noise by approximately 50%. When ranking the factors influencing NSS, the environmental noise floor increases the impact of interference noise by approximately 160%. The AP's transmit power has a more significant impact on (MCS, NSS). Reason: When ranking the factors influencing both MCS and NSS, AP transmit power (eirp) ranks second. Threshold information and the synchronization / asynchrony status information determined by the threshold have a smaller impact on (MCS, NSS). Reason: Changes in the NAV threshold and synchronization / asynchrony status information (represented by category_0, category_1, and category_2) are ranked low in terms of influence.

[0127] By implementing a 5-fold cross-validation strategy on the loc_nav.csv dataset (8 training_set_APs with 3 APs and 3 APs), we generated 858 training data points. This approach not only ensures that the model is fully 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 data point distribution shown cleverly reflects 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 good consistency between the model's predictions and the measured data. Therefore, we can conclude that the model demonstrates high accuracy in predicting (MOS, NSS).

[0129] Secondly, the prediction accuracy of the statistical model is that 830 out of 858 training data accurately predict NSS, with an accuracy of 96.74% ( Figure 16 (b)), MSE is 0.04315; 559 data accurately predict 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 MSE is low, which shows that although there is a small deviation in the prediction, the prediction result is still relatively accurate. Figure 16 In addition, the trained gradient boosting decision tree algorithm model can give the importance ranking of the impact (MCS, NSS) of the number of APs in three environments, as shown in Figure 17 As shown in (a) and (b).

[0130] According to the importance ranking of the three impacts (MCS, NSS) based on the number of APs, the following conclusions can be drawn:

[0131] The STA's signal-to-noise ratio 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 STA's signal-to-noise ratio has the greatest impact on (MCS, NSS).

[0132] Interference noise is more important in an environment with three APs than in an environment with two APs. Reason: In the ranking of factors affecting MCS and NSS, the importance of ambient noise and interference noise is roughly the same, with ambient noise being slightly more important than interference noise.

[0133] AP transmit power has a significant impact on MCS and NSS. Reason: AP transmit power (eirp) ranks fourth and third among the factors influencing MOS and NSS, respectively. Threshold information and the synchronization / asynchrony status information determined by the threshold have a smaller impact on MCS and NSS. Reason: Changes in the NAV threshold and synchronization / asynchrony status information (represented by category_0, category_1, and category_2) are ranked low in terms of influence.

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

[0135] The (MCS, NSS) results of the prediction test set using the gradient boosting decision tree algorithm prediction model trained on the training set are 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 the present application, a system for predicting a modulation and coding scheme and the number of spatial streams is provided, including the following modules:

[0144] A data acquisition module, configured to acquire received signal strength indication data and preset threshold information between at least two access points and a station;

[0145] A state determination module, configured to determine the synchronous / asynchronous communication state between the access points based on a dynamic relationship between the received signal strength indication data and the preset threshold information;

[0146] a signal classification module, configured to classify a signal sent by an adjacent access point to a station as an interference signal and a signal sent by an adjacent station as environmental noise based on the synchronous / asynchronous communication state;

[0147] A signal-to-noise ratio calculation module, used to calculate the signal-to-noise ratio of a site based on the signal classification result;

[0148] A model input module, configured to input the signal-to-noise ratio, synchronous / asynchronous communication state, and access point transmit power into a 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 an embodiment of the present application, an electronic device is 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 description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made 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. Among them, the device embodiments described above are only exemplary. For example, the division of the units can be a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0153] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0154] If the integrated unit is implemented in the form of 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, 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. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk, etc. Various media that can store program codes.

[0155] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A method for predicting a modulation and coding scheme and the number of spatial streams, characterized in that: The steps include: Obtaining received signal strength indication data and preset threshold information between at least two access points and a station; Determining the synchronous / asynchronous communication state between the access points according to the dynamic relationship between the received signal strength indication data and the preset threshold information; Based on the synchronous / asynchronous communication state, classify the signal sent by the adjacent access point to the station as an interference signal, and classify the signal sent by the adjacent station as environmental noise; Calculate the signal-to-noise ratio of the site based on the signal classification results; Inputting the signal-to-noise ratio, synchronous / asynchronous communication state and access point transmit power into a gradient boosting decision tree model; The modulation and coding scheme of the target access point and the prediction result of the number of spatial streams are output through the gradient boosting decision tree model.

2. The method for predicting the modulation and coding scheme and the number of spatial streams according to claim 1, wherein: 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 values ​​of the received signal strength indicator data of the downlink data signal of the first access point and the downlink data signal of the second access point do not reach the packet detection threshold and the average values ​​of the received signal strength indicator data do not reach the network allocation vector threshold, it is determined that the two access points are in a synchronous communication state; When the maximum value of the received signal strength indicator data of only one of the downlink data signal of the first access point and the downlink data signal of the second access point reaches the packet detection threshold or the average value of the received signal strength indicator reaches the network allocation vector threshold, it is determined that the two access points are in an asynchronous communication state; When the maximum values ​​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 values ​​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 method for predicting the modulation and coding scheme and the number of spatial streams according to claim 1, wherein: The signal classification rules are: Interference signals are defined as signals from non-associated access points; The ambient noise floor is defined as the signal from non-target sites.

4. The method for predicting the modulation and coding scheme and the number of spatial streams according to claim 1, wherein: Calculate the signal-to-noise ratio value of the site: When APs are synchronized, the signal-to-noise ratio calculation ignores the ambient noise floor intensity. When APs are in an asynchronous state, the signal-to-noise ratio calculation ignores the interference signal strength; When APs are in a mixed synchronous and asynchronous state, this value aggregates the linear power of the ambient noise floor and the interference signal.

5. The method for predicting the modulation and coding scheme and the number of spatial streams according to claim 4, wherein: Methods for aggregating the linear power values ​​of environmental noise floor and interference signals include: Convert all signal strength values ​​to be aggregated from dBm to mW. Sum the converted mW unit values; Convert the summation result back to dBm units.

6. The method for predicting the modulation and coding scheme and the number of spatial streams according to claim 1, wherein: The training methods for the gradient boosting decision tree model include: Constructing a training data set using the synchronous / asynchronous communication state, access point transmit power, and signal-to-noise ratio as input features and a combination of a modulation and coding scheme and a number of spatial streams after convergence of an adaptive modulation algorithm as output labels; The gradient boosting decision tree algorithm was used to perform 5-fold cross validation training on the training data set, and the model parameters were optimized by minimizing the mean square error between the predicted value and the output label.

7. The method for predicting the modulation and coding scheme and the number of spatial streams according to claim 6, wherein: During the training of the gradient boosting decision tree model: When the number of access points is two, the signal-to-noise ratio is used as the dominant feature; When there are three or more access points, increase the weight factor of the interference signal strength.

8. A system for predicting a modulation and coding scheme and the number of spatial streams, characterized in that: Includes the following modules: A data acquisition module, configured to acquire received signal strength indication data and preset threshold information between at least two access points and a station; A state determination module, configured to determine the synchronous / asynchronous communication state between the access points based on a dynamic relationship between the received signal strength indication data and the preset threshold information; a signal classification module, configured to classify a signal sent by an adjacent access point to a station as an interference signal and a signal sent by an adjacent station as environmental noise based on the synchronous / asynchronous communication state; A signal-to-noise ratio calculation module, used to calculate the signal-to-noise ratio of a site based on the signal classification result; A model input module, configured to input the signal-to-noise ratio, synchronous / asynchronous communication state, and access point transmit power into a 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 a program that supports the processor to execute the method for predicting the modulation and coding scheme and the number of spatial streams as claimed in any one of claims 1 to 7, and the processor is configured to execute the program stored in the memory.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for predicting the modulation and coding scheme and the number of spatial streams in any one of claims 1 to 7 are executed.

Images