Channel twin modeling method and system based on electromagnetic interference dynamic coupling factor

Abstract

The application relates to the technical field of twin modeling, in particular to a channel twin modeling method and system based on an electromagnetic interference dynamic coupling factor, which comprises the following steps: constructing a channel twin model; obtaining a dynamic coupling weight of a single communication carrier; extracting a communication carrier set for transmitting an emergency communication signal to the single communication carrier; obtaining an electromagnetic interference component of the single communication carrier, and then obtaining an overall electromagnetic interference influence degree at the current moment; obtaining a signal-to-interference-noise ratio factor at the current moment based on the signal-to-interference-noise ratio of the receiving channel of all communication carriers at the current moment; obtaining a modulation and coding index at the current moment; determining a target modulation and coding mode, so as to adjust the communication channel coding mode and support the channel twin model to obtain continuous communication state information. The application can improve the applicability of the reselected modulation and coding mode, and then improve the channel twin modeling precision.

Description

Technical Field

[0001] This application relates to the field of twin modeling technology, specifically to a channel twin modeling method and system based on electromagnetic interference dynamic coupling factor. Background Technology

[0002] With the increasing demand for wireless communication technology in emergency scenarios, communication quality is crucial to the safety of emergency personnel and facilities. Airborne communication vehicles are often used to enhance network communication transmission quality. However, rain, lightning, and fog in harsh environments such as mountainous areas cause complex electromagnetic interference, which severely attenuates and scatters communication signals, affecting the quality of emergency communication.

[0003] Digital twin technology supports channel twin modeling, enabling precise monitoring of emergency communication status and real-time optimization of communication strategies. Adaptive adjustment of modulation and coding schemes is one of the mainstream optimization strategies; by selecting appropriate modulation and coding schemes for channel coding, signal transmission quality can be effectively improved and bit error rate reduced.

[0004] Existing adaptive modulation and coding methods mainly determine the modulation and coding threshold based on the signal-to-interference-plus-noise ratio (SINR) to select a suitable modulation and coding scheme. This is suitable for terrestrial cellular networks with stable transmission scenarios, but it does not fully consider the relationship between communication status and electromagnetic interference, making it difficult to adapt to emergency communication in adverse weather conditions. Furthermore, the long-distance transmission of emergency communication further exacerbates channel attenuation and scattering. If the selected modulation and coding scheme cannot effectively resist dynamic electromagnetic interference, it will lead to an increase in the bit error rate of the communication link and frequent interruptions. This makes it impossible for the channel twin model to obtain continuous communication status information, making it difficult to accurately characterize the dynamic evolution of the channel. Summary of the Invention

[0005] To address the above shortcomings, this application provides a channel twin modeling method and system based on the dynamic coupling factor of electromagnetic interference. While retaining the original principle of adaptive modulation and coding scheme selection based on signal-to-interference-plus-noise ratio, it further considers the relationship between communication state and electromagnetic interference, improving the applicability of the reselected modulation and coding scheme, and thus improving the accuracy of channel twin modeling. In a first aspect, embodiments of this application provide a channel twin modeling method based on electromagnetic interference dynamic coupling factors, the method comprising the following steps: (1) Construct a channel twin model based on the scene point cloud data, geographical coordinates, communication status information and weather status information of the communication vehicle within a preset time period, including the current time. (2) Based on the correlation between the weather status information within the preset time period and the Doppler frequency shift of the receiving channel of a single communication vehicle, obtain the dynamic coupling weight of a single communication vehicle; (3) Extract the set of communication vehicles that transmit emergency communication signals to a single communication vehicle; by using the spatial distance between each communication vehicle in the set and the single communication vehicle, the attenuation and scattering of the emergency communication signal, and the dynamic coupling weight in (2), obtain the electromagnetic interference component of a single communication vehicle, and then obtain the overall electromagnetic interference impact at the current moment. (4) Based on the signal-to-interference-plus-noise ratio (SINR) of the receiving channels of all communication carriers at the current time, obtain the SINR factor at the current time, and combine it with the overall electromagnetic interference influence in (3) to obtain the modulation and coding index at the current time, so as to determine the target modulation and coding method from a variety of preset modulation and coding methods, thereby adjusting the communication channel coding method and supporting the channel twin model to obtain continuous communication state information.

[0006] In one embodiment, the dynamic coupling weights are obtained through the following process: Calculate the time-series correlation coefficients between the normalized values ​​of rainfall, wind speed, and the normalized value of Doppler frequency shift of the receiving channel of a single communication vehicle within the preset time period; The dynamic coupling weights are positively correlated with the absolute values ​​of all the calculated correlation coefficients.

[0007] In one embodiment, the dynamic coupling weight is a weighted sum of the absolute values ​​of all the calculated correlation coefficients.

[0008] In one embodiment, the attenuation scattering degree is positively correlated with the attenuation of the transmitted signal strength of each communication carrier in the communication carrier group when it reaches a single communication carrier.

[0009] In one embodiment, the electromagnetic interference component is obtained through the following process: Calculate the average of the dynamic coupling weights of all communication carriers in the communication carrier set and the dynamic coupling weights of a single communication carrier; Calculate the product of the normalized value of the spatial distance, the mean value, and the attenuation scattering degree; The electromagnetic interference component is positively correlated with the product of all communication carriers in the communication carrier set.

[0010] In one embodiment, the overall electromagnetic interference impact is obtained by the average level of the electromagnetic interference components of all communication vehicles at the current moment.

[0011] In one embodiment, the signal-to-interference-plus-noise ratio (SINR) factor is the inverse normalized result of the arithmetic mean of the SINR of the receiving channels of all communication carriers at the current time.

[0012] In one embodiment, the modulation and coding metric is a weighted sum of the signal-to-interference-plus-noise ratio (SINR) and the overall electromagnetic interference (EMI) effect.

[0013] In one embodiment, the target modulation and coding scheme is determined by the following method: Each preset modulation and coding scheme corresponds to a specific modulation order and target code rate; The value range of the modulation and coding index is divided into equal parts, and each of the divided parts is mapped to a modulation and coding scheme with increasing modulation order and target code rate in descending order of value, so as to obtain the modulation and coding index range corresponding to each modulation and coding scheme. Determine the modulation and coding index range at the current moment, and then determine the mapped modulation and coding scheme as the target modulation and coding scheme.

[0014] Secondly, embodiments of this application also provide a channel twin modeling system based on electromagnetic interference dynamic coupling factor, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any of the above-described channel twin modeling methods based on electromagnetic interference dynamic coupling factor.

[0015] This application has the following advantages: This application analyzes the correlation between weather condition information and the Doppler frequency shift of the receiving channel of a single communication vehicle to obtain dynamic coupling weights, which can assess the significant impact of environmental electromagnetic interference on a single communication vehicle. Furthermore, it obtains a set of communication vehicles transmitting emergency communication signals to a single communication vehicle, and uses the spatial distance between each communication vehicle in the set and the individual communication vehicle, the attenuation and scattering degree of the emergency communication signal, combined with the dynamic coupling weights, to obtain the electromagnetic interference components of a single communication vehicle. This helps to accurately quantify the comprehensive impact of environmental electromagnetic interference on emergency communication signals during transmission. By obtaining the average level of electromagnetic interference components of all communication vehicles at the current moment, the overall electromagnetic interference impact can be obtained, reflecting the significant impact of electromagnetic interference on emergency communication signals during the entire transmission process under rainy weather in mountainous areas, thus providing a quantitative basis for adaptive adjustment of modulation and coding methods. This application obtains the signal-to-interference-plus-noise ratio (SINR) factor based on the SINR of all communication carriers' receiving channels at the current moment, and combines it with the overall electromagnetic interference (EMI) impact to obtain the modulation and coding index. While retaining the original principle of adaptively selecting modulation and coding schemes based on SINR, it further considers the relationship between communication status and EMI. Based on the modulation and coding index, it determines the target modulation and coding scheme from a variety of preset modulation and coding schemes. By adjusting the communication channel coding scheme, it enhances the EMI resistance of emergency communication signals, ensures the stability of emergency communication signals, enables the channel twin model to obtain continuous communication status information in real time, more accurately depicts the dynamic evolution of the channel, and improves the accuracy of channel twin modeling. Attached Figure Description

[0016] Figure 1 A flowchart illustrating the steps of the channel twin modeling method based on the dynamic coupling factor of electromagnetic interference provided in this application embodiment; Figure 2 This is a schematic diagram illustrating the process of obtaining modulation and coding indicators. Detailed Implementation

[0017] The following description, in conjunction with the accompanying drawings, details the specific scheme of the channel twin modeling method and system based on electromagnetic interference dynamic coupling factor provided in this application.

[0018] like Figure 1 The diagram illustrates a flowchart of the channel twin modeling method based on electromagnetic interference dynamic coupling factor provided in this application embodiment. The method includes the following steps: Step 1: Construct a channel twin model based on the scene point cloud data, geographical coordinates, communication status information, and weather status information of the communication vehicle within a preset time period, including the current time.

[0019] (1) Real-time acquisition of scene point cloud data, geographical coordinates, communication status information and weather status information of the communication vehicle.

[0020] 1) Use the LiDAR sensor on the communication vehicle to scan the scene point cloud data of the communication vehicle in real time. The scene point cloud data refers to the point cloud data of the three-dimensional scene in which the communication vehicle is located.

[0021] 2) Use Global Positioning System (GPS) technology to obtain the geographic coordinates of each communication vehicle in real time. The geographic coordinates consist of longitude, latitude and altitude.

[0022] 3) Communication status information is acquired in real time through communication chips mounted on communication vehicles, such as the Huawei MH5000. This information includes the Doppler frequency shift and signal-to-interference-plus-noise ratio of the receiving channel for each communication vehicle, as well as the transmitted and received signal strengths for each vehicle, both measured in dBm. The receiving channel for each communication vehicle refers to the channel through which other communication vehicles transmit communication signals to each other. Each communication vehicle typically transmits or receives multiple communication signals simultaneously, generally employing orthogonal frequency division multiple access (OFDMA) technology to separate the strength of each transmitted and received signal. Therefore, the communication chip in each communication vehicle can acquire multiple transmitted and received signal strengths.

[0023] For the same emergency communication signal forwarded by multiple communication carriers, the source IP address and destination IP address of the data packets sent and received by each communication carrier can be compared to identify the signals of each transmission segment belonging to the same emergency communication.

[0024] 4) Obtain real-time weather status information of the environment where the communication vehicle is located from the meteorological platform where the communication vehicle is located. The weather status information includes rainfall and wind speed.

[0025] In this embodiment, the communication vehicle refers to a drone.

[0026] In this embodiment, the scanning frequency of the scene point cloud data is 10Hz, and the sampling frequency of the communication status information and weather status information is 1Hz. The implementer can set the specific values ​​of the scanning frequency and sampling frequency according to the actual situation. This application does not impose any restrictions on this.

[0027] It should be noted that LiDAR scanning, GPS technology, orthogonal frequency division multiple access technology, and the acquisition of communication status information using communication chips are all well-known technologies, and will not be described in detail in this application.

[0028] (2) Construct a channel twin model.

[0029] In order to adjust the modulation and coding scheme for the next moment according to the actual situation, it is necessary to analyze various data within a preset time period, including the current moment.

[0030] In this embodiment, the length of the preset time period is 5 minutes. The implementer can set the specific value of the preset time period length according to the actual situation. This application does not impose any restrictions on this.

[0031] Based on the scene point cloud data, geographical coordinates, communication status information and weather status information of the communication vehicle within the preset time period, a channel twin model is constructed using channel modeling tools.

[0032] In this embodiment, the channel modeling tool is a DeepMIMO tool. The DeepMIMO tool is based on ray tracing technology and achieves high-precision channel modeling by simulating the electromagnetic wave propagation path. The DeepMIMO tool is a well-known technology and will not be described in detail in this application. As other implementation methods, based on the ability to achieve channel modeling, implementers may choose other existing feasible technologies at their own discretion. This application does not impose any restrictions on this.

[0033] Step 2: Based on the correlation between the weather status information within the preset time period and the Doppler frequency shift of the receiving channel of a single communication vehicle, obtain the dynamic coupling weight of the single communication vehicle.

[0034] Based on the channel twin model, the communication status information of emergency communication in mountainous areas under rainfall is extracted in real time, and the electromagnetic interference characteristics of emergency communication in mountainous areas under rainfall are analyzed.

[0035] In mountainous emergency communication scenarios during rainy weather, such as flood, landslide, and mudslide rescues, direct communication between different rescue areas is difficult due to the impact of rain and strong winds. It is necessary to use airborne communication vehicles for signal relay to meet the needs of wide coverage and smooth communication. For example, communication signals sent by personnel in area A are transmitted to personnel in area B after being relayed by several communication vehicles.

[0036] However, rainfall and strong winds in mountainous areas usually generate strong electromagnetic interference, which affects the quality of emergency communications. Moreover, the greater the rainfall and the higher the wind speed, the more severe the electromagnetic interference, which exacerbates the oscillation of airborne communication vehicles and makes the Doppler frequency shift of the receiving channel more significantly affected by meteorological factors.

[0037] The following analysis will take a single communication vehicle as an example.

[0038] To analyze the time-varying correlation between Doppler frequency shift and mountainous meteorological environment, the Doppler frequency shift of the receiving channel of a single communication vehicle within the preset time period, as well as the rainfall and wind speed within the preset time period, are extracted from the channel twin model. The Z-Score method is used to normalize the Doppler frequency shift, rainfall, and wind speed within the preset time period. The Z-Score method not only eliminates dimensions, but also effectively preserves the relative direction of change between data after normalization. Specifically, the closer a positive number in the normalization result is to 1, the larger the data is above the average level, i.e., the more significant the upward fluctuation of the data. The closer a negative number in the normalization result is to -1, the smaller the data is below the average level, i.e., the more significant the downward fluctuation of the data. Compared with the maximum value normalization method, it is more robust and can effectively avoid the maximum value in the data compressing most of the data distribution space. The Z-Score method is a well-known technology and will not be described in detail in this application.

[0039] It should be added that: when normalizing the Doppler shift, rainfall, and wind speed within the preset time period, if the standard deviation of the rainfall within the preset time period is 0, the normalized value of the rainfall is set to 0, and the temporal correlation coefficient between the normalized value of the rainfall and the normalized value of the Doppler shift is subsequently set to 0; if the standard deviation of the wind speed within the preset time period is 0, the normalized value of the wind speed is set to 0, and the temporal correlation coefficient between the normalized value of the wind speed and the normalized value of the Doppler shift is subsequently set to 0; if the standard deviation of the Doppler shift within the preset time period is 0, the dynamic coupling weight is subsequently set to 0.

[0040] Calculate the time-series correlation coefficients between the normalized values ​​of rainfall, wind speed, and the normalized value of Doppler frequency shift of the receiving channel of a single communication vehicle within the preset time period; The dynamic coupling weights are positively correlated with the absolute values ​​of all the calculated correlation coefficients.

[0041] It should be noted that positive correlation means that the variables change in the same direction, that is, if one increases, they all increase, and if one decreases, they all decrease.

[0042] In this embodiment, the process of calculating the time-series correlation coefficient between the normalized values ​​of rainfall, wind speed, and the normalized value of the Doppler frequency shift of the receiving channel of a single communication vehicle is as follows: First, the normalized values ​​of rainfall, wind speed, and the Doppler frequency shift of the receiving channel of a single communication vehicle within the preset time period are arranged in time sequence to form a rainfall sequence, a wind speed sequence, and a Doppler frequency shift sequence, respectively. Then, the correlation coefficient between the rainfall sequence, the wind speed sequence, and the Doppler frequency shift sequence is calculated respectively.

[0043] In this embodiment, the correlation coefficient is specifically the Pearson correlation coefficient. The calculation of the Pearson correlation coefficient is a well-known technique and will not be described in detail in this application. As other implementation methods, based on the ability to separately realize the correlation between the normalized value of rainfall, the normalized value of wind speed and the normalized value of the Doppler frequency shift of the receiving channel of a single communication vehicle, the implementer may choose other existing feasible techniques, such as the Spearman correlation coefficient, etc. This application does not impose any restrictions on this.

[0044] In this embodiment, considering that the absolute values ​​of the calculated correlation coefficients can assess the degree of influence of environmental electromagnetic interference on a single communication vehicle during switching from two perspectives, the weighted sum of the absolute values ​​of all the calculated correlation coefficients is used as the dynamic coupling weight of a single communication vehicle. Rainfall and wind speed are equally important; therefore, the weight of the absolute values ​​of all the correlation coefficients is set to 0.5. In actual implementation scenarios, the implementer can adjust the specific values ​​of the weights according to the degree of influence of rainfall and wind speed; this application does not impose any restrictions on this.

[0045] It should be noted that the larger the absolute value of all the correlation coefficients calculated, the higher the time-varying correlation between the Doppler frequency shift of the receiving channel of a single communication vehicle and rainfall and wind speed, that is, the greater the influence of electromagnetic interference on a single communication vehicle.

[0046] Step 3: Extract the set of communication vehicles that transmit emergency communication signals to a single communication vehicle; by using the spatial distance between each communication vehicle in the set and the individual communication vehicle, the attenuation and scattering degree of the emergency communication signal, and the dynamic coupling weight in Step 2, obtain the electromagnetic interference component of a single communication vehicle, and then obtain the overall electromagnetic interference impact at the current moment.

[0047] (1) Extract the set of communication vehicles that transmit emergency communication signals to a single communication vehicle.

[0048] In mountainous electromagnetic interference environments, emergency communication signals are highly susceptible to attenuation and scattering during transmission. Moreover, the greater the transmission distance between communication vehicles, the more severe the attenuation and scattering of emergency communication signals, resulting in weaker signal strength received by the communication vehicles and poorer signal quality when relayed to personnel in the target area.

[0049] To analyze the significant characteristics of attenuation and scattering of emergency communication signals due to electromagnetic interference, the communication vehicles that the emergency communication signals pass through are sequentially obtained according to the transmission path of the emergency communication signals. Combined with the signal transmission direction between the communication vehicles, a communication vehicle set consisting of all communication vehicles transmitting emergency communication signals to a single communication vehicle is extracted.

[0050] (2) To measure the attenuation and scattering of emergency communication signals between each communication carrier in the communication carrier group and a single communication carrier.

[0051] The attenuation scattering degree is positively correlated with the attenuation of the transmitted signal strength of each communication carrier in the communication carrier group when it reaches a single communication carrier.

[0052] In this embodiment, the formula is used. Calculate the attenuation and scattering degree of the emergency communication signal between the j-th communication vehicle in the communication vehicle set and a single communication vehicle; where, This represents the attenuation and scattering degree of the emergency communication signal between the j-th communication vehicle in the communication vehicle set and a single communication vehicle; This represents the transmitted signal strength of the j-th communication vehicle in the communication vehicle set; This represents the received signal strength of a single communication vehicle; max() indicates the operation of taking the maximum value. This indicates the absolute value operation; This indicates a preset value greater than 0, used to avoid a denominator of 0. In this embodiment, The value is 0.01, and it is consistent with... With the same unit, implementers can set it up according to the actual situation. The specific value of is not limited in this application. This characterizes the attenuation of the transmitted signal strength of the j-th communication carrier in the communication carrier set as it reaches a single communication carrier. ,and , The numbers are generally negative, so the absolute value operation is used to ensure that... It is a non-negative number; Used for Normalization is performed.

[0053] It should be noted that the lower the received signal strength of a single communication vehicle is compared to the transmitted signal strength of the communication vehicle transmitting the emergency communication signal to it, the more severe the signal attenuation and scattering of the emergency communication signal in the mountainous area is due to complex electromagnetic interference under rainy weather. This makes it more urgent to adjust the modulation and coding scheme and re-code the channel to resist electromagnetic interference and improve signal quality.

[0054] (3) By combining the spatial distance between each communication carrier and the individual communication carrier, the attenuation and scattering degree of the emergency communication signal, and the dynamic coupling weight in step two, the electromagnetic interference component of the individual communication carrier is obtained.

[0055] 1) Furthermore, the attenuation and scattering of emergency communication signals in mountainous areas are affected not only by environmental electromagnetic interference but also by transmission distance; the greater the transmission distance, the more severe the signal attenuation and scattering. Therefore, by obtaining the geographical coordinates of a single communication vehicle and each communication vehicle in a group of communication vehicles, and transforming them to a geocentric rectangular coordinate system, the spatial distance between the single communication vehicle and each communication vehicle in the group is calculated to reflect the transmission distance. The geocentric rectangular coordinate system is a known technology and will not be described in detail in this application.

[0056] In this embodiment, the spatial distance between communication carriers is specifically Euclidean distance. The calculation of Euclidean distance is a well-known technique and will not be described in detail in this application.

[0057] 2) The greater the dynamic coupling weight of each communication carrier, the more susceptible the communication carrier is to electromagnetic interference during rainy weather in mountainous areas; the greater the spatial distance between communication carriers, the more the degree of electromagnetic interference will be aggravated, and the attenuation and scattering of emergency communication signals will be aggravated.

[0058] Based on the above analysis, the electromagnetic interference component of a single communication vehicle is obtained by considering the spatial distance between each communication vehicle and the attenuation and scattering of the emergency communication signal, combined with the dynamic coupling weight in step two. The specific process is as follows: Calculate the average of the dynamic coupling weights of all communication carriers in the communication carrier set and the dynamic coupling weights of a single communication carrier; Calculate the product of the normalized value of the spatial distance, the mean value, and the attenuation scattering degree; The electromagnetic interference component is positively correlated with the product of all communication carriers in the communication carrier set.

[0059] In this embodiment, the formula is used. Calculate the electromagnetic interference components of a single communication vehicle; where, M represents the electromagnetic interference component of a single communication carrier; M represents the total number of communication carriers in the communication carrier set of a single communication carrier. This represents the average of the dynamic coupling weight of the j-th communication vehicle in the communication vehicle set and the dynamic coupling weight of a single communication vehicle. This represents the spatial distance between a single communication vehicle and the j-th communication vehicle in the communication vehicle set; This represents the upper limit of the maximum communication range of a single communication vehicle, used for... Perform normalization processing; This represents the attenuation and scattering degree of the emergency communication signal between the j-th communication vehicle in the communication vehicle set and a single communication vehicle. Wherein, Normalized value representing spatial distance.

[0060] It should be added that for communication vehicles that do not need to receive emergency communication signals transmitted by other communication vehicles, the electromagnetic interference component of the communication vehicle should be set to 0.

[0061] It should be noted that in data analysis, if a target is affected by a certain factor and multiple characteristic indicators have a synergistic relationship, linear multiplication is usually used to represent the degree of influence of that factor on the target. In this application, when the emergency communication signal of a communication vehicle is transmitted in mountainous areas during rainfall, it is affected by complex electromagnetic interference. The larger the dynamic coupling weight, the higher the correlation between the emergency communication signal and the electromagnetic interference environment in the mountainous area. Furthermore, the farther the transmission distance and the greater the signal attenuation and scattering, the more significant the impact of electromagnetic interference on the emergency communication signal. Therefore, the three characteristic parameters of the mean dynamic coupling weight, spatial distance, and attenuation and scattering are linearly multiplied to obtain the electromagnetic interference component of a single communication vehicle. The larger the calculated electromagnetic interference component, the more significant the impact of environmental electromagnetic interference on the emergency communication signal received by a single communication vehicle in mountainous areas during rainfall.

[0062] (4) Obtain the overall electromagnetic interference impact at the current moment by using the average level of electromagnetic interference components of all communication carriers at the current moment.

[0063] In this embodiment, the average value of the electromagnetic interference components of all communication carriers at the current moment is taken as the overall electromagnetic interference impact at the current moment.

[0064] It should be noted that the greater the calculated overall electromagnetic interference impact, the more significant the impact of environmental electromagnetic interference on emergency communication signals during the entire transmission process under rainy weather in mountainous areas. The more severe the signal quality degradation, the more necessary it is to adjust the modulation and coding scheme in a timely manner to enhance the electromagnetic interference resistance of emergency communication signals.

[0065] Step 4: Based on the signal-to-interference-plus-noise ratio (SINR) of the receiving channels of all communication carriers at the current moment, obtain the SINR factor at the current moment. Then, combine it with the overall electromagnetic interference impact in Step 3 to obtain the modulation and coding index at the current moment.

[0066] Existing adaptive modulation and coding methods mainly determine the modulation and coding threshold based on the signal-to-interference-plus-noise ratio (SINR), which is suitable for terrestrial cellular wireless communication in stable transmission scenarios. However, because they do not fully consider the relationship between communication status and electromagnetic interference, they are difficult to adapt to emergency communication in severe weather environments with complex electromagnetic interference, resulting in poor communication quality even after the modulation and coding method is optimized. This application further considers the relationship between communication status and electromagnetic interference to adaptively adjust the modulation and coding method.

[0067] (1) Calculate the signal-to-interference-plus-noise ratio factor at the current moment.

[0068] The arithmetic mean of the signal-to-interference-plus-noise ratio (SIR) of the received channels of all communication carriers at the current time is calculated. The arithmetic mean is then reverse-normalized using the Min-Max normalization method, and the result of this reverse normalization is used as the SIR factor for the current time. Since the SIR is usually negative, the reverse normalization can be achieved using the Min-Max normalization method, which is a well-known technique and will not be described in detail here.

[0069] In this embodiment, the signal-to-interference-plus-noise ratio (SINR) factor at the current moment can be expressed by the following formula: In the formula, This represents the signal-to-interference-plus-noise ratio (SIR) factor at the current moment. This indicates the preset maximum signal-to-interference-plus-noise ratio; This indicates the preset minimum signal-to-interference-plus-noise ratio; This represents the arithmetic mean of the signal-to-interference-plus-noise ratio (SIR) of the receiving channels of all communication vehicles at the current moment.

[0070] In this embodiment, , The values ​​are 40 and -25, respectively. , The values ​​are all derived from experimental data.

[0071] It should be noted that the larger the calculated signal-to-interference-plus-noise ratio (SIR / NNR) factor, the more severe the electromagnetic interference affecting the emergency communication signal during transmission, resulting in more noise in the emergency communication signal and poorer signal quality.

[0072] (2) Calculate the modulation and coding index at the current time.

[0073] The signal-to-interference-plus-noise ratio (SIR) at the current moment is weighted and summed with the overall electromagnetic interference effect as the modulation and coding index at the current moment. Figure 2 This is a schematic diagram illustrating the process of obtaining modulation and coding indicators.

[0074] In this embodiment, the signal-to-interference-plus-noise ratio (SIRR) factor and the overall electromagnetic interference (EMI) impact are equally important. Therefore, the weights of both the SIRR factor and the EMI impact are set to 0.5. In actual implementation scenarios, implementers can adjust the specific values ​​of the weights according to the degree of influence of the SIRR factor and the EMI impact. This application does not impose any restrictions on this.

[0075] It should be noted that by combining the signal-to-interference-plus-noise ratio (SINR) factor with the overall electromagnetic interference impact, the modulation and coding index is calculated. This allows for the optimization of the adaptive modulation and coding scheme by further considering the relationship between communication status and electromagnetic interference while retaining the adaptive selection principle of the original modulation and coding scheme.

[0076] Step 5: Based on the modulation and coding index at the current moment, determine the target modulation and coding scheme from a variety of preset modulation and coding schemes, thereby adjusting the communication channel coding scheme and supporting the channel twin model to obtain continuous communication state information.

[0077] Based on the current modulation and coding parameters, an appropriate modulation and coding scheme is adaptively selected. The communication channel coding scheme is then adjusted promptly based on the newly selected modulation and coding scheme to enhance the electromagnetic interference resistance of emergency communication signals. Specifically: Based on a variety of preset modulation and coding schemes, the value range of the modulation and coding index is divided into intervals and mapped to each modulation and coding scheme. Specifically, in this embodiment, 20 modulation and coding schemes are preset, and their modulation order and target code rate are (2,78), (2,193), (2,308), (2,449), (2,602), (4,378), (4,490), (4,616), (4,796), (6,466), (6,567), (6,666), (6,772), (6,873), (6,948), (8,624), (8,711), (8,797), (8,885), (8,948); among which the modulation order 2, 4, 6, and 8 correspond to QPSK, 16QAM, 64QAM, and 256QAM modulation types, respectively. Generally, the higher the modulation order, the more bits each symbol carries, but the smaller the distance between constellation points, the higher the target code rate, and the higher the data transmission rate, but the weaker the error correction capability. Therefore, modulation and coding methods with high modulation order and high target code rate have poor electromagnetic interference resistance. The value range of the modulation and coding index is divided into equal parts, and each of these parts is mapped in descending order of value to a modulation and coding scheme with increasing modulation order and target code rate, thus obtaining the range of the modulation and coding index for each scheme; that is, the modulation and coding index falls within the range... The first modulation and coding scheme corresponds to this. At this point, a high modulation and coding index indicates that the emergency communication signal is more significantly affected by environmental electromagnetic interference. A modulation and coding scheme with a low modulation order and target code rate should be selected, and so on. The modulation and coding index should fall within the specified range. The second modulation and coding scheme corresponds to this. The modulation and coding index is in the range The time corresponds to the third modulation and coding scheme (2,308), until the modulation and coding index is within the range This corresponds to the 20th modulation and coding scheme (8,948); Before determining the modulation and coding index range at the current moment, if the value of the modulation and coding index at the current moment is greater than 1, it is corrected and truncated to 1; if the value of the modulation and coding index at the current moment is less than 0, it is corrected and truncated to 0; then the corrected value is used as the target for judgment to determine the target modulation and coding method.

[0078] Determine the modulation and coding index range at the current moment, and determine the mapped modulation and coding scheme as the target modulation and coding scheme to achieve adaptive adjustment of the modulation and coding scheme, thereby adjusting the coding scheme of the communication channel.

[0079] By optimizing the communication channel coding method, the electromagnetic interference resistance of emergency communication signals is enhanced, ensuring the stability of emergency communication signals. This enables the channel twin model to acquire continuous communication status information in real time, accurately depict the dynamic evolution of the channel, and improve the accuracy of channel twin modeling. The modulation and coding methods and channel coding are well-known technologies and will not be described in detail in this application.

[0080] Based on the same inventive concept as the above methods, this application also provides a channel twin modeling system based on electromagnetic interference dynamic coupling factor, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above-described channel twin modeling methods based on electromagnetic interference dynamic coupling factor.

[0081] In summary, this application analyzes the correlation between weather condition information and the Doppler frequency shift of the receiving channel of a single communication vehicle to obtain dynamic coupling weights, which can assess the significant impact of environmental electromagnetic interference on a single communication vehicle. Furthermore, it obtains a set of communication vehicles transmitting emergency communication signals to a single communication vehicle, and uses the spatial distance between each communication vehicle in the set and the individual communication vehicle, the attenuation and scattering degree of the emergency communication signal, combined with the dynamic coupling weights, to obtain the electromagnetic interference components of a single communication vehicle. This helps to accurately quantify the comprehensive impact of environmental electromagnetic interference on emergency communication signals during transmission. By obtaining the average level of electromagnetic interference components of all communication vehicles at the current moment, the overall electromagnetic interference impact can be obtained, reflecting the significant impact of electromagnetic interference on emergency communication signals during the entire transmission process under rainy weather in mountainous areas, thus providing a quantitative basis for adaptive adjustment of modulation and coding methods. This application obtains the signal-to-interference-plus-noise ratio (SINR) factor based on the SINR of all communication carriers' receiving channels at the current moment, and combines it with the overall electromagnetic interference (EMI) impact to obtain the modulation and coding index. While retaining the original principle of adaptively selecting modulation and coding schemes based on SINR, it further considers the relationship between communication status and EMI. Based on the modulation and coding index, it determines the target modulation and coding scheme from a variety of preset modulation and coding schemes. By adjusting the communication channel coding scheme, it enhances the EMI resistance of emergency communication signals, ensures the stability of emergency communication signals, enables the channel twin model to obtain continuous communication status information in real time, more accurately depicts the dynamic evolution of the channel, and improves the accuracy of channel twin modeling.

Claims

1. A channel twin modeling method based on electromagnetic interference dynamic coupling factor, characterized in that, The method includes the following steps: (1) Construct a channel twin model based on the scene point cloud data, geographical coordinates, communication status information and weather status information of the communication vehicle within a preset time period, including the current time. (2) Based on the correlation between the weather status information within the preset time period and the Doppler frequency shift of the receiving channel of a single communication vehicle, obtain the dynamic coupling weight of a single communication vehicle; (3) Extract the set of communication vehicles that transmit emergency communication signals to a single communication vehicle; by using the spatial distance between each communication vehicle in the set and the single communication vehicle, the attenuation and scattering of the emergency communication signal, and the dynamic coupling weight in (2), obtain the electromagnetic interference component of a single communication vehicle, and then obtain the overall electromagnetic interference impact at the current moment. (4) Based on the signal-to-interference-plus-noise ratio (SINR) of the receiving channels of all communication carriers at the current time, obtain the SINR factor at the current time, and combine it with the overall electromagnetic interference influence in (3) to obtain the modulation and coding index at the current time, so as to determine the target modulation and coding method from a variety of preset modulation and coding methods, thereby adjusting the communication channel coding method and supporting the channel twin model to obtain continuous communication state information.

2. The channel twin modeling method based on electromagnetic interference dynamic coupling factor as described in claim 1, characterized in that, The dynamic coupling weights are obtained through the following process: Calculate the time-series correlation coefficients between the normalized values ​​of rainfall, wind speed, and the normalized value of Doppler frequency shift of the receiving channel of a single communication vehicle within the preset time period; The dynamic coupling weights are positively correlated with the absolute values ​​of all the calculated correlation coefficients.

3. The channel twin modeling method based on electromagnetic interference dynamic coupling factor as described in claim 2, characterized in that, The dynamic coupling weight is the weighted sum of the absolute values ​​of all the calculated correlation coefficients.

4. The channel twin modeling method based on electromagnetic interference dynamic coupling factor as described in claim 1, characterized in that, The attenuation scattering degree is positively correlated with the attenuation of the transmitted signal strength of each communication carrier in the communication carrier group when it reaches a single communication carrier.

5. The channel twin modeling method based on electromagnetic interference dynamic coupling factor as described in claim 1, characterized in that, The electromagnetic interference component is obtained through the following process: Calculate the average of the dynamic coupling weights of all communication carriers in the communication carrier set and the dynamic coupling weights of a single communication carrier; Calculate the product of the normalized value of the spatial distance, the mean value, and the attenuation scattering degree; The electromagnetic interference component is positively correlated with the product of all communication carriers in the communication carrier set.

6. The channel twin modeling method based on electromagnetic interference dynamic coupling factor as described in claim 1, characterized in that, The overall electromagnetic interference impact is obtained by the average level of electromagnetic interference components of all communication vehicles at the current moment.

7. The channel twin modeling method based on electromagnetic interference dynamic coupling factor as described in claim 1, characterized in that, The signal-to-interference-plus-noise ratio (SINR) factor is the inverse normalized result of the arithmetic mean of the SINR of the receiving channels of all communication carriers at the current time.

8. The channel twin modeling method based on electromagnetic interference dynamic coupling factor as described in claim 1, characterized in that, The modulation and coding metric is a weighted sum of the signal-to-interference-plus-noise ratio factor and the overall electromagnetic interference effect.

9. The channel twin modeling method based on electromagnetic interference dynamic coupling factor as described in claim 1, characterized in that, The target modulation and coding scheme is determined by the following method: Each preset modulation and coding scheme corresponds to a specific modulation order and target code rate; The value range of the modulation and coding index is divided into equal parts, and each of the divided parts is mapped to a modulation and coding scheme with increasing modulation order and target code rate in descending order of value, so as to obtain the modulation and coding index range corresponding to each modulation and coding scheme. Determine the modulation and coding index range at the current moment, and then determine the mapped modulation and coding scheme as the target modulation and coding scheme.

10. A channel twin modeling system based on electromagnetic interference dynamic coupling factor, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the channel twin modeling method based on the electromagnetic interference dynamic coupling factor as described in any one of claims 1-9.

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