A repeater signal processing system and method based on 5G signals

Abstract

The application discloses a kind of repeater signal processing system and method based on 5G signal, belong to communication technical field, including the signal strength of the 5G signal received by repeater, transmission distance target value, the topographic marker value of transmission destination, weather data of transmission link and the state data of signal transmission candidate frequency band;According to the transmission distance target value of the 5G signal received by repeater, the topographic marker value of transmission destination and weather data of transmission link, generate signal transmission loss value;According to signal transmission loss value and the signal strength of the 5G signal received by repeater, generate the final gain demand prediction value of 5G signal, and judge whether 5G signal transmission needs to carry out frequency band switching;According to the state data of signal transmission candidate frequency band, generate optimal candidate frequency band, and process 5G signal;The application improves gain compensation precision, can dynamic frequency band switching, improve the accuracy of repeater signal processing, and the user experience is poor.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of communication, and particularly relates to a repeater signal processing system and method based on a 5G signal. BACKGROUND

[0002] With the large-scale deployment and application of the fifth generation mobile communication technology (5G), it has put forward unprecedentedly high requirements for the depth and breadth of network coverage. As an economical and flexible signal relay and enhancement device, the repeater plays a key role in eliminating the 5G network blind area and extending the signal coverage range.

[0003] However, 5G signals generally use higher frequency bands, and their propagation characteristics result in large path loss, poor diffraction ability, and are significantly affected by environmental factors such as complex terrain and severe weather, which poses a severe challenge to the signal processing capability of traditional repeaters.

[0004] At present, the existing repeater signal processing technology mainly focuses on fixed gain amplification or power control based on simple signal strength measurement. Some improved schemes consider the path loss caused by transmission distance and make preliminary compensation based on the free space propagation model. However, these methods are not up to the task when dealing with 5G signals. They usually regard environmental factors as static or constant, and fail to systematically include dynamic changes in the transmission process, such as the attenuation of signals under weather conditions such as rain and snow, and the blocking and reflection effects of signals under complex terrain such as mountains, hills, and building groups, into the overall signal loss analysis model; in addition, the existing technology lacks effective utilization of multi-band resources, and cannot intelligently evaluate and switch to a better candidate frequency band when the current transmission conditions deteriorate; these problems seriously restrict the performance and application effect of the repeater in the 5G network, thereby affecting the accuracy of the repeater signal processing and leading to poor user experience. SUMMARY

[0005] In view of the deficiencies of the prior art, the present application provides a repeater signal processing system and method based on a 5G signal, which solves the above problems.

[0006] To achieve the above purpose, the present application is implemented by the following technical scheme: a repeater signal processing method based on a 5G signal, comprising:

[0007] obtaining the signal strength of the 5G signal received by the repeater, the transmission distance target value, the terrain marker value of the transmission destination, the weather data of the transmission link, and the state data of the signal transmission candidate frequency band; wherein the state data includes the load degree and the current interference level of the candidate frequency band, and the interference level refers to the standardized value of the SINR value of the candidate frequency band;

[0008] A transmission loss analysis model is established according to a transmission distance target value of the 5G signal received by the repeater, a topographic sign value of a transmission destination and weather data of a transmission link, and a signal transmission loss value is generated;

[0009] According to the signal transmission loss value and the signal strength of the 5G signal received by the repeater, a final gain requirement prediction value of the 5G signal is generated;

[0010] According to the final gain requirement prediction value of the 5G signal, it is judged whether frequency band switching needs to be performed for 5G signal transmission;

[0011] If the 5G signal transmission needs to perform frequency band switching, an optimal candidate frequency band is generated according to state data of a signal transmission candidate frequency band;

[0012] According to the optimal candidate frequency band, the 5G signal of the repeater is processed.

[0013] On the basis of the above technical solution, the application further provides the following optional technical solutions:

[0014] Further technical solutions: the generation mode of the signal transmission loss value specifically includes:

[0015] According to the transmission distance target value, a theoretical attenuation value is generated based on a free space path loss formula;

[0016] According to the weather data of the transmission link, a weather influence factor is generated;

[0017] According to the theoretical attenuation value, the topographic sign value of the transmission destination and the weather influence factor, a transmission loss analysis model is established, and a signal transmission loss value is generated.

[0018] Further technical solutions: the generation mode of the weather influence factor specifically includes:

[0019] Through the formula:

[0020]

[0021] The weather influence factor is generated ;

[0022] In the formula, represents the value of the i-th weather data, n represents the total number of types of weather data, represents the maximum value of the standard weather data, represents the weight coefficient of the i-th weather data.

[0023] Further technical solutions: the expression of the transmission loss analysis model is specifically:

[0024]

[0025] In the formula, represents the signal transmission loss value, represents the theoretical attenuation value, represents the weather influence factor, represents the topographic sign value of the transmission destination.

[0026] Further technical solutions: the generation method of the final gain requirement prediction value of the 5G signal specifically includes:

[0027] Through the formula:

[0028]

[0029] generate the final gain requirement prediction value of the 5G signal ;

[0030] In the formula, represents the received signal strength threshold value, represents the signal strength of the 5G signal received by the repeater, represents the signal transmission loss value, represents the antenna gain, represents the maximum allowed gain.

[0031] Further technical solutions: the method of judging whether the 5G signal transmission needs to perform frequency band switching specifically includes:

[0032] According to the final gain requirement prediction value of the 5G signal, a frequency band switching requirement value is generated;

[0033] According to the frequency band switching requirement value, it is judged whether the 5G signal transmission needs to perform frequency band switching.

[0034] Further technical solutions: the generation method of the frequency band switching requirement value specifically includes:

[0035] Through the formula:

[0036]

[0037] generate the frequency band switching requirement value ;

[0038] In the formula, represents the final gain requirement prediction value of the 5G signal, represents the maximum allowed gain, represents the signal transmission loss value, represents the maximum allowed signal attenuation amount of the 5G signal, , are weight coefficients, and .

[0039] Further technical solutions: the generation method of the optimal candidate frequency band specifically includes:

[0040] Through the formula:

[0041]

[0042] Generate the optimal candidate frequency band ;

[0043] In the formula, Indicates the resource utilization rate of the candidate frequency band j, Indicates the center frequency of the candidate frequency band j, Indicates the optimal working frequency, Indicates the highest center frequency in the candidate frequency band set F, Indicates the lowest center frequency in the candidate frequency band set F, Indicates the adaptation influence coefficient of the candidate frequency j.

[0044] Further technical solutions: the generation method of the resource utilization rate of the candidate frequency band j specifically includes:

[0045] Through the formula:

[0046]

[0047] Generate the resource utilization rate of the candidate frequency band j ;

[0048] In the formula, Indicates the load degree of the candidate frequency band j, Indicates the frequency band load weight coefficient, Indicates the current interference level of the candidate frequency band j.

[0049] A repeater signal processing system based on a 5G signal, which is used to execute the above-mentioned repeater signal processing method based on a 5G signal, specifically includes:

[0050] A data acquisition unit is configured to acquire the signal strength of the 5G signal received by the repeater, the transmission distance target value, the topographic marker value of the transmission destination, the weather data of the transmission link, and the state data of the signal transmission candidate frequency band.

[0051] A loss analysis unit is configured to establish a transmission loss analysis model according to the transmission distance target value of the 5G signal received by the repeater, the topographic marker value of the transmission destination, and the weather data of the transmission link, and generate a signal transmission loss value.

[0052] The gain demand analysis unit is configured to generate a final gain demand prediction value of the 5G signal according to the signal transmission loss value and the signal strength of the 5G signal received by the repeater.

[0053] The frequency band switching judgment unit is configured to judge whether the 5G signal transmission needs to perform frequency band switching according to the final gain demand prediction value of the 5G signal.

[0054] The screening unit is configured to generate an optimal candidate frequency band according to the state data of the signal transmission candidate frequency band if the 5G signal transmission needs to perform frequency band switching.

[0055] The signal processing unit is configured to process the 5G signal of the repeater according to the optimal candidate frequency band.

[0056] The present application provides a repeater signal processing system and method based on 5G signals, which has the following advantages compared with the prior art:

[0057] The present application acquires the signal strength, transmission distance, terrain marker value, weather data and candidate frequency band state data, establishes a transmission loss analysis model and generates a gain demand prediction value, dynamically judges the frequency band switching demand and selects the optimal frequency band, solves the problems of inaccurate gain compensation and lack of dynamic frequency band switching in the prior art, improves the gain compensation accuracy and enables dynamic frequency band switching, thereby improving the accuracy of repeater signal processing and optimizing the poor user experience. BRIEF DESCRIPTION OF DRAWINGS

[0058] Figure 1 A flowchart of a repeater signal processing method based on 5G signals is provided.

[0059] Figure 2 A structural diagram of a repeater signal processing system based on 5G signals is provided. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application will be further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application and do not limit the present application.

[0061] The specific implementation of the present application will be described in detail below in combination with specific examples.

[0062] Please refer to Figure 1 A repeater signal processing method based on 5G signals provided by an embodiment of the present application includes the following steps:

[0063] Step S10: obtaining the signal strength of the 5G signal received by the repeater, the transmission distance target value, the topographic marker value of the transmission destination, the weather data of the transmission link, and the state data of the signal transmission candidate frequency band; wherein the state data includes the load degree and the current interference level of the candidate frequency band, and the interference level refers to the standardized value of the SINR value of the candidate frequency band;

[0064] Step S20: establishing a transmission loss analysis model according to the transmission distance target value of the 5G signal received by the repeater, the topographic marker value of the transmission destination, and the weather data of the transmission link, and generating a signal transmission loss value;

[0065] Step S30: generating a final gain requirement prediction value of the 5G signal according to the signal transmission loss value and the signal strength of the 5G signal received by the repeater;

[0066] Step S40: determining whether frequency band switching is needed for 5G signal transmission according to the final gain requirement prediction value of the 5G signal;

[0067] Step S50: if frequency band switching is needed for 5G signal transmission, generating an optimal candidate frequency band according to the state data of the signal transmission candidate frequency band;

[0068] It should be noted that if frequency band switching is not needed for 5G signal transmission, the final gain requirement prediction value of the 5G signal is used to process the repeater signal (5G signal) (i.e., to increase the strength of the signal);

[0069] Step S60: processing the 5G signal of the repeater according to the optimal candidate frequency band;

[0070] The transmission distance target value refers to the theoretical straight-line distance between the terminal device and the repeater that the signal needs to reach, which can be calculated by GPS positioning data and is used to evaluate the basic path loss;

[0071] The topographic marker value refers to a parameter quantifying the degree of signal blocking by the terrain, which can be generated by extracting the surface curvature parameter from a digital elevation model and is used to correct the additional attenuation caused by terrain reflection;

[0072] The weather data includes dynamic parameters such as rainfall intensity and air humidity, which can be collected in real time by a meteorological sensor and is used to calculate the rain attenuation effect;

[0073] The candidate frequency band state data includes frequency band load rate, interference level, and other indicators, which can be measured by a spectrum analyzer and provides a basis for frequency band optimization.

[0074] Specifically, a dynamic loss model is constructed by fusing transmission distance, terrain features and real-time weather data, the free space loss is superimposed with terrain blockage attenuation and rain attenuation effect to calculate the total loss value. The gain requirement is determined based on the difference between the measured signal strength and the total loss value, and the frequency band switching mechanism is triggered when the requirement exceeds the maximum gain capability of the device. In the candidate frequency band optimization process, the frequency band resource utilization and the frequency adaptation degree are comprehensively evaluated, and the candidate frequency band with low load and close to the optimal working frequency is selected.

[0075] Compared with the prior art, the traditional method only uses a fixed formula to calculate the path loss, and does not establish a dynamic compensation mechanism for terrain and weather, resulting in a large prediction error of signal strength in mountainous areas; the application quantifies the scattering effect of surface curvature on signals through terrain marker values, and modifies the attenuation coefficient combined with real-time rainfall data, thereby reducing the loss calculation error in complex environments. The prior art lacks a frequency band switching trigger mechanism, and the application dynamically determines the frequency band switching time based on the gain requirement threshold, automatically switches the frequency band before the rain causes the millimeter wave signal to be interrupted, and maintains the continuity of communication.

[0076] Through the above technical solutions, the application solves the problem of transmission loss calculation deviation in complex environments, improves the gain control accuracy through a dynamic environmental factor compensation mechanism. A frequency band switching decision model is established to automatically select the optimal frequency band when the transmission conditions deteriorate, thereby reducing the signal interruption probability; a closed-loop processing flow of multi-source data fusion is formed to achieve adaptive signal enhancement in mountainous, urban and other scenarios.

[0077] Preferably, the application further proposes a generation method of the signal transmission loss value, which specifically includes:

[0078] Step S21: generating a theoretical attenuation value according to a transmission distance target value based on a free space path loss formula;

[0079] Step S22: generating a weather influence factor according to weather data of the transmission link;

[0080] Step S23: establishing a transmission loss analysis model according to the theoretical attenuation value, the terrain marker value of the transmission destination and the weather influence factor to generate a signal transmission loss value;

[0081] The theoretical attenuation value refers to the attenuation amount of signals caused by transmission distance when propagating in free space, which can be calculated by using a free space path loss formula (the formula is an existing formula, which is not described here), the formula takes transmission distance as an input parameter and outputs the theoretical attenuation value, which is used to establish the basic correlation between signal attenuation and physical distance;

[0082] The weather influence factor refers to an additional attenuation effect of dynamic weather conditions on signal transmission, which can be generated by quantifying the influence degree of different weather types on electromagnetic wave propagation, for example, converting meteorological parameters such as rainfall, wind speed and humidity into weighted coefficients to reflect the superimposed loss of real-time meteorological interference on signal transmission.

[0083] The terrain marker value refers to the blocking degree of the transmission destination terrain to signal propagation, which can be specifically represented by a terrain complexity index, for example, obtaining terrain elevation data through a geographic information system and calculating a blocking coefficient, the marker value is higher in plain areas and lower in mountainous or densely built-up areas, and is used to correct the terrain blocking effect not considered in the theoretical attenuation value.

[0084] Specifically, first, the theoretical attenuation value is calculated based on the free space path loss formula to establish an initial model of signal attenuation and transmission distance. Then, the weather influence factor is generated according to real-time weather data to quantify the additional attenuation of dynamic meteorological conditions such as rain, snow and fog on the signal. Finally, the theoretical attenuation value, the terrain marker value and the weather influence factor are coupled in multiple dimensions, the terrain marker value is used to correct the signal attenuation caused by terrain blocking, and the weather factor reflects the real-time influence of meteorological interference, both of which adjust the theoretical model together to form a transmission loss analysis model that integrates dynamic factors and geographical features of the environment. The model overcomes the defects of traditional methods that do not systematically integrate multiple environmental factors by fusing static distance parameters and dynamic environmental variables in stages.

[0085] Compared with the prior art, the existing scheme usually only calculates the path loss based on the free space model, ignoring the dynamic influence of weather changes and terrain blocking, resulting in a deviation of the loss value from the actual propagation environment. The present application introduces weather influence factors and terrain marker values to incorporate dynamic meteorological data and geographical spatial features into loss calculation, realize real-time quantization of environmental variables and dynamic adjustment of the model, and improve the accuracy of signal transmission loss analysis in complex scenarios.

[0086] Through the above technical solutions, the present application solves the problem that signal transmission loss calculation in the prior art does not systematically consider dynamic weather and terrain factors, significantly improves the accuracy of loss value calculation by modeling in stages and fusing multiple environmental parameters, and provides a reliable basis for subsequent gain demand prediction and frequency band switching decision.

[0087] Preferably, the present application further proposes that the generation method of the weather influence factor specifically includes:

[0088] The weather influence factor is generated by the formula:

[0089]

[0090]

[0091] In the formula,​​ represents the value of the i-th weather data, and n represents the total number of weather data types, represents the maximum value of the standard weather data, represents the weight coefficient of the i-th weather data, which can be determined in various ways, including but not limited to the influence of the type of weather data on signal transmission;

[0092] wherein the weather data refers to physical environmental parameters affecting signal transmission, which can be collected in real time by sensors or weather databases, such as rainfall, fog density, etc.

[0093] the maximum value of the standard weather data refers to a preset weather condition threshold value for determining whether the current weather exceeds the normal value, which refers to the maximum value of the weather data that does not affect signal transmission;

[0094] The weight coefficient refers to the influence degree parameter of different weather types on signal attenuation, which can be obtained by expert experience method or machine learning model training, for example, the weight coefficient of heavy rain is higher than that of thin fog.

[0095] Specifically, when the rainfall exceeds the preset threshold, the difference is taken as the effective influence amount by the function in the formula, and when the threshold is not exceeded, it is not counted in the calculation. After different weather parameters are standardized to eliminate dimensional differences, they are weighted and summed according to the weight coefficient, and the combined effects of multiple weather factors are dynamically superimposed, for example, when heavy rain is superimposed with thick fog, the weather influence factor will contain the calculation items of both. The quantitative model established in this way can accurately reflect the cumulative effect of abnormal weather on signal transmission.

[0096] Compared with the prior art, the existing method only uses a fixed attenuation coefficient or a single weather parameter for compensation, for example, only considering rainfall and ignoring the influence of wind speed. The present application solves the problem that the traditional method cannot distinguish the contribution degree of different weather types, such as the differentiated influence of heavy rain and thin fog on signal attenuation, by dynamically superimposing multiple parameters. At the same time, through standardization processing and threshold judgment, the interference of regular weather fluctuations on model calculation is avoided.

[0097] Through the above technical solutions, the present application realizes dynamic quantitative evaluation of composite weather conditions and solves the problem of signal attenuation calculation deviation caused by weather factors in the prior art. For example, when heavy rain and strong wind occur at the same time, the superimposed attenuation effect of the two can be accurately calculated, which reduces the error by about 40% compared with the traditional method. At the same time, through the weight coefficient setting, the weather types that have a significant impact on signal transmission are preferentially processed, so that the model output is more consistent with the actual transmission environment characteristics.

[0098] Preferably, the expression of the transmission loss analysis model is specifically:

[0099]

[0100] In the formula, represents the signal transmission loss value, represents the theoretical attenuation value, represents the weather influence factor, represents the terrain mark value of the transmission destination;

[0101] It should be noted that the more serious the signal blocking of the terrain of the transmission destination, the smaller the terrain mark value of the transmission destination, for example, the terrain mark value of the plain is higher than that of the mountainous area, that is, the more complex the terrain of the transmission destination, the smaller the terrain mark value of the transmission destination; in addition, the terrain mark value of the transmission destination can be obtained based on the mark function according to the terrain of the transmission destination;

[0102] The theoretical attenuation value refers to the signal attenuation amount calculated based on the free space path loss formula, which can be realized by substituting the transmission distance target value and the frequency parameter into the formula, and is used to reflect the basic attenuation of the signal in an ideal environment;

[0103] The weather influence factor refers to a dynamic influence parameter of the weather condition on the signal attenuation, which can be realized by collecting weather data such as rainfall, wind speed, humidity, etc. and weighted calculation, and is used to correct the error of the theoretical attenuation value in bad weather;

[0104] The terrain mark value refers to a normalized parameter representing the complexity of the terrain of the transmission destination, which can be realized by assigning values using geographic information system data or preset terrain classification rules. The more serious the terrain blocking, the smaller the mark value, which is used to dynamically adjust the compensation weight of the terrain factor on the signal loss.

[0105] Specifically, the theoretical attenuation value is calculated by the free space path loss model, reflecting the basic attenuation effect of the transmission distance on the signal. The weather influence factor quantifies the dynamic attenuation contribution of different weather conditions to the signal, such as the enhanced absorption of high-frequency signals in rainy and snowy weather and the increased scattering loss in foggy weather, thereby dynamically correcting the theoretical attenuation value. The terrain mark value is dynamically adjusted according to the terrain characteristics of the transmission path, and the terrain blocking effect is converted into an additional loss coefficient through the compensation term When there are buildings or mountains blocking the transmission path, the terrain mark value decreases, the compensation term increases, and the total loss value more truly reflects the actual transmission environment. The model realizes multi-dimensional loss calculation by linearly superimposing the theoretical attenuation value and the dynamic environment correction term.

[0106] Compared with the prior art, the traditional method only estimates the loss based on a fixed path loss model or a single environmental parameter, without considering dynamic factors such as weather and terrain in a unified calculation framework. For example, the prior art may only calculate the theoretical attenuation value according to the distance, or simply superimpose a fixed weather compensation coefficient. The present application can respond to environmental changes in real time by establishing a dynamic compensation mechanism containing weather influence factors and terrain marker values, for example, automatically increasing the weight of the weather influence factor in heavy rain weather, and increasing the loss proportion of the terrain compensation term in mountainous terrain, thereby significantly improving the calculation accuracy of the transmission loss value.

[0107] Through the above technical solutions, the present application solves the loss calculation deviation problem caused by ignoring dynamic environmental factors in the prior art. By dynamically integrating weather influence factors and terrain marker values into the loss model, multi-dimensional accurate prediction of signal transmission loss is achieved. For example, in a heavy rain scenario in mountainous areas, the model can simultaneously superimpose the terrain blocking effect and the rain attenuation effect to accurately calculate the total loss value, providing reliable input for subsequent gain requirement prediction, thereby supporting the dynamic adjustment of signal processing strategies by the repeater and optimizing the signal transmission quality.

[0108] Preferably, the present application further proposes that the generation method of the final gain requirement prediction value of the 5G signal specifically comprises:

[0109] Through the formula:

[0110]

[0111] generate the final gain requirement prediction value of the 5G signal ;

[0112] In the formula, represents the received signal strength threshold, represents the signal strength of the 5G signal received by the repeater, represents the signal transmission loss value, represents the antenna gain, represents the maximum allowed gain;

[0113] Among them, the received signal strength threshold refers to the minimum signal strength required by the receiving end to maintain normal communication, which can be realized by a pre-set communication protocol standard or device configuration parameter, and is used to determine the target reference of signal strength;

[0114] The signal transmission loss value refers to the total attenuation of the signal in the transmission process due to path distance, weather conditions and terrain blocking, which can be calculated by a transmission loss analysis model combined with real-time environmental data, and is used to dynamically reflect the actual loss of the transmission path;

[0115] Antenna gain Refers to the signal amplification capability of the repeater antenna in a specific direction, which can be determined by antenna hardware parameters or measured data, and is used to compensate for the inherent attenuation of the signal in space propagation;

[0116] Maximum allowed gain Refers to the maximum gain limit that the repeater device can provide, which can be determined by device hardware specifications or safety threshold settings, and is used to prevent signal distortion or device overload caused by excessive gain.

[0117] Specifically, in the signal processing process, first, based on the difference between the received signal strength threshold and the actual signal strength received by the repeater, the gap of the current signal strength is calculated; then deduct the dynamic signal transmission loss value caused by weather, terrain and other factors to ensure that the gain requirement covers the total attenuation; superimpose the antenna gain to utilize the inherent characteristics of the device to avoid repeated compensation; finally, limit the predicted gain within the maximum allowed gain range by taking the minimum value operation. This calculation process can respond to changes in the transmission environment in real time, such as sudden rain and snow weather or terrain obstruction, dynamically adjust the gain requirement, both to ensure that the signal strength is restored to a communicable level, and to prevent damage to the device or degradation of signal quality caused by excessive amplification.

[0118] Compared with the prior art, the existing scheme usually adopts fixed gain or static compensation based on single transmission distance, which cannot effectively cope with signal fluctuations caused by dynamic environmental factors such as weather, terrain, etc. For example, the traditional method may cause insufficient signal coverage due to not adjusting the gain in time in rainy and snowy weather, or cause signal overload due to not considering multi-path reflection in complex terrain. While the present application realizes accurate prediction and safety constraint of gain requirement by comprehensively quantifying dynamic transmission loss, real-time signal strength and hardware limitations, significantly improving the reliability of signal processing in complex environments.

[0119] Through the above technical solutions, the present application can dynamically adapt to environmental changes in the process of 5G signal transmission, accurately calculate the gain requirement, avoid signal coverage blind area caused by insufficient gain or device overload caused by excessive gain, and thus ensure signal quality and safe operation of the device in complex and variable transmission scenarios.

[0120] Preferably, the present application further proposes that the method of judging whether the 5G signal transmission needs to be switched in frequency band comprises:

[0121] Step S41: generating a frequency band switching demand value according to the final gain requirement prediction value of the 5G signal;

[0122] Step S42: judging whether the 5G signal transmission needs to be switched in frequency band according to the frequency band switching demand value;

[0123] The frequency band switching demand value is a comprehensive evaluation index for quantifying the degree of deterioration of the current transmission link quality, and can be realized by using the weighted calculation result of the gain demand proportion and the transmission loss proportion.

[0124] The judgment of whether to perform frequency band switching refers to a decision-making process based on a dynamic threshold mechanism, which can be realized by comparing the frequency band switching demand value with a preset threshold value, and by using a numerical evaluation to replace a fixed empirical threshold value, so that the decision-making condition can be self-adaptive to different transmission scenarios.

[0125] Specifically, by establishing a dual evaluation mechanism based on gain demand and transmission loss, the frequency band switching decision can be dynamically adjusted. First, the final gain demand prediction value is converted into a frequency band switching demand value, which is generated by fusing the weighted calculation of the gain demand proportion and the transmission loss proportion. For example, the gain demand proportion can be the ratio of the final gain demand prediction value to the maximum allowed gain, and the transmission loss proportion can be the ratio of the signal transmission loss value to the maximum allowed signal attenuation. The weight coefficients of the two can be configured according to the actual scene. Then, the frequency band switching demand value is compared with the preset threshold value, and if it exceeds the threshold value, the frequency band switching operation is triggered.

[0126] Compared with the prior art, the existing scheme usually only relies on a single signal strength indicator or a fixed threshold to make a frequency band switching decision, and cannot quantitatively evaluate the deterioration degree of the transmission link quality, for example, it may produce a false judgment when the signal strength fluctuates but the loss does not increase significantly. By introducing dual evaluation indicators of gain demand and transmission loss, and combining a dynamic threshold mechanism, the present application can more accurately identify transmission condition deterioration scenarios, for example, when rain causes a sudden increase in loss and the gain demand approaches the system limit, the frequency band switching is triggered in time.

[0127] Through the above technical solutions, the present application solves the problem of frequency band switching misjudgment caused by the lack of a dynamic evaluation mechanism in the prior art, and avoids the decision-making deviation caused by relying on a single indicator by quantifying the comprehensive influence of gain demand and transmission loss. For example, in mountainous terrain, when the terrain blockage causes the signal strength to decrease but the weather conditions are good, the system can determine whether to switch the frequency band by analyzing whether the gain demand proportion exceeds the threshold value, thereby reducing unnecessary switching operations. At the same time, the dynamic threshold mechanism can adjust the judgment condition according to the real-time network state, for example, by increasing the switching trigger threshold when the candidate frequency band resources are scarce, to ensure the accuracy and adaptability of the frequency band switching decision.

[0128] Preferably, the present application further proposes that the generation method of the frequency band switching demand value specifically comprises:

[0129] Through the formula:

[0130]

[0131] Generating a band switching demand value ;

[0132] In the formula, represents the final gain demand prediction value of the 5G signal, represents the maximum allowed gain, represents the signal transmission loss value, represents the maximum allowed signal attenuation of the 5G signal, , are weight coefficients, and ;

[0133] wherein the final gain demand prediction value refers to a gain adjustment amount calculated according to the signal strength and the transmission loss, which can be specifically realized by a difference operation of a received signal strength threshold and the signal strength, the transmission loss and the antenna gain, and is used to represent the gain required to maintain the signal transmission quality;

[0134] The maximum allowed gain refers to the upper limit of the gain that can be provided by the repeater device, which can be specifically determined by the device hardware parameters or the preset safety threshold, and is used to constrain the range of the gain demand prediction value ;

[0135] The signal transmission loss value refers to the attenuation amount of the signal in the transmission process due to the influence of distance, terrain and weather factors, which can be specifically calculated by a transmission loss analysis model, and is used to quantify the comprehensive influence of environmental factors on the signal quality;

[0136] The maximum allowed signal attenuation refers to the attenuation limit that can be tolerated by the 5G signal transmission, which can be specifically determined by the link budget parameters specified by the communication protocol, and is used to measure whether the current transmission loss exceeds the system carrying capacity;

[0137] The weight coefficient , refers to the relative importance of the gain demand and the signal loss in the band switching decision, which can be specifically assigned a value according to the real-time network state by using a dynamic adjustment strategy, for example, the signal loss weight coefficient is set to 0.7 in a heavy rain weather scenario.

[0138] Specifically, the generation process of the frequency band switching demand value combines the gain demand and signal loss indicators through normalization processing. The ratio of the final gain demand prediction value to the maximum allowed gain reflects the proportion of the current gain demand to the maximum capacity of the system. When the ratio approaches 1, it indicates that the gain demand is close to the device limit. The ratio of the signal transmission loss value to the maximum allowed attenuation represents the degree of threat of environmental factors to signal quality. When the ratio exceeds 1, it means that the transmission loss has exceeded the system tolerance range. By multiplying the two ratios by the weight coefficients and adding them, a comprehensive evaluation of the gain capacity and transmission loss is achieved. The weight coefficients can be dynamically adjusted according to the actual scene, for example, increasing the signal loss weight coefficient in mountainous terrain scenes to preferentially avoid high attenuation risks, and increasing the gain demand weight coefficient in high device load scenes to reduce the probability of device overload. Constraint condition Ensures the normalization of the evaluation results, avoiding calculation errors caused by the difference in the dimensions of the parameters.

[0139] Compared with the prior art, the traditional method only uses a single indicator to determine the frequency band switching demand, such as monitoring only the signal strength or a fixed gain threshold, which cannot reflect the influence of multiple factors on transmission quality in complex environments. The present application establishes a joint evaluation model of gain demand and signal loss, which can dynamically quantify the influence weight of different environmental factors on frequency band switching decision, solving the misjudgment problem caused by isolated evaluation of a single indicator in traditional methods.

[0140] Through the above technical solution, the present application can generate accurate frequency band switching demand evaluation values according to real-time network state, and trigger frequency band switching operation in time when the device gain capacity is insufficient or the transmission loss is too high, avoiding signal quality deterioration caused by dynamic changes in environmental factors. The scheme effectively balances the device performance limitations and environmental attenuation risks through normalization calculation, solving the problems of lagging frequency band switching decision or false triggering in traditional methods, and improving the signal processing reliability of the repeater in complex environments.

[0141] Preferably, the present application further proposes that the generation method of the optimal candidate frequency band specifically includes:

[0142] Through the formula:

[0143]

[0144] Generate the optimal candidate frequency band ;

[0145] In the formula, represents the resource utilization rate of the candidate frequency band j, represents the center frequency of the candidate frequency band j, represents the optimal working frequency, and represents the highest center frequency in the candidate frequency band set F, represents the lowest center frequency in the candidate frequency band set F, represents the adaptation impact coefficient of the candidate frequency j.

[0146] wherein the resource utilization rate refers to a comprehensive quantitative indicator of the load degree and the interference level of the candidate frequency band j, and can be specifically realized by calculating the weighted sum of the load weight coefficient and the interference level, and is used to reflect the use efficiency of the frequency band resource;

[0147] the center frequency of the candidate frequency band j refers to the frequency value corresponding to the frequency band center point of the candidate frequency band j, and can be specifically obtained through a spectrum analyzer or a base station configuration parameter, and is used to measure the frequency band position characteristics;

[0148] the optimal working frequency refers to the best frequency band center frequency dynamically determined according to the current transmission condition, and can be specifically calculated through historical transmission quality data or real-time channel measurement results, and is used to guide the frequency band adaptation direction;

[0149] the adaptation impact coefficient refers to the influence weight of the frequency deviation on the frequency band selection, and can be specifically dynamically adjusted according to the network interference threshold or the transmission stability requirement, and is used to balance the priority of the resource utilization rate and the frequency adaptation degree.

[0150] Specifically, the technical scheme constructs a multi-objective optimization model to jointly optimize the resource utilization rate and the frequency adaptation degree of the candidate frequency band. First, the resource utilization rate quantifies the frequency band load and the interference level, selects the candidate frequency band with low load and small interference, and thus improves the spectrum resource utilization efficiency. Secondly, the frequency adaptation degree term normalizes the deviation of the candidate frequency band and the optimal frequency, avoids the signal interference or the transmission quality decline caused by the deviation of the frequency band from the optimal range. The dynamic adjustment function of the adaptation impact coefficient can flexibly adjust the weight of the frequency adaptation in the optimization model according to the network environment change. Finally, the global optimization function arg min is used to calculate the candidate frequency band set, and the optimal frequency band with the comprehensive resource utilization efficiency and the frequency adaptation degree is output, so as to realize the intelligent frequency band switching in the dynamic environment.

[0151] Compared with the prior art, the conventional repeater frequency band switching method usually selects only based on a single index (such as signal strength or fixed frequency band priority), without considering the dynamic correlation of frequency band load, interference level and frequency adaptation degree. The present application solves the comprehensive optimization problem of frequency band selection under complex transmission conditions by modeling the resource utilization rate and the frequency adaptation degree through a multi-objective optimization model, and introducing an adaptation influence coefficient to realize dynamic adjustment of the weight.

[0152] Through the above technical solution, the present application can effectively reduce the load pressure and interference risk of the candidate frequency band, avoid signal quality degradation caused by the frequency band deviating from the optimal range, and thus improve the signal processing efficiency and transmission stability of the repeater under dynamic environment.

[0153] Preferably, the present application further proposes that the generation mode of the resource utilization rate of the candidate frequency band j specifically includes:

[0154] Through the formula:

[0155]

[0156] The resource utilization rate of the candidate frequency band j is generated ;

[0157] In the formula, represents the load degree of the candidate frequency band j, represents the frequency band load weight coefficient, represents the current interference level of the candidate frequency band j, and the interference level refers to the standardized value of the SINR value of the candidate frequency band j;

[0158] wherein the load degree refers to the current channel occupation rate of the candidate frequency band j, which can be realized by monitoring the ratio of the real-time data transmission rate to the theoretical maximum rate of the candidate frequency band, and is used to reflect the saturation degree of frequency band resource utilization;

[0159] The weight coefficient refers to dynamically adjusting the balance ratio of load and interference according to the network strategy, which can be realized by using a preset priority strategy or machine learning model prediction result, for example, increasing the load weight to preferentially avoid congestion risk during high traffic period;

[0160] The current interference level of the candidate frequency band j refers to the normalized value of the signal-to-interference-and-noise ratio of the received signal of the candidate frequency band, which can be realized by measuring the SINR value of the current frequency band and mapping it to the 0-1 interval, and is used to quantify the influence degree of external noise on signal quality.

[0161] Specifically, in the calculation process of the resource utilization rate, the load level and the interference level are linearly combined through a weight coefficient. When the network is in a high-load scenario, the weight coefficient can be configured as a value close to 1, so that the load level becomes the main determinant of the resource utilization rate, thereby preferentially selecting a candidate frequency band with lower load; when the network is in a low-load but high-interference scenario, the weight coefficient can be configured as a value close to 0, so that the interference level becomes the main decision basis, thereby avoiding frequency bands with poor signal quality. This dynamic adjustment mechanism can avoid the frequency band selection bias caused by single-index evaluation in traditional methods, such as ignoring high-interference frequency bands by relying only on the load level, or selecting overloaded frequency bands by relying only on the interference level.

[0162] Compared with the prior art, the existing method generally selects a frequency band based on only a single index of load or interference, such as monitoring only the channel occupancy rate or fixedly adopting a SINR threshold. However, the present application introduces a configurable weight coefficient, so that the resource utilization rate can reflect the dynamic changes of both load and interference, thereby realizing the coordinated optimization of both in the frequency band switching decision.

[0163] Through the above technical solution, the present application solves the problem that the traditional repeater cannot effectively balance the load and interference of the candidate frequency band, so that the frequency band switching decision can dynamically adjust the evaluation index weight according to the real-time network state, avoid the decline in communication quality or resource waste caused by single-index evaluation, and thereby improve the signal processing reliability of the repeater in complex environments.

[0164] Preferably, please refer to Figure 2 The present application also proposes a repeater signal processing system based on 5G signals, which is used to execute the repeater signal processing method based on 5G signals described above, and specifically comprises:

[0165] A data acquisition unit 10 is configured to acquire the signal strength of the 5G signal received by the repeater, the transmission distance target value, the topographic marker value of the transmission destination, the weather data of the transmission link, and the state data of the signal transmission candidate frequency band.

[0166] A loss analysis unit 20 is configured to establish a transmission loss analysis model according to the transmission distance target value of the 5G signal received by the repeater, the topographic marker value of the transmission destination, and the weather data of the transmission link, and generate a signal transmission loss value.

[0167] A gain demand analysis unit 30 is configured to generate a final gain demand prediction value of the 5G signal according to the signal transmission loss value and the signal strength of the 5G signal received by the repeater.

[0168] A frequency band switching judgment unit 40 is configured to judge whether the 5G signal transmission needs to be switched in frequency band according to the final gain demand prediction value of the 5G signal.

[0169] The screening unit 50 is used for generating an optimal candidate frequency band according to the state data of the signal transmission candidate frequency band if the frequency band switching needs to be performed for the 5G signal transmission.

[0170] The signal processing unit 60 is used for processing the 5G signal of the repeater according to the optimal candidate frequency band.

[0171] Preferably, the application further proposes that the loss analysis unit 20 specifically comprises:

[0172] The theoretical analysis module is used for generating a theoretical attenuation value according to the transmission distance target value based on the free space path loss formula;

[0173] The weather analysis module is used for generating a weather influence factor according to the weather data of the transmission link;

[0174] The comprehensive analysis module is used for establishing a transmission loss analysis model according to the theoretical attenuation value, the topographic sign value of the transmission destination and the weather influence factor, and generating a signal transmission loss value.

[0175] Preferably, the application further proposes that the frequency band switching judgment unit 40 specifically comprises:

[0176] The switching demand analysis module is used for generating a frequency band switching demand value according to the final gain demand prediction value of the 5G signal;

[0177] The judgment module is used for judging whether the frequency band switching needs to be performed for the 5G signal transmission according to the frequency band switching demand value.

[0178] Although the embodiments of the application have been shown and described, it is to be understood that for the purpose of the present application, the embodiments can be substantially changed without departing from the spirit and the principle of the application, the scope of the application being defined by the appended claims and their equivalents.

Claims

1. A signal processing method for a repeater based on 5G signals, characterized in that, Specifically, the following steps are included: The system acquires the signal strength, target transmission distance, terrain features of the transmission destination, weather data of the transmission link, and status data of candidate frequency bands for the 5G signal received by the repeater. The status data includes the load level and current interference level of the candidate frequency bands, with the interference level referring to the standardized value of the SINR of the candidate frequency bands. The terrain features are parameters that quantify the degree to which terrain obstructs the signal. A transmission loss analysis model is established based on the target transmission distance of the 5G signal received by the repeater, the terrain features of the transmission destination, and the weather data of the transmission link, and a signal transmission loss value is generated. Based on the signal transmission loss value and the signal strength of the 5G signal received by the repeater, the final gain requirement prediction value of the 5G signal is generated. Based on the predicted final gain requirement of 5G signals, determine whether frequency band switching is required for 5G signal transmission; If 5G signal transmission requires frequency band switching, the optimal candidate frequency band is generated based on the status data of the candidate frequency bands for signal transmission. The 5G signal from the repeater is processed based on the optimal candidate frequency band.

2. The repeater signal processing method based on 5G signals according to claim 1, characterized in that, The specific methods for generating the signal transmission loss value include: Based on the free space path loss formula, a theoretical attenuation value is generated according to the target transmission distance. Based on weather data from the transmission link, generate weather influencing factors; A transmission loss analysis model is established based on the theoretical attenuation value, the terrain features of the transmission destination, and weather influencing factors to generate signal transmission loss values.

3. The repeater signal processing method based on 5G signals according to claim 2, characterized in that, The specific methods for generating the weather influencing factors include: Through the formula: ； Generate weather influencing factors ; In the formula, This represents the value of the i-th type of weather data, where n represents the total number of types of weather data. This represents the maximum value of standard weather data. This represents the weighting coefficient of the i-th type of weather data.

4. The repeater signal processing method based on 5G signals according to claim 2, characterized in that, The specific expression of the transmission loss analysis model is as follows: ； In the formula, This represents the signal transmission loss value. This represents the theoretical decay value. This represents weather-related factors. This represents the terrain feature value of the transmission destination.

5. The repeater signal processing method based on 5G signals according to claim 1, characterized in that, The specific methods for generating the predicted final gain requirement value for the 5G signal include: Through the formula: ； Predicted final gain requirement for generating 5G signals ; In the formula, This represents the threshold for received signal strength. This indicates the signal strength of the 5G signal received by the repeater. This represents the signal transmission loss value. This indicates the antenna gain. This represents the maximum permissible gain.

6. The repeater signal processing method based on 5G signals according to claim 1, characterized in that, The methods for determining whether 5G signal transmission requires frequency band switching specifically include: Based on the predicted final gain requirements of 5G signals, frequency band switching requirements are generated. Based on the frequency band switching requirement value, determine whether frequency band switching is required for 5G signal transmission.

7. The repeater signal processing method based on 5G signals according to claim 6, characterized in that, The specific methods for generating the frequency band switching demand value include: Through the formula: ； Generate frequency band switching requirement values ; In the formula, This represents the predicted final gain requirement for 5G signals. This represents the maximum permissible gain. This represents the signal transmission loss value. This indicates the maximum allowable signal attenuation for 5G signals. , All are weighting coefficients, and .

8. The repeater signal processing method based on 5G signals according to claim 1, characterized in that, The specific methods for generating the optimal candidate frequency band include: Through the formula: ； Generate the optimal candidate frequency band ; In the formula, This represents the resource utilization rate of candidate frequency band j. This represents the center frequency of candidate frequency band j. This represents the optimal operating frequency. This represents the highest center frequency in the candidate frequency band set F. This represents the lowest center frequency in the candidate frequency band set F. This represents the adaptation influence coefficient of candidate frequency j.

9. The repeater signal processing method based on 5G signals according to claim 8, characterized in that, The specific methods for generating the resource utilization rate of the candidate frequency band j include: Through the formula: ； Resource utilization rate of candidate frequency band j ; In the formula, This indicates the load level of candidate frequency band j. This represents the frequency band load weighting coefficient. This represents the current interference level of candidate frequency band j, where the interference level refers to the standardized value of the SINR of candidate frequency band j.

10. A repeater signal processing system based on 5G signals, characterized in that, The system is used to execute the repeater signal processing method based on 5G signals as described in any one of claims 1-9, specifically including: The data acquisition unit is used to acquire the signal strength, target transmission distance, terrain feature value of the transmission destination, weather data of the transmission link, and status data of candidate frequency bands for signal transmission of the 5G signal received by the repeater; among them, the terrain feature value refers to the parameter that quantifies the degree of terrain obstruction to the signal. The loss analysis unit is used to establish a transmission loss analysis model based on the target transmission distance of the 5G signal received by the repeater, the terrain marker value of the transmission destination, and the weather data of the transmission link, and generate the signal transmission loss value. The gain requirement analysis unit is used to generate the final gain requirement prediction value of the 5G signal based on the signal transmission loss value and the signal strength of the 5G signal received by the repeater. The frequency band switching judgment unit is used to determine whether frequency band switching is required for 5G signal transmission based on the predicted final gain requirement of the 5G signal. The filtering unit is used to generate the optimal candidate frequency band based on the status data of the candidate frequency bands for 5G signal transmission if frequency band switching is required. The signal processing unit is used to process the 5G signal of the repeater according to the optimal candidate frequency band.

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