Adaptive power adjustment and active interference suppression method for 5g communication device

Abstract

The application discloses a 5G communication device adaptive power adjustment and active inhibition method of adjacent frequency interference, relates to the technical field of wireless communication, and comprises the following steps: collecting multi-dimensional environment characteristic data in real time, calculating an interference risk coefficient, and generating a global transmission power upper limit constraint standard; carrying out multi-target joint calculation to obtain a group of optimal device operation configuration parameters; the optimal device operation configuration parameters are sent to the device baseband unit and the radio frequency unit to execute signal transmission; the residual adjacent frequency interference power value after the transmission is completed is collected in real time, and the real-time collected adjacent frequency interference power value is compared with a preset interference protection threshold for judgment; by collecting multi-dimensional wireless environment characteristics, the online gradient boosting decision tree is used to predict the adjacent frequency interference risk in advance, the global transmission power upper limit constraint standard with edge compression is generated by the frequency domain weighting window, the radio frequency out-of-band radiation is controlled from the source, and the defects of interference disposal lag and easy link fluctuation disconnection are solved.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and in particular to a method for adaptive power adjustment and active suppression of adjacent channel interference in 5G communication equipment. Background Technology

[0002] With the large-scale commercialization of 5G networks and the gradual implementation of 5G-A technology, a large number of urban macro base stations, indoor micro base stations, industrial private network base stations, and vehicle-to-everything (V2X) terminals are being deployed in a dense, co-located manner. As spectrum resources become increasingly congested, adjacent channel interference between adjacent channels has become a core bottleneck restricting network throughput and communication reliability.

[0003] Adjacent channel interference mainly originates from out-of-band radiation from the transmitter. On one hand, the nonlinear characteristics of the power amplifier itself cause spectrum regeneration; on the other hand, the non-ideal amplitude-frequency characteristics of the RF filter can lead to adjacent channel signal leakage. Existing adjacent channel interference suppression methods are all passive, meaning that once the equipment detects excessive interference in the adjacent channel, it directly reduces the overall transmit power or shuts down some frequency band resources. The above-mentioned existing solutions have three inherent drawbacks: 1. The adjustment response has a significant lag, and it can only be dealt with after the interference occurs and the user's communication quality deteriorates. In high-speed mobile and industrial sudden interference scenarios, link interruption and a large amount of data retransmission are likely to occur. 2: The power adjustment method is simple and crude, relying solely on overall power reduction to suppress interference, without coordinated optimization of spatial domain, frequency domain, and hardware parameters, which will significantly sacrifice cell coverage and system spectrum capacity; 3. The lack of a tiered compensation mechanism means that when power and beam adjustment reach hardware limits, only frequency band resources can be silenced. There is no differentiated protection for different services, making it prone to latency exceeding limits and communication failures in high-reliability, low-latency industrial services. Therefore, this invention proposes an adaptive power adjustment and adjacent-channel interference active suppression method for 5G communication equipment to address the shortcomings of existing technologies. Summary of the Invention

[0004] To address the aforementioned issues, the present invention aims to provide a method for adaptive power adjustment and active suppression of adjacent channel interference in 5G communication equipment. By collecting multi-dimensional wireless environment characteristics, and using an online gradient boosting decision tree to predict the risk of adjacent channel interference within a period T1, the method generates a global transmit power upper limit constraint standard with edge suppression through a frequency domain weighted window. This enables control of out-of-band radio frequency radiation from the source. Compared with traditional passive interference suppression schemes, this method solves the defects of traditional schemes, such as delayed interference handling and easy link fluctuations and disconnections, and provides more targeted interference suppression.

[0005] To achieve the above objectives, the present invention provides the following technical solution: A method for adaptive power adjustment and active suppression of adjacent channel interference in 5G communication equipment includes the following steps: S1: Real-time collection of multi-dimensional environmental feature data including resource block occupancy rate, neighboring cell load information, local historical transmit power statistics, adjacent channel recalculation, and access terminal moving speed. Input all multi-dimensional environmental feature data into the online learning model to predict the probability and intensity of adjacent channel harmful interference within a fixed time period T1. Then calculate the interference risk coefficient and finally generate the global transmit power upper limit constraint standard based on the interference risk coefficient. S2: Perform multi-objective joint calculations on antenna signal transmission direction, transmission power of each sub-band, and power amplifier nonlinearity compensation parameters to obtain a set of optimal equipment operation configuration parameters; S3: The optimal equipment operation configuration parameters obtained from S2 are sent to the equipment baseband unit and radio frequency unit to execute signal transmission. The equipment collects the residual adjacent channel interference power value after the transmission is completed in real time. S4: Compare the real-time acquired adjacent channel interference power value with the preset interference protection threshold: If the adjacent channel interference power value is less than the preset interference protection threshold, the device will maintain the current operating configuration and continue to work; if the adjacent channel interference power value is greater than the preset interference protection threshold, a differentiated frequency band isolation processing strategy will be implemented according to the latency and reliability level of the current transmission service, and the entire closed-loop adjacent channel interference suppression process will be completed in sequence.

[0006] A further improvement is that S1 includes inputting all multi-dimensional environmental features into an online gradient boosting decision tree model, then outputting the probability and intensity of adjacent-channel harmful interference, calculating the interference risk coefficient, and then generating a global transmit power upper limit constraint standard through a frequency domain weighted window function.

[0007] A further improvement is that when S2 performs multi-target joint calculations, the adjustable parameters during the calculation include the digital beamforming matrix, the analog phase shifting matrix, the power amplifier bias voltage, the power amplifier digital predistortion coefficient, and the transmit power of each resource block.

[0008] Further improvements include: the S2 multi-objective joint calculation process also includes setting three types of constraints: upper limit constraint of power spectral density, spatial null constraint of adjacent frequency device direction, and power amplifier adjacent channel leakage ratio constraint. Then, the antenna signal transmission direction, the transmission power of each sub-band, and the power amplifier nonlinear compensation parameters are iteratively solved to obtain a set of optimal equipment operation configuration parameters.

[0009] Further improvements are made in that the power amplifier adjacent channel leakage ratio constraint is calculated through a memory polynomial model. Specifically, the memory polynomial model calculates the out-of-band radiated power of each antenna branch in real time, and then uses the overall adjacent channel leakage index of the array as the basis for determining the power amplifier adjacent channel leakage ratio constraint. Finally, the predistortion coefficient and the power amplifier power supply bias are adjusted synchronously to achieve nonlinear compensation.

[0010] Further improvements are made in the following aspects: The differentiated frequency band isolation processing strategy in S4 specifically includes: if the current service is a non-latency-sensitive broadband service, the resource block with the highest contribution to frequency band edge interference is identified, and then the transmission power of this part of the resource block is reduced or silenced to construct an equivalent protection frequency band, and the original data is migrated to the resource block in the center of the frequency band for transmission; if the current service is a high-reliability low-latency service, micro-slot spectrum hopping transmission is adopted, the sub-band with a lower adjacent channel interference risk level is selected to carry data, and the power margin is borrowed from the idle time slot to compensate for the bandwidth loss caused by the hopping.

[0011] Further improvements include: the method further includes neighboring cell cooperative interference prediction and radio frequency front-end cooperative tuning control; when performing neighboring cell cooperative interference prediction, the load, transmit power and beam azimuth information of neighboring base stations are obtained through the base station X2 interface, and the signal quality and interference measurement information of neighboring cells are obtained through the terminal uplink measurement report. The above information is used as new environmental features and input into the online gradient boosting decision tree model to realize multi-cell joint interference risk prediction; when performing radio frequency front-end cooperative tuning control, the out-of-band suppression depth of the tunable radio frequency filter and the antenna array coupling matching parameters are adjusted synchronously according to the predicted adjacent channel interference risk level.

[0012] Further improvements include: the method also includes abnormal operating condition degradation and fault tolerance control. When there is distortion in the measurement data of the radio frequency detection channel, or the prediction confidence of the online learning model is lower than the threshold, or the multi-objective optimization solution cannot converge, it automatically switches to the preset static power template and fixed spatial null beam configuration to ensure continuous and stable transmission of the communication link.

[0013] Further improvements include: the method also includes hierarchical scheduling control of computing power, adaptively switching the complexity of the online learning model according to the hardware computing power capability of the 5G communication equipment, high computing power base stations adopt the complete online gradient boosting decision tree model, and low computing power terminals enable a lightweight simplified prediction model to reduce the real-time computing consumption of the baseband.

[0014] Further improvements are made in that the method also includes long-term self-iterative optimization control, which periodically counts the adjacent channel interference level, system throughput, and overall power consumption of the entire network at a fixed statistical period of T2, and then automatically updates the hyperparameters of the online learning model, the power envelope mapping coefficients used to generate the global transmit power upper limit constraint standard, and the interference protection threshold based on the statistical results.

[0015] The beneficial effects of this invention are as follows: The method of this invention collects multi-dimensional wireless environment features, uses an online gradient boosting decision tree to predict adjacent channel interference risks in advance within period T1, and generates a global transmit power upper limit constraint standard with edge suppression through a frequency domain weighted window. This enables control of radio frequency out-of-band radiation from the source, solving the defects of traditional solutions such as delayed interference handling and easy link fluctuation and disconnection. The interference suppression is more targeted. This invention optimizes the beam, frequency band power, and all adjustable parameters of the power amplifier by uniformly optimizing the beam, frequency band power, and superimposing triple transmission constraints. It also relies on a memory polynomial model to compensate for the nonlinear distortion of the power amplifier in real time. Without simply backing up the transmission power, it can improve spectral efficiency and reduce the power consumption of the whole machine while suppressing adjacent channel leakage, and the overall spectrum utilization can be improved. This invention employs a differentiated frequency band isolation processing strategy, which can suppress adjacent channel interference without sacrificing high-reliability service transmission performance, thus meeting the communication quality requirements of 5G services such as high-volume downloads and real-time industrial control. This invention achieves multi-cell joint prediction by acquiring neighboring cell load through the X2 interface, synchronously tunes RF devices, and suppresses interference through a two-layer hardware and software approach, adapting to complex deployment scenarios involving multiple standards and millimeter waves. An anomaly degradation and fault tolerance mechanism automatically switches static parameters when measurement or algorithm failures occur, ensuring link stability and improving reliability under extreme conditions. Through hierarchical scheduling of computing power, it can switch between light and heavy prediction models as needed, reducing baseband computing power consumption. Lightweight terminals can adapt without hardware modifications. By iteratively updating the model, power mapping coefficients, and interference thresholds on a T2 cycle, it can autonomously adapt to dynamic electromagnetic environments, reducing manual debugging and maintenance costs. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the method flow of the present invention. Detailed Implementation

[0017] To enhance understanding of the present invention, the present invention will be further described in detail below with reference to embodiments. These embodiments are only used to explain the present invention and do not constitute a limitation on the scope of protection of the present invention.

[0018] Example 1 according to Figure 1 As shown, this embodiment proposes an adaptive power adjustment and adjacent channel interference active suppression method for 5G communication equipment. Taking a 5G indoor micro base station as an example, considering the limited computing resources of indoor micro base stations, a lightweight online learning model is adopted to balance performance and resource consumption (interference prediction period T1=100ms, long-term self-iterative statistical period T2=1h, and the preset interference protection threshold is fixed according to the RF regulatory standard of the 3.5GHz mainstream commercial frequency band): S1: The device collects multi-dimensional environmental feature data every 20ms, including the current resource block occupancy rate, load information broadcast by neighboring cells, the average and variance of local transmit power in the past 5 minutes, downlink ACK / NACK repetition calculation of adjacent channel, and the real-time moving speed of the access terminal; at the same time, it receives the load, transmit power, and antenna beam pointing data of 3 neighboring micro base stations through the X2 interface, and uses the neighboring cell load as a new feature input to the online gradient boosting decision tree lightweight simplification model; the model is updated every T1=100ms, outputting the probability of adjacent channel harmful interference and the interference intensity level within the next 100ms window, and calculating the comprehensive interference risk coefficient; combined with the frequency domain weighted window function and the iteratively updatable power envelope mapping coefficient, a global transmit power upper limit constraint standard is generated, which automatically reduces the allowable transmit power at the frequency band edge near the adjacent channel.

[0019] S2: Based on the global transmit power upper limit constraint standard output by S1, multi-objective joint calculation is carried out. Adjustable parameters include digital beamforming matrix, analog phase shift matrix, power amplifier bias voltage, power amplifier digital predistortion coefficient, and transmit power of each resource block. During the calculation process, three types of hard constraints are set simultaneously: power spectral density upper limit constraint, adjacent channel equipment azimuth spatial domain null constraint, and power amplifier adjacent channel leakage ratio constraint. The out-of-band radiated power of each antenna branch is calculated in real time using a memory polynomial model. The overall adjacent channel leakage index of the array is used as the constraint judgment basis. The predistortion coefficient and power amplifier power supply bias voltage are iteratively adjusted synchronously. Finally, the optimal equipment operation configuration parameters are obtained by solving. The radio frequency front-end coordinated tuning control is executed synchronously. The out-of-band suppression depth of the tunable radio frequency filter and the antenna array coupling matching parameters are adjusted according to the current interference risk level to assist in reducing adjacent channel leakage at the hardware level.

[0020] S3: The optimal equipment operation configuration parameters are sent to the baseband unit and radio frequency unit to complete signal transmission; the radio frequency detection channel continuously collects the signal power of adjacent channel and obtains the residual adjacent channel interference power value in real time; the sensor acquisition status, model prediction confidence, and optimization iteration convergence status are monitored in real time. Once sensor data distortion, confidence below the threshold, or optimization solution non-convergence occurs, abnormal operating condition degradation fault tolerance control is immediately executed, switching to the factory preset static power template and fixed spatial null beam to ensure uninterrupted communication.

[0021] S4: Compare the measured residual adjacent channel interference power with the preset interference protection threshold; if the current service is a non-latency-sensitive broadband service such as high-definition video or file download, determine the resource block with the highest interference contribution at the frequency band edge, reduce the transmission power of this resource block or completely silence it, construct an equivalent protection frequency band, and migrate all the original service data to the low-interference resource block in the center of the frequency band for transmission; if the current service is a high-reliability, low-latency service such as real-time control of industrial robots or remote equipment monitoring, enable micro-timeslot spectrum jump transmission, select the sub-band with lower historical average interference risk to carry data, and extract the power margin of idle time slots to compensate for the bandwidth loss caused by the jump, so as to ensure that the service latency and transmission reliability indicators meet the standards.

[0022] Finally, the device performs long-term self-iterative optimization control once every T2=1 hour, statistically analyzes the average level of adjacent channel interference across the entire network, the overall throughput of the cell, and the average power consumption of the entire device. Based on the statistical results, it automatically updates the hyperparameters of the online gradient boosting decision tree model, the power envelope mapping coefficients used to generate the global transmit power upper limit constraint standard, and the preset interference protection threshold, continuously adapting to the daily dynamic changes in user access and electromagnetic interference environment.

[0023] Example 2 according to Figure 1 As shown, this embodiment proposes a method for adaptive power adjustment and active suppression of adjacent channel interference in 5G communication equipment. Taking a 5G macro base station applied in an industrial scenario as an example, this scenario requires high computing power. Therefore, a complete online gradient boosting decision tree model is adopted (interference prediction period T1=100ms, long-term self-iterative statistical period T2=1h, and the preset interference protection threshold is fixed according to the RF regulatory standard of the 3.5GHz mainstream commercial frequency band): S1: Completely collects multi-dimensional environmental features and receives operational data from 5 neighboring base stations via the X2 interface to complete neighboring cell collaborative interference prediction; the complete gradient boosting tree model completes interference prediction every T1=100ms, outputs a high-precision interference risk coefficient, and generates a global transmit power upper limit constraint standard by combining frequency domain weighted window and power envelope mapping coefficient; the computing power hierarchical scheduling control automatically activates the complete prediction model without lightweight pruning, resulting in higher interference prediction accuracy.

[0024] S2: Multi-objective joint calculation fully traverses all adjustable parameters, strictly implements triple constraints, memorizes the high-order fitting of the polynomial model to the nonlinear characteristics of the power amplifier, and accurately suppresses spectrum regeneration under high-power transmission conditions; the radio frequency front-end is coordinated and tuned to synchronously switch the filter with multiple suppression levels, adapting to sudden strong electromagnetic interference in industrial parks.

[0025] S3: Continuously collects residual interference power, monitors equipment operating status in real time, and automatically switches to fault-tolerant static configuration when measurement or algorithm anomalies occur, ensuring uninterrupted transmission of industrial control services.

[0026] S4: Differentiate spectrum isolation strategies for high-definition monitoring broadband services and URLLC control services for automated equipment in the industrial park. Automatically collect network operation indicators every T2=1 hour, iteratively update model hyperparameters, power envelope mapping coefficients, and interference protection thresholds, and adapt to the differentiated load and interference scenarios during morning and evening peak hours in industrial parks in the long term.

[0027] By collecting multi-dimensional wireless environment characteristics and using online gradient boosting decision trees to predict adjacent channel interference risks within period T1, and generating a global transmit power upper limit constraint standard with edge suppression through a frequency domain weighted window, it is possible to control out-of-band radio frequency radiation from the source, solving the shortcomings of traditional solutions such as delayed interference handling and easy link fluctuations and disconnections, and making interference suppression more targeted. By uniformly optimizing the entire set of adjustable parameters of beam, frequency band power, and power amplifier, superimposing triple transmit constraints, and relying on a memory polynomial model to compensate for power amplifier nonlinear distortion in real time, it is possible to improve spectrum efficiency and reduce overall power consumption while suppressing adjacent channel leakage, thus comprehensively improving spectrum utilization. Through differentiated frequency band isolation processing strategies, it is possible to suppress adjacent channels. It can overcome interference without sacrificing high-reliability service transmission performance, and can meet the communication quality requirements of 5G services such as high-volume downloads and real-time industrial control. It can obtain neighbor cell load through the X2 interface to achieve multi-cell joint prediction, synchronously tune radio frequency devices, and suppress interference through both hardware and software layers, adapting to complex deployment scenarios of multiple standards and millimeter waves. The abnormal degradation fault tolerance mechanism can automatically switch static parameters when measurement and algorithm failures occur to ensure link stability and improve the reliability of operation under extreme conditions. Through hierarchical scheduling of computing power, it can switch between light and heavy prediction models as needed to reduce baseband computing power consumption. Lightweight terminals can be adapted without hardware modification. By updating the model, power mapping coefficient and interference threshold in a T2 cycle, it can autonomously adapt to dynamic electromagnetic environments and reduce manual debugging and maintenance costs.

[0028] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

A method for adaptive power adjustment and active suppression of adjacent channel interference in 1.5G communication equipment, characterized in that, Includes the following steps: S1: Real-time collection of multi-dimensional environmental feature data including resource block occupancy rate, neighboring cell load information, local historical transmit power statistics, adjacent channel recalculation, and access terminal moving speed. Input all multi-dimensional environmental feature data into the online learning model to predict the probability and intensity of adjacent channel harmful interference within a fixed time period T1. Then calculate the interference risk coefficient and finally generate the global transmit power upper limit constraint standard based on the interference risk coefficient. S2: Perform multi-objective joint calculations on antenna signal transmission direction, transmission power of each sub-band, and power amplifier nonlinearity compensation parameters to obtain a set of optimal equipment operation configuration parameters; S3: The optimal equipment operation configuration parameters obtained from S2 are sent to the equipment baseband unit and radio frequency unit to execute signal transmission. The equipment collects the residual adjacent channel interference power value after the transmission is completed in real time. S4: Compare the real-time adjacent channel interference power value with the preset interference protection threshold: If the adjacent channel interference power value is less than the preset interference protection threshold, the device will maintain the current operating configuration and continue to work. If the adjacent channel interference power value is greater than the preset interference protection threshold, a differentiated frequency band isolation processing strategy will be implemented according to the latency and reliability level of the current transmission service, and the entire closed-loop adjacent channel interference suppression process will be completed in sequence.

2. The method for adaptive power adjustment and active suppression of adjacent channel interference in 5G communication equipment according to claim 1, characterized in that: S1 includes inputting all multi-dimensional environmental features into an online gradient boosting decision tree model, then outputting the probability of occurrence and intensity of adjacent-channel harmful interference, calculating the interference risk coefficient, and then generating a global transmit power upper limit constraint standard through a frequency domain weighted window function.

3. The method for adaptive power adjustment and active suppression of adjacent channel interference in 5G communication equipment according to claim 1, characterized in that: When S2 performs multi-target joint calculations, the adjustable parameters during calculation include the digital beamforming matrix, the analog phase shifting matrix, the power amplifier bias voltage, the power amplifier digital predistortion coefficient, and the transmit power of each resource block.

4. The method for adaptive power adjustment and active suppression of adjacent channel interference in 5G communication equipment according to claim 3, characterized in that: The S2 multi-objective joint calculation process also includes setting three types of constraints: upper limit constraint of power spectral density, spatial null constraint of adjacent frequency device direction, and adjacent channel leakage ratio constraint of power amplifier. Then, the antenna signal transmission direction, transmission power of each sub-band, and power amplifier nonlinear compensation parameters are iteratively solved to obtain a set of optimal equipment operation configuration parameters.

5. The method for adaptive power adjustment and active suppression of adjacent channel interference in 5G communication equipment according to claim 4, characterized in that: The adjacent channel leakage ratio constraint of the power amplifier is calculated by a memory polynomial model. Specifically, the memory polynomial model calculates the out-of-band radiated power of each antenna branch in real time, and then uses the overall adjacent channel leakage index of the array as the basis for determining the adjacent channel leakage ratio constraint of the power amplifier. Finally, the predistortion coefficient and the power supply bias of the power amplifier are adjusted synchronously to achieve nonlinear compensation.

6. The method for adaptive power adjustment and active suppression of adjacent channel interference in 5G communication equipment according to claim 1, characterized in that: The differentiated frequency band isolation processing strategy in S4 specifically includes: if the current service is a non-latency-sensitive broadband service, then identify the resource block with the highest contribution to frequency band edge interference, then reduce the transmission power of the resource block or silence it, construct an equivalent protection frequency band, and migrate the original data to the resource block in the center of the frequency band for transmission; if the current service is a high-reliability low-latency service, then use micro-slot spectrum hopping transmission, select the sub-band with a lower adjacent channel interference risk level to carry the data, and borrow power margin from idle time slots to compensate for the bandwidth loss caused by the hopping.

7. The method for adaptive power adjustment and active suppression of adjacent channel interference in 5G communication equipment according to claim 1, characterized in that: The method further includes neighboring cell cooperative interference prediction and radio frequency front-end cooperative tuning control. When performing neighboring cell cooperative interference prediction, the load, transmit power, and beam orientation information of neighboring base stations are obtained through the base station X2 interface, and the signal quality and interference measurement information of neighboring cells are obtained through the terminal uplink measurement report. The above information is used as new environmental features and input into the online gradient boosting decision tree model to realize multi-cell joint interference risk prediction. When performing radio frequency front-end cooperative tuning control, the out-of-band suppression depth of the tunable radio frequency filter and the antenna array coupling matching parameters are adjusted synchronously according to the predicted adjacent channel interference risk level.

8. The method for adaptive power adjustment and active suppression of adjacent channel interference in 5G communication equipment according to claim 1, characterized in that: The method also includes abnormal operating condition degradation fault tolerance control. When there is distortion of measurement data in the radio frequency detection channel, or the prediction confidence of the online learning model is lower than the threshold, or the multi-objective optimization solution cannot converge, it automatically switches to the preset static power template and fixed spatial null beam configuration to ensure continuous and stable transmission of the communication link.

9. The method for adaptive power adjustment and active suppression of adjacent channel interference in 5G communication equipment according to claim 1, characterized in that: The method also includes hierarchical scheduling control of computing power, adaptively switching the complexity of the online learning model according to the hardware computing power capability of 5G communication equipment. High computing power base stations adopt a complete online gradient boosting decision tree model, while low computing power terminals enable a lightweight simplified prediction model to reduce the real-time computing consumption of the baseband.

10. The method for adaptive power adjustment and active suppression of adjacent channel interference in 5G communication equipment according to claim 1, characterized in that: The method also includes long-term self-iterative optimization control, which periodically counts the adjacent channel interference level, system throughput, and overall power consumption of the entire network at a fixed statistical period of T2, and then automatically updates the hyperparameters of the online learning model, the power envelope mapping coefficients used to generate the global transmit power upper limit constraint standard, and the interference protection threshold based on the statistical results.

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