5G wireless repeater signal expansion combination system

Abstract

The application relates to the technical field of wireless communication, and discloses a 5G wireless repeater signal expansion combination system, which comprises the following modules: a distributed repeater module for receiving and amplifying 5G signals from a base station; an intelligent signal processing module for monitoring signal quality in real time and optimizing signal processing; a dynamic resource management module for dynamically allocating frequency spectrum and power according to signal quality and user demand; a self-organizing network mechanism module for monitoring environmental changes and analyzing signal optimization processing strategies; and a user interface and feedback module for displaying real-time signal states. Through the technical scheme of combining the self-organizing network mechanism module and the dynamic resource management module, the application achieves the effect of real-time adaptation to network environment changes. This makes the system capable of quickly responding to user demand and environmental fluctuations, and solves the problem of unstable signal quality caused by network load changes.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and in particular to a 5G wireless repeater signal extension combination system. Background Technology

[0002] Currently, the rapid development of 5G wireless communication technology has placed higher demands on signal coverage and quality. To achieve a high-quality user experience in different environments, many operators have adopted repeater technology to enhance signal coverage areas. However, existing repeater solutions typically use fixed configurations and cannot flexibly respond to changes in network load and user needs.

[0003] Regarding the technologies mentioned above, fixed-configuration repeaters often cannot adjust their signal in real time when faced with fluctuations in user traffic. When the number of users surges or the environment changes, signal quality may be significantly affected, leading to communication interruptions or quality degradation, which is particularly prominent in high-traffic scenarios.

[0004] Furthermore, traditional signal processing methods rely heavily on static parameter settings and lack intelligent optimization techniques. This makes it difficult for the system to quickly adapt to environmental changes when performing signal enhancement, resulting in a degraded user experience. Users often cannot provide timely feedback when the signal is poor, which further leads to lag in network management.

[0005] Furthermore, there are shortcomings in the management of spectrum resources. Some existing technologies often lack the ability to dynamically adjust spectrum allocation, resulting in resource waste. Because there is no effective mechanism to adjust spectrum and power in real time, users in different regions may face resource shortages, thus affecting signal stability. Summary of the Invention

[0006] The purpose of this invention is to provide a 5G wireless repeater signal extension combination system, which solves the problems of existing repeater technology in dynamically adapting to changes in the network environment, optimizing signal quality, and incorporating user feedback.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a 5G wireless repeater signal extension combination system, comprising;

[0008] A distributed repeater module is used to receive and amplify 5G signals from a base station. The distributed repeater module consists of multiple repeaters distributed in the target area and interconnected through a network to achieve collaborative operation.

[0009] The intelligent signal processing module, which is electrically connected to the distributed repeater module, is used to monitor signal quality in real time and optimize signal processing.

[0010] The dynamic resource management module, which is connected to the intelligent signal processing module, dynamically allocates spectrum and power according to signal quality and user needs;

[0011] The self-organizing network mechanism module, which is connected to the intelligent signal processing module, is used to monitor environmental changes and analyze signal optimization processing strategies, and transmit adjustment commands to the distributed repeater module.

[0012] The user interface and feedback module, which connects to the intelligent signal processing module and the dynamic resource management module, is used to display the real-time signal status and transmit user experience information and user needs to the intelligent signal processing module and the dynamic resource management module through feedback channels.

[0013] Preferably, the intelligent signal processing module includes;

[0014] The signal quality assessment unit is used to calculate the signal-to-noise ratio of the received signal and determine whether the signal needs to be enhanced based on a preset threshold.

[0015] The deep learning processing unit is used to process and analyze signal data by using convolutional neural networks to extract signal features and optimize signal output.

[0016] The feedback control unit is used to adjust the signal processing strategy in real time based on user feedback and environmental monitoring.

[0017] Preferably, the dynamic resource management module includes:

[0018] The spectrum allocation unit is used to dynamically adjust the spectrum resource allocation according to user needs and signal quality.

[0019] The power control unit is used to adjust the repeater's transmit power based on real-time monitoring data.

[0020] Preferably, the self-organizing network mechanism module includes:

[0021] The environmental monitoring unit is responsible for collecting key environmental parameters, such as user traffic, channel status, and interference levels.

[0022] The adaptive adjustment unit automatically adjusts the operating parameters and configurations between repeaters based on environmental monitoring data to adapt to network changes.

[0023] Preferably, the user interface and feedback module includes:

[0024] The user signal quality display unit is used to display the current signal strength, connection status and network performance to the user in real time;

[0025] The user feedback collection mechanism unit allows users to provide feedback on network performance and user experience, thereby providing real-time feedback data to the system.

[0026] Preferably, the signal-to-noise ratio calculation formula of the signal quality assessment unit is:

[0027] ;

[0028] in, Indicates signal power. Indicates noise power. Represents the logarithm to base 10. This indicates the signal-to-noise ratio.

[0029] Preferably, the spectrum allocation unit employs a multi-objective optimization algorithm, with the objective function being:

[0030] ;

[0031] in, Represents the objective function value. For users With repeater Cost coefficients between This indicates whether to allocate repeater spectrum resources to the user. As a weighting factor, Indicates the first Resource requirements or constraints in other resource sets This represents the total number of users. This indicates the total number of other resource requirements.

[0032] Preferably, the adaptive adjustment unit dynamically adjusts the repeater configuration using a deep learning model, the model expression of which is:

[0033] ;

[0034] in, Indicates the adjusted signal strength. Indicates the current signal strength. Indicates environmental parameters, function.

[0035] Preferably, the power control unit adjusts the repeater's transmission power based on real-time monitoring data, including:

[0036] Real-time monitoring and feedback of signal strength, interference levels, and user connection quality;

[0037] Based on the received monitoring data, determine whether the current transmission power meets the coverage requirements;

[0038] The transmit power of the repeater is dynamically adjusted to achieve optimal coverage and reduce signal interference.

[0039] Preferably, the environmental monitoring unit collects key environmental parameters such as user traffic, channel status, and interference level in real time, and preprocesses the collected data to generate an environmental analysis report.

[0040] In summary, the present invention has at least one of the following beneficial technical effects:

[0041] 1. This invention achieves real-time adaptation to changes in the network environment by employing a technical solution that combines a self-organizing network mechanism module with a dynamic resource management module. This enables the system to quickly respond to user needs and environmental fluctuations, solving the problem of unstable signal quality caused by changes in network load compared to fixed-configuration repeaters in existing technologies.

[0042] 2. This invention optimizes the signal quality monitoring and processing flow through deep learning technology in the intelligent signal processing module. This not only improves the accuracy of signal enhancement but also allows the system to self-adjust based on real-time data feedback. Compared to traditional manual or static adjustments, it solves the problem of untimely signal enhancement, ensuring users always receive a high-quality network experience.

[0043] 3. This invention introduces a user interface and feedback module, providing an intuitive display of signal quality and a convenient user feedback mechanism. This design allows users to understand the network status at any time and actively participate in system optimization. Compared with existing technologies, it solves the problem of inaccurate network management due to a lack of user participation.

[0044] 4. This invention improves the utilization rate of spectrum resources through a dynamic adjustment strategy of spectrum allocation and power control. This solution effectively overcomes the problems of unreasonable resource allocation and serious waste in the prior art, achieving maximum resource utilization and better serving the ever-growing network needs of users. Attached Figure Description

[0045] Figure 1 This is a system framework diagram of the present invention;

[0046] Figure 2 This is a schematic diagram of the intelligent signal processing module of the present invention;

[0047] Figure 3 This is a schematic diagram of the dynamic resource management module of the present invention;

[0048] Figure 4 This is a schematic diagram of the self-organizing network mechanism module of the present invention;

[0049] Figure 5 This is a schematic diagram of the user interface and feedback module of the present invention. Detailed Implementation

[0050] The following is in conjunction with the appendix Figure 1 - Appendix Figure 5 The present invention will be further described in detail below.

[0051] This invention provides a 5G wireless repeater signal extension and combination system, comprising:

[0052] A distributed repeater module is used to receive and amplify 5G signals from a base station. The distributed repeater module consists of multiple repeaters distributed in the target area and interconnected through a network to achieve collaborative operation.

[0053] Specifically, in this embodiment, the distributed repeater module includes multiple repeaters evenly distributed within the target area requiring signal coverage, interconnected via a network. These repeaters are not only configured to enhance signal strength but also to achieve intelligent signal distribution and dynamic management within the area through collaborative operation. The repeater structure and configuration are optimized based on the actual environment to ensure adaptability to signal processing needs in different scenarios.

[0054] Specifically, the distributed repeater module is responsible for receiving 5G signals from the base station, amplifying them, and retransmitting them, so that users can obtain high-quality 5G signals from any location. The repeater integrates multiple key functional units, including but not limited to the front-end receiving unit, signal amplification unit, transmission unit, and management and control unit.

[0055] In some embodiments, the front-end receiving unit receives base station signals using a segmented reception method. The repeater can selectively amplify specific signal frequencies based on different signal frequency bands. For example, the received signal frequency band may include NR bands (such as N78, N79, etc.), and the repeater can optimize processing for different frequencies according to user needs and network conditions.

[0056] The signal amplification unit uses a high-gain amplifier (HGA) to amplify the signal. This gain value can be calculated using the following formula:

[0057] ;

[0058] in, Indicates gain. Represents the logarithmic function with base 10. Indicates output power. Indicates input power. Indicates the power ratio.

[0059] Alternatively, the distributed repeater module can be equipped with adaptive gain control, which automatically adjusts the amplification factor when poor input signal quality is detected. This mechanism can significantly improve the reliability and stability of the system in practical applications.

[0060] Meanwhile, the transmitting unit is responsible for retransmitting the amplified signal through the antenna. The selection and deployment of the antenna need to be optimized in conjunction with environmental factors. For example, in urban high-rise environments, ultra-wideband antennas may be required to ensure signal coverage and penetration.

[0061] In one possible implementation, the distributed repeater module can also interact with the user interface to provide real-time feedback on user signal quality. This feedback mechanism allows the repeater to adjust its operating parameters more flexibly, such as increasing transmission power during peak user traffic periods to improve the user experience.

[0062] Furthermore, the distributed repeater module should possess network monitoring and coordination capabilities, enabling dynamic resource allocation based on its own signal status and that of neighboring repeaters. This process relies on instructions from the dynamic resource management module. Through effective data transmission and collaboration, multiple repeaters collectively form a unified network, improving signal coverage and quality.

[0063] The intelligent signal processing module, which is electrically connected to the distributed repeater module, is used to monitor signal quality in real time and optimize signal processing.

[0064] Specifically, in this embodiment, the intelligent signal processing module consists of a signal quality assessment unit, a deep learning processing unit, and a feedback control unit. These units work collaboratively, combining algorithms and hardware to achieve dynamic analysis of signal quality and real-time adjustment of processing strategies.

[0065] The signal quality assessment unit, specifically, performs quality quantification by calculating the signal-to-noise ratio (SNR) of the received signal. The calculation formula is as follows:

[0066] ;

[0067] in, Indicates signal power. Indicates noise power. Represents the logarithm to base 10. This indicates the signal-to-noise ratio.

[0068] In some embodiments, signal power is measured using a spectrum analyzer, while noise power is dynamically corrected based on interference data provided by an environmental monitoring module. For example, in a channel congestion scenario, noise power may increase significantly due to co-channel interference; in this case, the evaluation unit will automatically adjust the threshold judgment logic.

[0069] As an option, the deep learning processing unit employs a convolutional neural network (CNN) to extract and optimize signal features. The network structure includes an input layer, convolutional layers, pooling layers, and fully connected layers. The signal data received by the input layer includes time-domain waveforms, frequency-domain energy distribution, and user traffic characteristics.

[0070] In one possible implementation, the convolutional layer extracts local features through the following operation:

[0071] ;

[0072] in, This represents the first element in the convolution output feature map. Line number Column elements, Indicates from arrive Summation operation, This indicates the size or length of the convolution kernel, i.e., the number of weights it contains. Represents the first in the convolution kernel One weight parameter, Indicates input data Located in the middle line, number Column elements, Represents the input data matrix. Indicates the output feature map row and column indexes, This represents the bias term, a constant term added after the weighted summation.

[0073] The activation function uses ReLU. To enhance nonlinear expressive power

[0074] in, This represents the output of the activation function, i.e., the value after the nonlinear transformation. Indicates the function name. It is an input variable. This represents the input value, typically the linear output of the neuron. Indicates taking 0 and The maximum value in. If Then output Otherwise, output 0.

[0075] During training, the loss function is defined as the mean squared error (MSE):

[0076] ;

[0077] in, This represents the loss function, used to measure the error between the model's predicted values ​​and the actual values; in this formula... This is the result of the mean square error. This represents the normalization factor in the averaging operation, which averages the squared errors of all samples. Represents the total number of samples. This indicates that for all samples from arrive The summation operation, Indicates the first The true signal quality label of each sample Indicates the first The predicted value for each sample is calculated by the model based on the input. Squared error measures the degree of deviation between the predicted value and the true value for each sample. Squaring helps to amplify large errors and avoid sign cancellation.

[0078] The feedback control unit typically integrates user interface module experience data with real-time parameters from the environmental monitoring module to dynamically adjust signal processing strategies. For example, when a user reports high signal delay in a specific area, this unit will prioritize allocating resources to repeaters in that area based on the current channel status.

[0079] In one possible implementation, the adjustment strategy is implemented using a PID controller, whose output formula is:

[0080] ;

[0081] in, This represents the controller's output, used to adjust the system to achieve a target state. Represents a time variable. Indicates the error signal. This represents the proportionality coefficient, which determines the error. The direct impact on the controller output. Represents the integral coefficient. Represents the time from 0 to the current moment. The error integral reflects the cumulative effect of the error over time. Represents the integral variable. Represents the differential coefficient. The derivative (rate of change) of the error represents how quickly the error changes over time. The error function is the system's time error. Error signal at that time.

[0082] The operation of the feedback control unit relies primarily on real-time collected user feedback and environmental monitoring data. This data is crucial for adjusting signal processing strategies in real time. In this embodiment, the feedback control unit determines whether the operation meets user needs based on user feedback and adjusts the signal processing strategy accordingly. Specifically, when it detects that a user reports poor signal quality, the unit can respond quickly and adjust the signal enhancement strategy to optimize signal transmission performance.

[0083] Furthermore, the feedback control unit, combined with the dynamic resource management module, enables dynamic allocation of system resources. When a sudden increase in user traffic is detected in a certain frequency band, the system can automatically adjust the spectrum allocation and transmission power of that band to ensure that signal quality remains optimal. This dynamic adjustment mechanism enhances the network's flexibility and adaptability.

[0084] The dynamic resource management module, which is connected to the intelligent signal processing module, dynamically allocates spectrum and power according to signal quality and user needs;

[0085] Specifically, in this embodiment, the dynamic resource management module mainly includes a spectrum allocation unit and a power control unit. The main function of the spectrum allocation unit is to dynamically adjust spectrum resources according to the needs of different users and signal quality. The power control unit is responsible for dynamically adjusting the transmit power of the repeater based on real-time monitoring data. The coordinated operation of these functions can effectively improve the utilization rate of network resources and the signal experience of users.

[0086] The spectrum allocation unit uses a multi-objective optimization algorithm to manage spectrum resources. Specifically, the objective function of the optimization algorithm can be expressed as:

[0087] ;

[0088] in, Represents the objective function value. For users With repeater Cost coefficients between This indicates whether to allocate repeater spectrum resources to the user. As a weighting factor, Indicates the first Resource requirements or constraints in other resource sets This represents the total number of users. This represents the total number of other resource requirements. During this process, the system continuously evaluates the user's actual needs and signal quality, and calculates the optimal spectrum allocation scheme through algorithms.

[0089] Alternatively, the spectrum allocation unit can also dynamically adjust based on the signal-to-noise ratio (SNR) data provided by the signal quality assessment unit. When a signal quality degradation is detected in a certain frequency band, the unit can automatically reduce the resource allocation for that band and reallocate the resources to a frequency band with better signal quality, thereby ensuring the user's connection quality and signal stability.

[0090] In the implementation of the power control unit, as mentioned above, information such as signal strength, interference levels, and user connection quality is obtained through real-time data monitoring. Typically, this unit assesses whether the current transmit power meets coverage requirements. The required transmit power is calculated using the following formula:

[0091] ;

[0092] in, The adjusted transmission power, To meet the minimum transmit power required for basic coverage, This refers to the amount of power adjustment required based on dynamic feedback and environmental changes. Specifically, if the user connection quality is detected to be good and there is a high signal quality, the power can be appropriately reduced to reduce interference; if user feedback indicates poor signal quality, the transmission power will be increased to improve coverage.

[0093] In one possible implementation, the dynamic resource management module can also collaborate with the self-organizing network mechanism module to obtain key environmental parameters provided by the environmental monitoring unit. These environmental parameters may include user traffic changes, channel conditions, and interference levels. Based on this data, the dynamic resource management module can flexibly adjust resource allocation strategies to adapt to constantly changing network conditions.

[0094] When the number of users in a certain area increases sharply over a period of time, the dynamic resource management module can respond quickly by increasing the spectrum resources in that area through a spectrum allocation algorithm. At the same time, the power control unit can increase the transmission power to ensure that the signal maintains good quality under high load conditions.

[0095] The self-organizing network mechanism module, which is connected to the intelligent signal processing module, is used to monitor environmental changes and analyze signal optimization processing strategies, and transmit adjustment commands to the distributed repeater module.

[0096] Specifically, in this embodiment, the self-organizing network mechanism module mainly consists of an environmental monitoring unit and an adaptive adjustment unit. The core function of the environmental monitoring unit is to collect key environmental parameters in real time, including user traffic, channel status, and interference levels. This data provides basic information and decision-making basis for the optimization and adjustment of the entire system.

[0097] The environmental monitoring unit continuously monitors the current network environment using various sensors and data acquisition methods, and preprocesses the collected data. These processes include data cleaning, normalization, and feature extraction. The resulting environmental analysis report provides a scientific basis for subsequent adaptive adjustments.

[0098] Specifically, user traffic monitoring involves real-time statistics on the number of users connected to the network and their data demands. This parameter reflects the network load. Channel status refers to signal transmission quality, including fading and interference. Interference level is an important indicator for analyzing the impact of other signal sources on the current signal. Generally, environmental monitoring units can provide quantitative data, greatly improving the system's responsiveness to environmental changes.

[0099] Alternatively, the adaptive adjustment unit utilizes a deep learning model to dynamically adjust the repeater configuration. This deep learning model analyzes environmental data, and its expression can be represented as:

[0100] ;

[0101] in, Indicates the adjusted signal strength. Indicates the current signal strength. Indicates environmental parameters, function.

[0102] The training process of deep learning models is based on historical environmental data, enabling the system to make rapid and accurate adjustments when facing new environments. By continuously optimizing model parameters over time, the system gains increasingly stronger predictive capabilities. This data-driven approach allows repeaters to quickly make appropriate adjustments to optimize signal quality in response to changes in user demands and network conditions.

[0103] The adaptive adjustment unit can also be combined with the dynamic resource management module to provide real-time feedback and adjust spectrum and power allocation. When the environmental monitoring unit reports an increase in network load or a surge in user traffic, the adaptive adjustment unit can dynamically optimize signal parameters to ensure that the user experience is not affected.

[0104] The user interface and feedback module, which connects to the intelligent signal processing module and the dynamic resource management module, is used to display the real-time signal status and transmit user experience information and user needs to the intelligent signal processing module and the dynamic resource management module through feedback channels.

[0105] Specifically, in this embodiment, the user interface and feedback module mainly includes a user signal quality display unit and a user feedback collection mechanism. The user signal quality display unit is responsible for displaying the signal strength, network connection status, and other relevant network performance indicators to the user in real time. This information can be presented intuitively through a graphical interface, enabling users to quickly understand the current network status.

[0106] Specifically, signal strength can be displayed using numerical values ​​in decibels per milliwatt (dBm), and is typically calculated using the following formula:

[0107] ;

[0108] in, Indicates signal strength. This indicates the actual power of the current signal. This represents the reference power, which is a benchmark value for comparison. This represents a base-10 logarithmic function used to convert signal power to decibel levels.

[0109] In some embodiments, the user feedback collection mechanism allows users to submit feedback on network quality via an interface, including but not limited to issues such as signal interruption, buffering, and slow connection speeds. This mechanism supports not only text feedback but also options to allow users to provide ratings based on stars or satisfaction levels. Once a user submits feedback, this data is automatically recorded and transmitted to the intelligent signal processing module. Through further analysis, the system can optimize signal quality processing, ensuring that users' actual needs are addressed promptly.

[0110] Alternatively, user feedback processing can be combined with information from environmental monitoring units to form a complete feedback analysis system. In this way, the system can not only understand the user experience in a specific area but also adjust the overall signal strategy accordingly. For example, in areas with high user density, if feedback focuses on signal weakness or poor connectivity, the system can quickly utilize self-organizing network mechanisms to strengthen the local signal and ensure network stability.

[0111] In one possible implementation, the user interface module can be designed to support multi-platform access (such as mobile applications and web pages), ensuring that users can obtain the information they need on different devices. This cross-platform design enhances the interactivity between the user and the system, enabling users to effectively provide feedback on their experience and obtain necessary technical support in various situations.

[0112] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A 5G wireless repeater signal extension and combination system, characterized in that, include; A distributed repeater module is used to receive and amplify 5G signals from a base station. The distributed repeater module consists of multiple repeaters distributed in the target area and interconnected through a network to achieve collaborative operation. The intelligent signal processing module, which is electrically connected to the distributed repeater module, is used to monitor signal quality in real time and optimize signal processing. The dynamic resource management module, which is connected to the intelligent signal processing module, dynamically allocates spectrum and power according to signal quality and user needs; The dynamic resource management module includes: The spectrum allocation unit is used to dynamically adjust the spectrum resource allocation according to user needs and signal quality. The power control unit is used to adjust the repeater's transmission power based on real-time monitoring data; The spectrum allocation unit employs a multi-objective optimization algorithm, with the objective function being: ； in, Represents the objective function value. For users With repeater Cost coefficients between Indicates whether to allocate repeater spectrum resources to the user. As a weighting factor, Indicates the first Resource requirements or constraints in other resource sets This represents the total number of users. This represents the total number of other resource requirements. The self-organizing network mechanism module, which is connected to the intelligent signal processing module, is used to monitor environmental changes and analyze signal optimization processing strategies, and transmit adjustment commands to the distributed repeater module. The user interface and feedback module, which connects to the intelligent signal processing module and the dynamic resource management module, is used to display the real-time signal status and transmit user experience information and user needs to the intelligent signal processing module and the dynamic resource management module through feedback channels.

2. The 5G wireless repeater signal extension combination system according to claim 1, characterized in that, The intelligent signal processing module includes: The signal quality assessment unit is used to calculate the signal-to-noise ratio of the received signal and determine whether the signal needs to be enhanced based on a preset threshold. The deep learning processing unit is used to process and analyze signal data by using convolutional neural networks to extract signal features and optimize signal output. The feedback control unit is used to adjust the signal processing strategy in real time based on user feedback and environmental monitoring.

3. The 5G wireless repeater signal extension combination system according to claim 1, characterized in that, The self-organizing network mechanism module includes: The environmental monitoring unit is responsible for collecting key environmental parameters, such as user traffic, channel status, and interference levels. The adaptive adjustment unit automatically adjusts the operating parameters and configurations between repeaters based on environmental monitoring data to adapt to network changes.

4. A 5G wireless repeater signal extension and combination system according to claim 1, characterized in that, The user interface and feedback module includes: The user signal quality display unit is used to display the current signal strength, connection status and network performance to the user in real time; The user feedback collection mechanism unit allows users to provide feedback on network performance and user experience, thereby providing real-time feedback data to the system.

5. A 5G wireless repeater signal extension combination system according to claim 2, characterized in that, The signal-to-noise ratio calculation formula for the signal quality assessment unit is as follows: ； in, Indicates signal power. Indicates noise power. Represents the logarithm to base 10. This indicates the signal-to-noise ratio.

6. A 5G wireless repeater signal extension combination system according to claim 3, characterized in that, The adaptive adjustment unit dynamically adjusts the repeater configuration using a deep learning model, the model expression of which is: ； in, Indicates the adjusted signal strength. Indicates the current signal strength. Indicates environmental parameters, Represents a function.

7. A 5G wireless repeater signal extension and combination system according to claim 1, characterized in that, The power control unit adjusts the repeater's transmission power based on real-time monitoring data, including: Real-time monitoring and feedback of signal strength, interference levels, and user connection quality; Based on the received monitoring data, determine whether the current transmission power meets the coverage requirements; The transmit power of the repeater is dynamically adjusted to achieve optimal coverage and reduce signal interference.

8. A 5G wireless repeater signal extension combination system according to claim 3, characterized in that, The environmental monitoring unit collects key environmental parameters such as user traffic, channel status, and interference level in real time, and preprocesses the collected data to generate an environmental analysis report.

Images