5G wireless repeater signal extension combination system

Abstract

The invention relates to the technical field of wireless communication, and discloses a 5G wireless repeater signal extension combination system. The distributed repeater module is used for receiving and amplifying the 5G signal from the base station; the intelligent signal processing module is used for monitoring the signal quality in real time and optimizing the signal; the dynamic resource management module is used for dynamically distributing frequency spectrums and power according to signal quality and user requirements; the self-organizing network mechanism module is used for monitoring environment change and analyzing a signal optimization processing strategy; and the user interface and feedback module is used for displaying the real-time signal state. According to the technical scheme, the self-organizing network mechanism module and the dynamic resource management module are combined, so that the effect of adapting to the network environment change in real time is achieved. Therefore, the system can quickly respond to user requirements and environmental fluctuation, and the problem of unstable signal quality caused by network load change is solved.

Description

Technical Field

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

[0002] The rapid development of 5G wireless communication technology is placing higher demands on signal coverage and quality. To ensure a high-quality user experience in diverse environments, many operators are adopting repeater technology to enhance signal coverage. However, existing repeater solutions typically employ fixed configurations and lack the flexibility to adapt to changes in network load and user demand.

[0003] Regarding the aforementioned technologies, fixed repeaters often struggle to adjust signals in real time when user traffic fluctuates. When the number of users surges or the environment changes, signal quality can be significantly impacted, leading to communication interruptions or degradation. This is particularly true in high-traffic scenarios.

[0004] Furthermore, traditional signal processing methods rely heavily on static parameter settings and lack intelligent optimization. This prevents the system from quickly adapting to environmental changes when performing signal enhancement, resulting in a poor user experience. Users often lack timely feedback when experiencing poor signal conditions, further delaying network management.

[0005] Furthermore, spectrum resource management is inadequate. Existing technologies often lack the ability to dynamically adjust spectrum allocation, resulting in wasted resources. Without an effective mechanism for real-time spectrum and power adjustments, users in different areas may face resource scarcity, impacting signal stability. Summary of the Invention

[0006] The purpose of the present 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 network environment, optimizing signal quality and user feedback participation.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: A 5G wireless repeater signal extension combination system, comprising: A distributed repeater module, used to receive and amplify 5G signals from a base station. The distributed repeater module consists of multiple repeaters distributed within a target area and interconnected via a network to enable collaborative operation. An intelligent signal processing module, which is electrically connected to the distributed repeater module and is used to monitor signal quality in real time and optimize signal processing; Dynamic resource management module, which is connected to the intelligent signal processing module and dynamically allocates spectrum and power based on signal quality and user needs; The self-organizing network mechanism module is connected to the intelligent signal processing module 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 is connected to the intelligent signal processing module and the dynamic resource management module 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 the feedback channel.

[0008] Preferably, the intelligent signal processing unit includes: A 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. A deep learning processing unit is used to process and analyze signal data, extract signal features and optimize signal output by using a convolutional neural network. Feedback control unit, used to adjust signal processing strategies in real time based on user feedback and environmental monitoring.

[0009] Preferably, the dynamic resource management module includes: Spectrum allocation unit, used to dynamically adjust spectrum resource configuration based on user needs and signal quality; The power control unit is used to adjust the transmission power of the repeater based on real-time monitoring data.

[0010] Preferably, the self-organizing network mechanism module includes: Environmental monitoring unit, responsible for collecting key environmental parameters, user traffic, channel status and interference level; The adaptive adjustment unit automatically adjusts the operating parameters and configurations between repeaters based on environmental monitoring data to adapt to network changes.

[0011] Preferably, the user interface and feedback module includes: User signal quality display unit, used to display the current signal strength, connection status and network performance to users in real time; The user feedback collection mechanism unit allows users to provide feedback on network performance and usage experience, thereby providing real-time feedback data for the system.

[0012] Preferably, the signal-to-noise ratio calculation formula of the signal quality evaluation unit is: Where S represents the signal power, N represents the noise power, log 10 represents the logarithm with base 10, and SNR represents the signal-to-noise ratio.

[0013] Preferably, the spectrum allocation unit adopts a multi-objective optimization algorithm, and the objective function is: Among them, Z represents the objective function value, c i j is the cost coefficient between user i and repeater j, x i j indicates whether to allocate spectrum resources of repeater to the user, λ is the weight factor, d k represents the resource requirement or constraint item in the kth other resource set, n represents the total number of users, and m represents the total number of other resource requirement items.

[0014] Preferably, the adaptive adjustment unit realizes dynamic adjustment of the repeater configuration through a deep learning model, and the model expression is: S new =f(S old ,E); Among them, S new Indicates the adjusted signal strength, S old represents the current signal strength, E represents the environmental parameter, and f(.) represents the function.

[0015] Preferably, the power control unit adjusts the transmission power of the repeater according to the real-time monitoring data, including: Real-time monitoring 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; Dynamically adjust the transmission power of the repeater to achieve optimal coverage and reduce signal interference.

[0016] Preferably, the environment monitoring unit collects key environment parameters such as user traffic, channel status and interference level in real time, and pre-processes the collected data to generate an environment analysis report.

[0017] In summary, the present invention includes at least one of the following beneficial technical effects: 1. This invention combines a self-organizing network mechanism module with a dynamic resource management module to achieve real-time adaptation to network environment changes. This enables the system to quickly respond to user needs and environmental fluctuations. Compared to existing fixed-configuration repeaters, it solves the problem of unstable signal quality caused by network load fluctuations.

[0018] 2. This invention utilizes deep learning technology within its intelligent signal processing module to optimize signal quality monitoring and processing. This not only improves the accuracy of signal enhancement but also enables the system to self-adjust based on real-time data feedback. Compared to traditional manual or static adjustments, this approach addresses the issue of delayed signal enhancement, ensuring users consistently receive a high-quality network experience.

[0019] 3. This invention incorporates a user interface and feedback module, providing intuitive signal quality display and a convenient user feedback mechanism. This design allows users to understand network status at any time and actively participate in system optimization. Compared with existing technologies, it solves the problem of inaccurate network management caused by a lack of user participation.

[0020] 4. This invention improves spectrum resource utilization through dynamic adjustment strategies for spectrum allocation and power control. This solution effectively overcomes the problems of irrational resource allocation and severe waste in existing technologies, maximizes resource utilization, and better serves users' growing network needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a system framework diagram of the present invention; Figure 2 Schematic diagram of the intelligent signal processing module of the present invention; Figure 3 This is a schematic diagram of a dynamic resource management module of the present invention; Figure 4 Schematic diagram of the self-organizing network mechanism module of the present invention; Figure 5 Schematic diagram of the user interface and feedback module of the present invention. DETAILED DESCRIPTION

[0022] The following is combined with Figure 1 -Attached Figure 5 , the present invention is described in further detail.

[0023] The present invention provides a 5G wireless repeater signal extension combination system, comprising: A distributed repeater module, used to receive and amplify 5G signals from a base station. The distributed repeater module consists of multiple repeaters distributed within a target area and interconnected via a network to enable collaborative operation. Specifically, the distributed repeater module in this embodiment includes multiple repeaters evenly distributed throughout the target area requiring signal coverage and interconnected via a network. These repeaters not only enhance signal strength but also collaborate to achieve intelligent distribution and dynamic management of signals within the area. The repeater structure and configuration are optimized based on the actual environment to ensure they can adapt to the signal processing requirements of different scenarios.

[0024] Specifically, the distributed repeater module is responsible for receiving 5G signals from the base station, amplifying them and then retransmitting them, so that users can obtain high-quality 5G signals at any location. The repeater integrates multiple key functional units, including but not limited to the front-end receiving unit, signal amplification unit, transmitting unit and management control unit.

[0025] In some embodiments, the front-end receiving unit and base station signals receive signals using split-band reception. The repeater can selectively enhance specific signal frequencies based on the signal's frequency band. For example, if the received signal frequency band includes an NR band (such as N78, N79, etc.), the repeater can optimize processing for different frequencies based on user needs and network conditions.

[0026] The signal amplification unit uses a high-gain amplifier (HGA) to amplify the signal. The gain value can be calculated using the following formula: Where G represents the gain, log 10 represents the logarithmic function with base 10, P out Indicates output power, P in represents the input power, Indicates power ratio.

[0027] As an option, the distributed repeater modules 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 system reliability and stability in practical applications.

[0028] Meanwhile, the transmitter unit is responsible for retransmitting the amplified signal through the antenna. Antenna selection and deployment must be optimized based on environmental factors. For example, in urban high-rise environments, ultra-wideband antennas may be required to ensure signal coverage and penetration.

[0029] 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 more flexibly adjust its operating parameters, for example, increasing transmit power during peak traffic periods to improve user experience.

[0030] Furthermore, the distributed repeater module should have network monitoring and coordination capabilities, enabling dynamic resource allocation based on the signal status of its own repeater and neighboring repeaters. This process relies on instructions from the dynamic resource management module. Through effective data transmission and collaboration, multiple repeaters form an integrated network, improving signal coverage and quality.

[0031] An intelligent signal processing module, which is electrically connected to the distributed repeater module and is used to monitor signal quality in real time and optimize signal processing; Specifically, the intelligent signal processing module in this embodiment consists of a signal quality assessment unit, a deep learning processing unit, and a feedback control unit. These units work together to achieve dynamic analysis of signal quality and real-time adjustment of processing strategies through a combination of algorithms and hardware.

[0032] The signal quality evaluation unit, specifically, the signal quality evaluation unit quantifies the quality by calculating the signal-to-noise ratio (SNR) of the received signal. The calculation formula is: Where S represents the signal power, N represents the noise power, log 10 represents the logarithm with base 10, and SNR represents the signal-to-noise ratio.

[0033] In some embodiments, signal power is measured using a spectrum analyzer, while noise power is dynamically adjusted based on interference data provided by the environmental monitoring module. For example, in a congested channel scenario, noise power may increase significantly due to co-channel interference, in which case the evaluation unit will automatically adjust the threshold judgment logic.

[0034] The deep learning processing unit optionally uses a convolutional neural network (CNN) to extract and optimize signal features. The network structure includes an input layer, a convolutional layer, a pooling layer, and a fully connected layer. The input layer receives signal data including time domain waveforms, frequency domain energy distribution, and user traffic characteristics.

[0035] In one possible implementation, the convolutional layer extracts local features by: Among them, C ij Represents the element in row i and column j of the convolution output feature map (FeatureMap), represents the summation operation from k=1 to K, where K represents the size or length of the convolution kernel, that is, the number of weights included, and W k represents the kth weight parameter in the convolution kernel, X i+k-1,j represents the element at row i+k-1 and column j in the input data X, where X represents the input data matrix, i and j represent the row and column indices of the output feature map C, and b represents the bias term, a constant term added after the weighted summation.

[0036] The activation function uses ReLUf(x)=max(0,x) to enhance the nonlinear expression ability. Here, f(x) represents the output of the activation function, that is, the value after nonlinear transformation. f represents the function name, x is the input variable, x represents the input value, usually the linear output of the neuron, and max(0,x) represents the maximum value between 0 and x. If x > 0, then x is output; otherwise, 0 is output.

[0037] During training, the loss function is defined as the mean square error (MSE): Among them, L represents the loss function, which is used to measure the error between the model prediction value and the true value; L in this formula is the result of the mean square error, Represents the normalization factor in the averaging operation, averaging the squared errors of all samples, N represents the total number of samples, represents the summation operation of all samples from i=1 to i=N, y i represents the true signal quality label of the i-th sample, Represents the predicted value of the i-th sample, which is calculated by the model based on the input, Represents the squared error, which measures the degree of deviation between the predicted value of each sample and the true value. Squaring helps to amplify large errors and avoid sign cancellation.

[0038] The feedback control unit typically integrates user experience data from the user interface module with real-time parameters from the environmental monitoring module to dynamically adjust signal processing strategies. For example, if a user reports high signal latency in a specific area, the unit will prioritize resources to repeaters in that area based on current channel conditions.

[0039] In one possible implementation, the adjustment strategy is implemented by a PID controller, whose output formula is: Where u(t) represents the output of the controller, which is used to adjust the system to reach the target state, t represents the time variable, e(t) represents the error signal, and K p Represents the proportional coefficient, which determines the direct impact of the error e(t) on the controller output, K i represents the integral coefficient, It represents the error integral from 0 to the current time t, reflecting the cumulative effect of the error over time, τ represents the integral variable, K d represents the differential coefficient, The derivative (rate of change) of the error represents the rate of change of the error over time. The error function e(t) is the error signal of the system at time t.

[0040] The feedback control unit primarily relies on real-time 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 experience feedback and adjusts the signal processing strategy. Specifically, when it detects user feedback about poor signal quality, the unit can quickly respond and adjust the signal enhancement strategy to optimize signal transmission.

[0041] 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 in a particular frequency band is detected, the system automatically adjusts the spectrum allocation and transmit power for that band to ensure consistently optimal signal quality. This dynamic adjustment mechanism enhances network flexibility and adaptability.

[0042] Dynamic resource management module, which is connected to the intelligent signal processing module and dynamically allocates spectrum and power based on signal quality and user needs; Specifically, the dynamic resource management module in this embodiment primarily includes a spectrum allocation unit and a power control unit. The spectrum allocation unit's primary function is to dynamically adjust spectrum resources based on user needs and signal quality. The power control unit is responsible for dynamically adjusting the repeater's transmit power based on real-time monitoring data. The collaborative operation of these functions effectively improves network resource utilization and user signal experience.

[0043] 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: Among them, Z represents the objective function value, c i j is the cost coefficient between user i and repeater j, x i j indicates whether to allocate spectrum resources of repeater to the user, λ is the weight factor, d k represents the resource demand or constraint item in the kth other resource set, n represents the total number of users, and m represents the total number of other resource demand items. During this process, the system continuously evaluates the actual needs and signal quality of users and calculates the optimal spectrum allocation plan through an algorithm.

[0044] Optionally, the spectrum allocation unit can also dynamically adjust the signal-to-noise ratio (SNR) data provided by the signal quality assessment unit. When the unit detects a decrease in signal quality in a particular frequency band, it can automatically reduce the resource allocation for that band and reallocate resources to a band with better signal quality, thereby ensuring user connection quality and signal stability.

[0045] In the implementation of the power control unit, as mentioned above, real-time monitoring data is used to obtain information such as signal strength, interference level, and user connection quality. Generally, this unit will evaluate whether the current transmit power meets the coverage requirements. The required transmit power is calculated using the following formula: P out =P min +ΔP; Among them, P out is the adjusted transmit power, P minTo achieve the minimum transmit power required for basic coverage, ΔP is the power adjustment required based on dynamic feedback and environmental changes. Specifically, if the user connection quality is good and the signal quality is high, the power can be appropriately reduced to reduce interference. If the user feedback indicates poor signal quality, the transmit power is increased to improve coverage.

[0046] 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 changes in user traffic, channel status, and interference levels. Based on this data, the dynamic resource management module can flexibly adjust resource allocation strategies to adapt to changing network conditions.

[0047] When the number of users in a certain area increases dramatically over a period of time, the dynamic resource management module can quickly respond by increasing spectrum resources in that area through a spectrum allocation algorithm. At the same time, the power control unit can increase the transmit power to ensure that the signal quality can be maintained under high load conditions.

[0048] The self-organizing network mechanism module is connected to the intelligent signal processing module to monitor environmental changes and analyze signal optimization processing strategies, and transmit adjustment commands to the distributed repeater module; Specifically, the self-organizing network mechanism module in this embodiment primarily 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 fundamental information and decision-making basis for optimizing and adjusting the entire system.

[0049] The environmental monitoring unit continuously monitors the current network environment through various sensors and data collection methods, and preprocesses the collected data. This process involves data cleaning, normalization, and feature extraction. The resulting environmental analysis report provides a scientific basis for subsequent adaptive adjustments.

[0050] Specifically, user traffic monitoring involves real-time statistics on the number of users connected to the network and their data demands. This parameter reflects network load. Channel status refers to the quality of signal transmission, including fading and interference. Interference level is a key 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.

[0051] As an option, the adaptive adjustment unit uses a deep learning model to dynamically adjust the repeater configuration. The deep learning model analyzes the environmental data, and the model expression can be expressed as: S new=f(S old ,E); Among them, S new Indicates the adjusted signal strength, S old represents the current signal strength, E represents the environmental parameter, and f(.) represents the function.

[0052] The deep learning model's training process is based on historical environmental data, enabling the system to quickly and accurately adjust to new circumstances. By continuously optimizing the model parameters over time, the system gains increasingly stronger predictive capabilities. This data-driven approach enables repeaters to quickly make appropriate adjustments to optimize signal quality as user needs and network conditions change.

[0053] 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.

[0054] The user interface and feedback module is connected to the intelligent signal processing module and the dynamic resource management module 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; Specifically, the user interface and feedback module in this embodiment primarily includes a user signal quality display unit and a user feedback collection mechanism. The user signal quality display unit is responsible for displaying real-time signal strength, network connection status, and other relevant network performance indicators to the user. This information is intuitively presented through a graphical interface, allowing users to quickly understand the current network status.

[0055] Specifically, the signal strength can be displayed using a numerical value in decibel milliwatts (dBm), which is usually calculated using the following formula: Among them, S represents the signal strength, P represents the actual power of the current signal, P0 represents the reference power, which is the benchmark value for comparison, log 10 Represents the base 10 logarithm function used to convert signal power to decibel level.

[0056] In some embodiments, a user feedback collection mechanism can allow users to submit feedback on network quality through an interface, including but not limited to signal interruptions, buffering, slow connection speeds, and other issues. This mechanism not only supports text-based feedback but also includes options for users to provide star-based or satisfaction-based ratings. Once users submit feedback, this data is automatically recorded and transmitted to the intelligent signal processing module. Through further analysis, the system can optimize signal quality processing and ensure that users' actual needs are promptly addressed.

[0057] Alternatively, user feedback processing can be combined with information from environmental monitoring units to form a comprehensive feedback analysis system. This allows the system to not only understand the user experience in a specific area but also adjust the overall signal strategy based on this information. For example, in areas with high user density, if feedback is concentrated on signal weakening or poor connectivity, the system can quickly activate the self-organizing network mechanism module to locally strengthen the signal and ensure network stability.

[0058] In one possible implementation, the user interface module can be designed to support multi-platform access (e.g., mobile apps, web pages), ensuring that users can access the information they need across different devices. This cross-platform design enhances interactivity between users and the system, allowing users to effectively provide feedback on their experience and receive the technical support they need in different situations.

[0059] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A 5G wireless repeater signal extension combination system, characterized in that: include; A distributed repeater module, used to receive and amplify 5G signals from a base station. The distributed repeater module consists of multiple repeaters distributed within a target area and interconnected via a network to enable collaborative operation. An intelligent signal processing module, which is electrically connected to the distributed repeater module and is used to monitor signal quality in real time and optimize signal processing; Dynamic resource management module, which is connected to the intelligent signal processing module and dynamically allocates spectrum and power based on signal quality and user needs; The self-organizing network mechanism module is connected to the intelligent signal processing module 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 is connected to the intelligent signal processing module and the dynamic resource management module 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 the feedback channel.

2. A 5G wireless repeater signal extension combination system according to claim 1, characterized in that: The intelligent signal processing unit includes: A signal quality evaluation unit, configured 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; A deep learning processing unit, which processes and analyzes signal data by using convolutional neural networks to extract signal features and optimize signal output; Feedback control unit, used to adjust signal processing strategies in real time based on user feedback and environmental monitoring.

3. A 5G wireless repeater signal extension combination system according to claim 1, characterized in that: The dynamic resource management module includes: Spectrum allocation unit, used to dynamically adjust spectrum resource configuration based on user needs and signal quality; The power control unit is used to adjust the transmission power of the repeater according to real-time monitoring data.

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

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

6. A 5G wireless repeater signal extension combination system according to claim 2, characterized in that: The signal-to-noise ratio calculation formula of the signal quality evaluation unit is: Where S represents the signal power, N represents the noise power, log 10 represents the logarithm with base 10, and SNR represents the signal-to-noise ratio.

7. A 5G wireless repeater signal extension combination system according to claim 3, characterized in that: The spectrum allocation unit adopts a multi-objective optimization algorithm, and the objective function is: Among them, Z represents the objective function value, c i j is the cost coefficient between user i and repeater j, x i j indicates whether to allocate spectrum resources of repeater to the user, λ is the weight factor, d k represents the resource requirement or constraint item in the kth other resource set, n represents the total number of users, and m represents the total number of other resource requirement items.

8. A 5G wireless repeater signal extension combination system according to claim 4, characterized in that: The adaptive adjustment unit realizes dynamic adjustment of the repeater configuration through a deep learning model, and the model expression is: S new =f(S old ,E); Among them, S new Indicates the adjusted signal strength, S old represents the current signal strength, E represents the environmental parameter, and f(.) represents the function.

9. A 5G wireless repeater signal extension combination system according to claim 3, characterized in that: The power control unit adjusts the transmission power of the repeater according to the real-time monitoring data, including: Real-time monitoring 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; Dynamically adjust the transmission power of the repeater to achieve optimal coverage and reduce signal interference.

10. A 5G wireless repeater signal extension combination system according to claim 4, characterized in that: The environment monitoring unit collects key environmental parameters such as user traffic, channel status and interference level in real time, and pre-processes the collected data to generate an environment analysis report.

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