Channel quality monitoring and evaluating method and system capable of realizing real-time detection
By receiving and processing digital signals, calculating the spectrum curve and noise threshold curve, and forming recommended frequency bands, the problem of high cost and non-real-time operation of existing spectrum monitoring equipment is solved, and real-time and accurate spectrum assessment is achieved.
Patent Information
- Application Number
- CN202511405487.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-01-18
- Publication Date
- 2025-11-14
AI Technical Summary
Existing spectrum monitoring equipment is costly, complex to operate, and lacks real-time capability, making it difficult to effectively assess spectrum usage within a region.
By receiving signals and converting them into digital signals, sampling and storing spectral data, calculating the average spectral curve and background noise threshold curve, obtaining the intersection point and calculating the area of the region, a recommended frequency band is formed, and the optimal frequency band is used as the conclusion for channel quality monitoring and evaluation.
It enables real-time spectrum detection and assessment, improves the accuracy of channel quality assessment, and enriches the functional scenarios of spectrum monitoring.
Smart Images
Figure CN120956367A_ABST
Abstract
Description
[0001] This case is a divisional application based on the invention patent filed on January 18, 2024, with application number CN202410073235.7, entitled "A Channel Quality Monitoring and Evaluation Method and System". Technical Field
[0002] This invention relates to the field of communication technology, and in particular to a method and system for real-time monitoring and evaluation of channel quality. Background Technology
[0003] Electromagnetic spectrum resources have become a very important national resource. The effective utilization of electromagnetic spectrum resources is a national development strategy goal and plays an extremely important role in various fields such as military and civilian applications. It is also a prerequisite for the rapid development of wireless communication technology.
[0004] In recent years, with the rapid development of wireless communication technology, adaptive frequency hopping allocation technology, based on conventional frequency hopping, has gradually become one of the hot topics in military communication research. It is built upon spectrum monitoring and automatic channel quality assessment, combining frequency adaptation and power adaptation to greatly enhance the system's anti-fading, anti-interference, and anti-interception capabilities.
[0005] Currently, there are generally two common methods for spectrum monitoring: First, dispatching personnel to conduct one-time mobile detection of target frequency bands in the target area using expensive handheld spectrum analyzers. This method is costly in terms of manpower and resources, and the data obtained has a certain time sensitivity, making subsequent data processing less meaningful. Second, establishing fixed nodes in the target area for long-term detection and storing the data in a database for later processing. However, this method lacks portability, and due to the effects of multipath fading and shadowing, this single-node detection method cannot accurately represent the current spectrum usage in the area. Therefore, the concept of collaborative spectrum sensing has been proposed. This technology, by introducing spatial distribution factors, can more intuitively analyze the distribution of an infinite spectrum within a region.
[0006] However, currently available spectrum monitoring equipment offers limited solutions for distributed electromagnetic spectrum monitoring. Firstly, existing spectrum detection equipment is too expensive and requires highly skilled operators, making it difficult for the average person to identify valid information from the spectrum data. Summary of the Invention
[0007] The technical problem to be solved by the present invention is to provide a channel quality monitoring and evaluation method and system to improve the real-time performance of spectrum detection and evaluation.
[0008] To solve the above-mentioned technical problems, the first technical solution adopted by the present invention is as follows: A channel quality monitoring and evaluation method includes the following steps: S1. Receive signals from fixed frequency bands in the usage scenario and convert the received signals into digital signals; S2. Take a sampling point of the digital signal at every preset frequency starting from the starting frequency, and store the spectrum data of each sampling point within a preset time period; S3. Sum and average all the spectrum data within a preset time for each sampling point to obtain the average spectrum data for each sampling point; S4. Calculate the average spectrum curve and background noise threshold curve based on the average spectrum data of each sampling point; S5. Obtain the intersection points between the average spectrum curve and the background noise threshold curve, sort them from smallest to largest, and use each intersection point as the start and end point of the interval to form the recommended frequency band. Based on the intersection of the average spectrum curve and the background noise threshold curve, the area enclosed between two adjacent intersection points is calculated sequentially, and the recommended frequency band with the largest area is selected as the preferred frequency band.
[0009] The second technical solution adopted in this invention is: A channel quality monitoring and evaluation system includes one or more processors and a memory, wherein the memory stores a program that, when executed by the processor, performs the following steps: S1. Receive signals from fixed frequency bands in the usage scenario and convert the received signals into digital signals; S2. Take a sampling point of the digital signal at every preset frequency starting from the starting frequency, and store the spectrum data of each sampling point within a preset time period; S3. Sum and average all the spectrum data within a preset time for each sampling point to obtain the average spectrum data for each sampling point; S4. Calculate the average spectrum curve and background noise threshold curve based on the average spectrum data of each sampling point; S5. Obtain the intersection points between the average spectrum curve and the background noise threshold curve, sort them from smallest to largest, and use each intersection point as the start and end point of the interval to form the recommended frequency band. Based on the intersection of the average spectrum curve and the background noise threshold curve, the area enclosed between two adjacent intersection points is calculated sequentially, and the recommended frequency band with the largest area is selected as the preferred frequency band.
[0010] The beneficial effects of this invention are as follows: This scheme takes a sampling point at preset frequencies starting from the initial frequency of the digital signal and stores the spectrum data of each sampling point within a preset time period. It then sums and averages all the spectrum data within the preset time period for each sampling point to calculate the average spectrum data for each sampling point. Based on the average spectrum data of each sampling point, it calculates the average spectrum curve and the background noise threshold curve. The combination of time-domain and frequency-domain data enables real-time acquisition, improving the real-time performance of spectrum detection and evaluation. Furthermore, the background noise threshold obtained through the combined processing of time-domain and frequency-domain data is more real-time, improving the accuracy of channel quality assessment results. The scheme obtains the intersection points between the average spectrum curve and the background noise threshold curve, sorts them from smallest to largest, and uses each intersection point as the start and end point of an interval to form a recommended frequency band. Based on the intersection points between the average spectrum curve and the background noise threshold curve, it calculates the area of the region enclosed between adjacent intersection points, and selects the recommended frequency band with the largest area as the preferred frequency band. The preferred frequency band is used as the conclusion of channel quality monitoring and evaluation. This expands the application scenarios of the function from the original single-frequency point monitoring to the current frequency band quality evaluation. Attached Figure Description
[0011] Figure 1 The flowchart shows the steps of the channel quality monitoring and evaluation method according to the invention. Figure 2 Here is a system block diagram of the channel quality monitoring and evaluation system according to the invention; Figure 3 A simulation diagram of the channel quality monitoring and evaluation method according to the invention; Label Explanation: 1. Processor; 2. Memory. Detailed Implementation
[0012] To explain in detail the technical content, objectives, and effects of the present invention, the following description is provided in conjunction with the embodiments and accompanying drawings.
[0013] Please refer to Figure 1 This invention provides a channel quality monitoring and evaluation method, comprising the following steps: S1. Receive signals from fixed frequency bands in the usage scenario and convert the received signals into digital signals; S2. Take a sampling point of the digital signal at every preset frequency starting from the starting frequency, and store the spectrum data of each sampling point within a preset time period; S3. Sum and average all the spectrum data within a preset time for each sampling point to obtain the average spectrum data for each sampling point; S4. Calculate the average spectrum curve and background noise threshold curve based on the average spectrum data of each sampling point; S5. Obtain the intersection points between the average spectrum curve and the background noise threshold curve, sort them from smallest to largest, and use each intersection point as the start and end point of the interval to form the recommended frequency band. Based on the intersection of the average spectrum curve and the background noise threshold curve, the area enclosed between two adjacent intersection points is calculated sequentially, and the recommended frequency band with the largest area is selected as the preferred frequency band.
[0014] As can be seen from the above description, the beneficial effects of the present invention are as follows: This scheme takes a sampling point at preset frequencies starting from the initial frequency of the digital signal and stores the spectrum data of each sampling point within a preset time period. It then sums and averages all the spectrum data within the preset time period for each sampling point to calculate the average spectrum data for each sampling point. Based on the average spectrum data of each sampling point, it calculates the average spectrum curve and the background noise threshold curve. The combination of time-domain and frequency-domain data enables real-time acquisition, improving the real-time performance of spectrum detection and evaluation. Furthermore, the background noise threshold obtained through the combined processing of time-domain and frequency-domain data is more real-time, improving the accuracy of channel quality assessment results. The scheme obtains the intersection points between the average spectrum curve and the background noise threshold curve, sorts them from smallest to largest, and uses each intersection point as the start and end point of an interval to form a recommended frequency band. Based on the intersection points between the average spectrum curve and the background noise threshold curve, it calculates the area of the region enclosed between adjacent intersection points, and selects the recommended frequency band with the largest area as the preferred frequency band. The preferred frequency band is used as the conclusion of channel quality monitoring and evaluation. This expands the application scenarios of the function from the original single-frequency point monitoring to the current frequency band quality evaluation.
[0015] Furthermore, the specific steps for obtaining the background noise threshold curve are as follows: Assume the difference between two adjacent average spectral data is D. v The difference threshold is D, and the background noise points are n. i The sampling point is i, and the forward difference formula is as follows: ; When D v When n ≤ D i =data i ; When D v When >D, n i =data i-1 ; The background noise threshold curve is obtained based on the obtained background noise points and their corresponding frequencies.
[0016] As described above, the background noise threshold obtained by combining time-domain and frequency-domain data using the forward difference method is more real-time, thereby improving the accuracy of channel quality assessment results.
[0017] Furthermore, the frequency of the intersection point obtained in step S5 is similar to the frequency of each sampling point. The retrieved approximate points are used as the start and end points of the interval to form a recommended frequency band. The recommended frequency band is divided in units of preset frequency.
[0018] As described above, by searching for points whose frequencies of intersections obtained in step S5 are similar to the frequencies of each sampling point, the searched points are used as the start and end points of the interval to form a recommended frequency band. The recommended frequency band is divided in units of preset frequencies, which makes it easier to calculate the area and further improves the accuracy of the channel quality assessment results.
[0019] Furthermore, the area of the region in step S6 is calculated using the integral method.
[0020] As described above, by employing the integral concept and using approximate calculations to quantify the quality of the channel, the recommended frequency bands become more convincing.
[0021] Furthermore, in step S1, Fourier transform is used to convert the received signal into a digital signal.
[0022] The present invention also provides a channel quality monitoring and evaluation system, including one or more processors and a memory, wherein the memory stores a program that, when executed by the processor, performs the following steps: S1. Receive signals from fixed frequency bands in the usage scenario and convert the received signals into digital signals; S2. Take a sampling point of the digital signal at every preset frequency starting from the starting frequency, and store the spectrum data of each sampling point within a preset time period; S3. Sum and average all the spectrum data within a preset time for each sampling point to obtain the average spectrum data for each sampling point; S4. Calculate the average spectrum curve and background noise threshold curve based on the average spectrum data of each sampling point; S5. Obtain the intersection points between the average spectrum curve and the background noise threshold curve, sort them from smallest to largest, and use each intersection point as the start and end point of the interval to form the recommended frequency band. Based on the intersection of the average spectrum curve and the background noise threshold curve, the area enclosed between two adjacent intersection points is calculated sequentially, and the recommended frequency band with the largest area is selected as the preferred frequency band.
[0023] As can be seen from the above description, the beneficial effects of the present invention are as follows: This scheme takes a sampling point at preset frequencies starting from the initial frequency of the digital signal and stores the spectrum data of each sampling point within a preset time period. It then sums and averages all the spectrum data within the preset time period for each sampling point to calculate the average spectrum data for each sampling point. Based on the average spectrum data of each sampling point, it calculates the average spectrum curve and the background noise threshold curve. The combination of time-domain and frequency-domain data enables real-time acquisition, improving the real-time performance of spectrum detection and evaluation. Furthermore, the background noise threshold obtained through the combined processing of time-domain and frequency-domain data is more real-time, improving the accuracy of channel quality assessment results. The scheme obtains the intersection points between the average spectrum curve and the background noise threshold curve, sorts them from smallest to largest, and uses each intersection point as the start and end point of an interval to form a recommended frequency band. Based on the intersection points between the average spectrum curve and the background noise threshold curve, it calculates the area of the region enclosed between adjacent intersection points, and selects the recommended frequency band with the largest area as the preferred frequency band. The preferred frequency band is used as the conclusion of channel quality monitoring and evaluation. This expands the application scenarios of the function from the original single-frequency point monitoring to the current frequency band quality evaluation.
[0024] Furthermore, the specific steps for obtaining the background noise threshold curve are as follows: Assume the difference between two adjacent average spectral data is D. v The difference threshold is D, and the background noise points are n. i The sampling point is i, and the forward difference formula is as follows: ; When D v When n ≤ D i =data i ; When D v When >D, n i =data i-1 ; The background noise threshold curve is obtained based on the obtained background noise points and their corresponding frequencies.
[0025] As described above, the background noise threshold obtained by combining time-domain and frequency-domain data using the forward difference method is more real-time, thereby improving the accuracy of channel quality assessment results.
[0026] Furthermore, when the program is executed by the processor, it performs the following steps: The frequency of the intersection point obtained in step S5 is similar to the frequency of each sampling point. The retrieved approximate points are used as the start and end points of the interval to form a recommended frequency band. The recommended frequency band is divided in units of preset frequency.
[0027] As described above, by searching for points whose frequencies of intersections obtained in step S5 are similar to the frequencies of each sampling point, the searched points are used as the start and end points of the interval to form a recommended frequency band. The recommended frequency band is divided in units of preset frequencies, which makes it easier to calculate the area and further improves the accuracy of the channel quality assessment results.
[0028] Furthermore, when the program is executed by the processor, it performs the following steps: The area of the region in step S6 is calculated using the integral method.
[0029] As described above, by employing the integral concept and using approximate calculations to quantify the quality of the channel, the recommended frequency bands become more convincing.
[0030] Furthermore, when the program is executed by the processor, it performs the following steps: In step S1, Fourier transform is used to convert the received signal into a digital signal.
[0031] Please refer to Figure 1 and Figure 3 Embodiment 1 of the present invention is as follows: Please refer to Figure 1 A channel quality monitoring and evaluation method includes the following steps: S1. Receive signals from fixed frequency bands in the usage scenario and convert the received signals into digital signals; S2. Starting from the starting frequency, take a sampling point of the digital signal at every preset frequency, and store the spectrum data of each sampling point within a preset time period; in this embodiment, the preset frequency is 100kHz; since the spectrum data of the same sampling point will change with time, for example, the spectrum data of the 100ms level at the 100kHz sampling point is -81.5dBm, and the spectrum data of the 200ms level is -82.3dBm, so the digital signal is taken at every preset frequency starting from the starting frequency, and the spectrum data of each sampling point within a preset time period is stored.
[0032] S3. Sum and average all the spectrum data for each sampling point within a preset time period to calculate the average spectrum data for each sampling point; in this embodiment, the preset time period is 10s; the spectrum data includes the level value and its corresponding frequency. S4. Calculate the average spectrum curve and background noise threshold curve based on the average spectrum data of each sampling point; the horizontal axis of the average spectrum curve is frequency, and the vertical axis is level value; the horizontal axis of the background noise threshold curve is frequency, and the vertical axis is level value.
[0033] S5. Obtain the intersection points between the average spectrum curve and the background noise threshold curve, and sort them from smallest to largest. Use each intersection point as the start and end point of the interval to form the recommended frequency band; as in the simulation. Figure 3 As shown, the left bar represents the starting frequency sampling point of the 100kHz band, and the right bar represents the ending frequency sampling point of the 100kHz band. The intersection points of the bar with the average spectrum curve and the background noise threshold curve are used as the standard for the final recommended frequency band after calculation. Based on the intersection of the average spectrum curve and the background noise threshold curve, the area enclosed between two adjacent intersection points is calculated sequentially, and the recommended frequency band with the largest area is selected as the preferred frequency band.
[0034] The specific steps for obtaining the background noise threshold curve are as follows: Assume the difference between two adjacent average spectral data is D. v The difference threshold is D, and the background noise points are n. i The sampling point is i, and the forward difference formula is as follows: ; When D v When n ≤ D i =data i ; When D v When >D, n i =data i-1 ; The background noise threshold curve is obtained based on the obtained background noise points and their corresponding frequencies.
[0035] The frequency of the intersection point obtained in step S5 is similar to the frequency of each sampling point. The retrieved approximate points are used as the start and end points of the interval to form a recommended frequency band. The recommended frequency band is divided in units of preset frequency.
[0036] Assuming the frequencies of the intersection points obtained in step S5 are 0.5kHz, 100.1kHz, 199.3kHz, 300.2kHz, etc., then the points with similar frequencies for each sampling point are 0kHz, 100kHz, 200kHz, and 300kHz, and the recommended frequency bands are 0~100kHz, 100~200kHz, and 200~300kHz, respectively.
[0037] The area of the region in step S6 is calculated using the integral method.
[0038] In step S1, Fourier transform is used to convert the received signal into a digital signal.
[0039] Please refer to Figure 2Embodiment two of the present invention is as follows: A channel quality monitoring and evaluation system includes one or more processors 1 and a memory 2, wherein the memory 2 stores a program that, when executed by the processor 1, performs the following steps: S1. Receive signals from fixed frequency bands in the usage scenario and convert the received signals into digital signals; S2. Starting from the initial frequency, take a sampling point of the digital signal at preset frequencies, and store the spectrum data of each sampling point within a preset time period; in this embodiment, the preset frequency is 100kHz. S3. Sum and average all the spectrum data for each sampling point within a preset time period to calculate the average spectrum data for each sampling point; in this embodiment, the preset time period is 10s; S4. Calculate the average spectrum curve and background noise threshold curve based on the average spectrum data of each sampling point; S5. Obtain the intersection points between the average spectrum curve and the background noise threshold curve, sort them from smallest to largest, and use each intersection point as the start and end point of the interval to form the recommended frequency band. Based on the intersection of the average spectrum curve and the background noise threshold curve, the area enclosed between two adjacent intersection points is calculated sequentially, and the recommended frequency band with the largest area is selected as the preferred frequency band.
[0040] The specific steps for obtaining the background noise threshold curve are as follows: Assume the difference between two adjacent average spectral data is D. v The difference threshold is D, and the background noise points are n. i The sampling point is i, and the forward difference formula is as follows: ; When D v When n ≤ D i =data i ; When D v When >D, n i =data i-1 ; The background noise threshold curve is obtained based on the obtained background noise points and their corresponding frequencies.
[0041] When this program is executed by processor 1, it performs the following steps: The frequency of the intersection point obtained in step S5 is similar to the frequency of each sampling point. The retrieved approximate points are used as the start and end points of the interval to form a recommended frequency band. The recommended frequency band is divided in units of preset frequency.
[0042] Assuming the frequencies of the intersection points obtained in step S5 are 0.5kHz, 100.1kHz, 199.3kHz, 300.2kHz, etc., then the points with similar frequencies for each sampling point are 0kHz, 100kHz, 200kHz, and 300kHz, and the recommended frequency bands are 0~100kHz, 100~200kHz, and 200~300kHz, respectively.
[0043] When this program is executed by processor 1, it performs the following steps: The area of the region in step S6 is calculated using the integral method.
[0044] When this program is executed by processor 1, it performs the following steps: In step S1, Fourier transform is used to convert the received signal into a digital signal.
[0045] In summary, the present invention provides a channel quality monitoring and evaluation method and system capable of real-time detection. This method involves taking a sampling point from the digital signal at preset frequencies starting from the initial frequency, storing the spectral data for each sampling point within a preset time period, summing and averaging all spectral data within the preset time period for each sampling point to calculate the average spectral data for each sampling point, and calculating the average spectral curve and background noise threshold curve based on the average spectral data for each sampling point. The combination of time-domain and frequency-domain data enables real-time acquisition, improving the real-time performance of spectrum detection and evaluation. Furthermore, the processing of time-domain and frequency-domain data... The background noise threshold is more real-time, which can improve the accuracy of channel quality assessment results. The intersection points between the average spectrum curve and the background noise threshold curve are obtained and sorted from smallest to largest. Each intersection point is used as the start and end point of the interval to form a recommended frequency band. Based on the intersection points between the average spectrum curve and the background noise threshold curve, the area enclosed between two adjacent intersection points is calculated in turn. The recommended frequency band with the largest area is selected as the preferred frequency band. The preferred frequency band is used as the conclusion of channel quality monitoring and assessment. Furthermore, the function has been enriched from the original single frequency point monitoring to the current frequency band quality assessment.
[0046] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent modifications made based on the content of the present invention specification and drawings, or direct or indirect applications in related technical fields, are similarly included within the patent protection scope of the present invention.
Claims
1. A channel quality monitoring and evaluation method capable of real-time detection, characterized in that, Includes the following steps: S1. Receive signals from fixed frequency bands in the usage scenario and convert the received signals into digital signals; S2. Take a sampling point of the digital signal at preset frequencies starting from the starting frequency, and store the spectrum data of each sampling point within a preset time period; S3. Sum and average all the spectrum data within a preset time for each sampling point to obtain the average spectrum data for each sampling point; S4. Calculate the average spectrum curve and background noise threshold curve based on the average spectrum data of each sampling point; the horizontal axis of the average spectrum curve is frequency, and the vertical axis is the level value; the horizontal axis of the background noise threshold curve is frequency, and the vertical axis is the level value. S5. Obtain the intersection points between the average spectrum curve and the background noise threshold curve, sort them from smallest to largest, and use each intersection point as the start and end point of the interval to form the recommended frequency band. Based on the intersection of the average spectrum curve and the background noise threshold curve, the area enclosed between two adjacent intersection points is calculated sequentially, and the recommended frequency band with the largest area is selected as the preferred frequency band.
2. The channel quality monitoring and evaluation method with real-time detection capability according to claim 1, characterized in that, The specific steps for obtaining the background noise threshold curve are as follows: Assume the difference between two adjacent average spectral data is D. v The difference threshold is D, and the background noise points are n. i The sampling point is i, and the forward difference formula is as follows: ； When D v When n ≤ D i =data i ; When D v When >D, n i =data i-1 ; The background noise threshold curve is obtained based on the obtained background noise points and their corresponding frequencies.
3. The channel quality monitoring and evaluation method with real-time detection capability according to claim 1, characterized in that, The frequency of the intersection point obtained in step S5 is similar to the frequency of each sampling point. The retrieved approximate points are used as the start and end points of the interval to form a recommended frequency band. The recommended frequency band is divided in units of preset frequency.
4. The channel quality monitoring and evaluation method with real-time detection capability according to claim 1, characterized in that, The area of the region in step S6 is calculated using the integral method.
5. The channel quality monitoring and evaluation method with real-time detection capability according to claim 1, characterized in that, In step S1, Fourier transform is used to convert the received signal into a digital signal.
6. A channel quality monitoring and evaluation system capable of real-time detection, characterized in that, Includes one or more processors and a memory, wherein the memory stores a program that, when executed by the processor, performs the following steps: S1. Receive signals from fixed frequency bands in the usage scenario and convert the received signals into digital signals; S2. Take a sampling point of the digital signal at preset frequencies starting from the starting frequency, and store the spectrum data of each sampling point within a preset time period; S3. Sum and average all the spectrum data within a preset time for each sampling point to obtain the average spectrum data for each sampling point; S4. Calculate the average spectrum curve and background noise threshold curve based on the average spectrum data of each sampling point; the horizontal axis of the average spectrum curve is frequency, and the vertical axis is the level value; the horizontal axis of the background noise threshold curve is frequency, and the vertical axis is the level value. S5. Obtain the intersection points between the average spectrum curve and the background noise threshold curve, sort them from smallest to largest, and use each intersection point as the start and end point of the interval to form the recommended frequency band. Based on the intersection of the average spectrum curve and the background noise threshold curve, the area enclosed between two adjacent intersection points is calculated sequentially, and the recommended frequency band with the largest area is selected as the preferred frequency band.
7. The channel quality monitoring and evaluation system capable of real-time detection according to claim 6, characterized in that, When the program is executed by the processor, it performs the following steps: The specific steps for obtaining the background noise threshold curve are as follows: Assume the difference between two adjacent average spectral data is D. v The difference threshold is D, and the background noise points are n. i The sampling point is i, and the forward difference formula is as follows: ； When D v When n ≤ D i =data i ; When D v When >D, n i =data i-1 ; The background noise threshold curve is obtained based on the obtained background noise points and their corresponding frequencies.
8. The channel quality monitoring and evaluation system capable of real-time detection according to claim 6, characterized in that, When the program is executed by the processor, it performs the following steps: The frequency of the intersection point obtained in step S5 is similar to the frequency of each sampling point. The retrieved approximate points are used as the start and end points of the interval to form a recommended frequency band. The recommended frequency band is divided in units of preset frequency.
9. The channel quality monitoring and evaluation system capable of real-time detection according to claim 6, characterized in that, When the program is executed by the processor, it performs the following steps: The area of the region in step S6 is calculated using the integral method.
10. The channel quality monitoring and evaluation system capable of real-time detection according to claim 6, characterized in that, When the program is executed by the processor, it performs the following steps: In step S1, Fourier transform is used to convert the received signal into a digital signal.