A spectrum occupation degree calculation method based on full-band spectrum data

Abstract

The present application relates to a kind of spectrum occupation degree calculation method based on full-band spectrum data, belong to radio signal spectrum analysis field.The present application is based on the full-band spectrum data of same sampling time, and the full-band signal is divided into several sub-bands;Based on the full-band spectrum data of once sampling time, the level threshold of all sub-bands is calculated;Based on the spectrum data of different sampling time in measurement time, the occupation degree is estimated.The present application obtains multiple interval threshold values in each sub-band by histogram, interclass variance and other means, which can be used in abnormal electromagnetic signal monitoring scene, further reduces the false alarm rate of abnormal signal alarm, and improves electromagnetic space safety supervision ability.

Description

Technical Field

[0001] This invention belongs to the field of radio signal spectrum analysis, specifically relating to a method for calculating spectrum occupancy based on full-band spectrum data. Background Technology

[0002] Electromagnetic environments contain various types and sources of electromagnetic signals, and spectrum occupancy serves as an important indicator for assessing the complexity of the electromagnetic environment. Higher spectrum occupancy typically signifies a more complex electromagnetic environment with a greater likelihood of signal interference, which significantly impacts the normal operation of electronic equipment, the reliability of wireless communication systems, and electromagnetic compatibility design. Long-term monitoring of spectrum occupancy allows for understanding trends in electromagnetic environment changes, providing a basis for assessment and management. Firstly, regarding abnormal signal monitoring, some illegal FM broadcasts interfere with VHF communication systems (118MHz-137MHz) in civil aviation, posing a threat to aviation safety. The existence of "fake base stations" also interferes with normal mobile communications. By performing spectrum analysis on signals received at the receiver, and given known legal frequency information, illegal frequency components outside the normal communication range can be captured. By comparing the spectra of illegal and legal signals, the illegal signals can be reasonably distinguished, providing a theoretical basis for monitoring and locating them.

[0003] Currently, in spectrum monitoring methods based on energy monitoring principles, the environmental noise threshold has a significant impact on spectrum occupancy assessment. Choosing a noise threshold that is too low will result in the frequency band being continuously occupied due to environmental noise. Conversely, choosing a high noise threshold may lead to an underestimation of spectrum occupancy. Therefore, the noise threshold value is crucial in spectrum monitoring. Existing spectrum occupancy estimation methods, such as those mentioned in the ITU recommendations, use only the lowest 20% of samples to calculate the average root mean square (RMS) noise level threshold. However, this approach may result in an excessively low noise level, causing some unoccupied channels to be detected as "occupied," leading to a high false alarm rate.

[0004] This invention addresses the issue of occupancy of the full spectrum data pushed in real time by electromagnetic monitors. First, it groups the full frequency bands and performs dynamic threshold statistics based on the service frequency bands. This can be used in abnormal electromagnetic signal monitoring scenarios to further reduce the false alarm rate of abnormal signal alarms and improve the ability to supervise electromagnetic space safety. Summary of the Invention

[0005] (a) Technical problems to be solved

[0006] The technical problem to be solved by this invention is how to provide a spectrum occupancy calculation method based on full-band spectrum data, so as to solve the problem of high false alarm rate caused by existing spectrum occupancy estimation methods.

[0007] (II) Technical Solution

[0008] To address the aforementioned technical problems, this invention proposes a method for calculating spectrum occupancy based on full-band spectrum data, which includes the following steps:

[0009] S1. Based on the full-band spectrum data at the same sampling time, divide the frequency band into several sub-bands;

[0010] S2. Based on the full-band spectrum data at a single sampling time, calculate the level thresholds for all sub-bands.

[0011] S21. Generate histograms of spectrum data for all sub-bands.

[0012] For the Tth n The full-band data collected in the second acquisition will be the Ath n All frequency points within a sub-band range are sorted in ascending order of power value and divided into R power intervals. The number of frequency points falling within each power interval is counted.

[0013] S22. Calculate the level threshold for all sub-bands.

[0014] Traverse each power interval of the sub-band spectrum data histogram, and select the median value of each power interval as a possible threshold for that sub-band. Each sub-band has a total of R possible thresholds. Frequency points with power values ​​less than or equal to the possible thresholds within a sub-band are considered noise, and frequency points with power values ​​greater than the possible thresholds are considered signals. Calculate the inter-class variance of the sub-band spectrum data under each possible threshold. The possible threshold with the largest inter-class variance is the level threshold for that sub-band. Traverse all sub-bands of the single-sample full-band spectrum data to obtain the level thresholds for all sub-bands.

[0015] S3. Estimate occupancy based on frequency band spectrum data at different sampling times within the measurement period.

[0016] Based on the full-band spectrum data collected by the monitor at different times, the sub-band level thresholds are calculated respectively. The power value of any frequency point is compared with the corresponding sub-band level threshold to count whether a signal exists at that frequency point. If a signal exists, the count of signal presence at that frequency point is incremented by 1; otherwise, it is incremented by 0. n Under the full-band data set, the number of times the signal exists and T n The ratio of is the time occupancy of that frequency point.

[0017] In S1, the Tth monitoring unit... nThe spectrum data collected in this acquisition, covering the entire frequency band [9kHz-6GHz], is divided into A... n Each sub-band is labeled [1,2...,A]. n ].

[0018] In step S21, the formula for calculating the power range of the sub-band spectrum data is as follows:

[0019]

[0020] Where N is the number of frequency points within the sub-band, and R is the number of histogram intervals.

[0021] S21 includes: assuming that N frequency points are sampled within the sub-band, each frequency point has a power value, and the power value of the i-th frequency point is represented as P(i), where i = 1, 2, ..., i, ... N; after arranging the sub-band spectrum power values ​​of length N in ascending order, a 1×N dimension vector P = [P(1), ..., P(i), ..., P(N)] is obtained; the power range of the sub-band is divided into R equal parts, and the number of frequency points falling into each power interval is counted, thus obtaining a sub-band histogram with R power intervals.

[0022] Where N = 65536, R ∈ [256, 512].

[0023] In S22, it is assumed that the Tth n The Ath sampling n The possible threshold for the k-th power interval of each sub-band is M. k There are R possible thresholds M = [M1, ..., Mn] for R power intervals. k ,…,M R ], k = [1, ..., R];

[0024] After sorting in ascending order within the sub-bands, M k The position index within this sub-band is denoted as j, and all sub-bands less than or equal to M are represented by this index. k The frequency point is noise, and its power value is [P(1),…,P(j)], all of which are greater than M. k The frequency point is the signal, and its power value is [P(j+1),…,P(N)];

[0025] Calculate the cumulative probability and mean of signal and noise within each sub-band:

[0026] The cumulative probability w of noise noise (M k ) and mean μ noise (M k The formula is as follows:

[0027]

[0028] The cumulative probability w of the signal signal (M k ) and mean μ signal (M k The formula is as follows:

[0029]

[0030] Calculate the inter-class variance of the sub-band at this possible threshold. The formula is as follows:

[0031]

[0032] Where μ G M is the global mean of the spectrum data. k It is a possible threshold;

[0033] Traverse and calculate the Tth n Inter-class variance of all sub-bands of the sub-full-band data under R possible thresholds

[0034] In step S22, the inter-class variance of all sub-frequency bands under different possible thresholds is compared. The threshold M that maximizes the inter-class variance k The level threshold for this sub-frequency band:

[0035]

[0036] In S22, it is assumed that the Tth n The number of all sub-bands in the sub-full-band spectrum data is A. n Then, by iterating through all possible threshold values ​​for all sub-bands, we can obtain the Tth class variance. n The level thresholds for all sub-bands of the sub-full-band spectrum data are:

[0037] In S3, assuming the spectrum occupancy measurement duration is S seconds, and the electromagnetic monitoring device uploads full data every s seconds, then a total of T data will be uploaded within the statistical time. n = S / s full-band data; for T n Sub-full-band spectrum data, each group of full-band spectrum data is divided into A n For each sub-band, calculate the threshold for all sub-bands as follows:

[0038]

[0039] In S3, for T n For sub-full-band data, the number of times the signal exists at each frequency point is accumulated to L, then the spectrum occupancy is...

[0040]

[0041] (III) Beneficial Effects

[0042] This invention proposes a method for calculating spectrum occupancy based on full-band spectrum data. For the occupancy problem of the full spectrum data pushed in real time by electromagnetic monitors, the full frequency band is first grouped. Dynamic threshold statistics are then performed based on the inter-class variance of the sub-band spectrum data under each possible threshold for the service frequency band. Finally, the occupancy is estimated based on the frequency band spectrum data at different sampling times within the measurement period. This invention obtains multiple interval thresholds within each sub-band through histograms, inter-class variance, and other methods. It can be used in abnormal electromagnetic signal monitoring scenarios to further reduce the false alarm rate of abnormal signal alarms and improve electromagnetic space safety supervision capabilities. Attached Figure Description

[0043] Figure 1 This is a flowchart of the spectrum occupancy calculation method based on full-band spectrum data of the present invention;

[0044] Figure 2 This is a diagram showing the threshold division of the entire frequency band at the same sampling time;

[0045] Figure 3 This is a graph showing the calculation of the full-band level threshold at different sampling times during the measurement period. Detailed Implementation

[0046] To make the objectives, contents, and advantages of the present invention clearer, the specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples.

[0047] This invention discloses a method for calculating spectrum occupancy based on full-band spectrum data. The method calculates dynamic level thresholds based on inter-class variance, according to the service frequency band and the sampling time of the spectrum data. The method mainly includes: (1) dividing the full-band spectrum data into sub-bands based on the same sampling time; (2) calculating the level thresholds for each sub-band based on the full-band spectrum data at the same sampling time; and (3) dynamically estimating occupancy based on spectrum data from multiple frequency bands at different sampling times within the measurement period.

[0048] The spectrum occupancy calculation method based on full-band spectrum data of the present invention can be used in abnormal electromagnetic signal monitoring scenarios to further reduce the false alarm rate of abnormal signal alarms and improve the electromagnetic space safety supervision capability.

[0049] This invention proposes a method for calculating spectrum occupancy based on full-band spectrum data, comprising the following steps:

[0050] (1) Frequency band division is performed based on full-band spectrum data from the same sampling time.

[0051] The main types of indoor electromagnetic space signal distribution include broadcast television, trunked intercom, WIFI, Bluetooth, 4 / 5G mobile communication, RFID, etc. Based on the frequency band distribution range of mainstream wireless signals published by the Radio Regulatory Commission, the full frequency band signal is divided into several sub-frequency bands.

[0052] (2) Calculate the level thresholds of all sub-bands based on the full-band spectrum data at a single sampling time.

[0053] 1) Generate histograms of spectrum data for all sub-bands.

[0054] For the Tth n The full-band data collected in the second acquisition will be the Ath n All frequency points within a sub-band range are sorted in ascending order of power value and divided into R power intervals. The number of frequency points falling within each power interval is counted.

[0055] The number of power intervals is a key parameter in sub-band histogram generation. Too many power intervals can mask the true spectral distribution trend, such as bimodalities or skewness; too few power intervals can lead to the aggregation of key features. The formula for calculating the power interval of sub-band spectral data is as follows:

[0056]

[0057] Where N is the number of frequency points within the sub-band, and R is the number of histogram intervals.

[0058] 2) Calculate the level threshold for all sub-bands.

[0059] By traversing each power interval of the sub-band spectrum histogram, and considering that radio signal spectral power values ​​are typically affected by noise or signal strength fluctuations, the median value of each power interval is selected as a possible threshold for that sub-band. Thus, each sub-band has n possible thresholds. Frequency points within a sub-band whose power values ​​are less than or equal to a possible threshold are considered noise, and frequency points whose power values ​​are greater than a possible threshold are considered signals.

[0060] Calculate the inter-class variance of the sub-band spectral data for each possible threshold. The threshold with the largest inter-class variance is the level threshold for that sub-band. By traversing all sub-bands of the single-sample full-band spectrum data, the level thresholds of all sub-bands can be obtained.

[0061]

[0062] Where argmax() refers to taking the inter-class variance. Possible threshold M for maximizing the value k The value of .

[0063] (3) Estimate the occupancy rate based on the frequency spectrum data at different sampling times within the measurement period.

[0064] The level threshold of the full-band spectrum data changes dynamically at different sampling times. Therefore, the sub-band level threshold should be calculated based on the full-band spectrum data collected by the monitor at different times.

[0065] Assuming the spectrum occupancy measurement duration is S seconds, and the electromagnetic monitoring device uploads full data every s seconds, then a total of T data points are uploaded during the statistical time. n = S / s full-band data. For T n Sub-full-band spectrum data, each group of full-band spectrum data is divided into A n For each sub-band, calculate the threshold M for all sub-bands within the occupancy statistics period. * for

[0066]

[0067] in For the Tth n The full-band data sampled in the Ath sampling period, n Level threshold of each sub-band.

[0068] Compare the power value at any frequency point with the corresponding sub-band threshold to count whether a signal exists at that frequency point. If a signal exists, the count of signal presence at that frequency point is incremented by 1; otherwise, it is incremented by 0. The count of signal presence is calculated based on T sets of full-band data and T... n The ratio of the frequency points is the time occupancy degree of that frequency point.

[0069] For T n For sub-full-band data, the number of times the signal exists at each frequency point is accumulated to L, then the spectrum occupancy is...

[0070]

[0071] Example 1:

[0072] S1. Based on the full-band spectrum data at the same sampling time, divide the frequency band into several sub-bands.

[0073] The main types of indoor electromagnetic space signal distribution include broadcast television, trunked intercom, WIFI, Bluetooth, 4 / 5G mobile communication, RFID, etc. Based on the frequency band distribution range of mainstream wireless signals published by the Radio Regulatory Commission, the full frequency band signal is divided into several sub-frequency bands.

[0074] In practice, different signal types occupy different frequency bands. For example, computer video leakage signals mainly occupy the megahertz band; UHF broadcast television signals mainly occupy the 470-560MHz band; WIFI and Bluetooth signals are concentrated in the ISM band; and mobile communication signals mainly occupy the 2-5GHz band. During implementation, the monitoring device's T... n The spectrum data collected in this acquisition, covering the entire frequency band [9kHz-6GHz], is divided into A... n Each sub-band is labeled [1,2...,A]. n ],like Figure 2 As shown.

[0075] S2. Calculate the level thresholds of all sub-bands based on the full-band spectrum data at a single sampling time. S21. Generate histograms of spectrum data for all sub-bands.

[0076] For the Tth n The full-band data collected in the second acquisition will be the Ath n All frequency points within a sub-band range are sorted in ascending order of power value and divided into R power intervals. The number of frequency points falling within each power interval is counted.

[0077] For the Tth n The full-band data collected in the second acquisition will be the Ath n All frequency points within the sub-band range are processed, and each sub-band in the full-band data obtained at each sampling time is processed in the same way.

[0078] The number of power intervals is a key parameter in sub-band histogram generation. Too many power intervals can mask the true spectral distribution trend, such as bimodalities or skewness; too few power intervals can lead to the aggregation of key features. The formula for calculating the power interval of sub-band spectral data is as follows:

[0079]

[0080] Where N is the number of frequency points within the sub-band, and R is the number of histogram intervals.

[0081] In practice, assuming N frequency points are sampled within a sub-band, each with a power value, the power value of the i-th frequency point is represented as P(i), where i = 1, 2, ..., i, ... N. Arranging the sub-band spectral power values ​​of length N in ascending order yields a 1×N dimension vector P = [P(1), ..., P(i), ..., P(N)]. Assuming N = 65536, the number of power intervals in the sub-band histogram ranges from R ∈ [256, 512]. R can take any value within this range, such as R = 256. Dividing the sub-band power range into 256 equal parts and counting the number of frequency points falling into each power interval, a sub-band histogram with 256 power intervals is obtained.

[0082] S22. Calculate the level threshold for all sub-bands.

[0083] By traversing each power interval of the sub-band spectrum histogram, and considering that radio signal spectral power values ​​are typically affected by noise or signal strength fluctuations, the median value of each power interval is selected as a possible threshold for that sub-band. Thus, each sub-band has R possible thresholds. Frequency points within a sub-band with power values ​​less than or equal to the possible threshold are considered noise, and frequency points with power values ​​greater than the possible threshold are considered signals. The inter-class variance of the sub-band spectrum data under each possible threshold is calculated, and the possible threshold with the largest inter-class variance is the level threshold for that sub-band. By traversing all sub-bands of a single sample of the full-band spectrum data, the level thresholds for all sub-bands can be obtained.

[0084] In practical implementation, assume that the Tth n The Ath sampling n The possible threshold for the k-th power interval of each sub-band is M. k There are R possible thresholds M = [M1, ..., Mn] for R power intervals. k ,…,M R ], k = [1,…,R]. After sorting in ascending order within the sub-bands, M k The position index within this sub-band is denoted as j, and all sub-bands less than or equal to M are represented by this index. k The frequency point is noise, and its power value is [P(1),…,P(j)], all of which are greater than M. k The frequency point is the signal, and its power value is [P(j+1),…,P(N)]. Calculate the cumulative probability and mean of the signal and noise in each sub-band.

[0085] The cumulative probability w of noise noise (M k ) and mean μ noise (M k The formula is as follows:

[0086]

[0087] The cumulative probability w of the signal signal (M k ) and mean μ signal (M k The formula is as follows:

[0088]

[0089] Calculate the inter-class variance of the sub-band at this possible threshold. The formula is as follows:

[0090]

[0091] Where μ G M is the global mean of the spectrum data. k It is a possible threshold.

[0092] Traverse and calculate the Tth n Inter-class variance of all sub-bands of the sub-full-band data under R possible thresholds

[0093] Compare the inter-class variance of all sub-bands under different possible thresholds. The threshold M that maximizes the inter-class variance k This is the level threshold for this sub-frequency band.

[0094]

[0095] Assume the Tth n The number of all sub-bands in the sub-full-band spectrum data is A. n Then, by iterating through the inter-class variances at all possible thresholds for all sub-bands, we can obtain the Tth... n The level thresholds for all sub-bands of the sub-full-band spectrum data are:

[0096] S3. Estimate occupancy based on frequency band spectrum data at different sampling times within the measurement period.

[0097] The threshold values ​​of the full-band spectrum data change dynamically at different sampling times. Therefore, the sub-band level threshold values ​​should be calculated separately based on the full-band spectrum data collected by the monitor at different times.

[0098] Assuming the spectrum occupancy measurement duration is S seconds, and the electromagnetic monitoring device uploads full data every s seconds, then a total of T data points are uploaded during the statistical time. n = S / s full-band data. For T n Sub-full-band spectrum data, each group of full-band spectrum data is divided into A n For each sub-band, calculate the threshold for all sub-bands as follows:

[0099]

[0100] Compare the power value at any frequency point with the corresponding sub-band level threshold to count whether a signal exists at that frequency point. If a signal exists, the count of signal presence at that frequency point is incremented by 1; otherwise, it is incremented by 0. T n Under the full-band data set, the number of times the signal exists and T n The ratio of the frequency points is the time occupancy degree of that frequency point.

[0101] For T n For sub-full-band data, the number of times the signal exists at each frequency point is accumulated to L, then the spectrum occupancy is...

[0102]

[0103] Example 2:

[0104] In practice, the system collects one hour of full-band spectrum data, uploading one set of spectrum data every second, for a total of 3600 times. n =3600. Assuming the frequency point is a 9kHz spectrum, located in the first sub-band of the full-band data, with position number 1, its power value is P(1) = -40dBm, and the threshold for the first sampled full-band data is M. 11 * = -30dBm, then there is no signal at that frequency point in this sampling; the threshold for the full-band data of the second sampling is M. 21 * = -50dBm, then the signal at this frequency point exists in this sampling, L=1; and so on, finally we get L=1800, then the time occupancy of the frequency point is 50%.

[0105] This invention proposes a method for calculating spectrum occupancy based on full-band spectrum data. For the occupancy problem of the full spectrum data pushed in real time by electromagnetic monitors, the full frequency band is first grouped. Dynamic threshold statistics are then performed based on the inter-class variance of the sub-band spectrum data under each possible threshold for the service frequency band. Finally, the occupancy is estimated based on the frequency band spectrum data at different sampling times within the measurement period. This invention obtains multiple interval thresholds within each sub-band through histograms, inter-class variance, and other methods. It can be used in abnormal electromagnetic signal monitoring scenarios to further reduce the false alarm rate of abnormal signal alarms and improve electromagnetic space safety supervision capabilities.

[0106] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for calculating spectrum occupancy based on full-band spectrum data, characterized in that, The method includes the following steps: S1. Based on the full-band spectrum data at the same sampling time, divide the frequency band into several sub-bands; S2. Based on the full-band spectrum data at a single sampling time, calculate the level thresholds for all sub-bands. S21. Generate histograms of spectrum data for all sub-bands. For the The full-band data collected in the second acquisition will be the first All frequency points within each sub-band range are divided into categories after being sorted in ascending order of power value. R Each power range is counted, and the number of frequency points falling within each power range is counted. S22. Calculate the level threshold for all sub-bands. Traverse each power interval of the sub-band spectrum data histogram, and select the median value of each power interval as a possible threshold for that sub-band. Each sub-band has a total of R possible thresholds. Frequency points with power values ​​less than or equal to the possible thresholds within a sub-band are considered noise, and frequency points with power values ​​greater than the possible thresholds are considered signals. Calculate the inter-class variance of the sub-band spectrum data under each possible threshold. The possible threshold with the largest inter-class variance is the level threshold for that sub-band. Traverse all sub-bands of the single-sample full-band spectrum data to obtain the level thresholds for all sub-bands. S3. Estimate occupancy based on frequency band spectrum data at different sampling times within the measurement period. Based on the full-band spectrum data collected by the monitor at different times, the sub-band level thresholds are calculated respectively. The power value of any frequency point is compared with the corresponding sub-band level threshold to determine whether there is a signal at that frequency point. If a signal is present, the count of the signal at that frequency point is incremented by 1; otherwise, it is incremented by 0. The number of times the signal exists under the full frequency band data and The ratio of is the time occupancy of that frequency point; in, S21 includes: assuming sampling within the sub-frequency band. N There are n frequency points, each with a power value, and the nth frequency point has a power value. i The power value at each frequency point is expressed as: ,in i =1,2,…, i ,… N The length is N The sub-band spectral power values, when arranged in ascending order, can be used to obtain a 1× N dimensional vector P = Divide the power range of the sub-band into R equal parts, and count the number of frequency points falling into each power interval to obtain a sub-band histogram with R power intervals. In S22, it is assumed that the first The second sampling The first sub-band k The possible thresholds for each power range are: M k， R The power range has a total of R One possible threshold M =[ M 1, …, M k,…, M R ], k =[1,…, R ]; After sorting in ascending order within the sub-bands, M k The position number within this sub-band is represented as j All frequencies within the sub-band less than or equal to M k The frequency point is noise, and its power value is [ P (1),…, P ( j ], all greater than M k The frequency point is the signal, and its power value is [ P ( j +1),…, P ( N )]; Calculate the cumulative probability and mean of signal and noise within each sub-band: Cumulative probability of noise and mean The formula is as follows: Cumulative probability of the signal and mean The formula is as follows: Calculate the inter-class variance of the sub-band at this possible threshold. The formula is as follows: in It is the global mean of the spectrum data. It is a possible threshold; Traversal calculation of the first All sub-bands of sub-full-band data R Inter-class variance at possible thresholds .

2. The method for calculating spectrum occupancy based on full-band spectrum data as described in claim 1, characterized in that, In S1, the monitor will be the first The spectrum data within the full frequency band [9kHz-6GHz] collected in this acquisition is divided into: Each sub-band, marked as .

3. The method for calculating spectrum occupancy based on full-band spectrum data as described in claim 1, characterized in that, In step S21, the formula for calculating the power range of sub-band spectrum data is as follows: in N The number of frequency points within a sub-band. R This represents the number of intervals in the histogram.

4. The method for calculating spectrum occupancy based on full-band spectrum data as described in claim 1, characterized in that, N=65536, R∈[256,512].

5. The method for calculating spectrum occupancy based on full-band spectrum data as described in claim 1, characterized in that, In step S22, the inter-class variance of sub-frequency bands under different possible thresholds is compared. The threshold that maximizes the inter-class variance The level threshold for this sub-band: 。 6. The method for calculating spectrum occupancy based on full-band spectrum data as described in claim 5, characterized in that, In S22, it is assumed that the first The number of all sub-bands in the sub-full-band spectrum data is Then, by iterating through all possible threshold values ​​for all sub-bands, the inter-class variance is obtained. The level thresholds for all sub-bands of the sub-full-band spectrum data are: .

7. The method for calculating spectrum occupancy based on full-band spectrum data as described in claim 6, characterized in that, In S3, it is assumed that the duration of spectrum occupancy statistical measurement is... S Seconds, electromagnetic monitoring equipment per s If a full dataset is uploaded every second, then the total number of uploads within the statistical period is... = S / s Sub-full-band data; for Sub-full-band spectrum data, each group of full-band spectrum data is divided into: For each sub-band, calculate the threshold for all sub-bands as follows: 。 8. The method for calculating spectrum occupancy based on full-band spectrum data as described in claim 7, characterized in that, In S3, for Sub-full-band data, accumulating the number of times the signal exists at each frequency point. L Then the spectrum occupancy is 。

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