KR100916180B1 - An Enhanced Energy Detector for identification of the VSB and WMP signal in the IEEE 802.22 System and Detecting Method - Google Patents

Abstract

The present invention improves the performance of the conventional method by using the enhanced energy detector in the spectrum detection process. In the IEEE 802.22 system environment, the primary user, digital TV (DTV), wireless microphone signal (WMP), and noise ( It measures the characteristics of power of noise) and compares them and distinguishes them. The present invention is characterized by distinguishing the signal types of the preferred users by using the fact that they have different maximum-to-mean power ratios (MMPR) according to the types of signals of the preferred users. The energy of the signal is measured by the existing energy detector method to determine the presence of the signal, and if the signal exists, the control is transferred to the enhanced energy detector algorithm. The maximum power and average power can be measured through a sliding window of a certain size in the frequency domain, and the measured value can be used to obtain the maximum-to-mean power ratio (MMPR). It is the basic algorithm of the enhanced energy detector that distinguishes each signal from the noise through the threshold comparison using this value.

Energy Detector, Preferred User, Maximum Power, Average Power, MMPR, Threshold

Description

An enhanced energy detector for identification of the VSB and WMP signal in the IEEE 802.22 System and Detecting Method}

The present invention relates to an apparatus and method for detecting a signal by detecting energy of a signal among spectrum detection methods. More particularly, the present invention relates to a function of detecting the presence or absence of a signal of an existing energy detector. In addition, the present invention relates to an enhanced energy detector and detection method for identifying types of priority user signals that can increase the reliability of spectrum detection.

First, the main terms used in this technique and their meanings are defined.

Radio Cognitive Radio (CR): A technology that can detect and share an empty frequency band efficiently without using it, but share it efficiently.

Base Station (BS): A device that performs transmission and reception with a terminal through a wireless interface based on a radio recognition technology and controls communication control within its own communication area.

Primary User: A user who pays a fee and uses a licensed frequency band.

CR system: A communication system using radio recognition technology.

WRAN CPE (Customer Promise Equipmet): CR system user in WRAN (Wireless Regional Area Network) environment

Downlink: Direction from the base station to the terminal.

Uplink: Direction from the terminal to the base station.

It is difficult to use the same frequency band technically between wireless communication systems that are variously developed, and each wireless communication system is assigned a different frequency band. Almost all frequency bands suitable for voice communication and data communication have been allocated, and there is a shortage of frequency bands to be allocated to newly developed wireless communication systems.

However, there are frequency bands that are allocated to wireless communication systems but are not used locally or in time. Cognitive Radio (CR) technology is a method that can efficiently use this frequency band.

Wireless recognition is a technology that recognizes and uses an empty frequency band without interfering with an original primary user.

Based on the wireless recognition technology, in order to enable wireless Internet access in a wide area such as the US, Canada, Brazil, etc., a standard that provides services using unused channels among the TV bands of the VHF / UHF band is established. It is an IEEE 802.22 WRAN system.

1 is a diagram illustrating an overview of an IEEE 802.22 WRAN system.

Referring to FIG. 1, the WRAN system includes a primary user, a WRAN base station 110, and a WRAN CPE (Customer Promise Equipment) 120. Preferred users in FIG. 1 may be referred to as the TV receiver 130 and the wireless microphone 140. Service coverage of the WRAN system is 33Km ~ 100Km, spectrum efficiency is aimed at 0.5 ~ 3bps / Hz, downlink 1.5Mbps, uplink 384kbps or more.

The wireless recognition technology can be configured as an addition of a spectrum sensing technology for recognizing the external environment in the existing communication system and a part for resource allocation function that changes the efficient frequency and communication parameter information from the recognized external environment information. have. Among them, spectrum sensing technology can be classified into two functions as one of the core functions of wireless recognition technology that recognizes the environment including the presence of an external primary user. One of them is to find out whether or not a specific band is used, and the other is to identify characteristics and types of signals in the band being used. The first objective is necessary to secure the usable band, and the second can be seen as a function for efficient frequency operation. This spectrum sensing method can be largely classified into three methods.

One of the methods having good performance is a method of matching the same signal to the user signal and detecting the user signal. Matching filters are used to match the signals.

2 is a block diagram 200 illustrating a configuration of a method using a matched filter.

2, an arbitrary received signal passes through the matched filter 220 via the A / D converter 210. After the symbol time is restored 230 through the matching filter and the phase of the carrier 240 is restored 240, the signal may be detected through the threshold comparison 260 when the channel equalizer 250 passes. Although it is possible to maximize the signal-to-noise ratio (SNR) and it is a relatively accurate method, it is not possible to detect signals in various environments because it is necessary to know information about all signals in advance and performance may be greatly degraded if the information is not accurate. There is this.

Another spectral sensing method is a cyclostationary detection method that detects the shape of a signal by using the periodic properties of the signals. The modulated signal detects the signal by using the cyclostationary characteristic because the average and autocorrelation are statistically periodic.

3 is a block diagram 300 showing the configuration of a cyclostationary detection method.

Referring to FIG. 3, an arbitrary received signal moves to the frequency domain through the FFT 320 through the A / D converter 310. And this can be detected through the averaging step 340 via the autocorrelator 330. This detection has an excellent detection performance for interfering signals in a manner of autocorrelation of the received signal to detect the signal. This method has the advantage of being able to determine the presence of a signal even with a small signal-to-noise ratio and to find out the characteristics of the signal present in the current band. However, the resulting system requirements are not small, and the structure is complicated and expensive;

Finally, there is an energy detection method that detects the presence or absence of a signal according to the magnitude of the energy of the signal. The energy detection method can basically detect the primary user without information about the primary user.

4 is a block diagram 400 illustrating a configuration of an energy detection method.

Referring to FIG. 4, after an arbitrary received signal passes through the A / D converter 410, the received signal is transferred to the frequency domain through the FFT 430, averaged 440, and then moved to the frequency domain. The comparison of the thresholds in accordance with 420 can be implemented relatively easily.

One of the simplest methods is that it is easy to determine the presence or absence of the spectrum, but it is difficult to determine the type of the signal described above.

SUMMARY OF THE INVENTION The present invention has been made to solve the above-mentioned problems of the prior art, and uses an energy detector to improve the detection and detection performance of a VSB signal and a WMP signal designated as a preferred user in an IEEE 802.22 WRAN system, and to improve energy detection. Enhanced energy detector that distinguishes VSB and WMP signals in IEEE 802.22 system environment, which is improved by using the maximum power-to-average power ratio (MMPR) calculated for each section, which is a feature that distinguishes the user and noise, which is the biggest disadvantage of the scheme. It is an object to provide a detection method.

In addition, it is possible to implement the enhanced energy detector's algorithm to compare the new thresholds to distinguish the types of signals and to distinguish VSB and WMP signals in a reliable IEEE 802.22 system environment that can distinguish the types of detected signals. It is an object of the present invention to provide an energy detector and a detection method.

The enhanced energy detector for identifying the type of the preferred user signal according to the present invention for solving the above problems is a first energy detection module for identifying the presence or absence of a signal by comparing the energy of the received signal with a first threshold value; When the energy of the received signal is greater than the first threshold value, the power is measured using frequency values squared for each window while passing through the entire frequency domain with the first window size, and using the ratio of maximum power to average power, And a second energy detection module for identifying the type of user signal.

Here, the first energy detection module includes a bandpass filter for extracting a spectrum of interest, an A / D converter for converting an output of the bandpass filter into a digital signal, and an output of the A / D converter for a frequency domain signal. And an energy estimator for estimating the energy of the received signal by squaring and averaging the output of the frequency domain converter, and a threshold comparator for comparing the energy of the received signal with a first threshold.

In addition, the frequency domain converter converts the frequency domain signal using a fast fourier tramsform (FFT).

The second energy detection module includes a sliding window unit for measuring and storing power using frequency values squared for each window while passing the entire frequency domain with a first window size, and each window having the first window size. A power ratio calculator that calculates a ratio of the maximum power to the average power of the first user;

The second energy detection module may further include a controller configured to determine at least one of the first threshold value, the second threshold value, and the first window size.

In addition, the priority user identification unit identifies whether the priority user signal is a wireless microphone signal or a digital TV signal.

In addition, the enhanced energy detection method for identifying the type of the preferred user signal according to the present invention for solving the above problems is a first step of identifying the presence of the signal by comparing the energy of the received signal with the first threshold value And when the energy of the received signal is greater than a first threshold, the power is measured using frequency values squared for each window while passing the entire frequency domain with a first window size, and using the ratio of maximum power to average power. Firstly, a second step of identifying the type of user signal is included.

The first step includes a first step of filtering a frequency band to extract a spectrum of interest, a second step of sampling a signal filtered in the first step by a Nyquist rate, and a time sampled in the second step. A third process of converting a signal of a region into a signal of a frequency domain, a fourth process of calculating an energy of a received signal by squaring and averaging a sampled frequency of the frequency domain signal, and comparing the energy of the received signal with a first threshold value; And a fifth process of identifying the presence or absence of a signal by comparison.

The third process converts a frequency domain signal using Fast Fourier Tramsform (FFT).

The second step is a first process of measuring and storing power using frequency values squared for each window while passing through the entire frequency domain with the first window size, the maximum power of each window having the first window size. And a second step of calculating a ratio of average power and a third step of identifying a type of priority user signal by comparing the ratio of maximum power to average power with a second threshold.

The third process of the second step first identifies whether the user signal is a wireless microphone signal or a digital TV signal.

The enhanced energy detector and detection method for distinguishing the types of the preferred user signal according to the present invention configured as described above have the disadvantage of indistinguishable noise from the preferred users of the energy detector presented in the IEEE 802.22 WRAN system through the sliding window. This is solved by a relatively simple method of calculating the maximum power and the average power and comparing the thresholds, improving the performance and increasing the reliability of the spectrum detection.

Hereinafter, with reference to the accompanying drawings will be described specific details and embodiments of the present invention.

The newly defined terms in the present invention are as follows.

Sliding Window: The energy of each signal combined with the received signal can be efficiently measured by examining the entire band using a window of a specific size in the frequency domain band.

Maximum-to-mean Power Ratio (MMPR): The ratio of maximum power and average power measured in a window, and the method introduced in the present invention to increase the spectrum detection reliability. Have different MMPR values.

Pilot: In order to recover the synchronization signal well in the multi-path situation of the DTV signal, it has a power value higher than the total transmitted power to send an additional signal called a pilot.

AWGN: Additive White Gaussian Noise. It means that it is a Gaussian form as a random variable and is added to the original signal. A signal propagated into free space must take into account some noise that is always present in space, which is a commonly used concept.

Fading: A phenomenon in which the received field strength fluctuates in time due to a change in atmospheric refractive index in a wireless line and a change in the line due to interference, focusing, divergence, or obstruction of multiple waves at the receiving point. It can be used as noise existing in channel space.

Hereinafter, with reference to the accompanying drawings will be described in detail the contents according to the present invention.

5 is a block diagram showing the configuration of an enhanced energy detector for distinguishing types of preferred user signals in accordance with the present invention.

Referring to FIG. 5, an enhanced energy detector according to the present invention includes a first energy detection module 510, which is a conventional energy detector module, and a second energy detection module 520, which is an enhanced energy detector module. Have

First, the configuration of the first energy detection module 510 will be described.

The signal arriving at the radio recognition receiver passes through the band pass filter 511 and the A / D converter 512.

The bandpass filter 511 is an adjustable filter that can control the size of the filter in the controller 524 for the purpose of extracting the spectrum of interest.

The signal passing through the bandpass filter 511 is sampled by the Nyquist rate in the A / D converter 512. In order to convert the signal in the time domain into the frequency domain, the frequency domain transform unit 513 performs FFT (Fast Fourier Tramsform) to obtain N sampled frequencies, which is represented by Equation 1 below. Can be represented.

Here, Y (k) is frequency samples of the received signal, and when the value is squared and averaged by the energy estimator 514, the energy of the signal may be estimated. The energy of the received signal thus obtained is compared with the first threshold value in the threshold comparison unit 515 to determine the presence of the signal. If the first predefined threshold value is T and the estimated energy value is E, then the presence of the signal is determined as shown in [Equation 2].

E> T: Signal present

E <T: no signal present

The predefined first threshold value T may be appropriately changed by the controller 524. If the estimated energy E is less than the first threshold T, the absence information of the signal is known to the controller 524, and the first user is declared that no user exists. However, if the estimated energy value is greater than the first threshold, the algorithm of the enhanced second energy detection module 520 is first started to distinguish the detailed type of user.

The second energy detection module 520 includes a sliding window unit 521, a power ratio calculator 522, a priority user identification unit 523, and a control unit 524.

The sliding window unit 521 measures and stores power using frequency values squared for each window while passing through the entire frequency domain with the first window size.

That is, the power is measured and stored using the frequency values squared for each window while passing the entire frequency domain with a specific window size.

If the process is generalized, it can be expressed as [Equation 3].

Where τ is the delay of the frequency values and K is the size of the window.

Next, the power ratio calculator 522 calculates a ratio of the maximum power to the average power of each window having the first window size. As the window is moved, the average power for each window is obtained, and the maximum power value is obtained, and the ratio with the average power is calculated.

Further, the priority user identification unit 523 identifies the type of the priority user signal by comparing the ratio of maximum power to average power with a second threshold.

The controller 524 determines and adjusts at least one of the first threshold value, the second threshold value, and the first window size by the second energy detection module.

6A and 6B are flowcharts illustrating an enhanced energy detection method for identifying types of priority user signals according to the present invention.

In detail, FIG. 6A is a flowchart illustrating a process of determining the presence or absence of a priority user by estimating the energy of a received signal, and FIG. 6B illustrates a method of identifying a type of a priority user signal using a ratio of maximum power to average power. Is a flow chart.

In other words, the enhanced energy detection method according to the present invention consists of two steps.

First, the first step is to compare the energy of the received signal with the first threshold to identify the presence or absence of the signal. This process can use existing conventional methods.

An example of the first step is as follows.

First, a frequency band of interest is selected by filtering a frequency band (S10), and after the A / D conversion (S20), a signal in the time domain is converted into a signal in the frequency domain (S30).

In this case, the FFT can be used to convert a signal in the frequency domain.

Next, when the energy of the received signal is calculated (S40), and the energy of the received signal is greater than the first threshold value T (S50), the first user is present. If it is not (S60), if not, the user first determines that the absence (S70).

When the above first step is completed, the first step proceeds to the second step of identifying what type of user signal is.

Referring to FIG. 6B, the first process is a sliding window step (S110), and the sliding window measures power and stores power using frequency values squared for each window while passing through the entire frequency domain with a specific window size. do.

7 is a graph showing an example of a power spectral density (PSD) of an ATSC digital TV (DTV) signal, and FIG. 8 is a graph illustrating an example of a power spectral density (PSD) of a wireless microphone signal (WMP).

In the present invention, the maximum power to average power ratio (MMPR) is used as a criterion for distinguishing each priority user and the type of noise. First, a power spectral density (PSD) graph of a digital TV (DTV) and a wireless microphone (WMP) used as an example of a user signal is shown in FIGS. 7 and 8, respectively. In the power spectrum density graph of the DTV signal of FIG. 7, the sharpest line with the highest power level is a pilot, which is always higher than the peak power of the wireless microphone signal of FIG. 8. high.

7,8, when the average power is calculated based on the bandwidth of 6MHz, the average of the DTV signals distributed in the wide band is higher than the average of the narrow band wireless microphone signal. When the ratio of the maximum power to the average power is measured using these two values, it has a specific value depending on the type of signal. In the example of FIG. 7, 8, it can be seen that the MMPR value of the wireless microphone is larger than that of the DTV because the average power value of the wireless microphones distributed in the narrow band is considerably lower than the maximum power value. . The MMPR value of noise will naturally be less than the values of the two signals.

In this way, the ratio of the maximum power and the average power (MMPR) in the frequency domain is measured and stored and used as a criterion for distinguishing the types of user signals.

Referring to FIG. 6B again, in order to obtain a maximum power-to-average power ratio (MMPR), first, power values of respective sliding windows are compared to obtain a maximum power value (S120). In addition, in this step, if the average value of the power is also calculated at the same time (S130), the maximum power-to-average power ratio (MMPR) can be obtained through simple calculation with two values (S140). These processes calculate values within the size of the window, and therefore may produce different results depending on the size of the window. Therefore, it is necessary to set the appropriate window size according to the situation.

In step S160 and S180, the signal type is determined using the maximum power to average power ratio MMPR. This step is performed by the threshold comparison unit 515 or first user identification unit 523 stored in advance through the control unit 524.

The present invention identifies the preferred user by comparing the maximum power to average power ratio with a second threshold, wherein the second threshold consists of one or more thresholds according to the number of preferred users to distinguish.

Referring to FIG. 6B, since T1 (S160) is a threshold of a wireless microphone, T2 (S180) is a threshold of a DTV, and the MMPR of the wireless microphone is larger, the threshold of the wireless microphone is first compared and the signal is greater than the threshold. The type of is declared as a wireless microphone (S150).

If it is smaller than that, the process proceeds to the step of comparing the threshold value of the DTV, and is determined as a DTV signal (S170). If it is smaller than the threshold value of the DTV, the noise is determined to be the first user (S190).

9 is a diagram illustrating an embodiment of detecting a wireless microphone signal when the enhanced energy detector and the detection method according to the present invention are used.

Referring to FIG. 9, when the window size is 10 KHz, the average detection time is 0.015 ms, and the threshold value T1 of the wireless microphone signal is T1 = 5, the wireless microphone signal is enhanced in the AWGN and fading channel environments. The detection probability of the energy detector is shown. Pd and Pf shown in the following table of FIG. 7 indicate a correct detection probability and a false alarm probability, respectively, and a false detection probability is shown to indicate a case where a DTV signal is detected instead of a wireless microphone signal. Figure 9 also shows the correct detection probability of a conventional energy detector in an AWGN channel environment. Compared to the 90% detection probability (Pd = 0.9) in the enhanced energy detector graph of the same AWGN channel environment, the enhanced energy detector is shown in FIG. The graph shows that the signal-to-noise ratio (SNR) can detect signals with 2dB improvement in performance.

10 is a diagram illustrating an embodiment of detecting a DTV signal when the enhanced energy detector and the detection method according to the present invention are used.

Referring to FIG. 10, an additive white Gaussian noise (AWGN) and a fading channel when a window size of 10 KHz, an average detection time of 0.015 ms, a threshold value T1 of a wireless microphone signal and a threshold value T2 of a DTV signal are T2 = 2.8. The detection probability of the enhanced energy detector detecting the DTV signal under the environment is shown. As shown in FIG. 10, in the case of DTV signal detection, an apparent false detection probability of detecting a wireless microphone is shown when the signal-to-noise ratio (SNR) is from -20 dB to -9 dB. In addition, the 90% detection probability (Pd = 0.9) compared with the conventional energy detector in AWGN environment shows that the enhanced energy detector shows a performance of about 10dB lower. This is presumably because the weaker the signal, the lower the maximum power-to-average power ratio (MMPR) of the wireless microphone and the wireless microphone is detected in the detection range of the DTV signal. Degradation of detection performance can also be seen as a performance attenuation due to the cost of having to make two threshold comparisons to detect two signals. As a solution to this problem, we can propose to change the average time of window resizing and detection.

11 is a graph comparing the performance of the wireless microphone detection according to the window size when using the enhanced energy detector and the detection method according to the present invention.

Referring to FIG. 11, a graph shows detection performance as the window size changes from 2KHz to 50KHz when the detection time is 0.015ms and the wireless microphone threshold T1 = 5 in the AWGN channel environment. It has the best performance when the window size is small, and the 90% detection probability (Pd = 0.9) is 10dB better than the 50KHz at the smallest 2KHz. When using the proposed detector, it is necessary to select the appropriate window size.

12 is a graph comparing the performance of the wireless microphone detection according to the detection average time when using the enhanced energy detector and the detection method according to the present invention.

Referring to FIG. 12, a graph shows detection performance as the average detection time varies from 0.015 ms to 0.15 ms when the window size is 10 KHz and the wireless microphone threshold T1 = 5 in the AWGN channel environment. It is easy to see that the larger the average time, the better the performance.In the case of 90% detection probability (Pd = 0.9), it can be observed that the performance of about 10dB is improved when the average time is 0.15ms, which is the largest. . Therefore, a larger average detection time improves the performance of spectral sensing, but requires appropriate selection because it makes a trade-off in which the overall processing time is longer.

The invention can be implemented in hardware, software or a combination thereof. In hardware implementation, an application specific integrated circuit (ASIC), a digital signal processing (DSP), a programmable logic device (PLD), a field programmable gate array (FPGA), a processor, a controller, and a microprocessor are designed to perform the above functions. , Other electronic units, or a combination thereof. In the software implementation, the module may be implemented as a module that performs the above-described function. The software may be stored in a memory unit and executed by a processor. The memory unit or processor may employ various means well known to those skilled in the art.

As described above, an enhanced energy detector and a detection method for distinguishing types of priority user signals according to the present invention have been described with reference to the illustrated drawings, but the present invention is not limited to the embodiments and drawings disclosed herein. The technology can be applied within the scope of protection.

1 is a diagram illustrating a service outline of IEEE 802.22.

2 is a block diagram illustrating a method using a matched filter among spectrum detection methods.

3 is a block diagram illustrating a cyclostationary detection method among spectral detection methods.

4 is a block diagram illustrating a method using energy detection among spectral detection methods.

5 is a block diagram showing the configuration of an enhanced energy detector for distinguishing types of preferred user signals in accordance with the present invention.

6A and 6B are flowcharts illustrating an enhanced energy detection method for identifying types of priority user signals according to the present invention.

7 is a graph illustrating an example of a power spectral density (PSD) of an ATSC digital TV (DTV) signal.

8 is a graph illustrating an example of a power spectral density PSD of a wireless microphone signal WMP.

9 is a graph showing the performance when detecting a wireless microphone (WMP) signal using the enhanced energy detector and detection method according to the present invention.

10 is a graph showing the performance when detecting the DTV signal using the enhanced energy detector and detection method according to the present invention.

11 is a graph comparing the performance of the wireless microphone (WMP) detection according to the window size when using the enhanced energy detector and the detection method according to the present invention.

12 is a graph comparing the performance of wireless microphone (WMP) detection according to the average detection time when using the enhanced energy detector and the detection method according to the present invention.

<Explanation of symbols on main parts of the drawings>

510: first energy detection module 511: bandpass filter

512: A / D converter 513: frequency domain conversion unit

514: energy estimating unit 515: threshold comparison unit

520: second energy detection module 521: sliding window portion

522: power ratio calculation unit 523: first user identification unit

524: control unit

Claims (11)

A first energy detection module for comparing the energy of the received signal with a first threshold to identify the presence or absence of a signal; And When the energy of the received signal is greater than the first threshold value, the power is measured using frequency values squared for each window while passing through the entire frequency domain with the first window size, and using the ratio of maximum power to average power, An enhanced energy detector for identifying the type of preferred user signal comprising a second energy detection module for identifying the type of user signal. The method of claim 1, wherein the first energy detection module A bandpass filter for extracting the spectrum of interest; An A / D converter for converting the output of the band pass filter into a digital signal; A frequency domain converter for converting the output of the A / D converter into a frequency domain signal; An energy estimator for estimating the energy of a received signal by squaring and averaging the output of the frequency domain transform unit; And And a threshold comparison unit for comparing the energy of the received signal with a first threshold value. The method according to claim 2, And the frequency domain converter converts the type of the preferred user signal into a frequency domain signal using fast fourier tramsform (FFT). The method according to claim 1 or 2, wherein the second energy detection module A sliding window unit for measuring and storing power using frequency values squared for each window while passing the entire frequency domain in a first window size; A power ratio calculator configured to calculate a ratio of the maximum power to the average power of each window having the first window size; And And a priority user identifier for identifying a type of a priority user signal by comparing the ratio of maximum power to average power to a second threshold value. The method according to claim 4, The second energy detection module further includes a control unit for determining at least one or more of the first threshold value, the second threshold value, and the first window size. Detector. The method according to claim 4, And the priority user identification unit identifies a type of the priority user signal, wherein the priority user signal is a wireless microphone signal or a digital TV signal. A first step of identifying the presence or absence of the signal by comparing the energy of the received signal with a first threshold value; And When the energy of the received signal is greater than the first threshold value, the power is measured using frequency values squared for each window while passing through the entire frequency domain with the first window size, and using the ratio of maximum power to average power, An enhanced energy detection method for identifying types of preferred user signals comprising a second step of identifying types of user signals. The method of claim 7, wherein the first step is A first process of filtering the frequency band to extract a spectrum of interest; A second step of sampling the signal filtered in the first step by a Nyquist rate; A third process of converting a signal of a time domain sampled in the second process into a signal of a frequency domain; A fourth step of calculating an energy of a received signal by squaring and averaging a sampled frequency of the frequency domain signal; And And a fifth process of identifying the presence or absence of a signal by comparing the energy of the received signal with a first threshold value to identify the type of the preferred user signal. The method according to claim 8, The third step is an enhanced energy detection method for distinguishing the type of the preferred user signal, characterized in that the conversion to the frequency domain signal using the Fast Fourier Tramsform (FFT). The method according to claim 7 or 8, The second step includes a first step of measuring and storing power using frequency values squared for each window while passing the entire frequency domain in a first window size; Calculating a ratio of the maximum power to the average power of each window having the first window size; And a third step of identifying the type of the preferred user signal by comparing the ratio of the maximum power to the average power to a second threshold value. The method according to claim 10, The third step of the second step is to identify the type of priority user signal, characterized in that identifying the priority user signal is a wireless microphone signal or a digital TV signal.

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