Patent Publication Number: US-2021168008-A1

Title: Signal detecting device, signal detecting method, control circuit and computer readable storage medium

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application is a continuation application of International Application PCT/JP2018/030370, filed on Aug. 15, 2018, and designating the U.S., the entire contents of which are incorporated herein by reference. 
    
    
     BACKGROUND OF THE INVENTION 
     1. Field of the Invention 
     The present disclosure relates to a signal detecting device and a signal detecting method for detecting signals in a radio communication system. 
     2. Description of the Related Art 
     Because of shortage of frequency bands that can be allocated, there have recently been demands for radio systems in which same frequencies are shared. One example of such radio systems is a cognitive radio system constituted by a plurality of radio systems. In a cognitive radio system, while a certain radio system is communicating, another radio system does not communicate in some cases so as not to interfere with the certain radio system. In addition, while a certain radio system is not communicating, another radio system may communicate. In this case, a certain radio system needs to accurately obtain the communication condition of another radio system, and a signal detection technology for detecting whether or not a signal is present is therefore necessary. 
     Patent Literature 1 discloses a signal detecting device that calculates a cyclic autocorrelation function as cyclostationarity representing cyclic repetitive components of a signal when a received signal has a level equal to or lower than a threshold, and determines whether or not a signal is present by using the obtained cyclic autocorrelation function. 
     CITATION LIST 
     Patent Literature 
     Patent Literature 1: Japanese Patent No. 4531581 
     In the cognitive radio system as described in Patent Literature 1, a period during which a certain radio system stops communicating is set, and another radio system detects a signal during this period in some cases. In such a case, when the signal detection takes a long period, the period during which the certain radio system stops communication becomes longer, which lowers the communication efficiency. It is therefore necessary to improve the accuracy of signal detection to shorten the period for signal detection. 
     The present disclosure has been made in view of the above, and an object thereof is to provide a signal detecting device capable of improving the accuracy of signal detection. 
     SUMMARY OF THE INVENTION 
     A signal detecting device according to the present disclosure includes: a first calculation unit to calculate a peak-to-average power ratio as first feature data by using a received signal; a second calculation unit to calculate by using the received signal, as second feature data, at least one of a cyclic autocorrelation function, a spectral correlation function, a signal power, an amplitude correlation, and a phase difference; and a signal determining unit to determine whether or not a signal to be detected is present in the received signal by machine learning by using the first feature data and the second feature data. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a diagram illustrating functional blocks of a signal detecting device according to an embodiment. 
         FIG. 2  is a diagram illustrating a control circuit according to the embodiment. 
         FIG. 3  is a chart illustrating a frequency spectrum of a signal obtained by bandlimiting filtering according to the embodiment. 
         FIG. 4  is a graph illustrating a spectral correlation function of an OFDM signal according to the embodiment. 
         FIG. 5  is a table illustrating combinations of a frequency at which a peak of an SCF in  FIG. 4  occurs and a cyclic frequency according to the embodiment. 
         FIG. 6  is a diagram illustrating an example of a configuration when a signal determining unit according to the embodiment uses a neural network as a discriminator. 
     
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
     A signal detecting device and a signal detecting method according to an embodiment will be described in detail below with reference to the drawings. 
     Embodiment 
       FIG. 1  is a diagram illustrating functional blocks of a signal detecting device according to an embodiment. A signal detecting device  100  is equipped in a base station, a mobile station, or both of a base station and a mobile station, to receive signals from other systems and signals transmitted from the present system which includes the signal detecting device  100 . The signal detecting device  100  includes an antenna  200 , a feature data extracting unit  210 , and signal determining unit  220 . The antenna  200  receives signals to be detected. The feature data extracting unit  210  extracts feature data by using the received signals. The signal determining unit  220  determines whether or not a signal to be detected is present by machine learning using the feature data detected by the feature data extracting unit  210 . 
     The feature data extracting unit  210  and the signal determining unit  220  are implemented by processing circuitry that is electronic circuitry for carrying out respective processes. 
     The processing circuitry may be dedicated hardware, or may be a control circuit including a memory and a central processing unit (CPU) that executes programs stored in the memory. Note that the memory is nonvolatile or volatile semiconductor memory such as a random access memory (RAN), a read only memory (ROM) or a flash memory, a magnetic disk, or an optical disk, for example. In a case where the processing circuitry is a control circuit including a CPU, the control circuit is a control circuit  400  having a configuration illustrated in  FIG. 2 , for example. 
     As illustrated in  FIG. 2 , the control circuit  400  includes a processor  400   a , which is a CPU, and a memory  400   b . In a case of implementation by the control circuit  400  illustrated in  FIG. 2 , the processor  400   a  reads and executes programs, which correspond to the respective processes, stored in the memory  400   b . Furthermore, the memory  400   b  is also used as a temporary memory in processes performed by the processor  400   a.    
     The feature data extracting unit  210  includes a first calculation unit  211  and a second calculation unit  212 . The first calculation unit  211  calculates a peak-to-average power ratio, which is one of the feature data, by using received signals. The peak-to-average power ratio is also referred to as first feature data. The peak-to-average power ratio is one of information indicating the feature of a signal, and when an orthogonal frequency division multiplexing (OFDM) signal is to be detected, for example, the peak-to-average power ratio in an environment in which OFDM signals are present has a property of being higher than the peak-to-average power ratio in an environment without OFDM signals in which only noise is present. This is because an OFDM signal is a multiplexed signal of a plurality of subcarriers modulated with different data. Thus, an environment in which signals are present can be distinguished from an environment in which no signals are present by using the peak-to-average power ratio. In this manner, the peak-to-average power ratio can be used as feature data of a signal for signal detection. The first calculation unit  211  calculates the peak-to-average power ratio C by using the following formula on an input received signal. 
     
       
         
           
             
               
                 
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     In the formula (1), x[i] represents a received signal at a sampling timing i. p[i] represents a power of the received signal at the sampling timing i. K represents observation time of the received signal. |a| represents an absolute value of a complex number a. Note that a is x[i], etc. 
     The second calculation unit  212  calculates feature data of the input received signal other than the peak-to-average power ratio. Specifically, the feature data calculated by the second calculation unit  212  are at least one of: a cyclic autocorrelation function, a spectral correlation function (SCF), a signal power, an amplitude correlation, and a phase difference. The feature data calculated by the second calculation unit  212  are also referred to as second feature data. 
     The cyclic autocorrelation function can be calculated by the following formula. 
     
       
         
           
             
               
                 
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     In the formula (2), v represents a lag parameter. α represents a cyclic frequency. T s  represents a sampling period. a* represents a complex conjugate of the complex number a. 
     A specific example for the second calculation unit  212  calculating the cyclic autocorrelation function as feature data will be described in detail.  FIG. 3  is a chart illustrating a frequency spectrum of a signal obtained by bandlimiting filtering according to the embodiment. In  FIG. 3 , the vertical axis represents signal strength. The horizontal axis represents frequency. The frequency spectrum of a signal does not fall within the signal bandwidth W when the signal is band-limited by a bandlimiting filter. In this case, a region A 2  of a signal  10 , which is a signal before frequency shifting, has the same signal component as a region A 1  of the signal  10 . A region B 2  of the signal  10  has the same signal component as a region B 1  of the signal  10 . With use of this characteristic, when a signal  20 , which is a signal obtained by bandlimiting filtering, is assumed to be a signal with frequency shifted by the signal bandwidth W from the signal  10 , the region A 2  of the signal  10  and a region A 1 ′ of the signal  20  have the same signal component as each other. Similarly, the region B 1  of the signal  10  and a region B 2 ′ of the signal  20  have the same signal component as each other. Note that, because the cyclic autocorrelation function has a correlation value of a signal with frequency shifted by the cyclic frequency α when v=0 is set in the formula (2), the cyclic autocorrelation function has a correlation peak when v=0 and α=W. In the present embodiment, the correlation peak value of the cyclic autocorrelation function is calculated as feature data. In addition, when a signal to be detected is an OFDM signal, the cyclic autocorrelation function has peaks because of the cyclic property provided by guard intervals (GIs). Specifically, in the formula (2), the peaks occur when v=±N d  and α=m/N s T s . Note that N d  represents the data length of an OFDM symbol. N s  represents the symbol length of the OFDM symbol. m represents an integer. When an OFDM signal is to be detected, the peak values are calculated as feature data of the cyclic autocorrelation function. 
     The spectral correlation function can be calculated by the following formula. 
     
       
         
           
             
               
                 
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     In the formula (3), f represents frequency. Q represents the number of observed lag parameters. In addition, the formula (3) can be converted into the following formula by causing the number of observed samples and the number of observed lag parameters to asymptotically approach infinite values. 
     
       
         
           
             
               
                 
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     In the formula (4), X[f] represents a signal, that is, a frequency spectrum, the signal being obtained by Fourier transform of the received signal x[i]. M represents the number of observed symbols. N f  represents a fast Fourier transform (FFT) size. According to the formula (4), the spectral correlation function is a correlation value between X[f] and X[f-a] that is obtained by frequency shifting by the cyclic frequency α. 
     A specific example for calculating the spectral correlation function as feature data will be described. Assume that a signal to be detected is an OFDM signal, and that pilot signals are inserted in specified subcarriers. Here, assume that the GI length is 16 and that the FFT length is 64. In addition, the OFDM signal, in which nulls are allocated to subcarrier numbers −32 to −27, 0, 27 to 31; pilots are allocated to subcarrier numbers −24, −8, 8, and 24; and data are allocated to the remaining subcarrier numbers among the subcarrier numbers −32 to 31, will be described. 
       FIG. 4  is a graph illustrating the spectral correlation function of the OFDM signal according to the embodiment. The SCF has peaks at specific positions because of the pilots. The peaks occur at the specific positions because the SCF is a correlation value between the frequency spectrum X[f] and the frequency spectrum X[f−α] that is obtained by frequency shifting by the cyclic frequency α. Accordingly, the SCF becomes a correlation between pilots if a frequency of a pilot and a frequency that is obtained by frequency shifting a frequency of another pilot by the cyclic frequency α coincide to each other. 
       FIG. 5  is a table illustrating combinations of the frequency f at which a peak of the SCF in  FIG. 4  occurs and the cyclic frequency α according to the embodiment.  FIG. 5  includes numbers that indicate the number of SCF peak positions, the frequency f, and the cyclic frequency α. According to  FIG. 5 , there are twelve SCF peak positions. The number of SCF peak positions corresponds to the number of permutations of two pilots that are correlated out of four pilots inserted in the frequency direction. Note that the correlation value is the same regardless of the order of two frequency spectra that are correlated. For example, when correlation between a complex number “a” and a complex number “b” is assumed, the correlation between the complex number “a” and the complex number “b” and the correlation between the complex number “b” and the complex number “a” have the same value. Thus, six SCF peak positions are sufficient among the twelve SCF peak positions illustrated in  FIG. 5 . Specifically, a total of six including the numbers  4 ,  7 ,  8 ,  10 ,  11 , and  12  in  FIG. 5 . The feature data of the spectral correlation function may be the SCFs at the SCF peak positions determined by the pilots of the OFDM signal. Alternatively, the SCFs at all the positions excluding combinations of the same correlation values may be the feature data regardless of the SCF peak positions. 
     The signal power can be calculated by the following formula. 
     
       
         
           
             
               
                 
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     In addition, when a signal to be detected is an OFDM signal, a subcarrier power can be calculated as the signal power by the following formula. 
     
       
         
           
             
               
                 
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     When the subcarrier power is used as the feature data of the signal power, the power of all subcarriers may be used as the feature data, for example. Alternatively, power of subcarriers other than the subcarriers into which nulls are inserted may be used as the feature data. 
     The amplitude correlation can be calculated by the following formula. 
     
       
         
           
             
               
                 
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     In the formula (7), E[a] represents an average value of the complex number a. A range of i for obtaining the average is i=0 to K−1. In addition, r[i] represents the amplitude of the received signal x[i]. When the amplitude correlation is calculated as the feature data, the amplitude correlation C[m] in the formula (7) may be used without any change, for example. Alternatively, a statistic of the amplitude correlation in the formula (7) may be used as the feature data. Examples of the statistic include an average and a variance. The phase difference can be calculated by the following formula. 
       [Formula 8] 
         D [ i ]= arg{x [ i ]− x *[ i+ 1]}  (8)
 
     In the formula (8), arg{a} represents the phase of the complex number a. When the phase difference is calculated as the feature data, the phase difference D[i] in the formula (8) may be used without any change, for example. Alternatively, a statistic of the phase difference in the formula (8) may be used as the feature data. The statistic is an average or a variance, for example. 
     The signal determining unit  220  determines whether or not a signal to be detected is present in received signals by using two or more pieces of feature data extracted by the feature data extracting unit  210  using a learned discriminator, that is, the two or more pieces of feature data are the peak-to-average power ratio and at least one of: the cyclic autocorrelation function, the spectral correlation function, the signal power, the amplitude correlation, and the phase difference. For the discriminator, any learner such as a neural network, a decision tree, a Bayes classifier, or a support vector machine can be used, for example. Neural networks include a convolution neural network (CNN), a recurrent neural network (RNN), a residual network (ResNet), and the like. In addition, neural networks similarly include deep learning employing deeper layers of a neural network. 
       FIG. 6  is a diagram illustrating an example of a configuration when the signal determining unit  220  according to the embodiment uses a neural network as the discriminator. The signal determining unit  220  includes a discriminator  300  and a signal presence determining unit  340 . The discriminator  300  is a fully-connected neural network. In addition, the discriminator  300  is constituted by three layers, which are an input layer  310 , an intermediate layer  320 , and an output layer  330 . The discriminator  300  receives feature data J (J=1 to S; S is the number of feature data) extracted by the feature data extracting unit  210  each input to input nodes A J  (J=1 to S) in the input layer  310  of the discriminator. Weighting factors W J,K  associated with the input nodes A J , and intermediate nodes B K  (K=1 to M) of the intermediate layer  320 , are added to the feature data J input to the input nodes A J , and the weighted feature data are input to the intermediate nodes B K . In this process, bias values T K  associated with the intermediate nodes B K  are added to the values input to the intermediate nodes B K  from the input nodes, and values of the intermediate nodes B K  are thus obtained. An activating function such as a sigmoid function or a rectified linear unit (ReLU) function is applied to the values of the intermediate nodes B K , weighting factors V K,L  associated with the intermediate nodes B K  and output nodes C L  (L=1, 2) of the output layer  330  are then added to values obtained through the activation function, and the weighted values are input to the output nodes C L . In this process, bias values U L  associated with the output nodes C L  are added to the values input to the output nodes C L  from the intermediate nodes, and values of the output nodes C L  are thus obtained. The values of the output nodes C L  are input to the signal presence determining unit  340 , which outputs a result of determination on whether or not a signal is present. Note that, when assuming that the output node C 1  corresponds to presence of the signal and the output node C 2  corresponds to absence of the signal, the signal presence determining unit  340  outputs the presence of the signal (1 as an output value, for example) when the value of C 1 ≥the value of C 2 , or outputs the absence of the signal (0 as an output value, for example) when the value of C 1 &lt;the value of C 2 . 
     The weighting factors W J,K  and V K,L  and the bias values T K  and U L  are determined by performing a learning process by using training data, that is, the feature data J and a correct value of the presence/absence of a signal. For the learning process, a known technology such as backpropagation is used. Specifically, in a method for determining the weighting factors and the bias values, backpropagation or the like is used so as to reduce errors of the values of the output nodes output by the discriminator with respect to the correct values of signal presence/absence (the value of C 1 =1 and the value of C 2 =0 when the signal is present, and the value of C 1 =0 and the value of C 2 =1 when the signal is absent, for example). 
     As described above, in the present embodiment, the signal detecting device  100  inputs, in addition to the peak-to-average power ratio, any one or more of the cyclic autocorrelation function, the spectral correlation function, the signal power, the amplitude correlation, and the phase difference as feature data to the discriminator  300 , thereby enabling determination on whether or not a signal is present by machine learning. The signal detecting device  100  can therefore improve the accuracy of detecting a signal to be detected in received signals. 
     The configurations presented in the embodiment above are examples, and can be combined with other known technologies or can be partly omitted or modified without departing from the scope.