Abstract:
Systems and methods are described for processing a digital signal. In one embodiment, the method comprises receiving time-aligned input samples of an input signal; computing at least one moment using the time-aligned input samples; determining at least one of signal energy per symbol and noise power spectral density based on the at least one moment; and adjusting an input signal level based on the at least one of the signal energy per symbol and the noise power spectral density.

Description:
TECHNICAL FIELD 
       [0001]    The present disclosure generally relates to methods and systems for signal processing, and more particularly relates to methods and systems for signal processing for parameters used in demodulation systems. 
       BACKGROUND 
       [0002]    In systems that provide coherent demodulation of digital signals such as, but not limited to, Minimum Phase Shift Keying (MPSK), Minimum Shift Keying (MSK), and Gaussian Minimum Shift Keying (GMSK), the received signal must be properly aligned to a set of decision thresholds for optimum performance. Techniques to properly set a signal power as the input of a decision device typically require that both timing and carrier coherence are established prior to adjusting the signal level for optimum detection. However, many demodulator architectures allow for the establishment of signal timing prior to establishing carrier coherency. 
         [0003]    As a result, it is desirable to provide methods and systems for processing the time-aligned signals prior to establishing carrier coherency to optimize the performance of the demodulator. Other desirable features and characteristics will become apparent from the subsequent detailed description and the appended claims, taken in conjunction with the accompanying drawings and this background of the invention. 
       BRIEF SUMMARY 
       [0004]    According to various exemplary embodiments, systems and methods are described for processing a digital signal. In one embodiment, the method comprises receiving time-aligned input samples of an input signal; computing at least one moment using the time-aligned input samples; determining at least one of signal energy per symbol and noise power spectral density based on the at least one moment; and adjusting an input signal level based on the at least one of the signal energy per symbol and the noise power spectral density. 
         [0005]    In another embodiment, a system is provided for processing a digital signal. The system includes a first module that computes at least one moment using time-aligned input samples. A second module determines at least one of signal energy per symbol and noise power spectral density based on the at least one moment. A third module adjusts an input signal level based on the at least one of the signal energy per symbol and the noise power spectral density. 
         [0006]    In yet another embodiment, a computer program product is provided for processing a digital signal. The computer program product comprises a tangible storage medium readable by a processing circuit and storing instructions for execution by the processing circuit for performing a method comprising: receiving time-aligned input samples of an input signal; computing at least one moment using the time-aligned input samples; determining at least one of signal energy per symbol and noise power spectral density based on the at least one moment; a adjusting an input signal level based on the at least one of the signal energy per symbol and the noise power spectral density. 
         [0007]    Other embodiments, features and details are set forth in additional detail below. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0008]    The present invention will hereinafter be described in conjunction with the following figures, wherein like numerals denote like elements, and 
           [0009]      FIG. 1  is a functional block diagram illustrating a demodulator system including an estimation module in accordance with exemplary embodiments; 
           [0010]      FIG. 2  is a more detailed block diagram illustrating an estimation module of the demodulator system in accordance with exemplary embodiments; and 
           [0011]      FIG. 3  is a flowchart illustrating an estimation method that may be performed by the demodulator system in accordance with exemplary embodiments. 
       
    
    
     DETAILED DESCRIPTION 
       [0012]    The following detailed description of the invention is merely example in nature and is not intended to limit the invention or the application and uses of the invention. Furthermore, there is no intention to be bound by any theory presented in the preceding background or the following detailed description. As used herein, the term “module” refers to any hardware, software, firmware, electronic control component, processing logic, and/or processor device, individually or in any combination, including, without limitation: an application specific integrated circuit (ASIC), an electronic circuit, a processor (shared, dedicated, or group) and memory that executes one or more software or firmware programs, a combinational logic circuit, and/or other suitable components that provide the described functionality. 
         [0013]    Turning now to the figures and with initial reference to  FIG. 1 , a parameter estimation system  10  is shown to be associated with a demodulator system  11  in accordance with exemplary embodiments. As can be appreciated, the parameter estimation system  10  of the present disclosure is applicable to various signal processing systems such as, but not limited to, other demodulation systems, antenna processing systems, signal diversity combining systems, multiple input and multiple output antenna processing systems, open loop power control systems, and closed loop power control systems, and is not limited to the present example. For exemplary purposes, the disclosure will be discussed in the context of the demodulator system  11 . 
         [0014]    In various embodiments, the demodulator system  11  includes a demodulator  14  having a data decision device  16  that processes an input signal  18   a . The demodulator  14  is a digital signal processor such as, but not limited to a MPSK demodulator, a MSK demodulator, and a GMSK demodulator. 
         [0015]    The parameter estimation system  10  includes an estimation module  12 . The estimation module  12  processes an input signal  18   b  to estimate the signal energy per symbol (Es)  20  and the noise power spectral density (No)  22 . The input signal  18   b  includes time-aligned samples of the input signal  18   a.  The samples are time aligned but have not established carrier coherency. For example, the estimation module  12  estimates the Es  20  and the No  22  based on a relationship between characteristics of several moments and by a manipulation of these moments so that noise-only and signal-only moments become separable. By doing so, the estimation is made prior to carrier coherence tracking. 
         [0016]    The estimated signals Es  20  and No  22  are then used to adjust the input signal  18   a  to the data decision device  16  and/or the demodulator  14 . In particular, the estimated signals  20 ,  22  can be used to set a signal level at the input to one or both of the data decision device  16  and the demodulator  14 . 
         [0017]    Referring now to  FIG. 2 , a more detailed block diagram illustrates exemplary embodiments of the estimation module  12 . In various embodiments, the estimations performed by the estimation module  12  may be based upon fourth-order signal characteristics. For example, let the input  18   b  be: 
         [0000]    
       
      
       r=s+n,  
      
     
         [0000]    where s is the signal component, and n is the noise component. The model illustrates a processing method that separates signal characteristics from noise characteristics using time-based averages which are assumed to be equivalent to ensemble averages of the estimator. 
         [0018]    In  FIG. 2 , the estimation module  12  computes a second moment of the received signal. For example, by taking the magnitude square of the individual samples of the received signal and averaging over a predetermined number of the sample yields: 
         [0000]        E{|r|   2   }=E{|s|   2   +s*n+sn*+|n|   2   }=E{|s|   2   }+E{|n|   2   }=m   2ss*   +m   2nn*   E{|r|   2 }=σ s   2 +σ n   2 .  (1)
 
         [0019]    The fourth-moment of the received signal is then computed as: 
         [0000]    
       
         
           
             
               
                 
                   
                     
                       
                         
                           
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         [0020]    In moment notation, specifically noting which components are conjugated and which are not provides: 
         [0000]        E{|r|   4   }=m   ss*ss* +4 m   ss*   m   nn*   +m   nn*nn* .  (3)
 
         [0021]    To estimate the Es consider: 
         [0000]      2 E{|r|   2 } 2   −E{|r|   4 }=2 m   2ss*   m   2ss* +4 m   2ss*   m   2nn* +2 m   2nn*   m   2nn*   −m   ss*ss* −4 m   ss*   m   nn*   −m   nn*nn* .  (4)
 
         [0022]    A zero-mean Gaussian process provides: 
         [0000]        m   nn*nn* =−2 m   nn*   m   nn*   (5)
 
         [0000]    ( 5 ) 
         [0023]    Substituting equation 5 into equation 4 provides: 
         [0000]      2 E{|r|   2 } 2   −E{|r|   4 }=2 m   2ss*   m   2ss*   −m   ss*ss* .  (6)
 
         [0024]    For constant envelope signals and operation on samples at the output of the matched filter provides: 
         [0000]      2 E{|r|   2 } 2   −E{|r|   4 }=2 m   2ss*   m   2ss*   −m   2ss* =σ s   4 .  (7)
 
         [0025]    Thus, the normalized sampling provides: 
         [0000]      √{square root over (2 E{|r|   2 } 2   −E{|r|   4 })}=√{square root over (σ s   4 )}=σ s   2   =E   s .  (8)
 
         [0026]    The Es  20  can then be subtracted from the second moment to determine No  22  as subtracting equation 8 from equation 1 and considering operation on one sample per symbol provides: 
         [0000]        E{|r|   2 }−√{square root over (2 E{|r|   2 } 2   −E{|r|   4 })}=σ s   2 +σ n   2 −σ s   2   =E   s   +N   0   −E   s   =N   0 .  (9)
 
         [0027]    Referring now to  FIG. 3 , and with continued reference to  FIGS. 1 and 2 , a flowchart illustrates an estimation method that can be implemented by the parameter estimation system  10  of  FIG. 1  in accordance with the present disclosure. As can be appreciated in light of the disclosure, the order of operation within the method shown in  FIG. 3  is not limited to the sequential execution as illustrated in  FIG. 3 , but may be performed in one or more varying orders as applicable and in accordance with the present disclosure. As can further be appreciated, one or more steps of the method may be added or deleted without altering the spirit of the method. 
         [0028]    In various embodiments, the estimation method may be scheduled to run at various time intervals and/or may be run based one or more predetermined events. 
         [0029]    In one example, the method may begin at  100 . The time-aligned samples of the signal  18   b  are received at  110 . The second moment of the received signal is computed using the time-aligned samples  18   b  and, for example, equation 1 above at  120 . The fourth moment of the received signal is computed using the time-aligned samples  18   b  and, for example, equation 3 above at  130 . The Es  20  is set to an algebraic equation of the fourth and second moments of the received signal at  140 . The No  22  is computed by subtracting the Es  20  from the second moment of the received signal, for example, using equation 9 at  150 . The Es  20  and No  22  are then used to optimize the input signal  18   a,  for example, by using the values  20 ,  22  to adjust the input signal power to the modulator  14  at  160 . Thereafter, the method may end at  170 . 
         [0030]    As can be appreciated, one or more aspects of the present disclosure can be included in an article of manufacture (e.g., one or more computer program products) having, for instance, computer usable media. The media has embodied therein, for instance, computer readable program code means for providing and facilitating the capabilities of the present disclosure. The article of manufacture can be included as a part of a computer system or provided separately. 
         [0031]    Additionally, at least one program storage device readable by a machine, tangibly embodying at least one program of instructions executable by the machine to perform the capabilities of the present disclosure can be provided. 
         [0032]    While at least one example embodiment has been presented in the foregoing detailed description of the invention, it should be appreciated that a vast number of equivalent variations exist. It should also be appreciated that the embodiments described above are only examples, and are not intended to limit the scope, applicability, or configuration of the invention in any way. Rather, the foregoing detailed description will provide those skilled in the art with a convenient road map for implementing various examples of the invention. It should be understood that various changes may be made in the function and arrangement of elements described in an example embodiment without departing from the scope of the invention as set forth in the appended claims and their legal equivalents.