Abstract:
The present invention is a method and system for extracting information from a received signal with minimal loss due to noise. The system comprises of a transformer, for correlating the received signal to a wavelet function and producing wavelet decomposition coefficients, and a threshold circuit, which is responsive to the received signal, for applying predetermined threshold values based on the type of signal. Also included in the system is a filter, coupled to the transformer and threshold circuit, for altering the wavelet decomposition coefficients produced by the transformer using threshold values applied by the threshold circuit to produce altered wavelet coefficients from which the received signal is reconstructed with reduced noise.

Description:
BACKGROUND 
     This invention relates generally to a receiving system. More specifically, the present invention relates to a signal processor in a receiver system for real-time wavelet de-noising applications. 
     Communications systems, radar systems, sonar systems and the like have a receiver which is used to detect the presence of specific signals and a signal processor to extract the information being transmitted within the signal. A problem with many of these types of systems is the detection of the received signal in the presence of noise and clutter and extracting information from the detected received signal with minimal loss due to noise and clutter. 
     Current systems have employed de-noising methods in the signal processor of the receiver. In particular, current systems are utilizing wavelet techniques for de-noising received signals. De-noising exploits important characteristics of wavelets, including multi-resolution capabilities and perfect reconstruction. Wavelet theory involves representing general functions in terms of simpler fixed building blocks at different scales and positions in time. 
     The main goal of wavelet transforms is to decompose the information contained in a signal into characteristics of different scales. This can be thought of as a means to describe the input waveform over a unit of time at different resolutions in time and frequency or scale. This signal decomposition technique is performed with the Discrete Wavelet Transform. A principle advantage of decomposing the input signal over a multi-scale wavelet representation is that the desired signal has the degree of freedom to be designed to correlate with the transforming wavelet function, thus having the property of non-signal like features to not correlate as well with the transformation function. Thus, when the signal is seen in the wavelet domain, its representation is apparent by large coefficients while the undesired signal will be represented by much smaller coefficients and will also typically be equally distributed across all the wavelet decomposition scales. Therefore, when a wavelet transformation output is put through a threshold function by some rule such as the soft, hard, or gradient threshold rule, the noise-like coefficients can be removed from the wavelet coefficient sets across all scales. When the altered wavelet coefficients have been re-transformed back to the time domain via an Inverse Wavelet Transformation, the coefficients corresponding to the desired signal will remain with the noisy coefficients removed or de-emphasized and the reconstructed waveform can be considered de-noised and thus of a higher quality. 
     Current wavelet de-noising algorithms pick a wavelet decompositions scale-specific de-noising threshold based on the received signal&#39;s statistics. Some of the statistics used to calculate t are the number of input samples [N], noise standard deviation [σ], and correlation factors [σ j ,δ L,φ ,K N ] as shown in equations 1 and 2 below. 
     
       
           t =σ{square root over (2 log N)}  Equation 1 
       
     
     Equation 1 can be extended for wavelet decompositions that are not orthogonal, and thus produce correlated DWT coefficients, by the inclusion of a cross-correlation factor in the threshold equation. This is shown below where δ L,φ  is the j th  scale&#39;s cross-correlation of the non-orthogonal wavelet coefficients and K N  is the scale dependent data set&#39;s size. 
     
       
           t   N,φ,L (J)=σ j   {square root over (2( 1 +δ L,φ ) log ( K   N ))}   Equation 2 
       
     
     The more unbiased the statistics are, the more optimal and reliable the de-noising performance the thresholding solution will provide. The reliability of the statistics is therefore limited by the quality and size of the data set from which the statistics are derived. Reliable and unbiased statistical requirements naturally lead to larger and larger data sets and thus larger and larger memory. Sophisticated data handling issues therefore must be applied to store and manage said data sets. 
     A further complication in current systems is the decision to use global vs. local statistics. These data set boundaries from which the statistics are derived thus imply being either on a small packet scale, such as a single burst of communications from a single subscriber, or on a system level multi-packet scale, such as conglomerate statistics of subscriber serving groups or time variant single subscriber communications as are seen in a multi-carrier cable or wireless communications systems. These statistical requirements do not apply reliably or gracefully for latency sensitive applications, as latency is inherently ignored. One of the reasons that latency is ignored is the algorithm requires a-priori knowledge of the full data set&#39;s statistics prior to setting the de-noising threshold values and thus additional steps of data analysis and buffering prior to the wavelet thresholding stage must be performed. This is due to the desire to optimize the de-noising threshold. Again, the difficulty of choosing local vs. global statistics is a de-noising performance reliability variable. This further strains the memory and data handling issues and real-time requirements suffer further. Therefore the need for sufficient signal data to derive unbiased statistics exacerbates latency vs. performance issues and in real-time communications requires prohibitively long processing times. 
     The interpretation of the local and global statistics can also be misleading. In the case of local statistics, such as bursts communication between a subscriber and its infrastructure, the reliability of its statistical properties have a high probability of being skewed from its true characteristics due to insufficient data size. This will lead to a poor choice for the wavelet de-noising threshold value that either does not improve performance for the computational effort or mistakenly distorts the signal severely by over estimating the threshold values and acceptable/marginal performance is degraded/destroyed. 
     On the other hand global statistics, such as the conglomerate of many burst communications between single or multiple subscribers and its infrastructure, can be misleading. The communications medium cannot in many cases be assumed to have the same physical path characteristics for each subscriber in a serving group and/or may exhibit time invariant signaling performance for the single/multiple subscribers. From these perspectives local and global statistics are considered less than optimal and potentially very unreliable for real-time signal processing applications. 
     Accordingly, there exists a need for a signal processing approach/technique/algorithm to utilize wavelet de-noising techniques without the restrictions of the statistical, gradient searching, or memory and data handling issues of the current signal processing approaches/techniques/algorithms. 
     SUMMARY OF THE INVENTION 
     The present invention is a method and system for extracting information from a received signal with minimal loss due to noise. The system is comprised of a transformer, for correlating the received signal to a wavelet function and producing wavelet decomposition coefficients, a threshold circuit, which is responsive to the received signal, for applying predetermined threshold values based on the type of signal. Also included in the system is a filter, coupled to the transformer and threshold circuit, for altering the wavelet decomposition coefficients produced by the transformer using threshold values applied by the threshold circuit to produce altered wavelet coefficients from which the received signal is reconstructed with reduced noise. 
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS 
     The present invention will hereinafter be described in conjunction with the appended drawing figures, wherein like numerals denote like elements, and: 
     FIG. 1 is a block diagram of de-noising circuit for use in a signal processor of a receiver in accordance with the preferred embodiment of the present invention; 
     FIG. 2 is an exemplary block diagram of a CATV communication system; 
     FIG. 3 is a flow diagram of the de-noising circuit in accordance with the preferred embodiment of the present invention; 
     FIG. 4 is a block diagram of an alternative embodiment of the de-noising circuit of the present invention; and 
     FIGS. 5 and 6 are a flow diagram of the de-noising circuit in accordance with the alternative embodiment of the present invention. 
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
     The ensuing detailed description provides preferred exemplary embodiments only, and is not intended to limit the scope, applicability, or configuration of the invention. Rather, the ensuing detailed description of the preferred exemplary embodiments will provide those skilled in the art an enabling description for implementing a preferred exemplary embodiment of the invention. It being understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the invention as set forth in the appended claims. 
     FIG. 1 is a block diagram of a de-noising circuit  10  for use in a signal processor of a receiver in accordance with the preferred embodiment of the present invention. The de-noising circuit  10  comprises a demodulator  15 , a discrete wavelet transform (DWT)  12 , a filter  13 , an inverse discrete wavelet transform (IDWT)  14  and a threshold circuit  20 . The de-noising circuit  10  may be used in any type of system (i.e., communication, satellite, radar, etc.). An exemplary embodiment of the present invention will be described using a signal processor of a receiver in a CATV communication system. A CATV system is illustrated in FIG. 2, wherein a signal processor including a de-noising circuit  10  may be located at the subscriber station  210  or the headend  205 . 
     Referring back to FIG. 1, an input signal Y i  is received by the DWT  12 . As those skilled in the art know, the DWT correlates the input signal Y i  to a wavelet function, such as Daubechies  2 - 20 , and produces the DWT domain data of the input data. Due to the nature of the transform, the wavelet representation of the corrupted input signal will produce unique correlations with the uncorrupted signal buried in the noise that will produce large coefficients while the noise, because of its uncorrelated properties, will distribute wavelet basis correlation energy across all the dyadic scales at much smaller values. The wavelet decomposition data S(J—J,O), U(J—J,O) are then forwarded to the filter  13 . 
     The filter  13 , as disclosed above, alters or removes the coefficients represented by the noise. This is accomplished by applying threshold values to each dyadic scale output from the DWT  12 . In accordance with the preferred embodiment of the present invention, the threshold values are generated by the threshold circuit  20 . 
     The threshold circuit  20  stores application specific de-noising threshold values for use by the filter  13 . The threshold circuit  20  adapts the wavelet de-noising threshold levels through knowledge of a particular signal&#39;s application specific requirements. The properties used to adapt the wavelet de-noising threshold are predetermined and application and implementation specific. These properties include the signal&#39;s necessary dynamic range signal to noise ratio (SNR), peak to averages, and demodulation properties such as FEC performance, timing recovery degradation, clock jitter and real-time levels over time of the signal being received. These properties will be specific for the particular demodulator&#39;s implementation and can be measured in the lab. 
     With these properties, a table (not shown) is generated and stored in the threshold circuit  20  that directly correlates the application specific signal needs to a wavelet de-noising threshold value boundary. The threshold boundary stored in the threshold circuit  20  defines the maximum wavelet de-noising threshold value that can be applied without degrading the performance required of the application specific signal. 
     As stated above, the exemplary embodiment for the present invention is a DOCSIS CATV communication signal. As those skilled in the art know, DOCSIS CATV signals may include multiple types of communication signals (i.e., QPSK, 16 QAM, etc . . . ). Each of these signals has unique application specific requirements that must be realized to expect a given level of performance. These requirements are represented by the signal&#39;s required SNR, BER, etc . . . . In the exemplary block diagram of the CATV communication shown in FIG. 2, a Management Information Base (MIB) (not shown) located at the headend  205  houses the properties for each of the types of signals and sends this information to a receiver within the received communication signal. The subscriber  210  interprets this information using a Media Access Control (MAC) chip. 
     Once the information sent by the MIB is interpreted by the MAC chip, the MAC forwards the properties associated with the type of signal being received by the receiver to the threshold circuit  20 . The threshold circuit  20  receives the information from the MAC chip and picks the threshold values to be used by the filter  13 . The threshold value is picked in such a manner as to remove or de-emphasize the wavelet decomposition coefficients that represent noise. Thus, the uncorrupted input signal with minimized noise wavelet coefficients may be reconstructed with less corruption and thus increased signal to noise. Once the threshold values are obtained, the threshold values are forwarded to the filter  13  for processing with the input signal. 
     As states above, the filter  13  alters or removes the wavelet decomposition coefficients using the generated threshold values forwarded by the threshold circuit  20 . The altered wavelet coefficients {overscore (S)}(J—J,O),{overscore (U)}(J—J,O) . . . are then passed through the IDWT  14 . As those skilled in the art know, the IDWT  14  realigns the altered data across the dyadic decomposition scales and produces at its output the signal processor&#39;s best estimate of the received signal. The output signal from the IDWT  14  is then forwarded to the demodulator  15 , where the data being sent through the communication signal is recovered. 
     The flow diagram of the de-noising circuit  10  is illustrated in FIG. 3. A CATV communication signal is received by a de-noising circuit  10  of a receiver (step  101 ). The DWT  111  processes the received signal (step  102 ) and forwards the transformed signal to the filter circuit  112  (step  103 ). The MAC chip receives the signal type information transmitted within the received signal (step  104 ) and forwards the information to the threshold circuit  20  (step  105 ). The threshold circuit  20 , using the received information, obtains the predetermined threshold values associated with the received signal (step  106 ). The obtained threshold values are then forwarded to the filter circuit  13  (step  107 ). Once the filter circuit  13  receives the threshold values, the filter circuit  13  removes the noise from the transformed received signal using the threshold values (step  108 ). The filtered received signal is forwarded to the IDWT  14  (step  109 ). The IDWT  14  then reconstructs the received signal (step  110 ) and forwards it to the demodulator  15  (step  111 ) for extraction of the data being communicated over the received signal (step  112 ). 
     The de-noising circuit  10  disclosed above allows any real-time signal processing system to remain robust to bursts and thermal noise degradations in a communication channel in the presence of multi-rate and multi-mode communication systems. The present invention also eliminates the determination of threshold values based on the signals local or global statistics which require sufficient statistics for robust reliability, precluding the requirements of real-time applications. 
     An alternative embodiment of the de-noising circuit  10  for use in a signal processor of a receiver is illustrated in FIG.  4 . The alternate de-noising circuit  100  comprises a DWT  111 , a filter circuit  112 , an IDWT  113 , a demodulator  114 , a collector  115 , and a threshold circuit  110 . Similar to the de-noising circuit  10  disclosed above, a received signal is transformed by the DWT  111  and forwarded to the filter circuit  112 , which eliminates the noise present in the received signal utilizing the threshold values forwarded by the threshold circuit  110 . 
     The threshold circuit  110 , in accordance with this alternative embodiment, comprises a plurality of memory devices  102 ,  103 , for example two (2), and a memory device selector  104 . Although two (2) memory devices are illustrated, it should be apparent that any number of memory devices may be used. A first memory device  102 , similar to the threshold circuit  20  disclosed in the preferred embodiment, includes the predetermined threshold values associated with application specific signal types. Upon receipt of the communication signal, the first memory device  102  obtains the threshold values associated with the specific application and forwards them to the selector  104 . 
     The memory selector  104  initially determines from which of the plurality of memory devices  102 ,  103  the threshold values will be obtained. The selector  104  determines whether the threshold values to be obtained are for an initial de-noising of the received signal. There are many methods upon which this determination may be made. An exemplary method is using a signal generated by the MAC chip  101  which indicates whether the receiver is initially receiving the signal or if it has been in continuous reception thereof. If the former is the case, then the selector  104  receives the threshold values from the first memory device  102 . If the latter is the case, then the selector  104  receives the threshold values from the second memory device  103 , which is initially the same as the first memory device  102 . 
     The selector  104  forwards the threshold values to the filter circuit  112 . As disclosed above, the filter  112  eliminates the noise present in the received signal and forwards the filtered output to the IDWT  113 . The IDWT  113 , again, reconstructs the received signal without the noise and forwards the reconstructed signal to the demodulator  114   
     After the reconstructed signal is demodulated, the collector  115  determines the demodulator  114  properties (i.e., BER, SNR, etc . . . ). These properties are then forwarded to the second memory device  103 . The memory device  103  compares the properties forwarded by the collector  115  with the properties associated with the threshold values utilized by the filter  112 . If the comparison results in a difference greater than a predetermined value, the second memory device  103  may adjust the associated threshold values in accordance with this difference. Adjusting the threshold values based on this difference may be accomplished in a number of ways. The method of determining the adjustment is not germane to this alternate embodiment. Therefore, a detailed description of this method is not disclosed herein. 
     Once the second memory device  103  has adjusted the threshold values, the values are forwarded to the selector  104  and output to the filter  112  for processing. The demodulator  114  properties are compared to those stored in the second memory device  103  by the collector  115  until the demodulator  114  properties are within the predetermined range or a failed condition is met, in which case the received signal will have to be re-transmitted or the spectrum used by the received signal is marked unusable. 
     The flow diagram of the de-noising circuit  100  in accordance with this alternate embodiment is illustrated in FIG. 5. A CATV communication signal is received by a denoising circuit  100  of a receiver (step  501 ). The DWT  111  processes the received signal (step  502 ) and forwards the transformed signal to the filter circuit  112  (step  503 ). The MAC chip receives the signal type information transmitted within the received signal (step  504 ) and forwards the information to the threshold circuit  110  (step  505 ). The first memory device  102 , using the received information, obtaining the predetermined threshold values associated with the received signal (step  506 ). The obtained threshold values are then forwarded to the filter circuit  112  (step  507 ). Once the filter circuit  112  receives the threshold values, the filter circuit  112  removes the noise from the transformed received signal using the threshold values (step  508 ). The filtered received signal is forwarded to the IDWT  113  (step  509 ). The IDWT  113  then reconstructs the received signal (step  510 ) and forwards it to the demodulator  114  (step  511 ). The collector  115  calculates the demodulator properties for the reconstructed signal (step  512 ) and forwards them to the threshold circuit  110  (step  513 ). 
     Upon receipt of the calculated demodulator  114  properties, the second memory device  103  compares the calculated and predefined demodulator properties (step  514 ). If the difference between these values is greater than a predetermined threshold, and a failed condition has not been met, the second memory device  103  adjusts the threshold values associated with the received signal (step  515 ) and forwards these adjusted values via  104  to the filter  112  for processing (step  507 ). Otherwise, the data is extracted from the received signal (step  514 ). If a failed condition is met, the de-noising circuit  10  starts the process over (step  501 ). 
     While the principles of the invention have been described above in connection with specific apparatus, it is to be clearly understood that this description is made only by way of example and not as a limitation on the scope of the invention.