Patent Publication Number: US-2006013344-A1

Title: Method and system for maximum likelihood detection

Description:
BACKGROUND OF THE INVENTION  
      1. Field of the Invention  
      The presented invention relates to a method and system for maximum likelihood detection, especially relates to change the branch metrics weighting of maximum likelihood detector to improve detection performance.  
      2. Description of the Prior Art  
      Maximum likelihood (ML) detection is a common detection technique, which is widely used in different areas such as a communication system, image and voice process, digital data storage . . . etc. Generally speaking, the ML detection could be classified into two types: hard-decision and soft-decision. The hard-decision technique forces the received analog signals, in the communication system, for example, classified into some specific quantification levels in accordance to ideal signals, but soft-decision technique retains the original magnitude of received analog signals to perform maximum likelihood detection. Soft-decision gains better detection performance but within more complicated detection circuits. However, as the improvement of electronic circuits, soft-decision is becoming more and more popular.  
      Viterbi algorithm is commonly used for maximum likelihood detection, which includes 3 major steps: calculate the distance between received and ideal signals to obtain branch metrics; accumulate the branch metrics into path metrics for each state nodes; and determine a survivor path for the received signal sequence and then decode. Overview the steps of Viterbi algorithm, for each received signal sequence&#39;s component, first calculate the distance (for example, the square difference) with an ideal signal sequence component and obtain the branch metrics entering each state node of the component. Next, for each state, accumulate current branch metrics with accumulated path metrics of the previous component&#39;s to obtain the path metrics. After finishing the path metrics calculation for the last received signal sequence component, trace back to initial state node of first received signal sequence component to determine a survivor path with smallest path metrics, then decode received signal sequence and obtain original data signal according to the survivor path.  
      Although Viterbi algorithm is an optimum maximum likelihood detection algorithm, sometimes because of abnormal signals or noise influences, the errors between received and ideal signal sequence component become too large, which result in the path metrics after branch metrics calculation are not correct and obtain a wrong survivor path, generating decoding errors and influencing the accuracy of detection.  
      Referring to  FIG. 1 ,  FIG. 1  illustrate the trellis diagram for Viterbi algorithm decoding with a soft-decision. Assuming there is a data signal sequence D=(D[ 1 ],D[ 2 ],D[ 3 ], . . . ,D[ 12 ])=(0,1,1,1,0,0,0,0,1,1,1,1), and the channel response model of information channel is a partial response channel PR(1,2,1). The error-free ideal received signal sequence I should be: I=(I[ 1 ],I[ 2 ], . . . ,I[ 10 ])=(2,4,2,−2,−4,−4,−2,2,4,4), and the actual received signal sequence is R=(R[ 1 ],R[ 2 ],R[ 3 ], . . . ,R[ 10 ])=(1.7,4,3.8,−1.9,0.1,−3.8,−1.8,1.9,4,2,4). The four states of the trellis diagram are represented as S 0 , S 1 , S 2 , S 3  individually. Take the received signal sequence component R[ 5 ] for example.  
      The received signal sequence component R[ 5 ] is 0.1, but the ideal received signal sequence component I[ 5 ] is −4, which has a large error occurs. Calculate the branch metrics  10  from state S 0  of previous sequence component R[ 4 ] entering state S 0  of the current sequence component (represent by path S 0 -&gt;S 0 ), the branch metrics  10  is |0.1−(−4)| 2 =16.81, and the branch metrics  12  for path S 2 -&gt;S 0  is |0.1−(−2)| 2 =4.41. Then accumulate branch metrics  10  and  12  with the path metrics of state S 0  and S 2  of R[ 4 ] individually, and obtain the path metrics of state S 0  of R[ 5 ] is 16.81+3.34=20.15 and 15.34+4.41=19.75 individually. Therefore it concludes that the state of the previous signal sequence component is S 2 , in other words, determine the entering path for state S 0  of received signal sequence component R[ 5 ] is S 2 -&gt;S 0 , which makes the detection unable to achieve the correct survivor path  120  but the wrong survivor path  100 , and then influence the signal decoding result (received signal R[ 3 ] is decoded as 1 from 0).  
      As mentioned above, an abnormal signal occurs in the prior Viterbi algorithm detection that may cause serious problems, which makes an uncorrectable error. If it&#39;s possible to reduce the influence of abnormal signals, the detection accuracy will be improved.  
     SUMMARY OF THE INVENTION  
      It is therefore an object of the invention to provide a method for maximum likelihood detection, which changes the branch metrics weighting of the maximum likelihood detection via detecting the occurrence of abnormal signals, to reduce the influence caused by noise or abnormal signals and improving the detection accuracy.  
      The another object of the invention is to provide a system for maximum likelihood detection, the system includes: a signal receiving device, an abnormal signal-detecting device, a control device, and a maximum likelihood detection device with variable branch metrics weighting to carry out the operations of above method.  
      Also the invention provides different means to adjust branch metrics weighting in order to improve maximum likelihood detection accuracy.  
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
      Other objects, features, and advantages of the invention will become apparent from the following detailed description of the preferred but non-limiting embodiments. The description is made with reference to the accompanying drawings in which:  
       FIG. 1  illustrates the trellis diagram for Viterbi algorithm decoding with hard-decision;  
       FIG. 2  illustrates the trellis diagram for Viterbi algorithm decoding of the invention with soft-decision;  
       FIG. 3  shows a flow chart of an embodiment of the invention;  
       FIG. 4  shows the system of an embodiment of the invention; and  
       FIG. 5  shows an another embodiment of the invention applied for CD/DVD drive system. 
    
    
     DESCRIPTION OF THE PREFERRED EMBODIMENT  
      As discussed above, one characteristic of the invention is that the detector takes use of detected abnormal signals to improve the maximum likelihood detection before decoding a received signal. Hence when some specific signal pattern has been observed, it&#39;s able to determine if the signal is an abnormal signal or an error has occurred because of a large noise.  
      Now if original digital data signals are encoded in RLL(2,10) (run length limited) code, the ideal signal levels of a received signal which was transmitted through a partial response channel PR(1,2,1) are 4, 2, −2, −4, and the received signal sequence will not have some specific signal patterns, such as (2,−2,2),(−2,2,−2),(−2,4,−2),(2,−4,2) that positive and negative signal appears in turn, and the difference of consecutive two received signal&#39;s level will not be over 6. Hence if one of the above situations occurs and detected on the receiver end, it concludes the received signal is abnormal.  
       FIG. 2  illustrates the trellis diagram for Viterbi algorithm decoding of the invention with soft-decision. Referring to  FIG. 2 , the actual received signal sequence components R[ 3 ], R[ 4 ], R[ 5 ] are 3.8, −1.9, and 0.1. If quantify them in hard-decision, the received signal sequence has a (4,−2,2), in other words, a (positive, negative, positive) (represented in +,−,+ form) signal pattern. Because it&#39;s impossible for an ideal received signal which is generated after a RLL(2,10) encoded signal transmitting through a partial response channel PR(1,2,1), having such signal pattern, then determine R[ 5 ] is an abnormal signal. As determining the signal component R[ 5 ] is abnormal, the invention discloses a method to adjust the branch metrics weighting. In the embodiment, the invention discloses a mean, that multiplying the branch metrics weighting for each state of signal component R[ 5 ] with a coefficient ,such as 0.5, in other words, it means to halve original branch metrics of each state. For state S 0  of R[ 5 ], the branch metrics  20  which is entering from S 0  of previous component R[ 4 ] (path S 0 -&gt;S 0 ) becomes: 0.5*|0.1−(−4)| 2 =8.41, the branch metrics  22  (path S 2 -&gt;S 0 ) becomes: 0.5*|0.1−(−2)| 2 =2.21. Referring to  FIG. 1 , the original branch metrics is 16.81 and 8.41 individually. Accumulate branch metrics  20  and  22  with the path metrics for state S 0  and S 2  of R[ 4 ], we have the path metrics for state S 0  of R[ 5 ] which equals to 8.41+3.34=11.75 and 2.21+15.34=17.55 for each other, therefore determine the path entering state S 0  of R[ 5 ] is S 0 -&gt;S 0 . At the last step of determining a survivor path, it could achieve the correct survivor path  200  so that the receiver end could decode the received signal correctly. The calculations of path metrics and branch metrics for state S 1 , S 2 , and S 3  are the same to S 0 , which are not explained redundantly here.  
      As the method for adjusting branch metrics weighting discussed above, besides multiply the branch metrics weighting with a predetermined coefficient, it could also establish a look-up table to adjust branch metrics weighting in accordance to different abnormal signal pattern. For example, if the signal pattern of received signal sequence is (2,−2,4), multiply the branch metrics weighting with a coefficient 0.5; if the signal pattern of received signal sequence is (2,−2,2), the coefficient is 0.7; or if the signal pattern is (−2,2,−2), set the branch metrics for each state as some specific values directly. There are many different means to make the equivalent modification, and these means are unlimited in the invention.  
      In prior art, it shows the incorrect branch metrics will result in decoding error, and if makes use of the method of the invention, the decoding performance of maximum likelihood detection could be improved.  
      It&#39;s enhanced that the encoding method for digital data signal is not limited in RLL code, and the information channel is not only limited on the partial response channel PR(1,2,1). As long as the compositions of the encoding method and information channel could make the receiver end determining an abnormal signal before decoding procedure, that the invention could apply on them.  
       FIG. 3  is the flow chart of an embodiment of the invention. When a digital signal is transmitted through an information channel, and received a signal sequence which retains it original signal magnitude (step  300 ). In step  310 , first perform an abnormal signal detecting operation on received signal sequence, to determine if there are abnormal signals. If abnormal signal have been detected, adjust the branch metrics weighting of detected abnormal signal (step  320 ), and then calculate the branch metrics of the received signal sequence afterwards (step  330 ); if no abnormal signal is detected, calculate the branch metrics of the received signal sequence directly (step  330 ). After the calculations of branch metrics, accumulate every branch metrics and obtain the path metrics for each state node of received signal sequence (step  340 ). As the path metrics of the last signal sequence&#39;s component have been obtained, trace back to the initial state and determine a survivor path with the smallest accumulated path metrics (step  360 ). Finally the detector could decode the received signal sequence to the original digital data according to the survivor path (step  360 ). Herein step  330  to step  360  is the original decoding procedures of prior Viterbi algorithm.  
      The invention also discloses a system for adjusting the maximum likelihood detection, and  FIG. 4  is an embodiment of the invention. The disclosed system of the invention includes: a signal receiving device  400 , an abnormal signal-detecting device  410 , a control device  420 , and a maximum likelihood detection device with variable branch metrics weighting  430 . The signal receiving device  400  could be a RF receiver module, which is used to receive analog signals, wherein the analog signals are generated after an encoded digital data signal transmitted through an information channel. The abnormal signal-detecting device  410  detects if there are error occurs and inform the control device  420  the detection results. The control device  420  provides a control signal to the maximum likelihood detection device  430  in accordance to the control signal and adjusts the branch metrics weighting of maximum likelihood detection device  430 . Finally, the maximum likelihood detection device  430  is used to decode the received analog signals back to the original digital data signal, which changes the branch metrics weighting in accordance to the control signal of control device  420 .  
      The control device  420  is not only implemented in hardware circuit, but also in software program. And the system of the invention could also be integrated into a chip having the above functions, or implemented by compositions of electronic devices. The methods for adjusting branch metrics weighting of the control device  420  could be: multiply the original branch metrics weighting with a predetermined coefficient, or adjusted by a look-up table in accordance to different received signal patterns. The detail method for adjusting branch metrics weighting has been discussed above so that it&#39;s not explained redundantly here.  
      The presented invention may also apply on a CD/DVD drive system. The information channel of optical storage disk, has inter-symbol interference (ISI) situation. In order to reduce the influence to the reading performance caused by ISI, the pickup head of CD/DVD drive takes use of partial response sampling technique to reduce the influence of ISI, hence the sampling procedure of the pickup head could be thought as a digital data signal transmitted through a partial response channel such as PR(1,2,1) or PR(1,2,2,1) . . . etc. In optical storage disk, RLL code (especially RLL(1,7) and RLL(2,10)) is the most common encoding method. The RLL encoded digital data signal transmitting through a partial response channel such as PR(1,2,1) has a characteristic that, the difference between two consecutive received signals that are not over  6  (when available signal level is 4,2,−2, and 4) and signal pattern is (+,−,+) or (−,+,−). For CD/DVD drive, therefore, the read analog signal from disk could determine if there are abnormal signals that could apply on the disclosed maximum likelihood detection method.  FIG. 5  is an another embodiment of the invention, which is a flow chart of maximum likelihood detection for CD/DVD drive system. First the pickup head of CD/DVD drive reads out the digital data recorded on disk via partial response sampling technique and obtain an analog signal sequence (step  500 ). Next detecting and determining if there are abnormal signals that occur in the analog signal sequence (step  510 ). If no abnormal signal has been detected, decode the analog signal sequence via Viterbi algorithm directly (step  530 ). Otherwise, if there are abnormal signals detected, adjust the branch metrics weighting of the abnormal signal component first, and then decode via Viterbi algorithm. Detail operations of the partial response sampling is easily carried out for related professions skilled in the art and, the details about Viterbi algorithm procedures and method for adjusting branch metrics weighting have been discussed above that are not explained redundantly here.  
      The abnormal signal pattern changes with a different partial response channel model. When the storage density becomes larger such as developing technology HD-DVD (High Definition DVD) and BD (Blu-ray Disc) or applied the partial response sampling is different, the received analog signal will have a different signal pattern, and so do abnormal signal patterns. Therefore if the encoding method and sampling technique choose properly as designing a CD/DVD drive system, then the disclosed method for adjusting maximum likelihood detection could be applied to increase the detection accuracy.  
      The above-mentioned are only the preferred embodiments of the present invention, not intended to limit the scope thereof. It will be appreciated and carried out by those professions skilled in the art. Thus, many modifications of the embodiments that can be made without departing from the spirit of the present invention should be covered by the following claims.