Abstract:
An error correction encoding method is provided for encoding in parallel source digital data, having the form of a frame, wherein said data can be classified into N classes, where N is an integer at least equal to 2. 
     The encoding method includes:
       a first recursive systematic convolutional encoding step of data to be encoded, formed by the data of the class 1; and   an implementation of the following steps, for each n ranging from 1 to M, where M is a positive integer equal to or lower than N−1:
           n th  mixing of a set formed by the data of the class n+1 and the systematic data of the preceding encoding; and   (n+1) th  recursive systematic convolutional encoding of data to be encoded, formed by the result of the n th  mixing.   
               
 
     Also disclosed is a related decoding method, as well as an associated encoding and decoding devices.

Description:
TECHNICAL FIELD 
     The present invention relates to an error correction encoding method. 
     It also relates to a decoding method adapted to decode data that have been encoded using the error correction encoding method according to the invention. 
     It also relates to an encoding device for implementing the error correction encoding method according to the invention, as well as a decoding device for implementing the decoding method according to the invention. 
     The field of the invention is that of encoding digital data, for being transmitted in particular in the presence of a transmission noise, and of decoding said digital data after transmission. 
     The invention more particularly but in a non-limiting way relates to the field of optimization of digital data transmission, for example via a wide band radio network. 
     BACKGROUND 
     In telecommunications, error correction encoding methods (also called Forward Error Correction (FEC)) are used to protect so-called source data to be transmitted, from errors that will come from the transmission. To do this, redundancy is added to the source data in order to enable the recipient to detect and correct part of the errors. 
     The error correction encoding is followed with a modulation for transmission, that is why generally, the modulation and coding scheme (MCS) is used to designate both the error correction encoding and the modulation. 
     In prior art is known an error correction encoding method commonly called “turbo code”. This is an error correction encoding method, implementing in parallel at least two independent steps of systematic convolutive encoding of all the data to be encoded, and at least one time interleaving step changing the order for taking into account data for each of the encoding steps. Turbo codes are for example presented in French patent FR2675971. The decoding implements an iterative decoding algorithm based on the Bahl, Cocke, Jelinek and Raviv algorithm and an a posteriori maximum search. 
     One drawback of turbo codes however is that all the source data are equally protected. 
     UEP (Unequal Error Protection) codes, born with GSM technology, bring a response to this drawback by enabling digital data of a frame to be gathered into different classes depending on their importance, and each class to be protected depending on its priority level (a priority level all higher is assigned as the datum is important). 
     This principle enables transmission resources as well as the frequency band width used to be optimized. 
     A known drawback of UEP codes is that each class is separately processed. The different classes are first separated, and then separately encoded. The encoded data of each class are then separately modulated. After transmission, the data of a same frame are thus decorrelated. This involves a resource loss because there is for example a need for:
         further headers (that is further data used for defining a data packet, for example the data of a class in the case where the different classes are independently processed), and   further processings to resynchronize the data from different classes of a same frame after transmission.       

     Further, this resynchronization steps generate reception delays. 
     Such a resource loss goes against the current demand for a higher transmission rate, higher network capacity and shorter transmission delay. 
     SUMMARY 
     One purpose of the present invention is to provide error correction encoding/decoding methods and devices which do not have the drawbacks of prior art. 
     Another purpose of the present invention is to provide error correction encoding/decoding methods and devices which minimize the transmission and reception delays, in particular for applications such as sound or video transmission. 
     Another purpose of the present invention is to provide error correction encoding/decoding methods and devices which are less resource-heavy than the methods and devices of prior art. 
     Another purpose of the present invention is to provide error correction encoding/decoding methods and devices which require fewer transmission rates than the methods and devices of prior art. 
     Finally, one purpose of the present invention is to provide error correction encoding/decoding methods and devices which require less network capacity than the methods and devices of prior art. 
     The invention enables at least one of these purposes to be achieved by an error correction encoding method for encoding in parallel so-called source digital data, having the form of a frame, wherein said data can be classified in N classes, where N is an integer equal to at least 2. 
     The encoding method according to the invention comprises:
     a first recursive systematic convolutional encoding step of data to be encoded, formed by the data of the class 1; and   an implementation of the following steps, for each n ranging from 1 to M, where M is a positive integer equal to or lower than N−1
       n th  mixing of a set formed by the data of the class n+1 and the systematic data of a preceding encoding;   (n+1) th  recursive systematic convolutional encoding of data to be encoded, formed by the result of the n th  mixing.   
       

     Several intermediate encoding steps of the same data can also be provided before adding new information to be encoded. 
     A n th  mixing of a set formed by the data of the class n+1 and the systematic data of the preceding encoding step is thus referred to because
         the preceding encoding step can be the encoding step n;   the preceding encoding step can be an intermediate encoding step.       

     The term “systematic datum”, as well as the term “parity datum” used in the following, are terms relating to recursive systematic convolutional codes, and are known to those skilled in the art. They correspond to two outputs of a recursive systematic convolutional code. 
     Throughout the text, the systematic data and parity data can comprise tail bits. 
     The systematic datum is preferably identical to the datum to be encoded, whereas the parity datum can correspond to at least one redundancy datum. 
     The systematic data of an encoding step form the data to be encoded by said encoding step. 
     The invention advantageously provides adding new information to be encoded before some encoding steps. 
     Thus, a UEP type error correction encoding method is performed, that is with a non-uniform protection, wherein each class can benefit from a different protection with respect to errors occurring in particular during a transmission on a channel. 
     The invention takes up the general principle of turbo codes, since there are successive encoding steps and mixing steps in view of a further encoding of the same data. However, the known scheme has been changed to result in an encoding scheme wherein different source digital data of a same frame are more or less protected. 
     The different protection comes from a different number of redundancy information (or encoded data), as a function of the number of times the data of the class have been encoded. 
     Each class can indeed be encoded a different number of times, depending on a number of encodings performed taking into account the data of this class. The data of the class can be taken into account for an encoding, as data of the class 1, as data of the class n+1 and/or as data to be encoded formed by the result of the n th  mixing. 
     The method according to the invention is adapted to process entire frames. 
     The data protection can be called hierarchical, wherein more important data, in other words with a higher priority level, can be better protected. 
     The structure can be adapted to any frame type, regardless in particular of the number of classes. 
     A UEP encoding is performed which is directly applicable to an entire frame of digital data. 
     Each class of a frame can thus be encoded with a dedicated encoding scheme different from the encoding scheme applied to one or more other classes of the same frame. 
     The method according to the invention thus enables an encoding to be performed with fewer resources than state of the art methods. 
     Besides, the method according to the invention enables a quicker encoding to be performed by consuming less power than state of the art methods and by reducing the reception delay of applications such as voice and video. 
     Finally, data encoded with the method according to the invention can be transmitted with fewer transmission rates and less network capacity than data encoded with prior art methods and devices, with an equal protection. 
     The method according to the invention enables the different classes of a same frame to be encoded by a single error correction encoding method, unlike known UEP encoding methods wherein each class is encoded independently from other classes of the same frame. 
     It is no longer necessary to separate data of different classes of a same frame into several data flows, to encode them separately. 
     The method according to the invention thus enables the transmission of synchronization information to be avoided, and thus the resources of the transmission network to be optimized. 
     The method according to the invention thus enables a reception delay to be reduced, in particular for applications such as sound (for example voice) or video transmission. 
     The method according to the invention thus enables a resynchronization step to be avoided after transmission. 
     The method according to the invention thus enables the modulation of data that have been encoded to be simplified, wherein all the classes of a frame can be modulated together. It enables a single modulation scheme to be applied. 
     The at least one mixing step can provide a random distribution of the digital data in the final result. 
     The digital data can be any digital datum, in particular digital data representing a video or a voice. 
     The encoding method is preferably followed by a suitable modulation adapted to the transmission channel used. 
     The encoding and modulation scheme that can then be obtained is particularly robust to errors. 
     Some data of the frame can be provided not to be encoded. 
     An implementation of the puncturing can be provided following the implementation of a coding step. This can involve at least one depuncturing step during a decoding. The depuncturing consists in retrieving data of the same size such as data before a corresponding puncturing, for example by introducing zeros in the punctured data. 
     Preferably, a priority level is assigned to each class, the classes 1 to N being ranked in the decreasing order of the priority levels. 
     The method according to the invention thus enables each class to benefit from an adapted protection. Thereby, it enables the transmission of more redundancy information than necessary to be avoided, which enables the resources of the transmission network to be optimized while obtaining an optimum reception quality, since the most important information have been highly protected. 
     Each mixing step can consist in a simple concatenation. 
     Advantageously, each mixing step consists in an interleaving. 
     An interleaving can consist in organizing received data non-contiguously. Any type of known interleaving can be considered, in particular interleavings developed within the scope of turbo codes. 
     Generally, errors during a transmission on a channel occur in bursts rather than independently. If the number of errors exceeds the capacity of the error correction encoding, it fails to recover the source data. The interleaving is generally used to aid in solving this problem by changing the order for taking into account same digital data in several encodings, thus creating a more uniform error distribution. 
     According to a preferred embodiment of the encoding method according to the invention, M is chosen equal to N−1, so that the classes 1 to N are encoded. 
     Thus, all the source digital data can be protected. 
     Preferably, at the end of the implementation of all the steps of the encoding method according to the invention, that is at the output, parity data are obtained corresponding to each of the encoding steps as well as a systematic datum corresponding to the (M+1) th  encoding step. 
     The invention also relates to an encoding device for implementing the error correction encoding method according to the invention, capable of encoding so-called source digital data having the form of a frame, wherein said data can be classified into N classes. Said encoding device according to the invention comprises:
     a first recursive systematic convolutional encoding module for encoding data to be encoded formed by the data of the class 1; and   let n to range from 1 to M, wherein M is a positive integer equal to or lower than N−1, M sets each formed by a n th  mixer followed by a (n+1) th  recursive systematic convolutional encoding module, the n th  mixer being arranged to receive the data of the class n+1 and the systematic data of a preceding encoding module, and the (n+1) th  encoding module being arranged to encode data to be encoded formed by the output of the n th  mixer.   

     The invention also relates to a method for decoding digital data, arranged to decode digital data encoded in accordance with the encoding method of the invention. 
     Preferably, at the end of the implementation of all the steps of the encoding method according to the invention, parity data are obtained corresponding to each of the encoding steps, as well as the systematic datum of the (M+1) th  encoding step. 
     These obtained data are advantageously transmitted via a transmission channel. 
     So-called received data that may be affected by errors occurring in particular during the transmission can therefore be received after transmission. 
     Advantageously, the decoding method according to the invention is applied to such received data. 
     For reasons of clarity of the description, a datum before and after transmission is designated in the same way throughout the text. 
     Particularly advantageously, the decoding method according to the invention is such that, for any j, k, l between M+1 and 1:
     each j th  encoding step of the encoding method according to the invention, corresponds to a decoding step j ( 410   j ), adapted to decode encoded data resulting from the j th  encoding step;   at the end of each decoding step j, on the one hand so-called “soft” data are obtained for an assessment of the data of the class j, on the other hand so-called extrinsic data are obtained;
 
and the following steps are implemented:
   decoding k; and then   decoding l≠k as a function of at least one extrinsic datum provided by at least one other decoding step, used as an a priori datum.   

     So-called “a priori” data preferably represent probabilities on encoded data received from the channel. 
     These probabilities are available before any current decoding of said encoded data received, these probabilistic values coming from a source different from the encoded data received from the channel. 
     An a priori datum used to decode the encoded data resulting from the k th  encoding step can be relating to the parity data and systematic data of this k th  encoding step 
     The extrinsic data of a bit B advantageously designate the information produced by a decoder (based on the encoded information received from the channel and, if applicable, a priori data), except for the channel and a priori information of the bit B concerned. 
     These extrinsic data can represent the probability to have received this bit B as a function of values of all the other adjacent bits of the same frame. 
     The following book can in particular be referred to: Todd K Moon, “Error Correction Coding—Mathematical Methods and Algorithms”, John Wiley &amp; Sons 2005. 
     The extrinsic data resulting from the j th  encoding step are preferably relating to the parity data and systematic data of this j th  encoding step. 
     The extrinsic data preferably comprise so-called “a priori” data providing a further datum for assessing the data of other classes. 
     At each decoding step, an a priori datum can be used. 
     For the decoding step performed first in the chronological order, the a priori datum is set to zero. Then, each decoding step enables an a priori datum used for another decoding step to be obtained. 
     Each class can benefit from a different protection with respect to errors. A strongly protected class will benefit from an error rate all the less significant upon decoding. 
     A decoding l≠k as a function of at least one extrinsic datum provided by least one other decoding step, used as an a priori datum, enables the different encoded classes to benefit from the encoding of the other encoded classes. 
     A given bit error rate can thus be more quickly achieved for a less protected class. The invention thereby allows energy, redundancy, and delay savings. 
     Each decoding step can implement an iterative decoding, that is any type of algorithm based on the a posteriori maximum (MAP) search for assessing a posteriori probabilities. This a posteriori maximum can be calculated with the BCJR algorithm (Bahl, Cocke, Jelinek and Raviv algorithm), with a MAP derivation, in particular according to a so-called LOG MAP decoding using a likelihood ratio (“Log Likelihood Probabilities Ratios”), or a so-called MAX LOG MAP decoding, more suitable for the hardware implementation. 
     Before their use as an a priori datum for a decoding step j, the extrinsic data can be processed. The aim is to retrieve data of the same dimension and in the same order as the data at the output of the corresponding encoding step. 
     Preferably, the data assessments of the data of each class are gradually extracted from the soft data. The aim is to extract at the right moment some data relating to the respective classes. 
     A decoding step can be performed non successively for any class, regardless of the encoding order. 
     The first encoding step can be performed for an intermediate class, and the preceding and following decoding steps can be performed in any advantageous order, in particular according to a preset error rate to be achieved for each class. 
     Advantageously, a decoding step can be successively performed for all the classes n, where n is decreasing, ranging from M+1 to 1. 
     Preferably, the decoding method according to the invention further comprises an initial demultiplexing step performing the separation of the parity data of each class. 
     The parity data of each class can then each be used for a corresponding decoding step. 
     For any j between M+1 and 2, the decoding method according to the invention can further comprise after each decoding step j, the following operations:
     disentangling j−1 of the so-called extrinsic data, the disentangling j−1 performing a function reverse to that implemented in the mixing step j−1, for providing disentangled data;   demultiplexing the disentangled data to separate a priori data relating to the class j called extracted data from a priori data relating to the classes 1 to j−1 called useful a priori data;   providing said useful a priori data to be used in the decoding step j−1.   

     Throughout the text, when disentangling is mentioned to, it refers to a given mixing, the disentangling consisting in retrieving the order of the data before said mixing. 
     For any j between M+1 and 2, the decoding method according to the invention can further comprise after each decoding step j, the following operations:
     disentangling j−1 of the soft data, the disentangling j−1 performing a function reverse to that implemented in the mixing step j−1, to provide disentangled soft data;   demultiplexing the disentangled soft data to separate soft data relating to the class j called extracted soft data from soft data relating to the classes 1 to j−1.   

     The extracted soft data are used for assessing the data of the class j. 
     A specific step for assessing the extracted soft data can further be provided to retrieve the values of the class j. 
     At least one decoding step j can be reiterated at least once, as a function of a priori data corresponding to extrinsic data provided by at least one decoding step of the data of another class. 
     At each reiteration of a decoding step, one a priori datum can be used. 
     Each decoding step can be reiterated at least once, for example between 1 and 5 times. 
     Thus reiterated decoding step can then be followed by new decoding steps of data from following or preceding classes. 
     Before their use for a reiteration of the decoding step j, the extrinsic data can be processed before using some of them at least as a priori data. The aim is to retrieve data of the same dimension and in the same order as the data at the output from the corresponding encoding step. 
     At least one feedback is thus performed. The decoding method according to the invention can thus be considered as iterative, wherein each new iteration of a decoding step can improve the assessment of the data of the corresponding class. 
     Information from other classes can thus be used to improve the decoding of a class. 
     Each class benefits from a different protection with respect to errors. A strongly protected class will benefit from an error rate all the less important during decoding. During decoding, the at least one feedback enables to exploit the fact that during encoding, data corresponding to each of the classes are mixed. The different encoded classes can thereby benefit from the encoding of the other encoded classes. 
     A given bit error rate can thus be more quickly reached, for a less protected class. The invention thus allows energy, bit redundancy and transmission delay savings. 
     At least one decoding step j can be reiterated at least once as a function of a priori data corresponding to extrinsic data provided by at least one decoding step of the data of another class, and for j between M+1 and 2, and t strictly lower than j, the decoding step j is reiterated as a function of a priori data obtained in the decoding steps t to j−1. 
     Each decoding step can thus be reiterated using a priori data obtained in decoding steps of classes with higher priority. 
     In this case, said a priori data can comprise extrinsic information relating to the classes 1 to j−1 and information relating to the parity data of the classes 1 to j−1. 
     The decoding steps j−1 to t can then be reiterated. 
     At least one decoding step j can be reiterated at least once as a function of a priori data corresponding to extrinsic data provided by at least one decoding step of the data of another class, and for j between M and 1 and t strictly higher than j, the decoding step j is reiterated as a function of a priori data obtained in the decoding steps t to j+1. 
     Each decoding step can thus be reiterated using a priori data obtained in decoding steps of classes with a lower priority. 
     In this case, said a priori data can comprise extrinsic information relating to the classes t to j+1 and information relating to the parity data of the classes t to j+1. 
     The decoding steps j+1 to t can then be reiterated. 
     Preferably, the decoding method according to the invention comprises the following steps:
     the decoding step M+1 is reiterated as a function of a priori data obtained in the decoding steps 1 to M;   the decoding steps M to 1 are reiterated using a priori data corresponding to extrinsic data provided by the preceding decoding step (following the chronological order), so that the decoding steps M+1 to 1 make up a decoding phase;
 
and the decoding phase is reiterated at least once.
   

     Thus, a feedback is performed on all the decoding steps, and in each decoding phase, the decoding steps are successively performed for all the classes n, where n is decreasing, ranging from M+1 to 1. 
     From the second iteration, the decoding step M+1 can be performed as a function of extrinsic data provided by the decoding step 1. 
     Information from all the other classes can thus be used to improve the decoding of a class. 
     The invention also relates to a decoding device adapted to implement the decoding method according to the invention. 
     The decoding device according to the invention comprises M+1 decoding modules, each decoding module j (where j is an integer between 1 and M+1 inclusive) being capable of decoding encoded data resulting from the j th  encoding step of the encoding method according to the invention, and each decoding module j providing so-called extrinsic data capable of being used as a priori data by another decoding module, and at least one so-called “soft” datum for an assessment of the data of the class j. 
     The invention finds an application in all the fields of data transmission and any transmission system, whether it is a wire or a wireless transmission. It can be in particular the field of:
         terrestrial radio communications,   aerospace radio communications,   data transmission in robotics or electronics,   audio and/or video applications.       

     The invention also relates to a computer program product comprising instructions to perform the steps of the encoding method according to the invention when run on a computer device. 
     The invention also relates to a computer program product comprising instructions to perform the steps of the decoding method according to the invention when run on a computer device. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Further advantages and features of the invention will appear upon reading the detailed description of the implementations and embodiments in no way limiting, and of the following appended drawings wherein: 
         FIG. 1  diagrammatically illustrates an example of the so-called “in parallel” encoding method according to the invention, 
         FIG. 2  diagrammatically illustrates an example of the so-called “in parallel” decoding method according to the invention, 
         FIG. 3  illustrates a particular embodiment of the so-called “in parallel” encoding method according to the invention, 
         FIG. 4  illustrates a particular embodiment of the so-called “in parallel” decoding method according to the invention, 
         FIG. 5  illustrates bit error rate curves obtained with a decoding method according to the invention. 
     
    
    
     DETAILED DESCRIPTION 
     Throughout the text, a multiplexing can designate a concatenation, an interleaving or any other operation performed to rank data in a one-dimensional or multidimensional frame. 
     Throughout the text, when demultiplexing is mentioned to, it refers to a given multiplexing, the demultiplexing being the reverse operation of said multiplexing. 
     Throughout the text, when deinterleaving is mentioned to, it refers to a given interleaving, the deinterleaving consisting in retrieving the order of data before said interleaving. 
     The means for implementing each step of the method according to the invention are known to those skilled in the art, consequently only exemplary methods according to the invention will be described in detail. 
       FIG. 1  is a diagram representation of an example of a parallel encoding method in accordance with the method according to the invention. 
     Each encoder implements a recursive systematic convolutional code. 
     In the example represented in  FIG. 1 , a data frame  102  is encoded. The data of the frame  102  are classified in n classes  102   1 - 102   n . Each of the classes  102   i  is associated with a priority level. In the present example, in a non-limiting way, the priority level of the class  102   1  is greater than the priority level of the class  102   2 , and so on, the class with the lowest priority level being the class  102   n . 
     The method  300 , represented in  FIG. 3 , comprises a first coding step  302   1  which is limited to the encoding  306   1  of the data of the class  102   1 . 
     This coding step  302   1  outputs parity data which are redundancy data to enable the input data (and so-called systematic data which correspond to the input data) to be retrieved. 
     This step  302   1  is followed by a second coding step  302   2  performing an interleaving  304   2  of the data of the class  102   1  with the data of the class  102   2 . 
     The term “followed” is used, even though each coding step i can be performed simultaneously, before or after the encoding  306   i−1 . 
     The coding step  302   2  comprises an encoding  306   2  of the interleaved data provided by the interleaving  304   2 . 
     This coding step  302   2  outputs parity data which are redundancy data to enable the input data (and so-called systematic data which correspond to the input data) to be retrieved. 
     The method  100  comprises after step  302   2 , a coding step  302   3  and so on up to the step  302   n . Each of the steps  302   i  for i≧3 comprises the following operations:
         an interleaving  304   i  of the systematic data provided in step  302   i−1 , with the source data of the class  102   i ; and   an encoding  306   i  of the interleaved data provided by the interleaving  304   i .       

     Each step  302   i  for i≧2 outputs the parity data and systematic data which here correspond to the interleaved data obtained in the interleaving  304   i . 
     In output, the encoded frame A is obtained by multiplexing the parity data provided in each step  302   i, i=1     →     n , and the systematic datum provided by step  302   n . 
     It is noted that each interleaving step  304   i+1 , can also be implemented at the same time as the encoding step  306   i . 
     The data of the frame  102  are modulated and transmitted together as the data A, because they are not separated prior to the implementation of the encoding method according to the invention. 
     The data A are preferably modulated and then transmitted on a transmission channel. 
     After transmission, the data A that can be affected by errors are received. 
       FIG. 2  is a diagram representation of an example of a “in parallel” decoding method  400  in accordance with the method according to the invention, in the case where each encoder implements a recursive systematic convolutional code. 
     In the example represented in  FIG. 2 , data A are decoded. These data A have been encoded in accordance with the parallel encoding method  300  according to the invention. 
     After each encoding, a puncturing can be provided to be further performed. In this case, the decoding comprises depuncturing steps. This particular case is not represented here, but it is not excluded from the field of the invention. 
     The method  400  comprises a preliminary demultiplexing step  402  enabling to separate from data A, the parity data  404   1→n  obtained respectively at the encoding steps  306   1→n  of the parallel encoding method  300 , and the so-called systematic datum  406  corresponding to data to be encoded at the last encoding step of the parallel encoding method  300 . 
     A first decoding step  408   n  of the data of the class  102   n  comprises the following steps:
         a decoding  410   n  of the parity data  404   n  of the class  102   n , using the systematic datum  406  and an a priori datum (initially equal to zero), and providing so-called extrinsic data as well as so-called soft data for assessing the data of the class  102   n ;   a deinterleaving  412   n  of the so-called extrinsic data, to provide deinterleaved data, the deinterleaving  412   n  implementing a interleaving function reverse to the interleaving function implemented in the interleaving  304   n  of the parallel encoding method  300 ;   a demultiplexing  414   n  of the deinterleaved data to separate so-called useful a priori data which will be used in the following decoding step, and so-called a priori data relating to the data of the class  102   n .       

     The soft data for assessing the data of the class  102   n  undergo a deinterleaving step  416   n  implementing an interleaving function reverse to the interleaving function implemented in the interleaving  304   n  of the parallel encoding method  300 . A demultiplexing step  418   n  enables probabilities for the data of the class  102   n  (for each bit, probability to be 0 or 1) to be isolated. A step for assessing the data of the class  102   n  can further be provided. 
     The demultiplexing  414   n  is followed by a new decoding step  408   n−1  of the data of the class  102   n−1  comprising the following steps:
         a decoding  410   n−1  of the parity data of the class  102   n−1 , using:
           the useful a priori data obtained in the preceding decoding step, and   an assessed channel datum (the parity and the systematic data for the recursive systematic convolutional codes, the systematic one of which being formed by the systematic datum of the preceding decoding step that underwent a deinterleaving step  420   n−1  implementing an interleaving function reverse to the interleaving function implemented in the interleaving  304   n  of the parallel encoding method  300  and after removal of the systematic data corresponding to the class  102   n  therefrom).   
               

     The decoding  410   n−1  provides so-called extrinsic data corresponding to the classes  102   1→n−1  and data for assessing the data of the class  102   n−1 . 
     The decoding step  408   n−1  then comprises the following steps:
         a deinterleaving  412   n−1  of the so-called extrinsic data, to provide deinterleaved data, the deinterleaving  412   n−1  implementing an interleaving function reverse to the interleaving function implemented in the interleaving  304   n−1  of the parallel encoding method  300 ;   a demultiplexing  414   n−1  of the deinterleaved data to separate so-called useful a priori data relating to the data of the classes  102   1→n−2  which will be used in the following decoding step, from so-called a priori data relating to the data of the class  102   n−1 .       

     The soft data for assessing the data of the class  102   n−1  undergo a deinterleaving step  416   n−1  implementing an interleaving function reverse to the interleaving function implemented in the interleaving  304   n−1  of the parallel encoding method  300 . A demultiplexing step  418   n−1  enables probabilities for the data of the class  102   n−1  (for each bit, probability to be 0 or 1) to be isolated. A step for assessing data of the class  102   n−1  can further be provided. 
     The method  300  comprises after the step  408   n−1 , a decoding step  408   n−2  and so on up to the step  408   2 . Each step  408   i  for n−2≧i≧2 comprises the following operations:
         a decoding  410   i  of the parity data of the class  102   i , using:
           the useful a priori data obtained in the preceding decoding step, and   an assessed channel datum (formed by the parity and the systematic data for recursive systematic convolutional codes, the systematic one of which being formed by the systematic datum of the preceding decoding step that underwent a deinterleaving step  420   i  implementing an interleaving function reverse to the interleaving function implemented in the interleaving  304   i+1  of the parallel encoding method  300  and after removal of the systematic data corresponding to the classes  102   i+1→n  therefrom).   
               

     The decoding step  410   i  provides so-called extrinsic data and data for assessing the data of the class  102   i , called soft data. 
     The decoding step  408   i  then comprises the following steps:
         a deinterleaving  412   i  of the so-called extrinsic data, to provide deinterleaved data, the deinterleaving  412   i  implementing an interleaving function reverse to the interleaving function implemented in the interleaving  304   i  of the parallel encoding method  300 ;   a demultiplexing  414   i  of the deinterleaved data to separate so-called useful a priori data relating to the data of the classes  102   1→i−1  which will be used in the following decoding step, from so-called a priori data relating to the data of the class  102   i .       

     Data for assessing the data of the class  102   i  undergo a deinterleaving step  416   i  implementing an interleaving function reverse to the interleaving function implemented in the interleaving  304   i  of the parallel encoding method  300 . A demultiplexing step  418   i  enables probabilities for data of the class  102   i  (for each bit, probability to be 0 or 1) to be isolated. A step for assessing data of the class  102   i  can further be provided. 
     The method  400  comprises after the step  408   2 , a decoding step  408   1  comprising the following steps:
         a decoding  410   1  of the parity data  404   1  of the class  102   1 , using:
           the useful a priori data obtained in the preceding decoding step, and   an assessed channel datum (formed by the parity and the systematic data for the recursive systematic convolutional codes, the systematic one of which being formed by the systematic datum of the preceding decoding step that underwent a deinterleaving step  420   1  implementing an interleaving function reverse to the interleaving function implemented in the interleaving  304   2  of the parallel encoding method  300  and after removal of the systematic data corresponding to the classes  102   2→n  therefrom).   
               

     The decoding  410   1  provides so-called extrinsic data and data corresponding to an assessment of the data of the class  102   1 , called soft data. 
     The steps described since the decoding  410   n  are called decoding phase. 
     The decoding method  400  adapted to the parallel encoding also comprises a feedback, which consists in using extrinsic data provided in a decoding step to reiterate a preceding decoding step of the decoding phase. 
     The extrinsic data used for a reiteration of a decoding step are interleaved to retrieve data of the same dimension and in the same order as the data at the output from the corresponding encoding step. 
     The feedback comprises the following steps:
         interleaving  422   1  of the extrinsic data provided by the decoding step  410   1  and the so-called a priori data relating to the data of the class  102   2 , to obtain interleaved data provided by the interleaving  422   1 , the interleaving  422   1  implementing an interleaving function similar to the interleaving function implemented in the interleaving  304   2  of the parallel encoding method  300 ;   for i ranging from 2 to n−2, i interleaving steps  422   i  of the so-called a priori data relating to the data of the class  102   i  and the interleaved data provided by the interleaving  422   i−1 , the interleaving  422   i  implementing an interleaving function similar to the interleaving function implemented in the interleaving  304   i+1  of the parallel encoded method  300 ;   interleaving  422   n−1  of the interleaved data provided by the interleaving  422   n−2 , with a datum of the size of the a priori data relating to the data of the class  102   n  but set to zero, the interleaving  422   n−1  implementing an interleaving function similar to the interleaving function implemented in the interleaving  304   n  of the parallel encoding method  300 ;   taking into account the interleaved data of the interleaving  422   n−1  as an a priori datum during a second iteration of the decoding step  410   n .       

     This second iteration of the decoding step  410   n  can be followed by a third iteration of all the other steps of a decoding phase as described before. 
     Such a feedback can be implemented several times, for example three to fifteen times. After each iteration, the assessment of the data of each of the classes is improved. After at least five feedbacks, it is no longer always interesting to perform more feedbacks, since the gain on the assessment accuracy is negligible as compared to the further time required for another iteration 
     A particular embodiment of the error correction encoding method  300  according to the invention will now be described in reference to  FIG. 3 . 
     Each encoding implements a recursive systematic convolutional code. 
     Such a code enables encoded data formed by so-called “parity” (redundancy) data and so-called “systematic” data (identical to the data to be encoded) to be obtained. 
     The so-called source digital data  30  are formed by a frame  102  comprising three classes  102   1 ,  102   2  and  102   3 . 
     The method  300  according to the invention comprises an initial step  70  of separating the data of each of the classes  102   1 ,  102   2  and  102   3 . 
     The data of the class  102   1  are designated by the symbol a 1 . 
     The data of the class  102   2  are designated by the symbol a 2 . 
     The data of the class  102   3  are designated by the symbol a 3 . 
     The method  300  according to the invention comprises a first encoding step  306   1  of the data of the class  102   1 . 
     The parity data P 1 , that is the redundancy data relating to the data a 1  are obtained. 
     The obtained data P 1  are called “parity of the class  102   1 ”. 
     The method  300  according to the invention then (or simultaneously) comprises a step  304   2  of interleaving the data a 1  with the data a 2  of the class  102   2 . 
     Interleaved data b 1  are obtained. 
     The interleaved data b 1  are then encoded during an encoding step  306   2 , which provides parity data P 2 , that is redundancy data relating to the data b 1 . 
     Since data b 1  are formed by the mixed data a 1  and a 2 , the number of available redundancy data corresponding to data a 1  is increased. 
     The obtained data P 2  are called “parity of the classes  102   1  and  102   2 ”. 
     The method  300  according to the invention then (or simultaneously) comprises a step  304   3  of interleaving the data b 1  with the data a 3  of the class  102   3 . 
     Interleaved data b 2  are obtained. 
     The interleaved data b 2  are then encoded during an encoding step  306   3 , which provides parity data P 3 , that is redundancy data relating to the data b 2 . 
     Since data b 2  are formed by the mixed data a 1 , a 2  and a 3 , the number of available redundancy data corresponding to data a 1  and a 2  is increased. 
     The obtained data P 3  are called “parity of the classes  102   1 ,  102   2  and  102   3 ”. 
     Data A gathering all the parities P 1 , P 2  and P 3  are obtained in output, as well as a so-called systematic output S 3  corresponding to the data b 2  to be encoded during the last encoding step  306   3 . The systematic output is due to the use of recursive systematic convolutional codes. 
     A particular embodiment of the decoding method  400  according to the invention will now be described in reference to  FIG. 4 , corresponding to the encoding method of  FIG. 3 , and in the case where each encoder implements a recursive systematic convolutional code. 
     A first demultiplexing step  402  enables the parities P 1 , P 2 , P 3  and the systematic output S 3  to be separated among the received data A. 
     The method  400  according to the invention comprises a first decoding comprising a decoding step  410   3  of the parity P 3 , as a function of the systematic output S 3  and an a priori datum initially set to zero. 
     An output L soft (b 2 ) and so-called extrinsic data L ext (b 2 ) are obtained. 
     The output L soft (b 2 ) enables data b 2  to be assessed. 
     Throughout the text, L soft , L ext  and L priori  correspond to the logarithmic probabilities for each data bit to be 0 or 1, resulting from an advantageous use for this particular embodiment of the decoding algorithm called MAX LOG MAP. 
     On the one hand, the following steps are implemented:
         deinterleaving  416   3  of the output L soft (b 2 ), the deinterleaving  416   3  implementing a deinterleaving function reverse of the interleaving function implemented in the interleaving step  304   3 ;   demultiplexing  418   3  to separate the L soft (a 3 ) and L soft (b 1 ) data.       

     The output L soft (a 3 ) corresponds to an assessment of the data a 3  of the class  102   3 . 
     The output L soft (b 1 ) corresponds to an assessment of the data b 1 . 
     Indeed, the data b 2  correspond to the data a 3  interleaved with the data b 1 . 
     So-called extrinsic data L ext (b 2 ) comprise in particular information relating to an assessment of the data of the class  102   3 . 
     On the other hand, the following steps are implemented:
         deinterleaving  412   3  of the output L ext (b 2 ), the deinterleaving  412   3  implementing a deinterleaving function reverse of the interleaving function implemented in the interleaving step  304   3 ;   demultiplexing  414   3  to separate the L priori (a 3 ) and L priori (b 1 ) data.       

     The L priori (a 3 ) data correspond to the logarithmic probabilities for each data bit of the class  102   3  to be 0 or 1. 
     The L priori (b 1 ) data are used as a priori information at the following decoding step. 
     The demultiplexing  414   3  is followed by a second decoding comprising a decoding step  410   2  of the parity P 2 , as a function of L priori (b 1 ) and the systematic output S 3  at which a deinterleaving  420   2  has been applied implementing a deinterleaving function reverse of the interleaving function implemented in the interleaving step  304   3 , and a demultiplexing to separate systematic information corresponding to the data b 1  and the data of the class a 3 . Only the systematic data b 1  are useful for this decoding. 
     An output L soft (b 1 ) and so-called extrinsic data L ext (b 1 ) are obtained. 
     The output L soft (b 1 ) enables data b 1  to be assessed. 
     On the one hand, the following steps are implemented:
         deinterleaving  416   2  of the output L soft (b 1 ), the deinterleaving  416   2  implementing a deinterleaving function reverse of the interleaving function implemented in the interleaving step  304   2 ;   demultiplexing  418   2  to separate the L soft (a 2 ) and L′ soft (a 1 ) data.       

     The output L soft (a 2 ) corresponds to an assessment of the data a 2  of the class  102   2 . 
     The so-called extrinsic data L ext (b 1 ) comprise information relating to an assessment of the data of the classes  102   1  and  102   2 . 
     On the other hand, the following steps are implemented:
         deinterleaving  412   2  of the output L ext (b 1 ), the deinterleaving  412   2  implementing a deinterleaving function reverse of the interleaving function implemented in the interleaving step  304   2 ;   demultiplexing  414   2  to separate the L priori (a 2 ) and L priori (a 1 ) data.       

     The L priori (a 2 ) data correspond to the probabilities for each data bit of the class  102   2  to be 0 or 1. 
     The L priori (a 1 ) data are used as a priori information in the following decoding step. 
     The demultiplexing  414   2  is followed by a third decoding comprising a decoding step  410   1  of the parity P 1 , as a function of L priori (a 1 ) and the systematic output  5   3  at which a deinterleaving  420   2  and then  420   1  has been applied, implementing a deinterleaving function reverse of the interleaving function implemented in the interleaving step  304   3  respectively  304   2 , and the demultiplexing suitable for obtaining the systematic data of the class a 1 . 
     The extrinsic data L ext (a 1 ) and an assessment L soft (a 1 ) of the data of the class  102   1  are obtained. 
     The decoding method  400  has a feedback comprising the following steps:
         interleaving  422   1  of the L ext (a 1 ) and L priori (a 2 ) data, to obtain an interleaved datum L′ ext (b 1 ), and implementing an interleaving function similar to the interleaving function implemented in the interleaving  304   2  of the parallel encoding method  300 ;   interleaving  422   2  of the L′ ext (b 1 ) and L′ ext (a 3 ) data, to obtain an interleaved datum L priori (b 2 ) and implementing an interleaving function similar to the interleaving function implemented in the interleaving  304   3  of the parallel encoding method  300  (L′ ext (a 3 ) being a datum of the size of a 3  but assuming zero values);   new iteration of the decoding step  410   3 , taking into account an a priori datum L priori (b 2 );   new iteration of the steps following the decoding step  410   3 .       

     This feedback enables each class to benefit from the decoding accuracy obtained for the other classes. 
     Finally, the classes not much protected can be decoded with a better accuracy than if they had been encoded separately from better protected classes. 
     Described encodings use for example polynomial generators. 
     The sizes of the different processed classes can vary. 
     Some classes can be provided not to be encoded. 
     Bit error rate curves that can be obtained with a decoding method according to the invention are illustrated in  FIG. 5 . 
     The bit error rate is the number of erroneous bits in the assessments of the encoded data of a class, divided by the total number of bits analysed by the decoding method according to the invention. It is thus a quantity without unit. 
     The bit error rate is often expressed as a function of a signal to noise ratio. In  FIG. 5 , the abscissa axis corresponds to a bit error rate, the ordinate axis corresponds to the Eb/No ratio in dB, that is the ration in dB of an energy per bit to the power spectral density of the noise. 
     The example has been taken where:
         after encoding, a QPSK (Quadrature Phase-Shift Keying) modulation has been implemented on a AWGN (Additive White Gaussian Noise) channel;   the frame  102  only comprises two encoded classes  102   1  and  102   2 .       

     In  FIG. 5 , there is a ratio 2/3 between the size of the class  102   2  less protected and the size of the frame, and a frame size of 900 bits. 
     The curve  11  represents the bit error rate associated with the decoding of the class  102   1 , upon the first iteration of the decoding step of the data of the class  102   1 . 
     The curve  12  represents the bit error rate associated with the decoding of the class  102   2 , upon the first iteration of the decoding step of the data of the class  102   2 . 
     The curve  11 ′ represents the bit error rate associated with the decoding of the class  102   1 , upon the second iteration of the decoding step of the data of the class  102   1 . 
     The curve  12 ′ represents the bit error rate associated with the decoding of the class  102   2 , upon the second iteration of the decoding step of the data of the class  102   2 . 
     Thereby, it can be seen that:
         the class  102   1 , which is the first class that has been encoded, reaches upon the first iteration a very good bit error rate, since many redundancy information are available to retrieve the data of the class  102   1 ;   the data of the class  102   1  encoded in the first encoding step benefit from a decoding gain similar to that obtained in a turbo-type decoding upon the second iteration;   at the first iteration, the bit error rate associated with the data of the class  102   2  is rather low, because there are only little redundancy information available to retrieve the data of the class  102   2 ;   after an iteration, the bit error rate associated with the data of the class  102   2  is remarkably improved, and is closer to the bit error rate obtained for decoding the data of the class  102   1 , benefiting in particular from the turbo decoding gain.       

     The influence of a more strongly encoded class on a less encoded class depends in particular on the ratio of the size of the first class to the size of the second class, in number of bits. 
     After five iterations, a bit error rate of 10 −2  can for example be obtained for a signal to noise ratio lower than 2 dB, with a 2.5 dB gain between the first and the last iteration. 
     This property of the invention is particularly interesting, because it can be seen that each class benefits from the decoding accuracy obtained for the other classes and from the “turbo” effect. 
     Thus, a given class can be less protected than in prior art, for a given bit error rate. 
     It can then be seen that fewer redundancy data can be transmitted than in prior art, to obtain a given bit error rate. 
     Thus, the capacity of a transmission channel for a given coverage is increased. 
     Thus, the range of the transmission channel for a given capacity is increased. 
     Of course, the invention is not limited to the examples just described and numerous improvements can be provided to these examples without departing from the scope of the invention. 
     For example, any decoding type implementing in particular different feedback phases can be considered. 
     The invention can, for example, be combined with already existing techniques, for example with puncturing techniques, which consist in erasing bits of the already encoded frame to increase the coding ratio. In this case, the redundancy of the code for each class can be reduced. 
     The invention can also be combined with techniques of prior art consisting in separating data of a same frame, but each data packet gathering several classes and being apt to be processed according to the invention.