Error correction on burst channels by sequential decoding

Abstract

A sequential decoder for error correction on burst and random noise channels using convolutionally encoded data. The decoder interacts with a deinterleaver which time demultiplexes data from a data channel from its time multiplexed form into a predetermined transformed order. The decoder includes a memory for storing a table of likelihood values which are derived from known error statistics about the data channel such as the probabilities of random errors and burst errors, burst error severity and burst duration. The decoder removes an encoded subblock of data from the deinterleaver and enters it into a replica of the convolutional encoder which calculates a syndrome bit from a combination of the presently received subblock together with a given number of previous subblocks. The syndrome bit indicates if the current assumption of the path through the convolutional tree is correct. Where there is no error in the channel, then the received sequence is a code word and the syndrome bit indicates that the correct path in the convolution tree is taken. For each received bit, and indicator bit is calculated which is a function of the difference between the current path and the received sequence. The sequential decoder employs the syndrome bit together with burst indicator bits to calculate a table address in a table of likelihood values and error pattern values. The likelihood value is used to update a total likelihood of error value and the error pattern value is used to change the received subblock of data.