Iterative decoding of serially concatenated convolutional codes applying the SOVA

In numerous researches, the soft output Viterbi algorithm (SOVA) has been used for the iterative decoding of a parallel concatenated convolutional code (PCCC) instead of the maximum a posteriori (MAP) decoding algorithm having a suboptimal bounds to the maximum likelihood (ML) performance. The authors propose a modified SOVA for the iterative decoding of a serially concatenated convolutional code (SCCC). The most important reason for using the SOVA is that it can achieve real-time decoding without noticeable information losses. Another advantage is that the SOVA requires less structural modification of the conventional decoder than other iterative decoding algorithms such as maximum a posteriori (MAP). The simulation results show that the proposed SCCC decoding scheme using the SOVA is superior to the non-iterative SCCC. However, when compared with the scheme using the MAP algorithm, the performance gain is lower, as expected.