Design of Rate-Compatible Serially Concatenated Convolutional Codes
Paper in proceedings, 2006
Recently a powerful class of rate-compatible serially concatenated convolutional codes (SCCCs) have been proposed
based on minimizing analytical upper bounds on the error probability in the error ﬂoor region. Here this class of
codes is further investigated by combining analytical upper bounds with extrinsic information transfer charts analysis.
Following this approach, we construct a family of rate-compatible SCCCs with good performance in both the error
ﬂoor and the waterfall regions over a broad range of code rates.