Design of Rate-Compatible Serially Concatenated Convolutional Codes
  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. 
Alexandre Graell i Amat, Fredrik Brannstrom, and Lars K. Rasmussen