Block Markov Superposition Transmission: Construction of Big Convolutional Codes from Short Codes

Abstract: A construction of big convolutional codes from short codes called block Markov superposition transmission (BMST) is proposed. The BMST is very similar to superposition blockMarkov encoding (SBME), which has been widely used to prove multiuser coding theorems. The encoding process of BMST can be as fast as that of the involved short code, while the decoding process can be implemented as an iterative sliding-window decoding algorithm with a tunable delay. More importantly, the performance of BMST can be simply lower-bounded in terms of the transmission memory given that the performance of the short code is available. Numerical results show that, 1) the lower bounds can be matched with a moderate decoding delay in the low bit-error-rate (BER) region, implying that the iterative slidingwindow decoding algorithm is near optimal; 2) BMST with repetition codes and single parity-check codes can approach the Shannon limit within 0.5 dB at BER of 10^{-5} for a wide range of code rates; and 3) BMST can also be applied to nonlinear codes.