Digital Signal Processing Research Program

Contains table of contents for Section 2, an introduction, reports on twenty-two research projects and a list of publications.Sanders, a Lockheed-Martin Corporation Contract BZ4962U.S. Army Research Laboratory Contract DAAL01-96-2-0001U.S. Navy - Office of Naval Research Grant N00014-93-1-0686National Science Foundation Grant MIP 95-02885U.S. Navy - Office of Naval Research Grant N00014-96-1-0930National Defense Science and Engineering FellowshipU.S. Air Force - Office of Scientific Research Grant F49620-96-1-0072U.S. Navy - Office of Naval Research Grant N00014-95-1-0362National Science Foundation Graduate Research FellowshipAT&T Bell Laboratories Graduate Research FellowshipU.S. Army Research Laboratory Contract DAAL01-96-2-0002National Science Foundation Graduate FellowshipU.S. Army Research Laboratory/Advanced Sensors Federated Lab Program Contract DAAL01-96-2-000

Finite-Length Scaling of Spatially Coupled LDPC Codes Under Window Decoding Over the BEC

We analyze the finite-length performance of spatially coupled low-density parity-check (SC-LDPC) codes under window decoding over the binary erasure channel. In particular, we propose a refinement of the scaling law by Olmos and Urbanke for the frame error rate (FER) of terminated SC-LDPC ensembles under full belief propagation (BP) decoding. The refined scaling law models the decoding process as two independent Ornstein-Uhlenbeck processes, in correspondence to the two decoding waves that propagate toward the center of the coupled chain for terminated SC-LDPC codes. We then extend the proposed scaling law to predict the performance of (terminated) SC-LDPC code ensembles under the more practical sliding window decoding. Finally, we extend this framework to predict the bit error rate (BER) and block error rate (BLER) of SC-LDPC code ensembles. The proposed scaling law yields very accurate predictions of the FER, BLER, and BER for both full BP and window decoding.Comment: Published in IEEE Transactions on Communications (Early Access). This paper was presented in part at the IEEE Information Theory Workshop (ITW), Visby, Sweden, August 2019 (arXiv:1904.10410