2020 IEEE International Symposium
on Information Theory
21-26 June 2020 • Los Angeles, California, USA
| Paper ID | M.6.4 | ||
| Paper Title | Maximum Likelihood Decoding for Channels with Uniform Noise and Signal Dependent Offset | ||
| Authors | Renfei Bu, Jos H. Weber, Delft University of Technology, Netherlands; Kees A. Schouhamer Immink, Turing Machines Inc., Netherlands | ||
| Session | M.6: Coding for Storage and Memories II | ||
| Presentation | Lecture | ||
| Track | Coding for Storage and Memories | ||
| Manuscript | Click here to download the manuscript | ||
| Virtual Presentation | Click here to watch in the Virtual Symposium | ||
| Abstract | Maximum likelihood (ML) decision criteria have been developed for channels suffering from signal independent offset mismatch. Here, such criteria are considered for signal dependent offset, which means that the value of the offset may differ for distinct signal levels rather than being the same for all levels. An ML decision criterion is derived, assuming uniform distributions for both the noise and the offset. In particular, for the proposed ML decoder, bounds are determined on the standard deviations of the noise and the offset which lead to a word error rate equal to zero. Simulation results are presented confirming the findings. | ||
Plan Ahead
2021 IEEE International Symposium on Information Theory
11-16 July 2021 | Melbourne, Victoria, Australia
