Maximum Likelihood Decoding for Channels with Uniform Noise and Signal Dependent Offset

Conference paper (2020)

Authors

R. Bu Discrete Mathematics and Optimization - EEMCS
J.H. Weber Discrete Mathematics and Optimization - EEMCS
KA Schouhamer Immink Turing Machines Inc.
Research Group
Discrete Mathematics and Optimization (EEMCS) (TU Delft)
Maximum likelihood decoding Offset mismatch Signal dependent offset
More Info
expand_more

Published Date

2020

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Delft Institute of Applied Mathematics

Research Group

Discrete Mathematics and Optimization

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.

Files

09174270.pdf
(.pdf | 0.37 Mb)