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

None, None; None, None; None, None

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

Conference Paper (2020)

Author(s)

Renfei Bu (TU Delft - Electrical Engineering, Mathematics and Computer Science)

Jos H. Weber (TU Delft - Electrical Engineering, Mathematics and Computer Science)

Kees A. Schouhamer Immink (Turing Machines Inc.)

Research Group

Discrete Mathematics and Optimization

Maximum likelihood decoding Offset mismatch Signal dependent offset

DOI related publication

https://doi.org/10.1109/ISIT44484.2020.9174270 Final published version

To reference this document use

https://resolver.tudelft.nl/uuid:38c062ca-3748-4560-9a71-6c9248808490

More Info

expand_more

Publication Year

2020

Language

English

Research Group

Discrete Mathematics and Optimization

Article number

9174270

Pages (from-to)

706-710

ISBN (print)

978-1-7281-6433-5

ISBN (electronic)

978-1-7281-6432-8

Event

2020 IEEE International Symposium on Information Theory, ISIT 2020 (2020-07-21 - 2020-07-26), Los Angeles, United States

Downloads counter

179

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.