Maximum Likelihood Decoding for Gaussian Noise Channels with Gain or Offset Mismatch

Journal article (2018)

Authors

J.H. Weber Discrete Mathematics and Optimization - EEMCS
KA Schouhamer Immink Turing Machines Inc.
Research Group
Discrete Mathematics and Optimization (EEMCS) (TU Delft)
Copyright: © 2018 J.H. Weber, Kees A. Schouhamer Immink
DOI
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https://doi.org/10.1109/LCOMM.2018.2809749
Euclidean distance Standards Gaussian noise Maximum likelihood decoding Receivers Maximum likelihood detection
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Published Date

2018

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Delft Institute of Applied Mathematics

Research Group

Discrete Mathematics and Optimization

Abstract

Besides the omnipresent noise, other important inconveniences in communication and storage systems are formed by gain and/or offset mismatches. In the prior art, a maximum likelihood (ML) decision criterion has already been developed for Gaussian noise channels suffering from unknown gain and offset mismatches. Here, such criteria are considered for Gaussian noise channels suffering from either an unknown offset or an unknown gain. Furthermore, ML decision criteria are derived when assuming a Gaussian or uniform distribution for the offset in the absence of gain mismatch.