Article

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

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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.

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... Blackburn [61] investigates an ML criterion for channels with Gaussian noise and unknown gain and offset mismatch. In a subsequent study, ML decoding criteria are derived for Gaussian noise channels when assuming various distributions for the offset in the absence of gain mismatch [62]. This research aims to investigate possible coding techniques for noisy channels with gain and/or offset mismatch. ...
... Further, in 3.2. MAXIMUM LIKELIHOOD DECODING FOR CHANNELS WITH BOUNDED NOISE AND OFFSET 3 33 [56] and [62] a decoder was proposed based on minimizing a weighted sum of Euclidean and Pearson distances, which is proved to be optimal for channels with Gaussian noise and offset mismatch. ...
... In addition, for Gaussian distributed noise and offset mismatch, we derive the ML criterion considering successive channel outputs, which includes the results in [56,62] as its particular case. A concatenated coding scheme is proposed in the case of Gaussian noise and offset mismatch. ...
... Secondly, the proposed ML criterion provides a general framework, including the scaling-only case and the offset-only case. Some known criteria [13] [14] are shown to be special cases of this framework for particular a 1 , a 2 , b 1 , and b 2 settings. This paper aims to generalize ML decoding for multilevel cell channel with Gaussian noise and scaling and offset mismatch. ...
... In Theorem 2 of [13], the following ML criterion was presented for the case that there is bounded scaling (0 < a 1 ≤ a ≤ a 2 ) and no offset mismatch (b = 0): /a 1 ,x) if r,x > r, r /a 1 , L e (r/a 2 ,x) if r,x < r, r /a 2 , ...
... In Theorem 1 of [13], the following ML criterion was presented for the case that a = 1 and b 1 ≤ b ≤ b 2 : ...
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