Maximum Likelihood Decoding for Gaussian Noise Channels with Gain or Offset Mismatch
J.H. Weber (TU Delft - Discrete Mathematics and Optimization)
KA Schouhamer Immink (Turing Machines Inc.)
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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.