On the minimum mean $p$ th error in Gaussian noise channels and its applications

Alex Dytso, Ronit Bustin, Daniela Tuninetti, Natasha Devroye, H. Vincent Poor, Shlomo Shamai Shitz

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5 Scopus citations


The problem of estimating an arbitrary random vector from its observation corrupted by additive white Gaussian noise, where the cost function is taken to be the minimum mean $p$ th error (MMPE), is considered. The classical minimum mean square error (MMSE) is a special case of the MMPE. Several bounds, properties, and applications of the MMPE are derived and discussed. The optimal MMPE estimator is found for Gaussian and binary input distributions. Properties of the MMPE as a function of the input distribution, signal-To-noise-ratio (SNR) and order $p$ are derived. The 'single-crossing-point property' (SCPP) which provides an upper bound on the MMSE, and which together with the mutual information-MMSE relationship is a powerful tool in deriving converse proofs in multi-user information theory, is extended to the MMPE. Moreover, a complementary bound to the SCPP is derived. As a first application of the MMPE, a bound on the conditional differential entropy in terms of the MMPE is provided, which then yields a generalization of the Ozarow-Wyner lower bound on the mutual information achieved by a discrete input on a Gaussian noise channel. As a second application, the MMPE is shown to improve on previous characterizations of the phase transition phenomenon that manifests, in the limit as the length of the capacity achieving code goes to infinity, as a discontinuity of the MMSE as a function of SNR. As a final application, the MMPE is used to show new bounds on the second derivative of mutual information, or the first derivative of the MMSE.

Original languageEnglish (US)
Pages (from-to)2012-2037
Number of pages26
JournalIEEE Transactions on Information Theory
Issue number3
StatePublished - Mar 2018

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computer Science Applications
  • Library and Information Sciences


  • I-MMSE
  • estimation


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