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RLS Recovery with Asymmetric Penalty: Fundamental Limits and Algorithmic Approaches

Ali Bereyhi,M. A. Sedaghat,R. Müller

Published 2018 in arXiv.org

ABSTRACT

This paper studies regularized least square recovery of signals whose samples' prior distributions are nonidentical, e.g., signals with time-variant sparsity. For this model, Bayesian framework suggests to regularize the least squares term with an asymmetric penalty. We investigate this problem in two respects: First, we characterize the asymptotic performance via the replica method and then discuss algorithmic approaches to the problem. Invoking the asymptotic characterization of the performance, we propose a tuning strategy to optimally tune the algorithmic approaches for recovery. To demonstrate applications of the results, the particular example of BPSK recovery is investigated and the efficiency of the proposed strategy is depicted in the shadow of results available in the literature

PUBLICATION RECORD

  • Publication year

    2018

  • Venue

    arXiv.org

  • Publication date

    2018-05-30

  • Fields of study

    Mathematics, Computer Science

  • Identifiers
    arXiv 1805.11895
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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  • No concepts are published for this paper.

REFERENCES

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