RLS Recovery with Asymmetric Penalty: Fundamental Limits and Algorithmic Approaches

Bereyhi A, Sedaghat MA, Müller R (2018)

Publication Language: English

Publication Type: Conference contribution, Original article

Publication year: 2018

Event location: Podgorica ME

Open Access Link: https://arxiv.org/abs/1805.11895

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.

Authors with CRIS profile

Ali Bereyhi Lehrstuhl für Digitale Übertragung Mohammad Ali Sedaghat Lehrstuhl für Digitale Übertragung Ralf Müller Professur für Informationsübertragung

How to cite

APA:

Bereyhi, A., Sedaghat, M.A., & Müller, R. (2018). RLS Recovery with Asymmetric Penalty: Fundamental Limits and Algorithmic Approaches. In Proceedings of the International Balkan Conference on Communications and Networking (BalkanCom). Podgorica, ME.

MLA:

Bereyhi, Ali, Mohammad Ali Sedaghat, and Ralf Müller. "RLS Recovery with Asymmetric Penalty: Fundamental Limits and Algorithmic Approaches." Proceedings of the International Balkan Conference on Communications and Networking (BalkanCom), Podgorica 2018.

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