RLS Recovery with Asymmetric Penalty: Fundamental Limits and Algorithmic Approaches

Beitrag bei einer Tagung
(Originalarbeit)


Details zur Publikation

Autor(en): Bereyhi A, Sedaghat MA, Müller R
Jahr der Veröffentlichung: 2018
Sprache: Englisch


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.


FAU-Autoren / FAU-Herausgeber

Bereyhi, Ali
Lehrstuhl für Digitale Übertragung
Müller, Ralf Prof. Dr.-Ing.
Professur für Informationsübertragung
Sedaghat, Mohammad Ali
Lehrstuhl für Digitale Übertragung


Zitierweisen

APA:
Bereyhi, A., Sedaghat, M.A., & Müller, R. (2018). RLS Recovery with Asymmetric Penalty: Fundamental Limits and Algorithmic Approaches. 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.

BibTeX: 

Zuletzt aktualisiert 2018-22-09 um 15:23