%0 Conference Proceedings
%T GpLMS: Generalized Parallel Least Mean Square Algorithm for Partial Observations
%+ Institut supérieur de l'électronique et du numérique (ISEN)
%+ École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)
%+ Equipe Security, Intelligence and Integrity of Information (Lab-STICC_SI3)
%A Akkad, Ghattas
%A Nguyen, Viet-Dung
%A Mansour, Ali
%< avec comité de lecture
%B 14th International KES Conference on Intelligent Decision Technologies, KES-IDT 2022
%C Rhodes, Greece
%I Springer Nature Singapore
%3 Smart Innovation, Systems and Technologies
%V 309
%P 441-448
%8 2022-06-20
%D 2022
%R 10.1007/978-981-19-3444-5_38
%K Array processing
%K Adaptive beamforming
%K Least mean square (LMS)
%K Parallel LMS
%Z Engineering Sciences [physics]/Signal and Image processingConference papers
%X We propose a generalized parallel least mean square algorithm (GpLMS) to deal with partial observation scenarios. GpLMS takes advantage of a two stage parallel LMS architecture to enhance the convergence rate and updates weight vector based on observed entries to obtain a low computational complexity. We compare the results from our proposed algorithm with the state-of-the-arts in an adaptive beamforming context to illustrate its effectiveness.
%G English
%L hal-03772375
%U https://hal-ensta-bretagne.archives-ouvertes.fr/hal-03772375
%~ UNIV-BREST
%~ INSTITUT-TELECOM
%~ ENSTA-BRETAGNE
%~ CNRS
%~ UNIV-UBS
%~ ENSTA-BRETAGNE-STIC
%~ ENIB
%~ LAB-STICC
%~ INSTITUTS-TELECOM
%~ LAB-STICC_SI3
%~ LAB-STICC_T2I3