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Communication Dans Un Congrès Année : 2022

GpLMS: Generalized Parallel Least Mean Square Algorithm for Partial Observations

Résumé

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.
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Dates et versions

hal-03772375 , version 1 (08-09-2022)

Identifiants

Citer

Ghattas Akkad, Viet-Dung Nguyen, Ali Mansour. GpLMS: Generalized Parallel Least Mean Square Algorithm for Partial Observations. 14th International KES Conference on Intelligent Decision Technologies, KES-IDT 2022, Jun 2022, Rhodes, Greece. pp.441-448, ⟨10.1007/978-981-19-3444-5_38⟩. ⟨hal-03772375⟩
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