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GpLMS: Generalized Parallel Least Mean Square Algorithm for Partial Observations

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Intelligent Decision Technologies

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 309))

Abstract

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.

Supported by the AID-DGA and ANR in France to whom the authors are grateful.

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Correspondence to Ghattas Akkad .

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Akkad, G., Nguyen, VD., Mansour, A. (2022). GpLMS: Generalized Parallel Least Mean Square Algorithm for Partial Observations. In: Czarnowski, I., Howlett, R.J., Jain, L.C. (eds) Intelligent Decision Technologies. Smart Innovation, Systems and Technologies, vol 309. Springer, Singapore. https://doi.org/10.1007/978-981-19-3444-5_38

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