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Robust Precoding for Multi-User Visible Light Communications with Quantized Channel Information.
Muñoz, Olga; Pascual-Iserte, Antonio; San Arranz, Guillermo.
Afiliação
  • Muñoz O; Department of Signal Theory and Communications, Universitat Politècnica de Catalunya, 08034 Barcelona, Spain.
  • Pascual-Iserte A; Department of Signal Theory and Communications, Universitat Politècnica de Catalunya, 08034 Barcelona, Spain.
  • San Arranz G; Department of Signal Theory and Communications, Universitat Politècnica de Catalunya, 08034 Barcelona, Spain.
Sensors (Basel) ; 22(23)2022 Nov 28.
Article em En | MEDLINE | ID: mdl-36501940
ABSTRACT
In this paper, we address the design of multi-user multiple-input single-output (MU-MISO) precoders for indoor visible light communication (VLC) systems. The goal is to minimize the transmitted optical power per light emitting diode (LED) under imperfect channel state information (CSI) at the transmitter side. Robust precoders for imperfect CSI available in the literature include noisy and outdated channel estimation cases. However, to the best of our knowledge, no work has considered adding robustness against channel quantization. In this paper, we fill this gap by addressing the case of imperfect CSI due to the quantization of VLC channels. We model the quantization errors in the CSI through polyhedric uncertainty regions. For polyhedric uncertainty regions and positive real channels, as is the case of VLC channels, we show that the robust precoder against channel quantization errors that minimizes the transmitted optical power while guaranteeing a target signal to noise plus interference ratio (SNIR) per user is the solution of a second order cone programming (SOCP) problem. Finally, we evaluate its performance under different quantization levels through numerical simulations.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Sensors (Basel) Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Espanha

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Sensors (Basel) Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Espanha