Examinando por Autor "Segovia Vargas, Daniel"

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  • Miniatura
    Ítem
    Blind Adaptive Krylov Subspace Multiuser Detection
    (2001-01-01T08:12:44Z) Caamaño, Antonio J.; Segovia Vargas, Daniel; Ramos, Javier
    A new method for low-complexity Multiuser Detection (MUD)based in the Fast Subspace Decomposition (FSD) is proposed. The use of FSD allows the estimation of the number of users along with the multiuser detection on line. This leads to a fast multiuser estimation-detection scheme with ultra-low complexity. Furthermore, the method is proved to be strongly consistent and blind. This is applied here to the MMSE.Results included show MMSE performance at a fraction of the computational cost reported until now. The use for UMTS-TDD receivers is also proposed.
  • Miniatura
    Ítem
    Non-linear Diode Rectifier Analysis for Multi-Tone Wireless Power Harvesting
    (IEEE, 2019-11) López Yela, Ana; López Yela, Alberto; Segovia Vargas, Daniel; Popovic, Zoya
    This paper presents an analysis of the non-linear performance of a zero-bias Schottky diode under multi-tone excitation for wireless power harvesting applications. At low incident power levels, it has been shown that multi-tone inputs increase RF-DC conversion efficiency. Here we extend the theoretical analysis to include a more practical diode model which includes series resistance essential for determining efficiency. We show that the more complex theory approximates the real diode IV curve more accurately at low input power levels than previous models which neglect the series resistance. Index Terms—Non-linear model, zero-bias Schottky diode, Taylor expansion, multi-tone signals, energy harvesting.