Widely Linear System Estimation Using Superimposed Training

  • Authors:

  • Israel A. Arriaga-Trejo;Aldo G. Orozco-Lugo;Arturo Veloz-Guerrero;Manuel E. Guzman

  • Affiliations:

  • Communication Section of Cinvestav-IPN, Mexico City, Mexico;Communication Section of Cinvestav-IPN, Mexico City, Mexico;Intel Labs/IPR/SIA, Intel Guadalajara Design Center, Jalisco, Mexico;Intel Labs/IPR/SIA, Intel Guadalajara Design Center, Jalisco, Mexico

  • Venue:
  • IEEE Transactions on Signal Processing
  • Year:
  • 2011

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Abstract

In this correspondence, the use of superimposed training (ST) as a mean to estimate the finite impulse response (FIR) components of a widely linear (WL) system is proposed. The estimator here presented is based on the first-order statistics of the signal observed at the output of the system and its variance is independent of the channel components if suitable designed training sequences are employed. The construction of such sequences having constant magnitude both in time and frequency domains is also addressed.