Time-Varying Channel Estimation Using Two-Dimensional Channel Orthogonalization and Superimposed Training

@article{CarrascoAlvarez2012TimeVaryingCE,
  title={Time-Varying Channel Estimation Using Two-Dimensional Channel Orthogonalization and Superimposed Training},
  author={Roberto Carrasco-Alvarez and Ram{\'o}n Parra-Michel and Aldo G. Orozco-Lugo and Jitendra K. Tugnait},
  journal={IEEE Transactions on Signal Processing},
  year={2012},
  volume={60},
  pages={4439-4443}
}
In this correspondence, a method is presented for estimating double-selective channels using superimposed training (ST). The estimator is based on a subspace projection of the time-varying channel onto a set of two dimensional orthogonal functions. These functions are formed via the outer product of the discrete prolate spheroidal basis vectors and the universal basis vectors. This approach allows the channel to be expanded in both the time-delay and time dimensions with the fewest parameters… CONTINUE READING

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