Subspace techniques in blind mobile radio channel identification and equalization using fractional spacing and/or multiple antennas

Slock, Dirk

doi:10.1016/b978-044482107-2/50048-0

Cited by 10 publications

(1 citation statement)

References 5 publications

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“…(1) Here, d ( t ) E C' is the input signal, the vectors h(k) E CL are the taps in the impulse response of the system and where S ( T ) is the Kronecker delta and IL denotes the identity matrix of dimension L. In order to obtain a low rank model of the FIR-system, the output column vectors XI ( t ) to XI (t -N + 1) are collected in a larger vector in [6] and, hence, GNHN = 0. Since the structure in GN is rather complicated when L > 2, we refer to [6] for a more detailed description. Here, we restrict the description of the parameterization and simply state that there exists a one-to-one mapping between the parameters in GN and the parameters in H~J .…”

Section: Datamodelmentioning

confidence: 99%

Further results on optimally weighted subspace based blind channel estimation

Kristensson

Ottersten²

Conference Record of Thirty-Second Asilomar Conference on Signals, Systems and Computers (Cat. No.98CH36284)

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Subspace based identification of model parameters requires a low rank data model. To obtain such a model it is common to collect several snapshots cf the observed vector valued sequence in a larger vector In many cases, this "window" procedure results in a new vcctor valuedprocess which can be viewed as originating f r c m a low rank data model. When studying the performance of weighted subspace based techniques this window procedure complicates both the analysis and the implementation. In this paper an attempt is made to clarifi the theoretical and numerical difficulties when applying wt>ighting in such scenarios.

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Section: Datamodelmentioning

confidence: 99%