Shift covariant time-frequency distributions of discrete signals

O'Neill, J.C.; Williams, William J.

doi:10.1109/78.738246

Cited by 36 publications

(12 citation statements)

References 34 publications

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“…Note that (2) has also been proposed by O'Neill et al for odd-length signals [5]. The authors have used an axiomatic approach, which was extended in [6] to derive a discrete Cohen class. In [7] and [8], Stanković has derived a discrete distribution from the analysis of the WD defined in the frequency domain.…”

Section: A Discrete Wdsmentioning

confidence: 99%

Time-frequency-based detection using discrete-time discrete-frequency Wigner distributions

Richard

2002

IEEE Trans. Signal Process.

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Abstract-During the last decade, a comprehensive theory for optimum time-frequency (TF)-based detection has been developed. This was originally proposed in the continuous-time continuous-frequency case. This paper deals with detectors operating on discrete-time discrete-frequency Wigner distributions (WDs). The purpose is to discuss some existing definitions of this distribution within the context of TF-based detection and selecting those that do not affect the performance of the decision device with which they are associated. This question is of interest since there exist several approches for discretizing the WD, sometimes resulting in a loss of fundamental properties. First, the discrete-time discrete-frequency formulations of optimum detection are investigated. Next, the problem of the design of TF-based detectors from training data, keeping in mind severe effects of the curse of dimensionality, is considered.

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Section: A Discrete Wdsmentioning

confidence: 99%

Time-frequency-based detection using discrete-time discrete-frequency Wigner distributions

Richard

2002

IEEE Trans. Signal Process.

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“…The observation period is determined by the sampling block length NT, which should not be too small in order to reduce windowing effects. The resulting spectra can be interpreted as a time-varying power spectrum S ee acc e j2pfT ; nT À Á in the sense of a discrete-time Rihaczek spectrum [13][14][15]. Hence, the time average…”

Section: Comparison Of the Error Power Spectramentioning

confidence: 99%

Dirty RF: A New Paradigm

Fettweis

Löhning

Petrovic

et al. 2006

Int J Wireless Inf Networks

145

116

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The implementation challenge for new low-cost low-power wireless modem transceivers has continuously been growing with increased modem performance, bandwidth, and carrier frequency. Up to now we have been designing transceivers in a way that we are able to keep the analog (RF) problem domain widely separated from the digital signal processing design. However, with today's deep sub-micron technology, analog impairments -''dirt effects'' -are reaching a new problem level which requires a paradigm shift in the design of transceivers. Examples of these impairments are phase noise, non-linearities, I/Q imbalance, ADC impairments, etc. In the world of ''Dirty RF'' we assume to design digital signal processing such that we can cope with a new level of impairments, allowing lee-way in the requirements set on future RF sub-systems. This paper gives an overview of the topic and presents analytical evaluations of the performance losses due to RF impairments as well as algorithms that allow to live with imperfect RF by compensating the resulting error effects using digital baseband processing.

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“…A review of these distributions can be found in [7]. Many commonly used timefrequency distributions are members of the Cohen class [8]. It has been stated in [9] that the Cohen class, although introduced for deterministic signals, can be applied on non-stationary stochastic processes.…”

Section: Introductionmentioning

confidence: 99%