Separation of digital communication mixtures with the CMA: Case of unknown symbol rates

Jallon, Pierre; Chevreuil, Antoine; Loubaton, Philippe

doi:10.1016/j.sigpro.2010.03.002

Cited by 14 publications

(21 citation statements)

References 26 publications

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“…Proof: more solid arguments than the ones we give here are provided in [31]. The band of frequencies of the sampled version s k (n) of the source number k is…”

Section: Proposition 19 As the Kurtosis Of The Symbolsmentioning

confidence: 99%

“…In this case, indeed, ζ(s) is given by (3.30) and it is not possible to conclude directly that ζ(s) < 0. However, these statistics can be shown to express as integrals of the shaping filter c a (t), at least for a generic sampling frequency (see [31]). More precisely, except for four sampling frequencies that are irrelevant to consider, it can be shown quite simply that…”

Section: 9mentioning

confidence: 99%

“…In [31], it is shown rigorously that R (α) s and ζ(s) do not depend on the symbol period T . It is hence possible to compute numerically ζ(s) as a function of the excess bandwidth, while considering a few values of κ(d) corresponding to typical modulations.…”

Section: 9mentioning

confidence: 99%

“…When all the baud-rates are equal, the minimization of Υ (c) CM (r) is much more difficult and sufficient conditions on the sources have been set forth that assure that the minimizers of Υ (c) CM (r) are separating. We refer to [31]. However, the general case remains to be addressed (i.e.…”

Section: Proposition 19 As the Kurtosis Of The Symbolsmentioning

confidence: 99%

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Blind Signal Separation for Digital Communication Data

Chevreuil¹,

Loubaton²

2014

Academic Press Library in Signal Processing: Volume 2 - Communications and Radar Signal Processing

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Abstract. Blind source separation, often called independent component analysis, is a main field of research in signal processing since the eightees. It consists in retrieving the components, up to certain indeterminacies, of a mixture involving statistically independent signals. Solid theoretical results are known; besides, they have given rise to performent algorithms. There are numerous applications of blind source separation. In this contribution, we particularize the separation of telecommunication sources. In this context, the sources stem from telecommunication devices transmitting at the same time in a given band of frequencies. The received data is a mixed version of all these sources. The aim of the receiver is to isolate (separate) the different contributions prior to estimating the unknown parameters associated with a transmitter. The context of telecommunication signals has the particularity that the sources are not stationary but cyclo-stationary. Now, in general, the standard methods of blind source separation assume the stationarity of the sources. In this contribution, we hence make a survey of the well-known methods and show how the results extend to cyclo-stationary sources.

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“…Proof: more solid arguments than the ones we give here are provided in [31]. The band of frequencies of the sampled version s k (n) of the source number k is…”

Section: Proposition 19 As the Kurtosis Of The Symbolsmentioning

confidence: 99%

Section: 9mentioning

confidence: 99%

Section: 9mentioning

confidence: 99%

Section: Proposition 19 As the Kurtosis Of The Symbolsmentioning

confidence: 99%

See 2 more Smart Citations

Blind Signal Separation for Digital Communication Data

Chevreuil¹,

Loubaton²

2014

Academic Press Library in Signal Processing: Volume 2 - Communications and Radar Signal Processing

View full text Add to dashboard Cite

show abstract

“…In communications and radar/sonar, almost-cyclostationarity properties have been exploited for a variety of applications. They include weaksignal detection [122,123], synchronization [111,55,54], minimum mean-squared error (MMSE) filtering [115], source location [2,49,119,123,127,154,332,342], source separation [169], modulation format classification [81,327], and cognitive radio [23,144,233,329].…”

Section: Introductionmentioning

confidence: 99%