Performance Limits of Alphabet Diversities for FIR SISO Channel Identification

Delmas, Jean; Meurisse, Yann; Comon, Pierre

doi:10.1109/tsp.2008.2007926

Cited by 12 publications

(12 citation statements)

References 29 publications

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“…Therefore, researchers relied on the second order cyclic statistics (SOS) to invent and develop new blind methods as in [52 and 53]. In [54], the channel estimation based blind method is proposed in the frequency domain whereas, in [55], the single input-single output (SISO) FIR channel is blindly estimated by using SOS of transformed data. Necker and Stuber in [56] studied deterministic blind approach based on the ML for phase shift keying signals.…”

Section: Channel Estimation Using Semi-blind and Blind Techniquesmentioning

confidence: 99%

A Review and Comprehensive Study of Wireless Channel in Mobile Communication System: Fading Phenomena and Estimation

Jumaah

2019

QJES

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In wireless communication systems, the channel estimation problem has been played an essential challenge to accurately retrieve the channel state information (CSI) such that reliable communication & wide coverage can be provided. Due to the improvement and rapid growth of communication systems and in order to maintain a reliable data transmission, estimation of CSI has become necessary. This in turn results, precise receiver demodulation, accurate decoding, and equalization processes. This paper gives a survey on a fading phenomena and a comprehensive review of the recent works that have already been done and studied related to the problem of estimating channel parameters in wireless communication systems. Varieties of best channel estimation techniques that have been recently evolved are explored. Comparison between them in terms of computational cost, simplicity and appropriateness conditions is also discussed. This paper also provides a basic introduction of wireless channel model, SIMO and MIMO channel.

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Section: Channel Estimation Using Semi-blind and Blind Techniquesmentioning

confidence: 99%

A Review and Comprehensive Study of Wireless Channel in Mobile Communication System: Fading Phenomena and Estimation

Jumaah

2019

QJES

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“…This point will be illustrated in Section VI with the SISO channel modeling. Furthermore, note that the condition A is full column rank with p < r is not necessary to identify the useful parameter θ when specific a priori knowledge about the sources is available, see e.g., [28] for real-valued or QPSK modulations and [29] for offset linear modulations in SISO channel modeling.…”

Section: A Crb Expressionsmentioning

confidence: 99%

Survey and some new results on performance analysis of complex-valued parameter estimators

Delmas

Abeida

2015

Signal Processing

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Recently, there has been an increased awareness that simplistic adaptation of performance analysis developed for random real-valued signals and parameters to the complex case may be inadequate or may lead to intractable calculations. Unfortunately, many fundamental statistical tools for handling complex-valued parameter estimators are missing or scattered in the open literature. In this paper, we survey some known results and provide a rigorous and unified framework to study the statistical performance of complex-valued parameter estimators with a particular attention paid to properness (i.e., second order circularity), specifically referring to the second-order statistical properties. In particular, some new properties relative to the properness of the estimates, asymptotically minimum variance bound and Whittle formulas are presented. A new look at the role of nuisance parameters is given, proving and illustrating that the noncircular Gaussian distributions do not necessarily improve the CramerRao bound (CRB) with respect to the circular case. Efficiency of subspace-based complex-valued parameter estimators are presented with a special emphasis is put on noisy linear mixture. Index TermsCircular (proper) and noncircular (improper) complex-valued signals, statistical performance analysis, Cramer-Rao bound, asymptotically minimum variance bound, Slepian-Bangs and Whittle formulas.

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“…Other solutions aiming at estimating algebraically the best SISO equalizer, or to identify the SISO channel, when the input belongs to a known alphabet, have been proposed in [1,18,19,28,33,36,42,46,48,76,83,86,90]. The discrete alphabet hypothesis is then crucial, and replaces the assumption of statistical independence between symbols [14], which is no longer necessary.…”

Section: Algebraic Solutionsmentioning

confidence: 99%