A frequency domain-based approach for blind MIMO system identification using second-order cyclic statistics

Sabri, Khalid; Badaoui, Mohamed El; Guillet, François; Adib, Abdellah; Aboutajdine, Driss

doi:10.1016/j.sigpro.2008.07.011

Cited by 17 publications

(7 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Further applications are in the following subjects: capacity evaluation of second-order cyclostationary complex Gaussian noise channels [140], orthogonal overlay channels [371], and power line communication channels [319], timing and other signal parameter estimation [101,134,137,168,224,229,236,246,278,317,334], source separation [16,44,106,169], frequency-domain equalizer (FDE) design [374], blind multiple-input multiple-output (MIMO) system identification [299], signal power (SNR), and signal-to-interference-and-noise ratio (SINR) estimation [8,147,294], Doppler spread estimation [376], microDoppler estimation [214], noise modeling in power lines [130,182,318], and radio frequency interference (RFI) mitigation in radio astronomy [145].…”

Section: Miscellaneousmentioning

confidence: 99%

Cyclostationarity: New trends and applications

Napolitano

2016

Signal Processing

188

View full text Add to dashboard Cite

Section: Miscellaneousmentioning

confidence: 99%

Cyclostationarity: New trends and applications

Napolitano

2016

Signal Processing

188

View full text Add to dashboard Cite

“…Criteria measuring the closeness to diagonal matrices have been proposed. See also [47] and the references therein. Again, the diversity of the spectra is not always pertinent for digital communication contexts.…”

Section: Frequency-domain Approachesmentioning

confidence: 99%

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

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.

show abstract

“…They include training-based channel estimation (TBCE) schemes relying on training sequences that are known to the receiver, blind channel estimation (BCE) methods [13][14][15][16][17][18] identifying channel only from the received sequences, and semi-blind channel estimation (SBCE) approaches as a combination of the two aforementioned procedures [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

“…The optimal choice of training signals is usually investigated by minimizing mean square error (MSE) of the linear MIMO channel estimator. It is perceived that optimal design of training sequences for MIMO channel estimation is connected with the channel statistical characteristics, such as the fading model and the channel noise model [16][17][18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

A Novel Semi-blind Channel Estimation Scheme for Rayleigh Flat Fading MIMO channels (joint LS estimation and ML detection)

Moghaddam¹,

Saremi²

2010

IETE J Res

View full text Add to dashboard Cite

In this article, the training-based channel estimation (TBCE) and semi-blind channel estimation (SBCE) schemes in Rayleigh flat fading multiple-input multiple-output (MIMO) channels are investigated. First, least squares (LS), linear minimum mean square error (LMMSE), maximum likelihood (ML), and maximum a posteriori (MAP) channel estimators are presented and simulated. Owing to faster processing and lower bit error rate (BER), the LS estimator is the proper choice for both TBCE and SBCE-ML. It is illustrated that when the number of transmitter and/or receiver antennas increases, the performance of both TBCE and SBCE-ML schemes significantly improves. In addition, Alamouti coding has more effect on the performance of SBCE-ML rather than TBCE. Comparing LS-based TBCE and LS-based SBCE-ML, the simulation results introduce the most appropriate channel estimation method that uses an iterative algorithm. This new proposed method is based on LS estimator and ML detector. Simulation results of this investigation show that LS-based SBCE-ML method compared with LS-based TBCE method in different signal-to-noise ratios (SNRs) offers lower BER, 25% higher processing time, and 100 times lower training bits.

show abstract

A frequency domain-based approach for blind MIMO system identification using second-order cyclic statistics

Cited by 17 publications

References 21 publications

Cyclostationarity: New trends and applications

Cyclostationarity: New trends and applications

Blind Signal Separation for Digital Communication Data

A Novel Semi-blind Channel Estimation Scheme for Rayleigh Flat Fading MIMO channels (joint LS estimation and ML detection)

Contact Info

Product

Resources

About