A novel shifted type of SLS estimator for estimation of Rician flat fading MIMO channels

Nooralizadeh, Hamid; Moghaddam, Shahriar Shirvani

doi:10.1016/j.sigpro.2009.12.009

Cited by 13 publications

(22 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To estimate the above mentioned MIMO channel, the SSLS channel estimator of [1] is appropriate. It is shown that the presence of the Line of Sight (LOS) path is helpful for channel estimation.…”

Section: Training Based Rice Factor Estimationmentioning

confidence: 99%

“…where Ĉ H is the estimated matrix of channel co-variances and J LS is the error of the LS estimator [1]. Using (2) (5) and with some calculations, the MSE is given by 2 …”

Section: A Sensitivity Analysis Of the Ssls Estimatormentioning

confidence: 99%

“…In the system model of [1], the channel Rice factor is the only parameter which is needed to estimate at the receiver. Using the optimal training sequences and system model of [1], the minimum MSE of (7) is…”

Section: A Sensitivity Analysis Of the Ssls Estimatormentioning

confidence: 99%

“…However, in [1], we have investigated the Training Based Channel Estimation (TBCE) scheme in the more general MIMO Rician fading channels. By the new method of Shifted Scaled Least Squares (SSLS), it is shown that Rician fading not only can improve the performance of channel estimation but also may be useful to reduce the required Signal to Noise Ratio (SNR) and/or to increase the capacity of MIMO system.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Training-based MIMO channel Rice factor estimation algorithms

Nooralizadeh

Moghaddam

2010

IEEE 10th INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING PROCEEDINGS

Self Cite

View full text Add to dashboard Cite

Recently, to estimate the Rician flat fading MultipleInput Multiple-Output (MIMO) channels, we have proposed the Shifted Scaled Least Squares (SSLS) channel estimator. In this paper, it is analytically shown that the performance of this estimator is less sensitive to the erroneous estimation of the Rice factor. Moreover, to estimate the channel Rice factor in the above mentioned channel model, two algorithms are proposed. These algorithms work based on the optimal training signal and Least Squares (LS) technique. The estimated Rice factor is used in the SSLS estimator. The performance of these algorithms is numerically compared in the Rician fading channel estimation. Simulation results confirm the efficiency of these algorithms.

show abstract

Section: Training Based Rice Factor Estimationmentioning

confidence: 99%

“…where Ĉ H is the estimated matrix of channel co-variances and J LS is the error of the LS estimator [1]. Using (2) (5) and with some calculations, the MSE is given by 2 …”

Section: A Sensitivity Analysis Of the Ssls Estimatormentioning

confidence: 99%

Section: A Sensitivity Analysis Of the Ssls Estimatormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Training-based MIMO channel Rice factor estimation algorithms

Nooralizadeh

Moghaddam

2010

IEEE 10th INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING PROCEEDINGS

Self Cite

View full text Add to dashboard Cite

show abstract

“…In the literature, three classes of methods to estimate the channel response are presented. They include Training Based Channel Estimation (TBCE) schemes relying on training sequences that are known to the receiver (Xie et al, 2007: Biguesh & Gershman, 2006: Nooalizadeh et al, 2009: Nooralizadeh & Shirvani Moghaddam, 2010, Blind Channel Estimation (BCE) methods (Sabri et al, 2009: Panahi & Venkat, 2009: Chen & Petropulu, 2001, identifying channel only from the received sequences, and Semi Blind Channel Estimation (SBCE) approaches as combination of two aforementioned procedures (Cui & Tellambura, 2007: Abuthinien et al, 2007: Khalighi & Bourennane, 2008. One of the most usual approaches to identify MIMO CSI is TBCE.…”

mentioning

confidence: 99%

Joint LS Estimation and ML Detection for Flat Fading MIMO Channels

Moghaddam¹,

Saremi²

2011

MIMO Systems, Theory and Applications

View full text Add to dashboard Cite

Two‐step interference cancellation scheme in doubly selective channel estimation using superimposed training

Dou

Liu

et al. 2013

IET Communications

View full text Add to dashboard Cite

Channel estimation for single-input-multiple-output systems over doubly selective channel using superimposed training and discrete prolate spheroidal basis expansion model is considered. To remove the interference from unknown data, a two-step interference cancellation scheme is proposed, where in the first step, a first-order statistics-based estimator with cyclic mean removal before transmission is proposed. In this step, only interference components corresponding to cyclic mean of data sequence are removed, which avoids the conflict between total interference elimination and symbol recovery. In the second step, a low complexity iterative interference cancellation scheme is proposed to further improve estimation performance with detected symbols at the receiver's end. Simulation results show that the proposed iterative scheme has the symbol error rate (SER) performance slightly inferior to that of the partially-data-dependent approach but with less computational complexity and outperforms the data-dependent scheme in terms of SER as well as mean-square error over doubly selective channels.

show abstract

A novel shifted type of SLS estimator for estimation of Rician flat fading MIMO channels

Cited by 13 publications

References 26 publications

Training-based MIMO channel Rice factor estimation algorithms

Training-based MIMO channel Rice factor estimation algorithms

Joint LS Estimation and ML Detection for Flat Fading MIMO Channels

Two‐step interference cancellation scheme in doubly selective channel estimation using superimposed training

Contact Info

Product

Resources

About