Convergence-Enhanced Subspace Channel Estimation for MIMO-OFDM Systems with Virtual Carriers

Zhang, Biling; Yu, Jung-Lang; Yuan, Yipu; Wu, Chao-Yu

doi:10.1007/s00034-016-0415-3

Cited by 3 publications

(1 citation statement)

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“…Finally, the customary techniques reduce the divert limit in the MIMO OFDM system. One of the attractive solutions is to separate multiuser without the CSI in MIMO-OFDM [14]. Multiuser detection by numerous methods are selected as a new and effective method without prior constraints in MIMO OFDM systems [15].…”

Section: Introductionmentioning

confidence: 99%

Blind Channel Estimation Using Enhanced Independent Component Analysis for MIMO-OFDM System

2022

IJIES

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Blind Source Separation (BSS) is a process of separating a set of source signals from mixed-signal without the help of information of source signals. In some noisy acoustic surroundings, instinctive class detection is completely dependent on vocalization which remains a stimulating task. To identify the definite classes easily, the source signals have to be detached from the mixed signals and this separation procedure is considered as a substantial pre-processing phase before the detection procedure takes place. This research mainly focuses on the issues of BSS in bio-acoustic mixed signals. Independent Component Analysis (ICA) is current technique in the area of BSS that can discrete the mixed-signal and also utilizes Negentropy as its objective function. However, this method is penetrating to the separation matrix and it cannot diverge. So, the bootstrap ICA procedures with Fast and Robust Bootstrap (FRB) method is developed which is applicable for all the signals. The quality of separated source signals using Enhanced-ICA and other algorithms are compared and evaluated according to MATLAB toolbox metrics. The results show that Enhanced-ICA with negentropy is used for finding a maximum non-gaussianity which achieves the BER performances of 0.00019 which is better than existing Discrete Wavelet Transform based BSS (DWT-BSS) and Modified Newton with Improved Animal Migration Optimization (MN-IAMO).

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Section: Introductionmentioning

confidence: 99%