Forward–backward processing for efficient underground channel estimation in 60 GHz MISO FBMC systems. Issue 2 (1st January 2019)

• Record Type: Journal Article
• Authors: Belaoura, W.; Ghanem, K.; Nedil, M.; Bousbia‐Salah, H.
• Is Part Of: Electronics letters. Volume 55:Issue 2(2019)
• Page Start: 92
• Page End: 94
• Publication Date: 2019-01-01
• Subjects: channel bank filters -- space‐time codes -- channel estimation -- block codes -- filtering theory -- OFDM modulation -- quadrature amplitude modulation -- iterative methods; novel low complexity sparse channel estimation method -- forward–backward processing -- underground channel incorporating -- $2\times 1$ space‐time‐block coding filter bank multicarrier system -- STBC‐FBMC -- quadrature amplitude modulation -- estimation algorithm -- channel support -- backward removal -- support estimate -- conventional orthogonal matching pursuit algorithm -- effective channel reconstruction -- adaptive step size method -- higher estimation accuracy -- efficient underground channel estimation -- 60 GHz MISO FBMC systems -- frequency 60.0 GHz; Electronics -- Periodicals; 621.381
• DOI: 10.1049/el.2018.6406
• Languages: English
• ISSNs: 0013-5194

Abstract

Abstract : In this Letter, a novel low complexity sparse channel estimation method based on forward–backward processing is proposed for mm‐Wave underground channel incorporating a 2 × 1 space‐time‐block coding filter bank multi‐carrier (STBC‐FBMC) system, which employs quadrature amplitude modulation. The proposed estimation algorithm relies on the idea of the channel support forward selection and backward removal, which iteratively expands and compresses the support estimate, hence improving the ability to cover up for the errors made in the conventional orthogonal matching pursuit (OMP) algorithm. By contrast to the latter, the most innovative feature of the proposed approach is its capability of effective channel reconstruction without prior information of the sparsity level and its adaptive step size method to approach the true sparsity level. The proposed approach is shown to achieve a higher estimation accuracy than OMP alternative.