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Soft Computing

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16.04.2018 | Methodologies and Application

Enhanced channel estimation in OFDM systems with neural network technologies

verfasst von: Chia-Hsin Cheng, Yao-Hung Huang, Hsing-Chung Chen

Erschienen in: Soft Computing | Ausgabe 13/2019

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Abstract

Orthogonal frequency division multiplexing (OFDM) provides an effective and low complexity means of eliminating inter-symbol interference for transmission over frequency selective fading channels. In OFDM systems, channel state information (CSI) is required for the OFDM receiver to perform coherent detection or diversity combining, if multiple transmit and receive antennas are deployed. In practice, CSI can be reliably estimated at the receiver by transmitting pilots along with data symbols. In this paper, we investigate and compare various efficient pilot-based channel estimation schemes by neural network technologies for OFDM systems. We present further the application of functional link neural fuzzy network (FLNFN) for channel estimation in the investigated OFDM systems. We compared bit error rates of the proposed neural network with that of the other neural network technologies, the least square (LS) algorithm, and the minimum mean square error (MMSE) algorithm. Our results demonstrate that the proposed FLNFN algorithm can enhance the performance of channel estimation in existing OFDM channel environments.

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Titel: Enhanced channel estimation in OFDM systems with neural network technologies
verfasst von: Chia-Hsin Cheng
Yao-Hung Huang
Hsing-Chung Chen
Publikationsdatum: 16.04.2018
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 13/2019
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-018-3185-y

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