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    ASIA unversity > 資訊學院 > 資訊工程學系 > 期刊論文 >  Item 310904400/111601

    Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/111601

    Title: Enhanced channel estimation in OFDM systems with neural network technologies
    Authors: Ch, Chia-Hsin;Cheng, Chia-Hsin;Hua, Yao-Hung;Huang, Yao-Hung;陳興忠;Chen, Hsing-Chung;*
    Contributors: 資訊工程學系
    Date: 2018-04
    Issue Date: 2018-10-22 11:45:12 (UTC+8)
    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.
    Relation: SOFT COMPUTING
    Appears in Collections:[資訊工程學系] 期刊論文

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