Enhanced channel estimation in OFDM systems with neural network technologies
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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.
KeywordsFunctional link neural fuzzy network OFDM Channel estimation Least square Minimum mean square error Back-propagation neural network
This work was partially supported by the Ministry of Science and Technology (MOST), Taiwan, Republic of China, under Grant MOST 106-2632-E-468-003, and Asia University, Taiwan, and China Medical University Hospital, China Medical University, Taiwan (Grant No. ASIA-106-CMUH-04).
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Conflict of interest
The authors declare that they have no competing interests.
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