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Author: search.filters.author.Siriwanitpong, AphitchayaSubject: search.filters.subject.channel estimation

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    Performance Evaluation of the M-QAM Enhanced Subcarrier Index Modulation in the Multipath Fading Channel with the Non-Linear Amplifier
    (2023-01-01)
    Mi, Si Sar
    ;
    Siriwanitpong, Aphitchaya
    ;
    Boonsrimuang, Pornpawit
    ;
    Boonsrimuang, Pisit
    One of the specifications of the next-generation mobile communication is to reduce the power consumption to transmit the signal. Enhanced subcarrier index modulation (eSIM-OFDM) is a novel multicarrier modulation scheme. However, the eSIM-OFDM scheme degraded a little frequency utilization than the conventional orthogonal frequency division multiplexing (C-OFDM) scheme at the higher M-QAM modulation techniques. It has a special feature to transmit the signal when compared with the C-OFDM scheme. Its special feature increased the power efficiency and improved the bit error rate (BER) performance than the C-OFDM scheme. In the literature, the BER performance of the eSIM-OFDM scheme is analyzed in the additive white Gaussian noise (AWGN) channel and compared BER performance of the C-OFDM scheme. But in the literature, the BER performance of the eSIM-OFDM scheme and C-OFDM scheme were not evaluated in the multipath fading channel. Because eSIM-OFDM includes a special feature called inactive and active subcarriers. In this paper, we will evaluate the BER performance of higher order modulated eSIM-OFDM signal in the multipath Rician fading channel by using the Zadoff-Chu sequence pilot in the non-linear mobile communication system and will compare the BER performance with the C-OFDM scheme. From the simulation results, we can see that the BER performance of the eSIM-OFDM scheme is much better than the C-OFDM scheme in the multipath Rician fading non-linear channel. In this paper, computer simulation will evaluate the BER performance of the higher order modulated eSIM-OFDM scheme in the multipath Rician fading non-linear channel.
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    A Deep Learning-Based Channel Estimation for High-Speed Train Environments
    (2022-01-01)
    Siriwanitpong, Aphitchaya
    ;
    Boonsrimuang, Pornpawit
    ;
    Mori, Kazuo
    ;
    Boonsrimuang, Pisit
    A communication system in the railway track environment provides constant channel coefficient property as trains travel on a predetermined route and speed. This channel characteristic provides advantages in designing a channel estimation. This paper proposes a channel estimation algorithm based on a deep learning network called the Convolutional Neural Network (CNN). The CNN has trained with an average Channel Frequency Response (CFR) dataset on railway track environments with different multi-path fading and noises. The CFR can be estimated using a known pilot symbol as conventional methods. The estimated CFR is also used to select the right CFR for multi-path fading compensation. The CNN will then classify the estimated CFR by recognizing estimated channel characteristics and determining the most matched estimated channel for equalization of received signals in a data channel. The simulation results show that the performance of the deep learning algorithms outperforms that of the conventional algorithms. Furthermore, the proposed method delivers better Bit Error Rate (BER) performance since the deep learning-based channel estimation can categorize the features of the channel characteristics with different multi-path and doppler shifts.