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  • Open Access

    ARTICLE

    Convolutional Neural Network Auto Encoder Channel Estimation Algorithm in MIMO-OFDM System

    I. Kalphana1,*, T. Kesavamurthy2

    Computer Systems Science and Engineering, Vol.41, No.1, pp. 171-185, 2022, DOI:10.32604/csse.2022.019799

    Abstract Higher transmission rate is one of the technological features of prominently used wireless communication namely Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO–OFDM). One among an effective solution for channel estimation in wireless communication system, specifically in different environments is Deep Learning (DL) method. This research greatly utilizes channel estimator on the basis of Convolutional Neural Network Auto Encoder (CNNAE) classifier for MIMO-OFDM systems. A CNNAE classifier is one among Deep Learning (DL) algorithm, in which video signal is fed as input by allotting significant learnable weights and biases in various aspects/objects for video signal and capable of differentiating from… More >

  • Open Access

    ARTICLE

    Improved MIMO Signal Detection Based on DNN in MIMO-OFDM System

    Jae-Hyun Ro1, Jong-Gyu Ha2, Woon-Sang Lee2, Young-Hwan You3, Hyoung-Kyu Song2,*

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3625-3636, 2022, DOI:10.32604/cmc.2022.020596

    Abstract This paper proposes the multiple-input multiple-output (MIMO) detection scheme by using the deep neural network (DNN) based ensemble machine learning for higher error performance in wireless communication systems. For the MIMO detection based on the ensemble machine learning, all learning models for the DNN are generated in offline and the detection is performed in online by using already learned models. In the offline learning, the received signals and channel coefficients are set to input data, and the labels which correspond to transmit symbols are set to output data. In the online learning, the perfectly learned models are used for signal… More >

  • Open Access

    ARTICLE

    Improved Hybrid Precoding Technique with Low-Resolution for MIMO-OFDM System

    Seulgi Lee1, Ji-Sung Jung1, Young-Hwan You2, Hyoung-Kyu Song1,*

    CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2205-2219, 2021, DOI:10.32604/cmc.2021.017008

    Abstract This paper proposes an improved hybrid beamforming system based on multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) system. The proposed beamforming system improves energy efficiency compared to the conventional hybrid beamforming system. Both sub-connected and full-connected structure are considered to apply the proposed algorithm. In the conventional hybrid beamforming, the usage of radio frequency (RF) chains and phase shifter (PS) gives high power and hardware complexity. In this paper, the phase over sampling (POS) with switches (SW) is used in hybrid beamforming system to improve the energy efficiency. The POS-SW structure samples the value of analog beamformer to make lower… More >

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