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Browsing by Author Luping, Xiang

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    • Turbo Detection Aided Autoencoder for Multi-Carrier Wireless Systems: Integrating Deep Learning into Channel Coded Systems
    Authors: Chao, Xu; Thien, Van Luong; Luping, Xiang; Shinya, Sugiura; Robert, G. Maunder; Lie-Liang, Yang; Lajos, Hanzo;  Advisor: -;  Co-Author: - (2022)

    A variety of deep learning schemes have endeavoured to integrate deep neural networks (DNNs) into channel coded systems by jointly designing DNN and the channel coding scheme in specific channels. However, this leads to limitations concerning the choice of both the channel coding scheme and the channel paramters. We circumvent these impediments and conceive a turbo-style multi-carrier auto-encoder (MC-AE) for orthogonal frequency-division multiplexing (OFDM) systems, which is the first one that achieves the flexible integration of DNN into any given channel coded systems while achieving an iteration gain. More explicitly, first of all, we design the MC-AE independently of both the cha...
    • Turbo Detection Aided Autoencoder for Multicarrier Wireless Systems: Integrating Deep Learning Into Channel Coded Systems
    Authors: Chao, Xu; Thien Van, Luong; Luping, Xiang; Shinya, Sugiura;  Advisor: -;  Co-Author: - (2022)

    A variety of deep learning schemes have endeavoured to integrate deep neural networks (DNNs) into channel coded systems by jointly designing DNN and the channel coding scheme in specific channels. However, this leads to limitations concerning the choice of both the channel coding scheme and the channel paramters. We circumvent these impediments and conceive a turbo-style multi-carrier auto-encoder (MC-AE) for orthogonal frequency-division multiplexing (OFDM) systems, which is the first one that achieves the flexible integration of DNN into any given channel coded systems while achieving an iteration gain. More explicitly, first of all, we design the MC-AE independently of both the cha...
    • Unity-Rate Coding Improves the Iterative Detection Convergence of Autoencoder-Aided Communication Systems
    Authors: Luping, Xiang; Chao, Xu; Xiaoyu, Zhang;  Advisor: -;  Co-Author: - (2022)

    A forward error correction (FEC) and unity-rate coded (URC) autoencoder (AE)-assisted communication system is proposed for the first time, which relies on soft iterative decoding for attaining a vanishingly low error probability. The AE-demapper is specifically designed for directly calculating the extrinsic logarithmic likelihood ratios (LLRs), which can be directly entered into the URC decoder for soft iterative decoding. This avoids the potential degradation due to the conversion of symbol probabilities to bit LLRs. A comprehensive capacity analysis of the AE is performed, which demonstrates the capacity advantage of the AE-aided constellation design over its conventional quadratur...
    • Unity-Rate Coding Improves the Iterative Detection Convergence of Autoencoder-Aided Communication Systems
    Authors: Luping, Xiang; Chao, Xu; Xiaoyu, Zhang;  Advisor: -;  Co-Author: - (2022)

    A forward error correction (FEC) and unity-rate coded (URC) autoencoder (AE)-assisted communication system is proposed for the first time, which relies on soft iterative decoding for attaining a vanishingly low error probability. The AE-demapper is specifically designed for directly calculating the extrinsic logarithmic likelihood ratios (LLRs), which can be directly entered into the URC decoder for soft iterative decoding. This avoids the potential degradation due to the conversion of symbol probabilities to bit LLRs. A comprehensive capacity analysis of the AE is performed, which demonstrates the capacity advantage of the AE-aided constellation design over its conventional quadratur...