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    • Machine-Learning-Aided Optical OFDM for Intensity Modulated Direct Detection
    Authors: Xiaoyu, Zhang; Thien Van, Luong; Periklis, Petropoulos;  Advisor: -;  Co-Author: - (2022)

    End-to-end learning systems are conceived for Orthogonal Frequency Division Multiplexing (OFDM)-aided optical Intensity Modulation paired with Direct Detection (IM/DD) communications relying on the Autoencoder (AE) architecture in deep learning. We first propose an AE-aided Layered ACO-OFDM (LACO-OFDM) scheme, termed as LACONet, for exploiting the increased bandwidth efficiency of LACO-OFDM. LACONet employs a Neural Network (NN) at the transmitter for bit-to-symbol mapping, and another NN at the receiver for recovering the data bits, which together form an AE and can be trained in an end-to-end manner for simultaneously minimizing both the BER and PAPR. Moreover, the detection archite...
    • Machine-Learning-Aided Optical OFDM for Intensity Modulated Direct Detection
    Authors: Xiaoyu, Zhang; Thien, Van Luong; Periklis, Petropoulos; Lajos, Hanzo;  Advisor: -;  Co-Author: - (2022)

    End-to-end learning systems are conceived for Orthogonal Frequency Division Multiplexing (OFDM)-aided optical Intensity Modulation paired with Direct Detection (IM/DD) communications relying on the Autoencoder (AE) architecture in deep learning. We first propose an AE-aided Layered ACO-OFDM (LACO-OFDM) scheme, termed as LACONet, for exploiting the increased bandwidth efficiency of LACO-OFDM. LACONet employs a Neural Network (NN) at the transmitter for bit-to-symbol mapping, and another NN at the receiver for recovering the data bits, which together form an AE and can be trained in an end-to-end manner for simultaneously minimizing both the BER and PAPR. Moreover, the detection archite...
    • 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...