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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 architecture of LACONet is drastically simplified compared to classical LACO-OFDM, since the Fast Fourier T...
    • 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 architecture of LACONet is drastically simplified compared to classical LACO-OFDM, since the Fast Fourier T...