Autoencoder-Based End-to-End Underwater DCO-OFDM Communication System

Abstract

To mitigate the delay spread and inter-symbol interference (ISI) induced by optical scattering in underwater wireless optical communication (UWOC), this paper introduces long short-term memory (LSTM), a convolutional block attention module (CBAM), and residual connections into a convolutional neural network autoencoder (CNN-AE), and proposes a CNN-LSTM-AE-based end-to-end DC-biased optical orthogonal frequency division multiplexing (DCO-OFDM) system. In the proposed system, convolutional layers in the encoder serve to extract local features; CBAM adaptively weights salient features along the channel and spatial dimensions; LSTM layers model the temporal dependencies of signal sequences; and residual connections are incorporated to improve the learning capability for subtle signal features, thereby enhancing the robustness of the system against multipath channels. A symmetric structure is adopted at the receiver, ultimately enabling end-to-end signal recovery. Simulation results show that, under typical clear ocean and coastal ocean channel conditions, the proposed system outperforms end-to-end systems based on a fully connected autoencoder (FC-AE) and a CNN-AE at different modulation orders, i.e., different numbers of bits per symbol. For example, under strong scattering conditions in the coastal ocean channel, when the number of bits per symbol is 2 and the bit error rate (BER) is 10−3, the proposed system achieves signal-to-noise ratio (SNR) gains of approximately 3.33 dB and 1.82 dB over the two baselines. In terms of block error rate (BLER), SNR gains of approximately 4.52 dB and 2.19 dB are achieved over the two comparison systems, which substantiates the superior end-to-end transmission reliability of the proposed system.