Enhanced Machine Learning-Based SDM-QAM Transmission Using Low-Cost Fast-OFDM

This paper presents a novel integration of quadrature amplitude modulation (QAM)-based fast optical orthogonal frequency-division multiplexing (F-OFDM) with machine learning (ML)-based equalization in spatial division multiplexing (SDM) applications, using few-mode fibers (FMFs). The FMFs support four LP modes, resulting in a total of 12 orthogonal modes, each accommodating two polarizations. A digital multiple-input multiple-output channel equalizer is employed at the receiver’s digital signal processing (DSP) unit to effectively mitigate channel crosstalk. The study harnesses supervised ML-DSP techniques, in particular recurrent neural networks (RNNs) and deep neural networks (DNNs), achieving substantial reductions in bit error rates (BERs). In addition, higher-complexity architectures, namely convolutional neural networks (CNNs) and long short-term memory (LSTM) networks, are evaluated to assess the impact of advanced spatial and temporal feature extraction. It is shown that F-OFDM demonstrates superior performance over conventional optical OFDM, particularly when supported by ML techniques. Simulation results reveal that RNNs achieve a BER of 0.0019 over 15 km at 12 Gbaud (worst-case selected channel), showcasing a remarkable 52.5% improvement compared to linear equalization. DNNs achieve a BER of 0.0025, reflecting a 37.5% enhancement. While RNNs perform better, their computational demands pose challenges for real-time applications, and the more complex models (CNN and LSTM) do not provide additional performance gains. The paper also explores cyclic prefix management and subcarrier number strategies in F-OFDM to optimize performance, paving the way for future advancements in SDM networks.