Automatic Modulation Classification for Flexible OFDM-based Optical Networks

Abstract

Orthogonal frequency division multiplexing (OFDM) technology, a multi-carrier digital modulation technology, has been widely implemented in optical networks thanks to the effective provision of dispersion compensation for optical paths. To provide bandwidth abundant and flexible optical path services, OFDM-based optical networks may need to support several modulation formats, i.e., BPSK, QPSK, 8-PSK, and 16-QAM, and deploy them adaptively. Recently, automatic modulation classification (AMC) has become a promising solution for wireless networks to identify accurately the modulation formats of the received OFDM signals. In this paper, we propose an effective AMC using deep learning (DL) for flexible and adaptive OFDM-based optical networks. The proposed DL-based AMC is able to classify four typical modulation schemes such as binary phase-shift keying (BPSK), quadrature PSK (QPSK), 8-PSK, and 16-quadrature amplitude modulation (QAM) in dynamic network conditions. Numerical experiments are performed to verify the effectiveness of the developed solution. Our developed solution offers a significantly high accuracy, 95.83+%, even with a low SNR, says 4 dB, and its performance is improved when the SNR is enhanced.