FL-Based NN in 30.4-km MMW Transmission Using Full-Photonic Conversion and TWT Amplifier

This letter presents a focal loss (FL)-based neural network soft de-mapping method for 30.4-km millimeter-wave (MMW) transmission, utilizing full-photonic up- and down-conversion. A traveling wave tube (TWT) is employed to improve power budget to realize over 50-km equivalent distance. The proposed method is shown to effectively reduce the bit error rate (BER) in MMW transmission, particularly when the model parameters are small, highlighting its potential to lower complexity of receiver. The performance of the FL function is validated using convolutional neural networks (CNN), recurrent neural networks (RNN), and residual networks (ResNet) architectures, all of which lead to a significant reduction in BER, with ResNet achieving the best results. The field trial of 30.4-km MMW transmission and targeted over 50.0-km equivalent distance have been demonstrated successfully. Notably, a 10.43-Gb/s line rate is achieved over the 30.4-km near-sea surface wireless link at 0-dBm received optical power (ROP) of transmitter photodiode.