Long-range photonics-aided 17.6 Gbit/s D-band PS-64QAM transmission using gate recurrent unit algorithm with a complex QAM input.

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Abstract

D-band fiber-wireless technique that overcomes the bandwidth bottleneck of electrical devices has been popularized, but long-range D-band wireless transmission is still limited by the large absorption loss. So, the exploration of m-QAM formats is essential for the D-band long distance wireless transmission due to their different spectrum efficiency and SNR requirement. Moreover, nonlinearity in photonics-aided millimeter-wave (mm-wave) system is also a significant problem caused by fiber, photoelectrical devices and power amplifiers. So it is critical to employ a machine learning-based nonlinear compensation algorithm especially for long-distance D-band wireless delivery. A novel Gate Recurrent Unit (GRU) algorithm with a complex QAM input is proposed to further improve the receiver sensitivity of coherent D-band receiver, which effectively preserves the relative relationship between I/Q components of QAM signals and has memory capabilities with a better precision. We mainly discuss three learners with a complex QAM input, including complex-valued neural network (CVNN), single-lane Long Short-Term Memory (SL-LSTM) and single-lane Gate Recurrent Unit (SL-GRU). Thanks to these adaptive deep learning methods, we successfully realize 135 GHz 4Gbaud QPSK and PS-64QAM signal wireless transmission over 4.6 km, respectively. Considering the aspects of transmission capacity and recovery precision, CVNN equalizer is suitable for QPSK recovery, SL-GRU would be the best choice for PS-64QAM over D-band long range wireless transmission link up to km magnitude. The effective data rate can be achieved up to 17.6 Gbit/s. Therefore, we believe that the combination of high-order modulation and NN supervised algorithms with a complex input has an application prospect for the future 6 G mobile communication.