Novel Machine Learning Linearization Scheme for 6G A-RoF Systems

This article presents the design and implementation of a digital pre-distortion (DPD) scheme based on machine learning (ML) algorithm, which has been envisioned for the sixth generation of mobile communications (6G) analog radio over fiber (A-RoF) system. The DPD scheme employs an augmented real-valued time delay neural network (ARVTDNN) to compensate for the memory and memory-less non-linear effects introduced by the A-RoF communication chain. The ARVTDNN receives the base-band samples of the transmit waveform and stores them in a time-delay line (TDL), which means that the information necessary to compensate for time-correlated effects is available for the ML algorithm. Therefore, the ARVTDNN can compensate for both memory and memory-less effects, including the chromatic dispersion introduced by the optical fiber. The novel DPD can bring a significant positive impact in enhanced remote areas (eRAC) applications since the proposed DPD allows A-RoF to be used to connect the base-band unit with a low-cost radio frequency (RF) radio head installed in remote areas. In this case, the severe non-linearities introduced by the Mach-Zehnder optical modulator (MZM) and power amplifier (PA) and the time dispersive distortions introduced by the fiber can be mitigated, improving the quality of the signal. Furthermore, the novel ML-based linearization scheme can be flexibly tailored to cover variations in the operating scenario by adjusting its hyperparameters and training data. The root mean square error vector magnitude (EVMRMS), normalized mean square error (NMSE) and adjacent channel leakage ratio (ACLR) metrics have been used to evaluate the performance of the proposed DPD. Numerical results demonstrate promising linearization performance, reducing the signal in- and out-band distortions when the fronthaul link is extended to cover remote and rural areas.