Advanced Machine Learning-Powered Tunable Optical Signal Processor for Precise Chromatic Dispersion Compensation in Analog B5G/6G Mobile Fronthaul Networks

We introduce an ML-driven optical signal processor for dispersion compensation in B5G RAN. This approach leverages a reconfigurable, energy-efficient MRR structure, effectively mitigating power fading. Our study exploits M-QAM digitally up-converted A-IFoF transmission simulation results to fiber distances up to 25km to prove the capabilities of the designed machine learning-based analog photonic processing unit. Analytical MATLAB calculations show enhanced output power, corroborated by VPI simulations demonstrating improved EVM values, including 16.9% EVM for 1GBd QPSK at 8.5GHz over 25km, meeting the 3GPP standards.