Enhanced Radio Frequency System Integration in Optical Networks Using Adaptive Compensation Techniques

The combination of radio frequency (RF) systems with optical communication networks has made adaptive optical compensation methods an ideal means to improve communication. By reducing problems like chromatic dispersion (CD), polarization mode dispersion (PMD), and nonlinear effects, this study measures how efficiently RF systems work in optical networks. Researchers are investigating advanced algorithms such as Neural Networks (NN), Reinforcement Learning (RL), and Digital Signal Processing (DSP) methods, among other adaptable techniques. All of these helps optimize network parameters in real time. These flexible methods significantly improve key performance measures such as Bit Error Rate (BER), Signal-to-Noise Ratio (SNR), Optical Signal-to-Noise Ratio (OSNR), Error Vector Magnitude (EVM), lag, and speed. Because they can keep data quality and dependability high, these methods are necessary for next-generation networks like 5G. The study includes information about possible effects on network efficiency and proposes areas for future research, such as integrating machine learning (ML) methods deeper and developing cost-effective ways for networks to respond.