Utilizing Generative Adversarial Networks Compensator in 4X4 MIMO 5G Backhaul Convergence with Radio-over Fiber (RoF) System

The utilization of fiber systems for transmitting Millimeter-Wave (MMW) signals has gained significant traction in recent years, particularly for advanced wireless communication applications such as 5G and beyond. However, wirelessly transmitting MM-Wave radio signals via the atmosphere experiences significant signal deterioration, known as attenuation. To address this constraint, it is beneficial to utilize Radio Over Fiber (RoF) technology wherever possible. This study investigates developments in the combination of wireless and optical networks RoF and compensates for the error by Generative Adversarial Networks (GAN). The proposed system is constructed by utilizing the simulation programs VPIphotonics and Python. VPIphotonics (Ver.11.1) and Python (Ver. 3.7.6) are programming platforms designed to develop algorithms, analyze data and applications, and design models in real-time to compensate for the errors that occur in the proposed system. GANs used to compensate for errors in the RoF system shows a sophisticated application of machine learning techniques to improve signal integrity and reduce distortions. The 4X4 MIMO proposed system has a bandwidth of 17 GHz and reached a data rate of 133.33 Gb/s; the centre frequency is 160 GHz. This signal is transmitted over fiber lengths (50 km) with EVM ≈ 4.5 at the 50 km distance and 4.5 % with data rate Gb/s for the 4X4 MIMO proposed system.