Optoelectronic-aided machine learning-based stable routing protocol for MANET and beyond massive MIMO systems in 5G networks

Mobile Ad Hoc Networks (MANETs) are dynamic and self-organizing networks where nodes constantly change positions and communicate wirelessly. The stability of routing protocols in MANETs is crucial for effective data transmission, considering factors like node mobility, bandwidth, and signal strength. This research introduces an innovative approach to enhance MANET routing stability by integrating optoelectronic devices and machine learning. Optoelectronic components are leveraged to optimize signal detection in 5G networks and beyond, particularly in Massive Multiple-Input, Multiple-Output (MIMO) systems. The proposed protocol, "Optoelectronic-Aided Machine Learning-Based Stable Routing Protocol," utilizes machine learning algorithms to intelligently select routes based on real-time data, improving network efficiency. Moreover, the integration of optoelectronic devices enhances signal detection and quality. Comprehensive evaluations were conducted to validate the effectiveness of this approach, comparing it with conventional routing algorithms such as AODV. The Network Simulator 2 evaluated key performance indicators such as round-trip latency, packet delivery ratio, and route lifetime. Specifically for 5G networks and Massive MIMO systems, the results show that this Optoelectronic-Aided Machine Learning-Based Stable Routing Protocol has the potential to improve the stability and efficiency of MANETs considerably. This study aids in the development of cutting-edge wireless network communication protocols.