Experimental Design of Machine Learning based Enhanced Communication Efficiency and Noise Reduction over Mobile Cellular Networks

Modern communication relies heavily on mobile cellular networks, which can meet the increasing demands of users for higher data speeds, lower latency, and better communication quality. In order to improve communication efficiency and reduce noise in mobile cellular networks, this research presents a novel experimental design that uses machine learning. Essentially, this study makes use of the Learning Assisted Communication Evaluator Protocol (LACEP), a clever method that blends machine learning algorithms into the network architecture with ease. Notable gains result from LACEP’s intelligent optimization of network settings, adaptive response to changing environmental circumstances, and efficient mitigation of communication noise. Prominently, the suggested model attained a remarkable throughput of 97.24%, surpassing the throughput of 3.05% of current protocols such as Internet Protocol, Link Layer Protocol, and TDD Protocol. The experimental findings offer strong proof of the ability of machine learning-based techniques to deal with the dynamic and frequently noisy characteristics of mobile cellular networks. It’s also critical to emphasize that Python software is used to achieve this creative approach, guaranteeing scalability and adaptability. This experimental design, which adopts machine learning via LACEP, represents a positive step towards more effective and resilient mobile communication systems. It also adds to the continuous endeavors to enhance the performance of mobile cellular networks and provide high-quality communication services even in difficult settings.