Thesis Abstract

Abstract
In today's fast-paced world, the demand for efficient and reliable communication systems is ever-increasing. With the advent of the Internet of Things (IoT), smart cities, and advanced manufacturing processes, the need for robust communication networks is paramount. This research project focuses on the design and optimization of a novel communication system based on machine learning techniques for enhancing network performance and reliability. The proposed communication system leverages machine learning algorithms to dynamically adapt to changing network conditions, thereby optimizing the use of resources and ensuring seamless connectivity. By incorporating machine learning models into the communication system, it becomes possible to predict network congestion, prevent bottlenecks, and prioritize traffic based on real-time data analysis. One of the key objectives of this research is to develop a predictive model that can anticipate network failures and proactively implement solutions to mitigate their impact. By analyzing historical data and monitoring network performance metrics, the machine learning-based communication system can identify potential issues before they occur, thus enhancing the overall reliability of the network. Furthermore, the research seeks to optimize the allocation of resources within the communication system by employing machine learning algorithms to intelligently manage bandwidth, routing, and other network parameters. By dynamically adjusting these parameters based on real-time data inputs, the system can adapt to changing network conditions and ensure efficient utilization of resources. To evaluate the performance of the proposed communication system, extensive simulations will be conducted using realistic network scenarios and traffic patterns. By comparing the results of the machine learning-based system with traditional communication approaches, the research aims to demonstrate the superiority of the proposed approach in terms of network performance, reliability, and scalability. Overall, this research project aims to push the boundaries of traditional communication systems by harnessing the power of machine learning to create a more intelligent and adaptive network infrastructure. By developing a communication system that can learn from past experiences, anticipate future challenges, and optimize resource utilization, this research has the potential to significantly enhance the reliability and efficiency of modern communication networks.

Thesis Overview