Examining Machine Learning Applications in Communication Networks

This project explores the areas of intersection of machine learning (ML) techniques and modern communication networks to enhance efficiency, adaptability, and performance. The increasing complexity of wireless and wired networks, driven by 5G/6G advancements, software-defined networking (SDN), and the proliferation of Internet-of-Things (IoT) devices, necessitates intelligent automation for network management, resource allocation, and optimization.
The project will focus on key ML applications in communication networks, including:

• Network Traffic Prediction and Anomaly Detection: Utilizing ML models to forecast traffic patterns and detect network anomalies for improved reliability and security.
• Resource Allocation and Scheduling: Applying reinforcement learning (RL) and deep learning techniques for adaptive spectrum allocation, power control, and congestion management.
• Channel Estimation and Signal Processing: Exploring supervised and unsupervised learning methods to enhance signal detection, interference mitigation, and adaptive beamforming.
• Network Optimization and Fault Prediction: Investigating ML-driven approaches for predictive maintenance, fault detection, and self-healing mechanisms in communication networks.

Expected Outcomes:

• A comprehensive study of ML applications in communication networks, including state-of-the-art techniques and use cases.
• Development of hands-on assignments and case studies.
• Insights and potential research directions for future exploration in ML-driven communication systems.

This project will serve as a foundation for designing a structured and practical curriculum for students interested in the intersection of machine learning and communications.