1. Artificial Intelligence in Communication Systems
Machine Learning Fundamentals
– Supervised, unsupervised, reinforcement learning
– Model training, evaluation metrics, overfitting, hyperparameter tuning
Deep Learning and Neural Networks
– CNNs, RNNs, LSTMs, Transformers
– Application in traffic classification, anomaly detection, and predictive analytics
AI in Network Optimization
– Intelligent routing, QoS prediction, congestion avoidance
– Resource allocation using reinforcement learning
AI for Wireless and Mobile Networks
– Handoff optimization, channel estimation
– AI-driven power management and spectrum allocation
Security and Privacy
– AI for intrusion detection and threat modeling
– Secure learning and privacy-preserving algorithms
Practical Skills
– Tools: Python, TensorFlow, PyTorch, Scikit-learn
– Datasets: CICIDS, NSL-KDD, KDDCup, DARPA
– Simulation & testing in NS-3 or Mininet with AI modules
2. Advanced Computer Networks
Network Architecture & Protocols
– TCP/IP, BGP, MPLS, IPv6, SDN
– Routing algorithms, NAT, tunneling
Next-Generation Networks
– 5G/6G architecture, network slicing, NFV, edge computing
Wireless and Mobile Networking
– LTE/5G systems, mobility management, MAC protocols
Quality of Service (QoS) and Traffic Engineering
– Delay, jitter, throughput, SLA management
Network Security
– Protocol-level security, VPNs, firewalls, IDS/IPS
– AI-enhanced network security
