AI-Driven Radio Access Network · Network Intelligence · Wireless Systems

I’m exploring how AI can transform the Radio Access Network — from scheduling and beamforming to full-stack network optimization. My research sits at the intersection of deep learning, wireless communication, and large-scale system implementation.

📡 Radio Access Network (RAN)

1. Intelligent gNB scheduling & resource allocation

2. AI-driven HARQ prediction & link adaptation

3. Channel estimation & CSI compression with deep learning

4. Multi-cell coordination & interference management

🤖 AI for Wireless Systems

1. Reinforcement learning for dynamic RAN control

2. Graph neural networks for cell/UE topology modeling

3. Foundation models for wireless signal processing

🛰 System Engineering

1. O-RAN architecture research

2. Near-RT RIC intelligent control loops

3. Real-world dataset generation & RF environment simulation

🔬 AI-driven RAN Scheduler for 5G NR

📡 GNN-based CSI prediction for multi-cell coordination

📊 Reinforcement Learning for dynamic RAN slicing

🧬 Signal-processing foundation model prototype for wireless tasks

(If you want, I can add DOIs, papers, PDF links, or custom badges.)

📧 Email: zang03@uos.ac.kr

⭐️ If you’re into AI + Wireless

Feel free to follow, reach out, or star any project that interests you. Let’s build the future of AI-driven networks together.