This project leverages AI for optimizing resource allocation in network slicing. By using a deep learning model, it predicts optimal bandwidth, CPU, and memory distribution based on parameters like traffic load, latency, jitter, and slice type. The model helps improve network efficiency and performance by dynamically adjusting resource allocation.

*A technical essay on 6G networks, covering Terahertz frequencies, AI, ML, Massive MIMO, and Service-Based Architecture. It explores advanced applications like holographic telepresence and dynamic slicing, highlighting innovations in protocols and AI-driven resource optimization for ultra-reliable, low-latency communication.*