All ProjectsContact Dr. Hossain
6GNetwork SlicingISAC
AI-Native 6G Network Architecture
Next-generation 6G networks require fundamentally new design paradigms where AI is not an add-on but a native component of every network layer. This project investigates how deep learning, reinforcement learning, and generative AI can be embedded into radio access, core, and edge layers to achieve self-optimizing, zero-touch networks capable of supporting diverse services from eMBB to URLLC and massive IoT simultaneously.
Research Objectives
- Develop AI-native air interface designs that jointly optimize sensing and communication
- Build dynamic network slicing engines using deep reinforcement learning for multi-tenant QoS
- Design intelligent spectrum management and interference coordination for sub-THz bands
- Investigate generative AI for network digital twin creation and what-if scenario planning
Methods & Techniques
- Deep Reinforcement Learning (DRL) for resource allocation
- Transformer-based models for wireless channel prediction
- Multi-agent RL for distributed network slice management
- Diffusion models for radio environment map generation
Interested in this research?
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