Research

What I work on

My PhD work sits at the intersection of non-terrestrial networks and deep reinforcement learning: teaching UAVs and high-altitude platforms to position and move themselves to keep ground users connected. Around that core, I work more broadly across AI/ML and wireless communications.

Deep Reinforcement Learning

Policy-gradient and actor-critic methods for continuous positioning and trajectory control, applied to keep aerial base stations optimally placed under changing conditions.

  • PPO
  • TD3
  • Trajectory optimization
  • Positioning
  • Autonomous decision-making
  • Continuous control
  • Multi-agent coordination

Non-Terrestrial Networks

Integrating UAVs and high-altitude platform stations (HAPS) with terrestrial networks to keep ground and aerial users connected — including a GPS-free framework, using only radio-sensing and angle-of-arrival measurements, for coordinating multiple UAV base stations in disaster-response and wind-disturbed maritime scenarios.

  • UAV base stations
  • HAPS
  • Terrestrial/NTN integration
  • Disaster response
  • Maritime networks

AI & Machine Learning

Broader work in federated learning, autoencoder-based classification and detection, data valuation, and large language models.

  • Federated Learning
  • CNN Autoencoders
  • Large Language Models
  • Data valuation

Wireless Communications

MIMO systems and next-generation wireless standards, including indirect SINR estimation from SSB and CSI-RS reference signals in 5G NR.

  • MIMO
  • 5G/6G
  • SINR estimation
  • SSB / CSI-RS