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My research is on future network design for beyond-5G/6G services with a focus on AI-powered context-aware communications. Traditional networks have been developed with communication protocols of different layers operating independently and with minimal adaptation mechanisms and knowledge of the user/environment. This has led to "semi-static" protocols and networks that cannot provide ultra reliable low latency, high data-rates at scale. Enabling 6G applications such immersive and critical communications will require novel networking solutions and architectures that span 1) modeling and predicting user behavior and application traffic without deep packet inspection, 2) developing multi-layer networking protocols and functions that incorporate this knowledge, and 3) designing end-to-end AI-powered network functions that can efficiently adapt to user and network context. 

Focus Areas

5G/6G Communications and Networking

  • Radio Resource Management (RRM) for Low Latency Communications

  • 6G Architectures & Protocols that enable Intelligence and Cross-layer Designs

  • UAV, Non-Terrestrial Communications

Immersive & Multi-Sensory Communications

  • Traffic Modeling and Non-invasive Classification of 6G Applications

  • Predictive RRM & Cross-Layer Designs

  • Communication-Compute-Control Co-Design

  • Testbed, Prototyping & Development of Multi-Sensory Communications

Robust Intelligence for Future Networks

  • Interpretable, Scalable AI for Networks

  • Robust Reinforcement Learning Algorithms

  • Multi-Agent and Federated Learning

  • Low-Overhead Online Learning


  1. Robust Intelligence for Beyond-5G Networks and Applications - new*  Hiring

  2. 5G-Assisted Autonomous Vehicles

  3. Cognitive Machine Type Communications for Massive IoT Applications

  4. Navigation and Control of Drones over 5G Networks: Enhanced Communication, Adaptive Control and Drone Swarm Collision Avoidance

  5. Machine Intelligence and Big Data Analytics for 5G Networks and Beyond                   
    NSERC Strategic, Queen’s University – Ericsson
    Overview:  Topics of investigation include network traffic prediction using generative artificial intelligence models (for IoT, augmented reality, autonomous vehicles traffic), hybrid deep learning architectures for spatiotemporal mobility modeling, anticipatory edge computing and NFV using reinforcement learning, predictive mobility-aware scheduling.

Past Projects 

​I was 

  1. Autonomous Multi-RAT Radio Resource Management for 5G (OCE ENCQOR): The goal of this project is to investigate how reinforcement learning techniques can be used to improve the performance of carrier aggregation management in 5G networks.

  2. Sensor-less Sensing of 5G Application Traffic (OCE ENCQOR)The goal of this project is to develop artificial intelligence techniques that can model, classify and predict real-time traffic of 5G applications like virtual reality, autonomous vehicle communication, and massive MTC.

  3. Beyond Visual Line of Sight Drones Enabled by Enhanced Mobile Broadband - IndroRobotics

  4. Spatial and Temporal Prediction of Wireless Communication Channels for 5G - UoT

  5. Distributed Edge Caching using Multi-Agent Reinforcement Learning - McGill

  6. Coordinated Communication and Interference Management under Network Virtualization - UoT

  7. Situation-Aware Machine Type Communications,

    Intelligent Resource Allocation Schemes using Deep Reinforcement Learning and User Localization

  8. mmWave and Mid-band Channel Modeling and Beamforming for 5G

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