Research

PhD Thesis — Energy Efficiency in B5G/6G Applied to IoT​

LRT Team — LIGM — Université Gustave Eiffel (2022 – 2026)

My doctoral research investigates energy-efficient communication mechanisms for IoT devices in B5G/6G networks, with a particular focus on massive Machine-Type Communications (mMTC).

The work focuses on adaptive communication strategies and optimization techniques that reduce device energy consumption while maintaining reliable connectivity in large-scale IoT deployments.

The research relies primarily on large-scale network simulation and optimization methods.

mMTC Communication Optimization​

This research axis studies scalable and energy-efficient communication mechanisms for massive IoT deployments (mMTC).

The objective is to understand how network parameters and communication strategies can be adapted to reduce device energy consumption while maintaining reliable connectivity.

Main research directions include:

• Investigation of adaptive DRX (Discontinuous Reception) mechanisms to reduce IoT device energy consumption
• Energy-aware radio resource allocation strategies for IoT traffic
• Deep Reinforcement Learning (Q-Learning, DQN) for dynamic adaptation of network parameters
• Metaheuristic optimization approaches for radio resource management
• Device-to-device (D2D) relay selection for energy-efficient communication
• Drone-assisted connectivity to extend coverage in challenging environments

These mechanisms are evaluated through large-scale simulations using OMNeT++.

Experimental 5G Platform​

Alongside my PhD research, I contribute to the deployment of a private 5G experimental platform used for teaching and experimentation within the Master's program.

This platform is used to study system-level behavior of 5G networks and collect end-to-end performance metrics.

Main activities include:

• Deployment of private 5G infrastructures
• Experimental analysis of end-to-end network performance
• Identification of performance bottlenecks across the 5G stack
• Development of experimental setups for IoT connectivity and network monitoring

The platform relies on open-source 5G stacks:

• srsRAN
• Open5GS
• free5GC

Tools & Simulation Platforms​