Mehdi Bennis

Vehicular Cooperative Perception Through Action Branching and Federated Reinforcement Learning

2021 · IEEE Transactions on Communications

Cooperative perception plays a vital role in extending a vehicle’s sensing range beyond its line-of-sight. However, exchanging raw sensory data under limited communication resources is infeasible. Towards enabling an efficient cooperative perception, vehicles need to …

Distributed Learning in Wireless Networks: Recent Progress and Future Challenges

2021 · arXiv (Cornell University)

The next-generation of wireless networks will enable many machine learning (ML) tools and applications to efficiently analyze various types of data collected by edge devices for inference, autonomy, and decision making purposes. However, due to …

Predictive Closed-Loop Remote Control over Wireless Two-Way Split Koopman Autoencoder

2022 · arXiv (Cornell University)

Real-time remote control over wireless is an important-yet-challenging application in 5G and beyond due to its mission-critical nature under limited communication resources. Current solutions hinge on not only utilizing ultra-reliable and low-latency communication (URLLC) links …

Semantic-Native Communication: A Simplicial Complex Perspective

2022 · arXiv (Cornell University)

Semantic communication enables intelligent agents to extract meaning (or semantics) of information via interaction, to carry out collaborative tasks. In this paper, we study semantic communication from a topological space perspective, in which higher-order data …

Balancing Energy Efficiency and Distributional Robustness in Over-the-Air Federated Learning

2023 · arXiv (Cornell University)

The growing number of wireless edge devices has magnified challenges concerning energy, bandwidth, latency, and data heterogeneity. These challenges have become bottlenecks for distributed learning. To address these issues, this paper presents a novel approach …

Maze Discovery using Multiple Robots via Federated Learning

2024 · arXiv (Cornell University)

This work presents a use case of federated learning (FL) applied to discovering a maze with LiDAR sensors-equipped robots. Goal here is to train classification models to accurately identify the shapes of grid areas within …

RAG-Enabled Intent Reasoning for Application-Network Interaction

2026

Intent-based network (IBN) is a promising solution to automate network operation and management. IBN aims to offer human-tailored network interaction, allowing the network to communicate in a way that aligns with the network users' language, …