1 citations · 1 across the 15 of their papers we have counts for
16 papers
Relative Representations of Latent Spaces enable Efficient Semantic Channel Equalization
Tomás Hüttebräucker, Simone Fiorellino, Mohamed Sana +2
In multi-user semantic communication, language mismatche poses a significant challenge when independently trained agents interact. We present a novel semantic equalization algorith…
Soft Partitioning of Latent Space for Semantic Channel Equalization
Tomás Hüttebräucker, Mohamed Sana, Emilio Calvanese Strinati
Semantic channel equalization has emerged as a solution to address language mismatch in multi-user semantic communications. This approach aims to align the latent spaces of an enco…
Latent Space Alignment for Semantic Channel Equalization
Tomás Hüttebräucker, Mohamed Sana, Emilio Calvanese Strinati
We relax the constraint of a shared language between agents in a semantic and goal-oriented communication system to explore the effect of language mismatch in distributed task solv…
Distributed Intelligent Integrated Sensing and Communications: The 6G-DISAC Approach
Emilio Calvanese Strinati, George C. Alexandropoulos, Madhusudan Giyyarpuram +14
This paper introduces the concept of Distributed Intelligent integrated Sensing and Communications (DISAC), which expands the capabilities of Integrated Sensing and Communications…
Goal-Oriented and Semantic Communication in 6G AI-Native Networks: The 6G-GOALS Approach
Emilio Calvanese Strinati, Paolo Di Lorenzo, Vincenzo Sciancalepore +17
Recent advances in AI technologies have notably expanded device intelligence, fostering federation and cooperation among distributed AI agents. These advancements impose new requir…
Pragmatic Goal-Oriented Communications under Semantic-Effectiveness Channel Errors
Tomás Hüttebräucker, Mohamed Sana, Emilio Calvanese Strinati
In forthcoming AI-assisted 6G networks, integrating semantic, pragmatic, and goal-oriented communication strategies becomes imperative. This integration will enable sensing, transm…
Semantic Channel Equalizer: Modelling Language Mismatch in Multi-User Semantic Communications
Mohamed Sana, Emilio Calvanese Strinati
We consider a multi-user semantic communications system in which agents (transmitters and receivers) interact through the exchange of semantic messages to convey meanings. In this…
Federated Multi-Agent Deep Reinforcement Learning for Dynamic and Flexible 3D Operation of 5G Multi-MAP Networks
Esteban Catté, Mohamed Sana, Mickael Maman
This paper addresses the efficient management of Mobile Access Points (MAPs), which are Unmanned Aerial Vehicles (UAV), in 5G networks. We propose a two-level hierarchical architec…
Sensing of Side Lobes Interference for Blockage Prediction in Dense mmWave Networks
Mohamed Sana, Hiba Dakdouk, Benoit Denis
The integration of sensing capability in the design of wireless communication systems is foreseen as a key enabler for efficient radio resource management in next-generation networ…
Dual-Attention Deep Reinforcement Learning for Multi-MAP 3D Trajectory Optimization in Dynamic 5G Networks
Esteban Catté, Mohamed Sana, Mickael Maman
5G and beyond networks need to provide dynamic and efficient infrastructure management to better adapt to time-varying user behaviors (e.g., user mobility, interference, user traff…
Learning Hierarchical Resource Allocation and Multi-agent Coordination of 5G mobile IAB Nodes
Mohamed Sana, Benoit Miscopein
We consider a dynamic millimeter-wave network with integrated access and backhaul, where mobile relay nodes move to auto-reconfigure the wireless backhaul. Specifically, we focus o…
Cost-Efficient and QoS-Aware User Association and 3D Placement of 6G Aerial Mobile Access Points
Esteban Catté, Mohamed Sana, Mickael Maman
6G networks require a flexible infrastructure to dynamically provide ubiquitous network coverage. Mobile Access Points (MAP) deployment is a promising solution. In this paper, we f…
Learning Semantics: An Opportunity for Effective 6G Communications
Mohamed Sana, Emilio Calvanese Strinati
Recently, semantic communications are envisioned as a key enabler of future 6G networks. Back to Shannon's information theory, the goal of communication has long been to guarantee…
Transferable and Distributed User Association Policies for 5G and Beyond Networks
Mohamed Sana, Nicola di Pietro, Emilio Calvanese Strinati
We study the problem of user association, namely finding the optimal assignment of user equipment to base stations to achieve a targeted network performance. In this paper, we focu…
Energy Efficient Edge Computing: When Lyapunov Meets Distributed Reinforcement Learning
Mohamed Sana, Mattia Merluzzi, Nicola di Pietro +1
In this work, we study the problem of energy-efficient computation offloading enabled by edge computing. In the considered scenario, multiple users simultaneously compete for limit…
Multi-Agent Reinforcement Learning for Adaptive User Association in Dynamic mmWave Networks
Mohamed Sana, Antonio De Domenico, Wei Yu +2
Network densification and millimeter-wave technologies are key enablers to fulfill the capacity and data rate requirements of the fifth generation (5G) of mobile networks. In this…