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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…
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…