5 citations · 18 across the 8 of their papers we have counts for
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Deep Learning-Enabled Text Semantic Communication under Interference: An Empirical Study
Tilahun M. Getu, Georges Kaddoum, Mehdi Bennis
At the confluence of 6G, deep learning (DL), and natural language processing (NLP), DL-enabled text semantic communication (SemCom) has emerged as a 6G enabler since it minimizes b…
Fundamental Limits of Deep Learning-Based Binary Classifiers Trained with Hinge Loss
Tilahun M. Getu, Georges Kaddoum, M. Bennis
Although deep learning (DL) has led to several breakthroughs in many disciplines, the fundamental understanding on why and how DL is empirically successful remains elusive. To atta…
Tutorial-Cum-Survey on Semantic and Goal- Oriented Communication: Research Landscape, Challenges, and Future Directions
Tilahun M. Getu, Georges Kaddoum, Mehdi Bennis
SemCom and goal-oriented SemCom are designed to transmit only semantically-relevant information and hence help to minimize power usage, bandwidth consumption, and transmission dela…
Making Sense of Meaning: A Survey on Metrics for Semantic and Goal-Oriented Communication
Tilahun M. Getu, Georges Kaddoum, Mehdi Bennis
Semantic communication (SemCom) aims to convey the meaning behind a transmitted message by transmitting only semantically-relevant information. This semantic-centric design helps t…
Performance Limits of a Deep Learning-Enabled Text Semantic Communication under Interference
Tilahun M. Getu, Walid Saad, Georges Kaddoum +1
Although deep learning (DL)-enabled semantic communication (SemCom) has emerged as a 6G enabler by minimizing irrelevant information transmission -- minimizing power usage, bandwid…