no code implementations • 25 Nov 2021 • Tilahun M. Getu
In light of these questions, we derive error bounds in Lebesgue and Sobolev norms that comprise our developed deep approximation theory.
no code implementations • 30 May 2022 • Tilahun M. Getu, Nada T. Golmie, David W. Griffith
We provide a new generation solution to the fundamental old problem of a doubly selective fading channel estimation for orthogonal frequency division multiplexing (OFDM) systems.
no code implementations • 15 Feb 2023 • Tilahun M. Getu, Walid Saad, Georges Kaddoum, Mehdi Bennis
Although deep learning (DL)-enabled semantic communication (SemCom) has emerged as a 6G enabler by minimizing irrelevant information transmission -- minimizing power usage, bandwidth consumption, and transmission delay, its benefits can be limited by radio frequency interference (RFI) that causes substantial semantic noise.
no code implementations • 20 Mar 2023 • Tilahun M. Getu, Georges Kaddoum, Mehdi Bennis
Despite the surge in their swift development, the design, analysis, optimization, and realization of robust and intelligent SemCom as well as goal-oriented SemCom are fraught with many fundamental challenges.
no code implementations • 13 Sep 2023 • Tilahun M. Getu, Georges Kaddoum
Although deep learning (DL) has led to several breakthroughs in many disciplines as diverse as chemistry, computer science, electrical engineering, mathematics, medicine, neuroscience, and physics, a comprehensive understanding of why and how DL is empirically successful remains fundamentally elusive.
no code implementations • 30 Oct 2023 • 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 by promising to minimize bandwidth consumption, transmission delay, and power usage.