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Error Bounds for a Matrix-Vector Product Approximation with Deep ReLU Neural Networks
In light of these questions, we derive error bounds in Lebesgue and Sobolev norms that comprise our developed deep approximation theory.
Blind Estimation of a Doubly Selective OFDM Channel: A Deep Learning Algorithm and Theory
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.
Performance Limits of a Deep Learning-Enabled Text Semantic Communication under Interference
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.
Making Sense of Meaning: A Survey on Metrics for Semantic and Goal-Oriented Communication
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.
Fundamental Limits of Deep Learning-Based Binary Classifiers Trained with Hinge Loss
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.
Deep Learning-Enabled Text Semantic Communication under Interference: An Empirical Study
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.
