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eess.SP2025
Online Learning of Modular Bayesian Deep Receivers: Single-Step Adaptation with Streaming Data
Yakov Gusakov, Osvaldo Simeone, Tirza Routtenberg +1
Deep neural network (DNN)-based receivers offer a powerful alternative to classical model-based designs for wireless communication, especially in complex and nonlinear propagation…
cs.IT2025
In-Context Learning for Gradient-Free Receiver Adaptation: Principles, Applications, and Theory
Matteo Zecchin, Tomer Raviv, Dileep Kalathil +3
In recent years, deep learning has facilitated the creation of wireless receivers capable of functioning effectively in conditions that challenge traditional model-based designs. L…