3 papers
eess.SP2026
WiMamba: Linear-Scale Wireless Foundation Model
Tomer Raviv, Nir Shlezinger
Foundation models learn transferable representations, motivating growing interest in their application to wireless systems. Existing wireless foundation models are predominantly ba…
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…
cs.IT2025
Blind Channel Estimation and Joint Symbol Detection with Data-Driven Factor Graphs
Luca Schmid, Tomer Raviv, Nir Shlezinger +1
We investigate the application of the factor graph framework for blind joint channel estimation and symbol detection on time-variant linear inter-symbol interference channels. In p…