4 papers
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…
Reliable Narrowband Interference Detection via Backward Conformal Prediction
Xin Su, Meiyi Zhu, Osvaldo Simeone +2
Narrowband interference can severely degrade the performance of WiFi links by concentrating significant power on a small portion of the channel. Machine learning (ML) detectors tra…
From High-Level Requirements to KPIs: Conformal Signal Temporal Logic Learning for Wireless Communications
Jiechen Chen, Michele Polese, Osvaldo Simeone
Softwarized radio access networks (RANs), such as those based on the Open RAN (O-RAN) architecture, generate rich streams of key performance indicators (KPIs) that can be leveraged…
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…