3 citations · 8 across the 34 of their papers we have counts for
13 papers · 1 filter
Prediction-Powered Communication with Distortion Guarantees
Matteo Zecchin, Unnikrishnan Kunnath Ganesan, Giuseppe Durisi +2
The development of 6G wireless systems is taking place alongside the development of increasingly intelligent wireless devices and network nodes. The changing technological landscap…
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…
Conformal Calibration: Ensuring the Reliability of Black-Box AI in Wireless Systems
Osvaldo Simeone, Sangwoo Park, Matteo Zecchin
AI is poised to revolutionize telecommunication networks by boosting efficiency, automation, and decision-making. However, the black-box nature of most AI models introduces substan…
Online Conformal Compression for Zero-Delay Communication with Distortion Guarantees
Unnikrishnan Kunnath Ganesan, Giuseppe Durisi, Matteo Zecchin +2
We investigate a lossy source compression problem in which both the encoder and decoder are equipped with a pre-trained sequence predictor. We propose an online lossy compression s…
What If We Had Used a Different App? Reliable Counterfactual KPI Analysis in Wireless Systems
Qiushuo Hou, Sangwoo Park, Matteo Zecchin +3
In modern wireless network architectures, such as Open Radio Access Network (O-RAN), the operation of the radio access network (RAN) is managed by applications, or apps for short,…
Cell-Free Multi-User MIMO Equalization via In-Context Learning
Matteo Zecchin, Kai Yu, Osvaldo Simeone
Large pre-trained sequence models, such as transformers, excel as few-shot learners capable of in-context learning (ICL). In ICL, a model is trained to adapt its operation to a new…
In-Context Learning for MIMO Equalization Using Transformer-Based Sequence Models
Matteo Zecchin, Kai Yu, Osvaldo Simeone
Large pre-trained sequence models, such as transformer-based architectures, have been recently shown to have the capacity to carry out in-context learning (ICL). In ICL, a decision…
Forking Uncertainties: Reliable Prediction and Model Predictive Control with Sequence Models via Conformal Risk Control
Matteo Zecchin, Sangwoo Park, Osvaldo Simeone
In many real-world problems, predictions are leveraged to monitor and control cyber-physical systems, demanding guarantees on the satisfaction of reliability and safety requirement…
Federated Inference with Reliable Uncertainty Quantification over Wireless Channels via Conformal Prediction
Meiyi Zhu, Matteo Zecchin, Sangwoo Park +3
In this paper, we consider a wireless federated inference scenario in which devices and a server share a pre-trained machine learning model. The devices communicate statistical inf…
UAV-Aided Decentralized Learning over Mesh Networks
Matteo Zecchin, David Gesbert, Marios Kountouris
Decentralized learning empowers wireless network devices to collaboratively train a machine learning (ML) model relying solely on device-to-device (D2D) communication. It is known…
Asynchronous Decentralized Learning over Unreliable Wireless Networks
Eunjeong Jeong, Matteo Zecchin, Marios Kountouris
Decentralized learning enables edge users to collaboratively train models by exchanging information via device-to-device communication, yet prior works have been limited to wireles…
LIDAR and Position-Aided mmWave Beam Selection with Non-local CNNs and Curriculum Training
Matteo Zecchin, Mahdi Boloursaz Mashhadi, Mikolaj Jankowski +3
Efficient millimeter wave (mmWave) beam selection in vehicle-to-infrastructure (V2I) communication is a crucial yet challenging task due to the narrow mmWave beamwidth and high use…
Team Deep Mixture of Experts for Distributed Power Control
Matteo Zecchin, David Gesbert, Marios Kountouris
In the context of wireless networking, it was recently shown that multiple DNNs can be jointly trained to offer a desired collaborative behaviour capable of coping with a broad ran…