Nicola Piovesan — accepted AI-conference papers

2026

Goal-Oriented Time-Series Forecasting: Foundation Framework Design

Luca-Andrei Fechete, Mohamed Sana, Fadhel Ayed, Nicola Piovesan, Wenjie Li, Antonio De Domenico +1

AAAI 2026technical

Conventional time-series forecasting methods typically aim to minimize overall prediction error, without accounting for the varying importance of different forecast ranges in downstream applications. We propose a training methodology that enables forecasting models to adapt their focus to applicatio

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2020

Modeling the Environment in Deep Reinforcement Learning: The Case of Energy Harvesting Base Stations

Nicola Piovesan, Marco Miozzo, Paolo Dini

ICASSP 2020accepted

In this paper, we focus on the design of energy self-sustainable mobile networks by enabling intelligent energy management that allows the base stations to mostly operate off-grid by using renewable energy. We propose a centralized control algorithm based on Deep Reinforcement Learning. The single a…

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