The rapid advancement of artificial intelligence technologies has given rise to diversified
intelligent services, which place unprecedented demands on massive connectivity and …

This paper studies a new multi-device edge artificial-intelligent (AI) system, which jointly
exploits the AI model split inference and integrated sensing and communication (ISAC) to …

To keep pace with the explosive growth of Artificial Intelligence (AI) and Machine Learning
(ML)-dominated applications, distributed intelligence solutions are gaining momentum …

Over-the-air computation (AirComp) is a well-known technique by which several wireless
devices transmit by analog amplitude modulation to achieve a sum of their transmit signals …

Linear computations over quantum many-to-one communication networks offer opportunities
for communication cost improvements through schemes that exploit quantum entanglement …

Over-the-air computation (AirComp) is a known technique in which wireless devices transmit
values by analog amplitude modulation so that a function of these values is computed over …

In this study, we propose a digital over-the-air computation (OAC) scheme for achieving
continuous-valued (analog) aggregation for federated edge learning (FEEL). We show that …

In this study, we propose a non-coherent over-the-air computation scheme to calculate the
majority vote (MV) reliably in fading channels. The proposed approach relies on modulating …

A comprehensive coverage of the state-of-the-art in quantum machine learning (QML)
methodologies, with a unique perspective on their applications for wireless communications …

Federated learning (FL), as an emerging distributed machine learning paradigm, allows a
mass of edge devices to collaboratively train a global model while preserving privacy. In this …