Communication and computation are often viewed as separate tasks. This approach is very
effective from the perspective of engineering as isolated optimizations can be performed …

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

To satisfy the expected plethora of computation-heavy applications, federated edge learning
(FEEL) is a new paradigm featuring distributed learning to carry the capacities of low-latency …

As data generation increasingly takes place on devices without a wired connection, Machine
Learning (ML) related traffic will be ubiquitous in wireless networks. Many studies have …

Over-the-air computation (AirComp) has been recognized as a promising technique in
Internet-of-Things (IoT) networks for fast data aggregation from a large number of wireless …

The performance of over-the-air computation (AirComp) systems degrades due to the hostile
channel conditions of wireless devices (WDs), which can be significantly improved by the …

In this study, we propose an over-the-air computation (OAC) scheme to calculate the
majority vote (MV) for federated edge learning (FEEL). With the proposed approach, edge …

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 …

Over-the-air computation (AirComp) has been recognized as a promising technique of
enabling the fusion center (FC) to aggregate the data gleaned from massive distributed …

Over-the-air computing (AirComp) has recently attracted considerable attention as an
efficient method of data fusion by integrating uncoded communication transmissions with …