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 …

Due to the greatly improved capabilities of devices, massive data, and increasing concern
about data privacy, Federated Learning (FL) has been increasingly considered for …

To facilitate the deployment of digital twins in Metaverse, the paradigm with semantic
awareness has been proposed as a means for enabling accurate and task-oriented …

Abstract Heterogeneous Networks (HetNets) play an imperative role in enhancing the
quality-of-service (QoS) for end-users by increasing the spectral efficiency (SE) of the …

The flexible mobility feature of unmanned aerial vehicles (UAVs) leads to frequent
handovers and serious inter-cell interference problems in UAV-assisted cellular networks …

Federated learning (FL) is a distributed learning paradigm wherein users exchange FL
models with a server instead of raw datasets, thereby preserving data privacy and reducing …

In this letter, we explore the process of transmitting model parameters for federated learning
(FL) across cell-free massive multiple-input multiple-output (CFmMIMO) networks. We …

Extremely large-scale multiple-input multiple-output (XL-MIMO) has been deemed a
breakthrough technology that holds significant potential for next-generation communication …

This paper investigates the effect of low-resolution analog-to-digital converters (ADCs) on
the centralized cell-free massive MIMO network over spatially correlated fading channels …

Federated Learning (FL) is a distributed learning framework in wireless edge networks,
intended to bolster in-telligent application capabilities. This paper considers an over-the-air …