Related papers: Waveforms for Computing Over the Air

With the advent of the Internet-of-Things (IoT) era, the ever-increasing number of devices and emerging applications have triggered the need for ubiquitous connectivity and more efficient computing paradigms. These stringent demands have…

Over-the-air computation (OAC) leverages the physical superposition property of wireless multiple access channels (MACs) to compute functions while communication occurs, enabling scalable and low-latency processing in distributed networks.…

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 and privacy-preserving. To further improve the…

Recently, the ChannelComp framework has proposed digital over-the-air computation by designing digital modulations that enable the computation of arbitrary functions. Unlike traditional analog over-the-air computation, which is restricted…

This paper studies a hierarchical over-the-air computation (AirComp) network over a large area, in which multiple relays are exploited to facilitate data aggregation from massive WDs. We present a two-phase amplify-and-forward (AF) relaying…

Decentralized federated learning (DFL), inherited from distributed optimization, is an emerging paradigm to leverage the explosively growing data from wireless devices in a fully distributed manner.DFL enables joint training of machine…

The emerging concept of Over-the-Air (OtA) computation has shown great potential for achieving resource-efficient data aggregation across large wireless networks. However, current research in this area has been limited to the standard…

The stringent requirements for low-latency and privacy of the emerging high-stake applications with intelligent devices such as drones and smart vehicles make the cloud computing inapplicable in these scenarios. Instead, edge machine…

Via collaborative beamforming, nodes in a wireless network are able to transmit a common message over long distances in an energy efficient fashion. However, the process of making available the same message to all collaborating nodes…

A segmented waveguide-enabled pinching-antenna system (SWAN)-assisted over-the-air computation (AirComp) framework is proposed. Three transmission architectures, namely segment selection (SS), phase-shifter-free segment aggregation (SA),…

This paper studies Integrated Sensing, Communication, and Powering (ISCAP) as a novel framework designed to enhance Internet of Things (IoT) applications within sixth-generation wireless networks. In these applications, in addition to IoT…

In this paper, we consider decentralized federated learning (FL) over wireless networks, where over-the-air computation (AirComp) is adopted to facilitate the local model consensus in a device-to-device (D2D) communication manner. However,…

Over-the-air federated edge learning (Air-FEEL) has emerged as a promising solution to support edge artificial intelligence (AI) in future beyond 5G (B5G) and 6G networks. In Air-FEEL, distributed edge devices use their local data to…

To efficiently exploit the massive amounts of raw data that are increasingly being generated in mobile edge networks, federated learning (FL) has emerged as a promising distributed learning technique. By collaboratively training a shared…

We propose a framework to design integrated communication and computing (ICC) receivers capable of simultaneously detecting data symbols and performing over-the-air computing (AirComp) in a manner that: a) is systematically generalizable to…

Air components, including UAVs, planes, balloons, and satellites have been widely utilized since the fixed capacity of ground infrastructure cannot meet the dynamic load of the users. However, since those air components should be…

In this paper, we study unmanned aerial vehicles (UAVs) assisted wireless data aggregation (WDA) in multicluster networks, where multiple UAVs simultaneously perform different WDA tasks via over-the-air computation (AirComp) without…

Federated edge learning is a promising technology to deploy intelligence at the edge of wireless networks in a privacy-preserving manner. Under such a setting, multiple clients collaboratively train a global generic model under the…

At present, there is a trend to deploy ubiquitous artificial intelligence (AI) applications at the edge of the network. As a promising framework that enables secure edge intelligence, federated learning (FL) has received widespread…

Recently, over-the-air computation (AirComp) leverages the superposition property of wireless channels to enable efficient function computation over a multiple access channel (MAC). However, existing digital AirComp methods either rely on…