In this study, we propose circularly-shifted chirp (CSC)-based majority vote (MV)(CSC-MV),
a power-efficient over-the-air computation (OAC) scheme, to achieve long-range federated …

In this study, we propose an over-the-air computation (OAC) approach for power-efficient
localization in a sensor network. The proposed approach relies on a voting-based …

Over the past few years, significant advancements have been made in the field of machine
learning (ML) to address resource management, interference management, autonomy, and …

This paper studies the over-the-air computation (AirComp) in an orthogonal frequency
division multiplexing (OFDM) system with imperfect channel state information (CSI), in which …

In this work, we investigate federated edge learning over a fading multiple access channel.
To alleviate the communication burden between the edge devices and the access point, we …

Federated edge learning (FEEL) is a framework for training models in a distributed fashion
using edge devices and a server that coordinates the learning process. In FEEL, edge …

The imminent Internet of Things, fueled by 6G networks and machine learning technologies,
is set to shift wireless communication to machine-centric paradigms, revolutionizing sectors …

This dissertation presents circularly-shifted chirps (CSCs), synthesized within orthogonal
frequency domain multiplexing (OFDM) framework, as a novel solution for integrating radar …

A general-purpose synchronization method allows a set of software-defined radios (SDRs)
to transmit or receive any in-phase/quadrature (IQ) data with precise timings while …

The disclosure deals with a system and method for circularly-shifted chirp (CSC)-based
majority vote (MV)(CSC-MV), a low-peak-to-mean envelope power ratio (PMEPR) over-the …