Federated Edge Learning (FEEL) is a distributed machine learning technique where each device contributes to training a global inference model by independently performing local computations with their data. More recently, FEEL has been merged with …
Over-the-air computation (AirComp) has recently emerged as an efficient analog method for data acquisition from wireless sensor devices. In essence, AirComp exploits the signal superposition property of a multiple access channel to estimate functions …
Looking at the last decade of evolution, 4G and current 5G (4th/5 th generation of mobile communications) boosted the integration of services (other than voice and text) provided to end-users, marking a significant discontinuity with previous …
In this paper, we propose a scheme optimizing the per-user channel sensing duration in millimeter-wave (mmWave) multi-user multiple-input single-output (MU-MISO) systems. For each user, the BS predicts the effective rate to be achieved after pilot …
This paper deals with a distortion-based non-convex peak-to-average power ratio (PAPR) problem for large-scale multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. Our work is motivated by the observation …
Federated Learning (FL) is a distributed machine learning technique designed to utilize the distributed datasets collected by our mobile and internet-of-things devices. As such, it is natural to consider wireless communication for FL. In wireless …
In the Internet of Things, learning is one of most prominent tasks. In this paper, we consider an Internet of Things scenario where federated learning is used with simultaneous transmission of model data and wireless power. We investigate the …
In the resource management of wireless networks, Federated Learning has been used to predict handovers. However, non-independent and identically distributed data degrade the accuracy performance of such predictions. To overcome the problem, Federated …
We consider a set of binary random variables and address the open problems of inferring provable logical relations among these random variables, and prediction. We propose to solve these two problems by learning a Kolmogorov model (KM) for these …
The computational detection of musical patterns is widely studied in the field of Music Information Retrieval and has numerous applications. However, pattern detection in real-time has not yet received adequate attention. The real-time detection is …