This paper addresses the problem of distributed training of a machine learning model over the nodes of a wireless communication network. Existing distributed training methods are not explicitly designed for these networks, which usually have physical …
This paper considers a general class of iterative algorithms performing a distributed training task over a network where the nodes have background traffic and communicate through a shared wireless channel.Focusing on the carrier-sense multiple access …
An innovative feature of the 5th Generation mobile network (5G) is to consider industrial applications as use cases for which its new radio access, 5G New Radio, aims to provide ultra low latency and ultra high reliability performance. These …
This paper proposes a new class of augmented musical instruments, “Smart Instruments”, which are characterized by embedded computational intelligence, bidirectional wireless connectivity, an embedded sound delivery system, and an onboard system for …
In this paper we propose to extend the concept of the Internet of Things to the musical domain leading to a subfield coined as the Internet of Musical Things (IoMUT). IoMUT refers to the network of computing devices embedded in physical objects …
In this paper, we consider a mobile edge computing (MEC) network, that is wirelessly powered. Each user harvests wireless energy and follows a binary computation offloading policy, i.e., it either executes the task locally or offloads it to the MEC …
The integration of volatile renewable energy into distribution networks on a large scale will demand advanced voltage control algorithms. Communication will be an integral part of these algorithms, however, it is unclear what kind of communication …
Full-duplex base-stations with half-duplex nodes, allowing simultaneous uplink and downlink from different nodes, have the potential to double the spectrum efficiency without adding additional complexity at mobile nodes. Hybrid beam forming is …
To make the system available at low-cost, millimeter-ave (mmWave) multiple-input multiple-output (MIMO) architectures employ analog arrays, which are driven by a limited number of radio frequency (RF) chains. One primary challenge of using large …
Finding a dataset of minimal cardinality to characterize the optimal parameters of a model is of paramount importance in machine learning and distributed optimization over a network. This paper investigates the compressibility of large datasets. More …