Deep learning models for scheduling of potentially-interfering communication pairs, in
device-to-device (D2D) settings, require large training samples in the order of hundreds to …

In this paper, we propose two novel geometric machine learning (G-ML) methods for the
wireless link scheduling problem in device-to-device (D2D) networks. In dynamic D2D …

In this paper, we investigate the fundamental problem of wireless link scheduling in device-
to-device (D2D) networks, through the lens of Riemannian geometry. Our goal is to find a …

In wireless multi-hop networks, delay is an important metric for many applications. However,
the max-weight scheduling algorithms in the literature typically focus on instantaneous …

Efficient scheduling of transmissions is a key problem in wireless networks. The main
challenge stems from the fact that optimal link scheduling involves solving a maximum …

A fundamental problem in the design of wireless networks is to efficiently schedule
transmission in a distributed manner. The main challenge stems from the fact that optimal …

Abstract Scheduling of Device-to-Device (D2D) links in communication networks
conventionally relies on solving NP-hard combinatorial optimization problems. These types …

Driven by the expeditious evolution of the Internet of Things, the conventional vehicular ad
hoc networks will progress toward the Internet of Vehicles (IoV). With the rapid development …

While the industry is continuously evolving towards more flexible and modular
manufacturing environments, simultaneously requirements on the industrial network …

Time-sensitive networking (TSN) can offer deterministic low-latency communication, making
it a critical solution for high-level autonomous vehicle's in-vehicle network. The deterministic …