EECS Seminar: A Theoretical Model for Information Flow and Scheduling in mmWave Networks

McDonnell Douglas Engineering Auditorium
Yahya Ezzeldin, Ph.D. Candidate

Electrical & Computer Engineering Department

Abstract: Millimeter-Wave (mmWave) communications are expected to play a vital role in 5G mobile communications and beyond, expanding the available spectrum and enabling multi-gigabit services such as next-generation business solutions, virtual and augmented reality applications, and autonomous vehicle and drone communication. Although for single-hop mmWave networks, several works have examined channel modeling, performance bounds and algorithms, for multi-hop mmWave networks, fundamental performance bounds such as the information-theoretical capacity have not been well explored. 

In this talk, we will discuss a simple yet informative theoretical model for multi-hop mmWave networks called 1-2-1 networks. In such networks, the ineffectiveness of isotropic transmission is captured by assuming that two nodes can communicate only if they point beams at each other, otherwise, the signal is received well below the thermal noise level. The focus of the talk will be to highlight the operational benefits that arise from the beamforming nature in our model for mmWave networks, as well as the complexities that accompany the need to use and steer such beamforming. 

Time permitting, we will discuss some additional wireless communication network applications from the viewpoint of designing low-complexity algorithms that can achieve provable performance guarantees.

This talk is based on joint work with Martina Cardone, Christina Fragouli and Giuseppe Caire.

Bio: Yahya Ezzeldin is a doctoral candidate in the Department of Electrical & Computer Engineering at UCLA, working with Christina Fragouli. He received his bachelor's and master's degrees in electronics and communications engineering from Alexandria University in 2011 and 2014, respectively. His research interests are broadly in network information theory, wireless communications, distributed computing and machine learning. He worked as a machine learning platform engineer at Intel during the summer of 2018. He is the recipient of the 2019-2020 Dissertation Year Fellowship at UCLA, the graduate division award at UCLA in 2014-2015 and the Henry Samueli Fellowship in 2016.