I received my Ph.D. in Electrical Engineering from Ben-Gurion University in 2017, under the supervision of Dr. Ron Dabora. Prior to that I received my B.Sc. degree and my M.Sc. degree in 2011 and 2013, respectively, from Ben-Gurion University, all in Electrical Engineering.
From 2009 to 2013 I worked as an engineer at Yitran Communications.
My detailed CV can be found here.
Quantization refers to the representation of a continuous-amplitude signal using a finite number of bits. Quantizers are typically designed to obtain an accurate digital representation of the input signal, while operating independently of the system task, and are commonly implemented using serial scalar analog-to-digital converters (ADCs). In my work I study task-based quantization with serial scalar ADCs. This research combines lossy source coding and quantization theory with tools for handling the hardware limitations, including convex optimization and machine learning.
Here is a video of my talk on task-based quantization given in BIRS workshop (October 2018).
Massive MIMO – from theory to practice:
Massive multiple-input multiple-output (MIMO) systems are multi-user wireless networks in which the BS is equipped with an arbitrarily large number of antennas. While massive MIMO technology has the potential of increasing the spectral efficiency of wireless networks, using a large scale antenna array, and particularly, accurately quantizing the signal observed at the antennas, gives rise to various implementation challenges. Among these challenges are increased cost, high power consumption, and large memory usage. In my work I study massive MIMO systems, starting from the fundamental limits of such channels, computed assuming only standard power constraints, and proceeding to the realization of practical systems accounting for these constraints.