no code implementations • 22 Dec 2020 • Eren Balevi, Jeffrey G. Andrews
Communication at high carrier frequencies such as millimeter wave (mmWave) and terahertz (THz) requires channel estimation for very large bandwidths at low SNR.
no code implementations • 24 Jun 2020 • Eren Balevi, Akash Doshi, Ajil Jalal, Alexandros Dimakis, Jeffrey G. Andrews
This paper presents a novel compressed sensing (CS) approach to high dimensional wireless channel estimation by optimizing the input to a deep generative network.
no code implementations • 24 Sep 2019 • Eren Balevi, Jeffrey G. Andrews
It is empirically shown that this design gives nearly the same performance as to the hypothetically perfectly trained autoencoder, and we also provide a theoretical proof of why that is so.
no code implementations • 15 Mar 2019 • Eren Balevi, Jeffrey G. Andrews
Our results illustrate that the proposed algorithm approaches the performance of the multi-agent RL, which requires millions of trials, with hundreds of online trials, assuming relatively low environmental dynamics, and performs much better than a single agent RL.
no code implementations • 2 Nov 2018 • Eren Balevi, Jeffrey G. Andrews
For channel estimation (using pilots), we design a novel generative supervised deep neural network (DNN) that can be trained with a reasonable number of pilots.