August 08, 2019

Model-free Training of End-to-end Communication Systems

  • Ait Aoudia F.
  • Hoydis J.

The idea of end-to-end learning of communication systems through neural network-based autoencoders has the shortcoming that it requires a differentiable channel model. We present in this paper a novel learning algorithm which alleviates this problem. The algorithm enables training of systems with an unknown channel model or with non-differentiable components. It iterates between training of the receiver using the true gradient, and training of the transmitter using an approximation of the gradient. We show that this approach works as well as model-based training for a variety of channels and tasks. Moreover, we demonstrate the algorithm's practical viability through hardware implementation on software defined radios where it achieves state-of-the-art performance over a coaxial cable and wireless channel.

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Recent Publications

May 01, 2020

A Packaged 0.01-26-GHz Single-Chip SiGe Reflectometer for Two-Port Vector Network Analyzers

  • Chung H.
  • Ma Q.
  • Rebeiz G.
  • Sayginer M.

© 1963-2012 IEEE. This article presents a packaged SiGe BiCMOS reflectometer for 0.01-26-GHz two-port vector network analyzers (VNAs). The reflectometer chip is composed of a resistive bridge coupler and two wideband heterodyne receivers for coherent magnitude and phase detection. In addition, a high-linearity receiver channel is designed to accommodate 20 ...

August 01, 2019

Protecting photonic quantum states using topology

  • Blanco-Redondo A.

The use of topology to protect quantum information is well-known to the condensed-matter community and, indeed, topological quantum computing is a bursting field of research and one of the competing avenues to demonstrate that quantum computers can complete certain problems that classical computers cannot. In photonics, however, we are only ...

May 01, 2019

Digital networks at the nexus of productivity growth

  • Kamat S.
  • Prakash S.
  • Saniee I.
  • Weldon M.

This paper takes a fresh look at the debate over the relationship between digital technology and productivity. The argument of economic historian Robert J. Gordon is that digital technology will not lead to increases in productivity such as we saw in the last century, based on his analysis of the ...