January 01, 2017

An Introduction to Machine Learning Communications Systems

  • Hoydis J.
  • O'Shea T.

We introduce and motivate machine learning (ML) communications systems that aim to improve on and to even replace the vast expert knowledge in the field of communications using modern machine learning techniques. These have recently achieved breakthroughs in many different domains, but not yet in communications. By interpreting a communications system as an autoencoder, we develop a fundamental new way to think about radio communications system design as an end-to-end reconstruction optimization task that seeks to jointly optimize transmitter and receiver components in a single process. We further present the concept of Radio Transformer Networks (RTNs) as a means to incorporate expert domain knowledge in the ML model and study the application of convolutional neural networks (CNNs) on raw IQ time-series data for modulation classification. We conclude the paper with a deep discussion of open challenges and areas for future investigation.

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Recent Publications

August 09, 2017

A Cloud Native Approach to 5G Network Slicing

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  • Miller R.
  • Sharma S.

5G networks will have to support a set of very diverse and often extreme requirements. Network slicing offers an effective way to unlock the full potential of 5G networks and meet those requirements on a shared network infrastructure. This paper presents a cloud native approach to network slicing. The cloud ...

August 01, 2017

Modeling and simulation of RSOA with a dual-electrode configuration

  • De Valicourt G.
  • Liu Z.
  • Violas M.
  • Wang H.
  • Wu Q.

Based on the physical model of a bulk reflective semiconductor optical amplifier (RSOA) used as a modulator in radio over fiber (RoF) links, the distributions of carrier density, signal photon density, and amplified spontaneous emission photon density are demonstrated. One of limits in the use of RSOA is the lower ...

July 12, 2017

PrivApprox: Privacy-Preserving Stream Analytics

  • Chen R.
  • Christof Fetzer
  • Le D.
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How to preserve users' privacy while supporting high-utility analytics for low-latency stream processing? To answer this question: we describe the design, implementation and evaluation of PRIVAPPROX, a data analytics system for privacy-preserving stream processing. PRIVAPPROX provides three properties: (i) Privacy: zero-knowledge privacy (ezk) guarantees for users, a privacy bound tighter ...