2020 IEEE International Symposium
on Information Theory
21-26 June 2020 • Los Angeles, California, USA
| Paper ID | E.4.3 | ||
| Paper Title | Achievability of nearly-exact alignment for correlated Gaussian databases | ||
| Authors | Osman Emre Dai, Georgia Institute of Technology, United States; Daniel Cullina, Pennsylvania State University, United States; Negar Kiyavash, Ecole Polytechnique Fédérale de Lausanne, Switzerland | ||
| Session | E.4: Estimation and Applications | ||
| Presentation | Lecture | ||
| Track | Detection and Estimation | ||
| Manuscript | Click here to download the manuscript | ||
| Virtual Presentation | Click here to watch in the Virtual Symposium | ||
| Abstract | We study the conditions that allow for the alignment of correlated databases with multivariate Gaussian features. We present some analysis tools that allow us to go beyond the achievability result for exact alignment and derive the condition for nearly-exact alignment. Our main theorem gives an expression for the order of magnitude of the error in alignment as a function of mutual information between features. | ||
Plan Ahead
2021 IEEE International Symposium on Information Theory
11-16 July 2021 | Melbourne, Victoria, Australia
