2020 IEEE International Symposium
on Information Theory
21-26 June 2020 • Los Angeles, California, USA
| Paper ID | S.3.5 | ||
| Paper Title | Measurement Dependent Noisy Search with Stochastic Coefficients | ||
| Authors | Nancy Ronquillo, Tara Javidi, University of California, San Diego, United States | ||
| Session | S.3: Channels with Feedback | ||
| Presentation | Lecture | ||
| Track | Shannon Theory | ||
| Manuscript | Click here to download the manuscript | ||
| Virtual Presentation | Click here to watch in the Virtual Symposium | ||
| Abstract | Consider the problem of recovering an unknown sparse unit vector via a sequence of linear observations with stochastic magnitude and additive noise. An agent sequentially selects measurement vectors and collects observations subject to noise affected by the measurement vector. We propose two algorithms of varying computational complexity for sequentially and adaptively designing measurement vectors. The proposed algorithms aim to augment the learning of the unit common support vector with an estimate of the stochastic coefficient. Numerically, we study the probability of error in estimating the support achieved by our proposed algorithms and demonstrate improvements over random-coding based strategies utilized in prior works. | ||
Plan Ahead
2021 IEEE International Symposium on Information Theory
11-16 July 2021 | Melbourne, Victoria, Australia
