Technical Program

Paper Detail

Paper IDS.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.

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2021 IEEE International Symposium on Information Theory

11-16 July 2021 | Melbourne, Victoria, Australia

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