Demixing Structured Superposition Signals from Periodic and Aperiodic Nonlinear Observations

Mohammadreza Soltani; Chinmay Hegde

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arXiv · 1708.02999

Demixing Structured Superposition Signals from Periodic and Aperiodic Nonlinear Observations

Mohammadreza Soltani·Chinmay Hegde

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Abstract

We consider the demixing problem of two (or more) structured high-dimensional vectors from a limited number of nonlinear observations where this nonlinearity is due to either a periodic or an aperiodic function. We study certain families of structured superposition models, and propose a method which provably recovers the components given (nearly) $m = \mathcal{O}(s)$ samples where $s$ denotes the sparsity level of the underlying components. This strictly improves upon previous nonlinear demixing techniques and asymptotically matches the best possible sample complexity. We also provide a range of simulations to illustrate the performance of the proposed algorithms.

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BibTeX RIS

Mohammadreza Soltani, Chinmay Hegde. 2017-08-08. Demixing Structured Superposition Signals from Periodic and Aperiodic Nonlinear Observations. https://arxiv.org/abs/1708.02999

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