Information-Theoretic Thresholds for Sparse Binary Signal Recovery
September 7, 2026
This research establishes the sample-complexity requirements for maximum-likelihood recovery of sparse binary signals under noisy linear measurements. It identifies a regime where measurement sparsity incurs logarithmic sample-complexity loss while providing nearly linear computational gains.
HOW THIS AFFECTS YOU
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researcherYou can use these bounds to optimize the trade-off between measurement sparsity and recovery accuracy in high-SNR regimes.