Sample abundance
Sample abundance is a signal processing paradigm in which very large numbers of low-precision measurements—often one-bit samples produced by comparators with time-varying thresholds—are leveraged to recover signals or parameters with high fidelity and reduced computational cost. Instead of enforcing difficult constraints (e.g., positive-semidefiniteness or low rank) during reconstruction, many problems under sample abundance are reformulated as overdetermined linear feasibility tasks defined by half-space inequalities.