Information theoretic bounds for compressed sensing

In this paper we derive information theoretic performance bounds to sensing and reconstruction of sparse phenomena from noisy projections. We consider two settings: output noise models where the noise enters after the projection and input noise models where the noise enters before the projection. We consider two types of distortion for reconstruction: support errors and mean-squared errors. Our goal is to relate the number of measurements, m, and SNR, to signal sparsity, k, distortion level, d, and signal dimension, n. We consider support errors in a worst-case setting. We employ different variations of Fano’s inequality to derive necessary conditions on the number of measurements and SNR required for exact reconstruction. To derive sufficient conditions we develop new insights on max-likelihood analysis based on a novel superposition property. In particular this property implies that small support errors are the dominant error events. Consequently, our ML analysis does not suffer the conservatism of the union bound and leads to a tighter analysis of max-likelihood. These results provide order-wise tight bounds. For output noise models we show that asymptotically an SNR of Θ(log(n)) together with Θ(k log(n/k)) measurements is necessary and sufficient for exact support recovery. Furthermore, if a small fraction of support error