Papers Published

1. Kipnis, A; Reeves, G; Eldar, YC; Goldsmith, AJ, Compressed sensing under optimal quantization, IEEE International Symposium on Information Theory - Proceedings (August, 2017), pp. 2148-2152, IEEE [doi] .
   (last updated on 2024/04/18)

   Abstract:
   We consider the problem of recovering a sparse vector from a quantized or a lossy compressed version of its noisy random linear projections. We characterize the minimal distortion in this recovery as a function of the sampling ratio, the sparsity rate, the noise intensity and the total number of bits in the quantized representation. We first derive a singe-letter expression that can be seen as the indirect distortion-rate function of the sparse source observed through a Gaussian channel whose signal-to-noise ratio is derived from these parameters. Under the replica symmetry postulation, we prove that there exists a quantization scheme that attains this expression in the asymptotic regime of large system dimensions. In addition, we prove a converse demonstrating that the MMSE in estimating any fixed sub-block of the source from the quantized measurements at a fixed number of bits does not exceed this expression as the system dimensions go to infinity. Thus, under these conditions, the expression we derive describes the excess distortion incurred in encoding the source vector from its noisy random linear projections in lieu of the full source information.