A low-complexity recursive procedure is presented for minimum mean squared error
(MMSE) estimation in linear regression models. A Gaussian mixture is chosen as the prior …

We discuss the problem of designing channel access architectures for enabling fast, low-
latency, grant-free, and uncoordinated uplink for densely packed wireless nodes …

The replica method is a non-rigorous but widely-used technique from statistical physics used
in the asymptotic analysis of many large random nonlinear problems. This paper applies the …

The paper considers the problem of detecting the sparsity pattern of a k-sparse vector in
\BBR^n from m random noisy measurements. A new necessary condition on the number of …

We study the information-theoretic limits of exactly recovering the support set of a sparse
signal, using noisy projections defined by various classes of measurement matrices. Our …

Recovery of the sparsity pattern (or support) of an unknown sparse vector from a limited
number of noisy linear measurements is an important problem in compressed sensing. In …

Recovery of the sparsity pattern (or support) of an unknown sparse vector from a small
number of noisy linear measurements is an important problem in compressed sensing. In …

It is well known that the support of a sparse signal can be recovered from a small number of
random projections. However, in the presence of noise all known sufficient conditions …

In this paper, sensor network scenarios are considered where the underlying signals of
interest exhibit a degree of sparsity, which means that in an appropriate basis, they can be …

Orthogonal matching pursuit (OMP) is a widely used greedy algorithm for recovering sparse
vectors from linear measurements. A well-known analysis of Tropp and Gilbert shows that …