The standard linear regression (SLR) problem is to recover a vector x 0 from noisy linear
observations y= Ax 0+ w. The approximate message passing (AMP) algorithm proposed by …

Deep learning has gained great popularity due to its widespread success on many inference
problems. We consider the application of deep learning to the sparse linear inverse …

Approximate message passing (AMP) is a low-cost iterative signal recovery algorithm for
linear system models. When the system transform matrix has independent identically …

We examine a class of stochastic deep learning models with a tractable method to compute
information-theoretic quantities. Our contributions are three-fold:(i) We show how entropies …

The evolution of wireless communications has been significantly influenced by remarkable
advancements in multiple access (MA) technologies over the past five decades, shaping the …

Signal recovery from unitarily invariant measurements is investigated in this paper. A
message-passing algorithm is formulated on the basis of expectation propagation (EP). A …

Integrated sensing and communications (ISAC) is capable of circumventing the limitations of
existing frequency-division sensing and communications (FDSAC) techniques. Hence, it has …

We consider linear regression in the high-dimensional regime where the number of
observations n is smaller than the number of parameters p. A very successful approach in …

We put forward a new algorithmic solution to the massive unsourced random access (URA)
problem, by leveraging the rich spatial dimensionality offered by large-scale antenna arrays …

In this letter, we propose a turbo compressed sensing algorithm with partial discrete Fourier
transform (DFT) sensing matrices. Interestingly, the state evolution of the proposed algorithm …