This paper studies a large random matrix system (LRMS) model involving an arbitrary signal
distribution and forward error control (FEC) coding. We establish an area property based on …

Large multiple-input multiple-output (MIMO) appears in massive multi-user MIMO and
randomly-spread code-division multiple access (CDMA)-based wireless systems. In order to …

We study compressed sensing (CS) signal reconstruction problems where an input signal is
measured via matrix multiplication under additive white Gaussian noise. Our signals are …

The ability to understand and solve high-dimensional inference problems is essential for
modern data science. This chapter examines high-dimensional inference problems through …

We study the compressed sensing (CS) signal estimation problem where an input signal is
measured via a linear matrix multiplication under additive noise. While this setup usually …

Recently progress has been made in compressive sensing by replacing simplistic sparsity
models with more powerful denoisers. In this paper, we develop a framework to predict the …

Massive multi-user (MU) multiple-input multiple-output (MIMO) will be a core technology in
next-generation wireless systems. By equipping the infrastructure base-stations (BSs) with …

Page 1 ABSTRACT MA, YANTING. Solving Large-Scale Inverse Problems via Approximate
Message Passing and Optimization. (Under the direction of Dror Baron.) This work studies the …

Various examples of methods and systems are provided for approximate message passing
with universal denoising. In one example, a method includes applying an approximate …

In this dissertation, two classes of distributed algorithms are developed for sparse signal
recovery in large sensor networks. All the proposed approaches consist of local computation …