153 citations · 429 across the 48 of their papers we have counts for
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Vector Approximate Message Passing for the Generalized Linear Model
Philip Schniter, Sundeep Rangan, Alyson K. Fletcher
The generalized linear model (GLM), where a random vector is observed through a noisy, possibly nonlinear, function of a linear transform output $\boldsymbol{z}=\b…
AMP-Inspired Deep Networks for Sparse Linear Inverse Problems
Mark Borgerding, Philip Schniter, Sundeep Rangan
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 proble…
Denoising based Vector Approximate Message Passing
Philip Schniter, Sundeep Rangan, Alyson Fletcher
The denoising-based approximate message passing (D-AMP) methodology, recently proposed by Metzler, Maleki, and Baraniuk, allows one to plug in sophisticated denoisers like BM3D int…
Vector Approximate Message Passing
Sundeep Rangan, Philip Schniter, Alyson K. Fletcher
The standard linear regression (SLR) problem is to recover a vector from noisy linear observations . The approximate message pas…
Transport Layer Performance in 5G mmWave Cellular
Menglei Zhang, Marco Mezzavilla, Russell Ford +6
The millimeter wave (mmWave) bands are likely to play a significant role in next generation cellular systems due to the possibility of very high throughput thanks to the availabili…