S
Sundeep Rangan
Researcher at New York University
Publications - 401
Citations - 25852
Sundeep Rangan is an academic researcher from New York University. The author has contributed to research in topics: Belief propagation & Base station. The author has an hindex of 69, co-authored 382 publications receiving 21503 citations. Previous affiliations of Sundeep Rangan include University of Waterloo & University of Padua.
Papers
More filters
Journal ArticleDOI
Millimeter-Wave Cellular Wireless Networks: Potentials and Challenges
TL;DR: Measurements and capacity studies are surveyed to assess mmW technology with a focus on small cell deployments in urban environments and it is shown that mmW systems can offer more than an order of magnitude increase in capacity over current state-of-the-art 4G cellular networks at current cell densities.
Journal ArticleDOI
An Overview of Signal Processing Techniques for Millimeter Wave MIMO Systems
TL;DR: This article provides an overview of signal processing challenges in mmWave wireless systems, with an emphasis on those faced by using MIMO communication at higher carrier frequencies.
Journal ArticleDOI
Millimeter Wave Channel Modeling and Cellular Capacity Evaluation
Mustafa Riza Akdeniz,Yuanpeng Liu,Mathew K. Samimi,Shu Sun,Sundeep Rangan,Theodore S. Rappaport,Elza Erkip +6 more
TL;DR: Detailed spatial statistical models of the channels are derived and it is found that, even in highly non-line-of-sight environments, strong signals can be detected 100-200 m from potential cell sites, potentially with multiple clusters to support spatial multiplexing.
Journal ArticleDOI
Femtocells: Past, Present, and Future
TL;DR: This tutorial article overviews the history of femtocells, demystifies their key aspects, and provides a preview of the next few years, which the authors believe will see a rapid acceleration towards small cell technology.
Proceedings ArticleDOI
Generalized approximate message passing for estimation with random linear mixing
TL;DR: In this article, the generalized AMP (G-AMP) algorithm is proposed to estimate a random vector observed through a linear transform followed by a componentwise probabilistic measurement channel.