M
Muhammad Alrabeiah
Researcher at Arizona State University
Publications - 40
Citations - 1925
Muhammad Alrabeiah is an academic researcher from Arizona State University. The author has contributed to research in topics: Wireless & Overhead (computing). The author has an hindex of 14, co-authored 36 publications receiving 1026 citations. Previous affiliations of Muhammad Alrabeiah include King Saud University & McMaster University.
Papers
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Journal ArticleDOI
Enabling Large Intelligent Surfaces With Compressive Sensing and Deep Learning
TL;DR: In this article, a novel LIS architecture based on sparse channel sensors is proposed, where all the LIS elements are passive except for a few elements that are connected to the baseband.
Posted Content
Enabling Large Intelligent Surfaces with Compressive Sensing and Deep Learning
TL;DR: The achievable rates of the proposed solutions approach the upper bound, which assumes perfect channel knowledge, with negligible training overhead and with only a few active elements, making them promising for future LIS systems.
Journal ArticleDOI
Deep Learning for mmWave Beam and Blockage Prediction Using Sub-6 GHz Channels
TL;DR: It is proved that under certain conditions, there exist mapping functions that can predict the optimal mmWave beam and blockage status directly from the sub-6 GHz channel and that a large enough neural network can predict mmWave beams and blockages with success probabilities that can be made arbitrarily close to one.
Proceedings ArticleDOI
Deep Learning for TDD and FDD Massive MIMO: Mapping Channels in Space and Frequency
TL;DR: In this paper, the authors proposed a channel-to-channel mapping in space and frequency, where the channels at one set of antennas and one frequency band are mapped to the channels from another set of antenna and frequency band.
Proceedings ArticleDOI
Deep Learning for Large Intelligent Surfaces in Millimeter Wave and Massive MIMO Systems
TL;DR: An energy-efficient novel LIS architecture where all the LIS elements are passive except few non-uniformly distributed active elements (connected to the baseband) is proposed and an efficient solution to design the L IS reflection matrices is developed, with negligible training overhead, leveraging deep learning tools.