2.7k citations · 6.4k across the 135 of their papers we have counts for
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Deep Transfer Learning Based Downlink Channel Prediction for FDD Massive MIMO Systems
Yuwen Yang, Feifei Gao, Zhimeng Zhong +2
Artificial intelligence (AI) based downlink channel state information (CSI) prediction for frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems…
Millimeter Wave Base Stations with Cameras: Vision Aided Beam and Blockage Prediction
Muhammad Alrabeiah, Andrew Hredzak, Ahmed Alkhateeb
This paper investigates a novel research direction that leverages vision to help overcome the critical wireless communication challenges. In particular, this paper considers millim…
Deep Learning for mmWave Beam and Blockage Prediction Using Sub-6GHz Channels
Muhammad Alrabeiah, Ahmed Alkhateeb
Predicting the millimeter wave (mmWave) beams and blockages using sub-6GHz channels has the potential of enabling mobility and reliability in scalable mmWave systems. These gains a…
3D Scene Based Beam Selection for mmWave Communications
Weihua Xu, Feifei Gao, Shi Jin +1
In this paper, we present a novel framework of 3D scene based beam selection for mmWave communications that relies only on the environmental data and deep learning techniques. Diff…
ViWi: A Deep Learning Dataset Framework for Vision-Aided Wireless Communications
Muhammad Alrabeiah, Andrew Hredzak, Zhenhao Liu +1
The growing role that artificial intelligence and specifically machine learning is playing in shaping the future of wireless communications has opened up many new and intriguing re…
Deep Learning for Massive MIMO with 1-Bit ADCs: When More Antennas Need Fewer Pilots
Yu Zhang, Muhammad Alrabeiah, Ahmed Alkhateeb
This paper considers uplink massive MIMO systems with 1-bit analog-to-digital converters (ADCs) and develops a deep-learning based channel estimation framework. In this framework,…