150 citations · 470 across the 103 of their papers we have counts for
8 papers · 2 filters
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…
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,…
Deep Learning for Direct Hybrid Precoding in Millimeter Wave Massive MIMO Systems
Xiaofeng Li, Ahmed Alkhateeb
This paper proposes a novel neural network architecture, that we call an auto-precoder, and a deep-learning based approach that jointly senses the millimeter wave (mmWave) channel…
Deep Learning for TDD and FDD Massive MIMO: Mapping Channels in Space and Frequency
Muhammad Alrabeiah, Ahmed Alkhateeb
Can we map the channels at one set of antennas and one frequency band to the channels at another set of antennas---possibly at a different location and a different frequency band?…