Deep Learning-Based User Activity Detection and Channel Estimation in Grant-Free NOMA
Author:
Affiliation:
1. School of Information and Electronics, Beijing Institute of Technology, Beijing, China
2. Department of Electrical and Computer Engineering, University of Houston, Houston, TX, USA
Funder
Natural Science Foundation on Frontier Leading Technology Basic Research Project of Jiangsu
Beijing Natural Science Foundation
National Natural Science Foundation of China
NSF
Amazon Catalyst
Toyota Motor Corporation
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Applied Mathematics,Electrical and Electronic Engineering,Computer Science Applications
Link
https://ieeexplore.ieee.org/ielam/7693/10098671/9911178-aam.pdf
Reference46 articles.
1. Finite-Alphabet Signature Design for Grant-Free NOMA: A Quantized Deep Learning Approach
2. Deep Neural Network-Based Active User Detection for Grant-Free NOMA Systems
3. Deep Residual Learning Meets OFDM Channel Estimation
4. Model-Based Compressive Sensing
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