Data-aided Active User Detection with a User Activity Extraction Network for Grant-free SCMA Systems
Author:
Affiliation:
1. Korea University,School of Electrical Engineering,Seoul,Republic of Korea
2. Samsung Research,Seoul,Republic of Korea
Funder
National Research Foundation of Korea
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10008390/10008474/10008640.pdf?arnumber=10008640
Reference11 articles.
1. SCMA Codebook Design;taherzadeh;2014 IEEE 80th Vehicular Technology Conference (VTC2014-Fall),2017
2. Deep Learning-Based Active User Detection for Grant-free SCMA Systems
3. Deep Learning-based Codebook Design for Code-Domain Non-Orthogonal Multiple Access Approaching Single-User Bit-Error Rate Performance
4. Deep Neural Network-Based Active User Detection for Grant-Free NOMA Systems
5. MAP-Based Active User and Data Detection for Massive Machine-Type Communications
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1. Deep learning-based Collision-aware Multi-user Detection for Grant-free Sparse Code Multiple Access Systems;2023 28th Asia Pacific Conference on Communications (APCC);2023-11-19
2. Adaptive Federated Learning-based Joint Pilot Design and Active User Detection in Scalable Cell-free Massive MIMO Systems;2023 4th Information Communication Technologies Conference (ICTC);2023-05-17
3. On the Performance of Deep Learning-based Data-aided Active User Detection for GF-SCMA System;2022 27th Asia Pacific Conference on Communications (APCC);2022-10-19
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