Exploiting Classifier Diversity for Efficient Grant-Free Random Access
Author:
Affiliation:
1. Xidian University,State Key Laboratory of ISN,Xi’an,China,710071
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9837954/9838246/09838561.pdf?arnumber=9838561
Reference12 articles.
1. Block Sparse Bayesian Learning Based Joint User Activity Detection and Channel Estimation for Grant-Free NOMA Systems
2. Active user detection and channel estimation in uplink CRAN systems
3. High-Order Contrasts for Independent Component Analysis
4. Joint User Identification, Channel Estimation, and Signal Detection for Grant-Free NOMA
5. A matrix-theoretic approach for analyzing quasi-cyclic low-density parity-check codes
Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Variational Bayesian Inference Clustering-Based Joint User Activity and Data Detection for Grant-Free Random Access in mMTC;IEEE Internet of Things Journal;2023-06-01
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