A CNN Architecture for Learning Device Activity From MMV

被引：3

作者：

Wu, Xiaofu ^{[1
]}

Zhang, Suofei ^{[2
]}

Yan, Jun ^{[1
]}

机构：

[1] Nanjing Univ Posts & Telecommun, Natl Engn Res Ctr Commun & Networking, Nanjing 210003, Peoples R China

[2] Nanjing Univ Posts & Telecommun, Sch Internet Things, Nanjing 210003, Peoples R China

来源：

IEEE COMMUNICATIONS LETTERS | 2021年 / 25卷 / 09期

基金：

中国国家自然科学基金;

关键词：

Activity detection; CNNs; MLPs; deep learning; Internet of Things; GRANT-FREE NOMA; USER DETECTION; ACCESS;

D O I：

10.1109/LCOMM.2021.3091841

中图分类号：

TN [电子技术、通信技术];

学科分类号：

0809 ;

摘要：

Device activity detection has been extensively investigated for grant-free massive machine-type communications. Instead of using deep Multi-Layer Perception (MLP) networks, this letter proposes a novel convolutional neural network (CNN) architecture for learning device activity from multiple-measurement vectors (MMV). With the use of 1 x 1 convolutional layers, the proposed CNN could exploit the full potential of shared sparsity among multiple measurements. Extensive simulations show that the proposed CNN outperforms the existing deep MLP network in both performance and computational complexity, especially when the number of measurements increases.

引用

页码：2933 / 2937

页数：5

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