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 $1\times 1$ 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.

中文翻译：

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从 MMV 学习设备活动的 CNN 架构

设备活动检测已被广泛研究用于免授权的大规模机器类型通信。这封信没有使用深度多层感知 (MLP) 网络，而是提出了一种新颖的卷积神经网络 (CNN) 架构，用于从多测量向量 (MMV) 中学习设备活动。随着使用 $1\乘以 1$ 卷积层，提议的 CNN 可以充分利用多个测量之间共享稀疏性的全部潜力。大量模拟表明，所提出的 CNN 在性能和计算复杂度方面都优于现有的深度 MLP 网络，尤其是在测量次数增加时。