Dang, Xiaobing, Xiang, Wei, Yuan, Lei, Yang, Yuan, Cheng, Peng, and Hernandez, Alvaro (2025) Model-driven Deep Learning for Massive Access in Internet of Things Networks. IEEE Transactions on Communications, 73 (11). pp. 11259-11273.

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Abstract

In the context of massive machine-type communications (mMTC) within the Internet of Things (IoT), joint activity detection and channel estimation (JADCE) is a key challenge in enabling massive access due to sporadic device access patterns. In this work, we consider both single-antenna and multiple-antenna base station scenarios and formulate the JADCE problem using the least absolute shrinkage and selection operator (LASSO) framework. To address this problem, we propose a model-driven network that utilizes compressive sensing (CS) and deep learning techniques. Specifically, we first design a prediction-correction alternating direction method of multipliers (PC-ADMM) as the underlying algorithm of the model-driven network. Then, the network is developed based on the PC-ADMM and is designed to be complex-valued. Furthermore, we also model the proximal operator, typically used to generate sparse solutions in LASSO, as a channel attention module within the model-driven network to enhance robustness. Numerical results show that the proposed PC-ADMM framework outperforms existing LASSO-based methods in terms of channel estimation and device activity detection.

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