Deep Learning-Based Activity Detection for Grant-Free Random Access

Abstract

The cellular Internet of Things wireless network is a promising solution to provide massive connectivity for machine-type devices. However, designing grant-free random access (GF-RA) protocols to manage such connections is challenging, since they must operate in interference-aware scenarios with sporadic device activation patterns and a shortage of mutually orthogonal resources. Supervised machine learning models have provided efficient solutions for activity detection, noncoherent data detection, and nonorthogonal preamble design in scenarios with massive connectivity. In this article, we develop two deep learning (DL) sparse support recovery algorithms to detect active devices in massive machine-type communication random access. The DL algorithms, developed to deploy GF-RA protocols, are based on the deep multilayer perceptron and the convolutional neural network models. Unlike previous works, we investigate the impact of the type of sequences for preamble design on the activity detection accuracy. Our results reveal that preambles based on the Zadoff–Chu sequences, which present good correlation properties, achieve better activity detection accuracy with the proposed algorithms than random sequences. Besides, we demonstrate that our DL algorithms achieve activity detection accuracy comparable to state-of-the-art techniques with extremely low computational complexity.