Modern wireless networks must reliably support a wide array of connectivity
demands, encompassing various user needs across diverse scenarios. Machine-Type
Communication (mMTC) is pivotal in these networks, particularly given the
challenges posed by massive connectivity and sporadic device activation
patterns. Traditional grant-based random access (GB-RA) protocols face
limitations due to constrained orthogonal preamble resources. In response, the
adoption of grant-free random access (GF-RA) protocols offers a promising
solution. This paper explores the application of supervised machine learning
models to tackle activity detection issues in scenarios where non-orthogonal
preamble design is considered. We introduce a data-driven algorithm
specifically designed for user activity detection in Cell-Free Massive
Multiple-Input Multiple-Output (CF-mMIMO) networks operating under GF-RA
protocols. Additionally, this study presents a novel clustering strategy that
simplifies and enhances activity detection accuracy, assesses the resilience of
the algorithm to input perturbations, and investigates the effects of adopting
floating-to-fixed-point conversion on algorithm performance. Simulations
conducted adhere to 3GPP standards, ensuring accurate channel modeling, and
employ a deep learning approach to boost the detection capabilities of mMTC
GF-RA devices. The results are compelling: the algorithm achieves an
exceptional 99\% accuracy rate, confirming its efficacy in real-world
applications.

本文利用监督式机器学习模型来解决非正交前导序列设计条件下的用户活动检测问题，并介绍了一个针对 GF-RA 协议下的无线电波多输入多输出（CF-mMIMO）网络的数据驱动算法和新颖的聚类策略，模拟实验结果显示算法在真实场景中具有 99% 的准确率。