  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.
