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In this paper, we explore the use of machine learning methods as an efficientalternative to correlation in performing packet detection. Targetingsatellite-based massive machine type communications and internet of thingsscenarios, our focus is on a common channel shared among a large number ofterminals via a fully asynchronous ALOHA protocol to attempt delivery of shortdata packets. In this setup, we test the performance of two algorithms, neuralnetworks and random forest, which are shown to provide substantial improvementsover {traditional} techniques. Excellent performance is demonstrated in termsof detection and false alarm probability also in the presence of collisionsamong user transmissions. The ability of machine learning to extract furtherinformation from incoming signals is also studied, discussing the possibilityto classify detected preambles based on the level of interference they undergo.

Grant-Free Access: Machine Learning for Detection of Short Packets