March 3, 2026

Enhancing IoT Connectivity in NTN: A Machine Learning Approach for Packet Detection in Grant-Free Access

Key Points

Improved packet detection performance suggests a dominant role for machine learning in IoT communications.
The convolutional neural network showed better outcomes compared to traditional correlator methods in detection.
Assessment of methods focused on grant-free access, common in non-terrestrial network environments and IoT.
Implications indicate that machine learning could significantly enhance connectivity, especially in challenging conditions.

Abstract

Non-terrestrial networks (NTN) are key to the 6G-Internet of Things (IoT) ecosystem, providing broader coverage and reliable connectivity. As IoT adoption grows, the demand for scalable communication solutions increases. IoT communications, relying on grant-free random access protocols, face challenges in packet detection due to uncoordinated transmissions. Traditional detection methods struggle in NTN environments, especially due to interference and Doppler shifts. This paper proposes a convolutional neural network for efficient packet detection in IoT-NTN scenarios. The method outperforms a traditional correlator-based approach, showing superior detection performance under harsh conditions. Our results highlight the potential of machine learning for enhancing IoT connectivity over NTN.

Enhancing IoT Connectivity in NTN: A Machine Learning Approach for Packet Detection in Grant-Free Access

Key Points

Abstract

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