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

Kurzfassung

Non-terrestrial networks (NTN) are key to the 6GInternet 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 IoTNTN scenarios. The method outperforms a traditional correlatorbased approach, showing superior detection performance under harsh conditions. Our results highlight the potential of machine learning for enhancing IoT connectivity over NTN.