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Dimensioning cellular IoT network using stochastic geometry and machine learning techniques

Abstract : Narrowband Internet of Things (NB-IoT) is a Low Power Wide Area technology, which was standardized in the Third Generation Partnership Project release, specifies a new random access procedure and a new transmission scheme for IoT. The advantages of the NB-IoT network are providing deep coverage, low power consumption, and support of a huge number of connections. Especially, NB-IoT can efficiently connect up to 50,000 devices per NB-IoT network cell.We focus our work on the study of NB-IoT network dimensioning. In this regard, we use stochastic geometry and machine learning techniques along with the thesis to characterize key performance indicators of the NB-IoT network, such as coverage probability, the number of required radio resource blocks, and the traffic pattern recognition and prediction based on the downlink control information. The thesis is divided into three major studies. Firstly, we derive the performance of uplink coverage probability in single-cell and multi-cell of NB-IoT network. The analytical expressions of the coverage and successful access probabilities in a single-cell NB-IoT network are presented by considering the packet arrival distribution. In the multi-cell scenario, a prediction of coverage probability is determined directly from the network parameters by using a Deep Neural Network. The subsequent analysis consists of an analytical model to calculate the required radio resource blocks in the multi-cell NB-IoT network and determine the network outage probability. This model is beneficial for operators because it clarifies how they should manage the available spectrum. Finally, the thesis addresses the recognition and prediction traffic type problems using the data collected from the Downlink Control Information. A wide group of machine learning algorithms are implemented and compared to identify the highest performances.The analysis conducted in this thesis demonstrates that stochastic geometry and machine learning techniques can serve as powerful tools to analyze the performance of the NB-IoT network. The frameworks developed in this work provide general analytical tools that can be readily extended to facilitate other research in 5G networks.
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Submitted on : Tuesday, October 26, 2021 - 3:42:12 PM
Last modification on : Wednesday, October 27, 2021 - 3:11:38 AM


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  • HAL Id : tel-03404319, version 1



Tuan Anh Nguyen. Dimensioning cellular IoT network using stochastic geometry and machine learning techniques. Networking and Internet Architecture [cs.NI]. Institut Polytechnique de Paris, 2021. English. ⟨NNT : 2021IPPAT014⟩. ⟨tel-03404319⟩



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