Hidden Markov Model Based Machine Learning for mMTC Device Cell Association in 5G Networks
Journal article, Peer reviewed
Accepted version
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https://hdl.handle.net/11250/2649532Utgivelsesdato
2019Metadata
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Originalversjon
Balapuwaduge, I. A. M. & Li, F. Y. (2019). Hidden Markov Model Based Machine Learning for mMTC Device Cell Association in 5G Networks. IEEE International Conference on Communications. https://doi.org/10.1109/ICC.2019.8761913Sammendrag
Massive machine-type communication (mMTC) is expected to play a pivotal role in emerging 5G networks. Considering the dense deployment of small cells and the existence of heterogeneous cells, an MTC device can discover multiple cells for association. Under traditional cell association mechanisms, MTC devices are typically associated with an eNodeB with highest signal strength. However, the selected eNodeB may not be able to handle mMTC requests due to network congestion and overload. Therefore, reliable cell association would provide a smarter solution to facilitate mMTC connections. To enable such a solution, a hidden Markov model (HMM) based machine learning (ML) technique is proposed in this paper to perform optimal cell association.