Learning-Based Iterative Interference Cancellation for Cognitive Internet of Things

This paper is concerned with a machine learning approach to cancel the interference for cognitive Internet of Things (C-IoT) in the concurrent spectrum access (CSA) model, where the C-IoT system is noncooperative and has very limited knowledge on the interference. Our transceiver design uses an iter...

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Veröffentlicht in:	IEEE internet of things journal 2019-08, Vol.6 (4), p.7213-7224
Hauptverfasser:	Liu, Yi, Kuai, Xiaoyan, Yuan, Xiaojun, Liang, Ying-Chang, Zhou, Liang
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Clustering Clustering algorithms Cognitive Internet of Things (C-IoT) Constraints Decoding Demodulation Gaussian mixture model (GMM)-based expectation–maximization (EM) algorithms Interference Interference cancellation Internet of Things Iterative algorithms Iterative methods Machine learning Modulation Modules noncoordination Performance enhancement Receivers
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