Deep Learning in Downlink Coordinated Multipoint in New Radio Heterogeneous Networks

Faris B. Mismar; Brian L. Evans

doi:10.1109/lwc.2019.2904686

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arXiv · 1812.03421

Deep Learning in Downlink Coordinated Multipoint in New Radio Heterogeneous Networks

Faris B. Mismar·Brian L. Evans

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Abstract

We propose a method to improve the performance of the downlink coordinated multipoint (DL CoMP) in heterogeneous fifth generation New Radio (NR) networks. The standards-compliant method is based on the construction of a surrogate CoMP trigger function using deep learning. The cooperating set is a single-tier of sub-6 GHz heterogeneous base stations operating in the frequency division duplex mode (i.e., no channel reciprocity). This surrogate function enhances the downlink user throughput distribution through online learning of non-linear interactions of features and lower bias learning models. In simulation, the proposed method outperforms industry standards in a realistic and scalable heterogeneous cellular environment.

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BibTeX RIS

Faris B. Mismar, Brian L. Evans. 2019-03-14. Deep Learning in Downlink Coordinated Multipoint in New Radio Heterogeneous Networks. https://doi.org/10.1109/lwc.2019.2904686

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