Deep Learning in Downlink Coordinated Multipoint in New Radio Heterogeneous Networks
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.
Keywords
Cite
@article{arxiv.1812.03421,
title = {Deep Learning in Downlink Coordinated Multipoint in New Radio Heterogeneous Networks},
author = {Faris B. Mismar and Brian L. Evans},
journal= {arXiv preprint arXiv:1812.03421},
year = {2019}
}
Comments
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