IEEE Wireless Communications Letters,
accepted for publication.
Deep Learning in Downlink Coordinated Multipoint in New Radio Heterogeneous Networks
Faris B. Mismar and
Brian L. Evans
Department of Electrical and Computer Engineering,
Wireless Networking and Communications Group,
The University of Texas at Austin,
Austin, TX 78712 USA
Paper Draft (on arXiv.org)
Multiantenna Communications Project
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
This surrogate function enhances the downlink user throughput distribution
through online learning of non-linear interactions of features and lower bias
In simulation, the proposed method outperforms industry standards
in a realistic and scalable heterogeneous cellular environment.
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Last Updated 03/09/19.