IEEE Wireless Communications Letters,
vol. 8, no. 4, Aug. 2019, pp. 1040-1043, DOI 10.1109/LWC.2019.2904686.
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
faris.mismar@utexas.edu -
bevans@ece.utexas.edu
Paper draft on
arXiv.org and
IEEE Explore
Software Release
Multiantenna Communications Project
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.
Errata: In Table V, which has the caption "Downlink Throughput Improvement over Static CoMP",
the entry for SVM in the row for Peak (95%) results should have been 8.5% instead of 0.9%.
The other calculations are correct.
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Last Updated 11/08/20.