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 -

Paper draft on and IEEE Explore

Software Release

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 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.