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.