Deep Learning in Downlink Coordinated Multipoint in New Radio Heterogeneous Networks
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We propose a method to improve the performance of the downlink coordinated multipoint (DL CoMP) in heterogeneous fifth generation New Radio (NR) networks. The 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. Through simulation, we show that the proposed method outperforms industry standards in a realistic and scalable heterogeneous cellular environment.
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