# Deep Learning in Downlink Coordinated Multipoint in New Radio Heterogeneous Networks

## TL;DR

This paper introduces a deep learning-based surrogate function to enhance downlink CoMP performance in 5G heterogeneous networks, improving throughput distribution without relying on channel reciprocity.

## Contribution

It presents a standards-compliant deep learning approach to construct a surrogate CoMP trigger function for heterogeneous NR networks, outperforming existing industry standards.

## Key findings

- Outperforms industry standards in simulations
- Enhances user throughput distribution
- Effective in realistic heterogeneous environments

## 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.