Machine Learning in Downlink Coordinated Multipoint in Heterogeneous Networks
This work addresses performance optimization in 5G networks for telecom operators and users, but it is incremental as it applies existing ML methods to a specific network scenario.
The paper tackles the problem of improving downlink coordinated multipoint (DL CoMP) in heterogeneous 5G NR networks by proposing an algorithm that uses online machine learning with SVM classifiers to enhance user downlink throughput. Simulation results show improvements in macro and pico base station downlink throughputs due to informed triggering of multiple radio streams.
We propose a method for downlink coordinated multipoint (DL CoMP) in heterogeneous fifth generation New Radio (NR) networks. The primary contribution of our paper is an algorithm to enhance the trigger of DL CoMP using online machine learning. We use support vector machine (SVM) classifiers to enhance the user downlink throughput in a realistic frequency division duplex network environment. Our simulation results show improvement in both the macro and pico base station downlink throughputs due to the informed triggering of the multiple radio streams as learned by the SVM classifier.