Coordinated Spectral Efficiency Prediction for Real-World 5G CoMP Systems

TL;DR

This paper presents a data-driven, model-assisted approach for predicting spectral efficiency in real-world 5G CoMP systems, addressing the complexity of channel modeling and network dynamics to optimize resource utilization.

Contribution

It introduces a residual-based neural network model that leverages domain knowledge for effective CSE prediction in complex 5G environments.

Findings

The approach accurately predicts CSE using real-world data.

The model captures high-dimensional non-linear relationships.

Experimental results validate the method's effectiveness.

Abstract

Coordinated multipoint (CoMP) systems incur substantial resource consumption due to the management of backhaul links and the coordination among various base stations (BSs). Accurate prediction of coordinated spectral efficiency (CSE) can guide the optimization of network parameters, resulting in enhanced resource utilization efficiency. However, characterizing the CSE is intractable due to the inherent complexity of the CoMP channel model and the diversity of the 5G dynamic network environment, which poses a great challenge for CSE prediction in real-world 5G CoMP systems. To address this challenge, in this letter, we propose a data-driven model-assisted approach. Initially, we leverage domain knowledge to preprocess the collected raw data, thereby creating a well-informed dataset. Within this dataset, we explicitly define the target variable and the input feature space relevant to channel statistics for CSE prediction. Subsequently, a residual-based network model is built to capture the high-dimensional non-linear mapping function from the channel statistics to the CSE. The effectiveness of the proposed approach is validated by experimental results on real-world data.