Predicting Bit Error Rate from Meta Information using Random Forests

TL;DR

This paper introduces a Random Forest-based system to predict Bit Error Rate from meta-information, enabling better selection of interference mitigation techniques in signal processing.

Contribution

The paper presents a novel RF-based recommendation system for BER prediction that outperforms heuristic methods and identifies key predictive attributes.

Findings

RF predicts BER with high accuracy

Attribute importance reveals key meta-information factors

Prediction improves selection of mitigation strategies

Abstract

With the increasing power of machine learning-based reasoning, the use of meta-information (e.g., digital signal modulation parameters, channel conditions, etc.) to predict the performance of various signal processing techniques has become feasible. One such problem of practical interest is choosing a proper interference mitigation method based on the meta information of the received signal. Since heuristic table-based methods suffer from limited prediction capability for unseen cases, we propose a recommendation system based on the use of Random Forests (RF). Specifically, RF used to predict the Bit-Error-Rate (BER) of all mitigation approaches so as to determine the approach with the best performance. We found RF can predict BER with high accuracy, and its importance factor demonstrates which input attributes matter most. These BER prediction results can also benefit other functions such as adaptive modulation, channel sensing, beaming selection, etc.