Adjustment of Cluster-Then-Predict Framework for Multiport Scatterer Load Prediction

TL;DR

This paper introduces a two-stage cluster-then-predict framework for multiport scatterer load prediction, significantly reducing error and providing a new metric for performance trade-offs.

Contribution

It proposes a novel cluster-then-predict approach that captures the relation between S-parameters and load impedances, improving prediction accuracy.

Findings

Achieved up to 46% reduction in RMSE with gradient boosting.

K-means clustering combined with KNN is identified as the best setup.

Introduced the Real-world Unified Index (RUI) for performance analysis.

Abstract

Predicting interdependent load values in multiport scatterers is challenging due to high dimensionality and complex dependence between impedance and scattering ability, yet this prediction remains crucial for the design of communication and measurement systems. In this paper, we propose a two-stage cluster-then-predict framework for multiple load values prediction task in multiport scatterers. The proposed cluster-then-predict approach effectively captures the underlying functional relation between S-parameters and corresponding load impedances, achieving up to a 46% reduction in Root Mean Square Error (RMSE) compared to the baseline when applied to gradient boosting (GB). This improvement is consistent across various clustering and regression methods. Furthermore, we introduce the Real-world Unified Index (RUI), a metric for quantitative analysis of trade-offs among multiple metrics with conflicting objectives and different scales, suitable for performance assessment in realistic scenarios. Based on RUI, the combination of K-means clustering and k-nearest neighbors (KNN) is identified as the optimal setup for the analyzed multiport scatterer.