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

arXiv: https://arxiv.org/abs/2602.08129 · HTML: https://mlsanity.com/paper/2602.08129/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 20 references that are in the map.
- First posted on arXiv 8 Feb 2026.

## Builds on (most cited first, 7 of 7)

- [Smart Radio Environments Empowered by Reconfigurable Intelligent Surfaces: How it Works, State of Research, and Road Ahead](https://mlsanity.com/paper/2004.09352/) (2020)
- [Diverse Multiple Trajectory Prediction Using a Two-stage Prediction Network Trained with Lane Loss](https://mlsanity.com/paper/2206.08641/) (2022)
- [Variational Wasserstein Clustering](https://mlsanity.com/paper/1806.09045/) (2018)
- [Design of Reconfigurable Intelligent Surfaces by Using S-Parameter Multiport Network Theory -- Optimization and Full-Wave Validation](https://mlsanity.com/paper/2311.06648/) (2023)
- [Learned Global Optimization for Inverse Scattering Problems -- Matching Global Search with Computational Efficiency](https://mlsanity.com/paper/2107.00332/) (2021)
- [Virtual VNA: Minimal-Ambiguity Scattering Matrix Estimation with a Fixed Set of "Virtual" Load-Tunable Ports](https://mlsanity.com/paper/2403.08074/) (2024)
- [Mixtures of ensembles: System separation and identification via optimal transport](https://mlsanity.com/paper/2503.13362/) (2025)

## Abstract (arXiv)

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.