Comparison of Machine Learning Models for Predicting Indoor Materials from Channel Impulse Response

Kocevska, Teodora; Javornik, Tomaž; Švigelj, Aleš; Guan, Ke; Rashkovska, Aleksandra; Hrovat, Andrej

doi:10.23919/softcom55329.2022.9911422

Cited by 2 publications

(1 citation statement)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To validate the proposed approach, we specified a basic set of propagation characteristics of the multipath components (MPCs) as features of the RE signature, and we formalized an initial framework that enables the identification of the material of a single wall [19]. In [20], we evaluated the material identification using a baseline data set. Motivated by the promising results of our early studies, we extended our research to the identification of multiple properties of the propagation environment.…”

Section: Introductionmentioning

confidence: 99%

Identification of Indoor Radio Environment Properties from Channel Impulse Response with Machine Learning Models

et al. 2023

Self Cite

View full text Add to dashboard Cite

The design and optimization of next-generation indoor wireless communication networks require detailed and precise descriptions of the indoor environments. Environmental awareness can serve as a fundamental basis for the dynamic adaptation of the wireless system to channel conditions and can improve the system’s performance. Methods that combine wireless technology with machine learning are promising for identifying the properties of the indoor radio environment (RE) without requiring specialized equipment or manual intervention. In the paper, we propose an approach for identifying the materials of the surfaces using channel impulse response (CIR) and RE identification models built with machine learning. To train the models and assess their performance, we acquired radio propagation data from rooms with different sizes and materials using ray tracing. We explored tree-based methods, ensemble-based methods, kernel-based methods, and neural networks for training the models. The performance of the models is evaluated in three realistic scenarios defined by the location of the radio nodes and the room sizes. The multilayer perceptron models performed best in most of the evaluation settings. The results show that the models are capable of accurately predicting the materials in rooms with sizes that were not included in the training procedure. Including CIRs from a large number of rooms with different sizes and surface materials estimated with different radio node positions in the training process results in models with wider practical applicability.

show abstract

Section: Introductionmentioning

confidence: 99%