Work

Enabling SWIPT with Machine Learning-Based Multisine Signal Classification

Year: 2025

Type: conference-paper

Abstract: This paper presents a novel methodology for employing multisine waveforms in simultaneous wireless information and power transfer (SWIPT) systems, utilizing software-defined radio tools. The proposed approach encodes information by varying the number of carriers in the multisine signals, while simultaneously enabling the receiver to harvest energy. A comprehensive dataset is generated by transmitting various waveforms and measuring the harvested power across different distances. The primary objectives are to accurately classify the received waveforms to extract information and validate the dataset using established machine learning techniques. Experimental evaluations demonstrate that basic supervised machine learning models, specifically multinomial logistic regression and support vector machine, achieve a high accuracy of 99.2% and 100%, respectively. These results underscore the capability of the proposed system to effectively distinguish not only between binary signal classes but also among multiple signal classes.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.15225384

Authors: Stylianou, Petros, Faddoul, Elio, Korium, Mohamed, Krikidis, Ioannis

Institutions: University of Cyprus, Lappeenranta-Lahti University of Technology

Language: English

Topic: Energy Harvesting in Wireless Networks

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: green

Funder: European Commission