News Release

Pusan National University researchers propose backscatter communication technique for low-power internet of things communication

The new system is 40% more energy-efficient than conventional backscattering systems and enables integrated sensing and communication technology

Peer-Reviewed Publication

Pusan National University

Optimizing Backscatter Communication with Machine Learning and Polarization Diversity


The researchers used circuit modeling, advanced modulation techniques, and polarization diversity to design a MIMO transceiver system for BackCom applications, achieving a spectral efficiency of 2.0 bps/Hz and improving energy efficiency by 40% compared to conventional techniques.

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Credit: Professor Sangkil Kim from Pusan National University

Backscatter communication (BackCom) is a promising low-power method for the widespread adoption of the Internet of Things (IoT) technologies, where connected devices reflect and modulate existing signals by altering their load impedance, rather than generating signals themselves. To achieve low bit error rates and high data rates, higher-order modulation schemes such as Quadrature Amplitude Modulation (QAM) are selected based on accurately modeled reflection coefficients. However, discrepancies between simulations and real-world measurements make it challenging to accurately predict the optimal reflection coefficient.

In a recent study, a research team led by Professor Sangkil Kim from the Department of Electronics Engineering at Pusan National University used transfer learning to accurately model the in-phase/quadrature or I/Q load modulators. Additionally, they introduced polarization diversity to design a BackCom system that utilizes multiple antennas for simultaneous signal transmission and reception. Their paper was made available online on 20 March 2024 and published in Volume 11, Issue 12 of the IEEE Internet of Things Journal on 15 June 2024.

“As the technology for more efficient and reliable backscatter communication improves, it lowers the barrier for IoT adoption across numerous industries. This could lead to a proliferation of IoT devices and integrated sensing and communication (ISC), facilitating smart cities, more efficient industries, and enhanced personal and public services,” says Prof. Kim.

Transfer learning involves applying knowledge gained from one task to enhance performance on a related task. The researchers pretrained an artificial neural network (ANN) using simulated input bias voltages (VI and VQ). This initial training step familiarized the ANN with the load modulator behaviors across varying voltage conditions. The knowledge gained from the pretraining step was then used in a main training step, where the ANN was trained using experimental data to predict reflection coefficients based on VI and VQ inputs.

This transfer of knowledge enabled the ANN to improve its predictions, achieving a minimal deviation of only 0.81% between modeled and measured reflection coefficients. Using these accurate models, researchers selected optimal 4- and 16-QAM schemes by aligning predicted reflection coefficients with specific points in the QAM constellation. This optimization ensured energy-efficient data transmission, with total consumption below 0.6 mW, much lower than conventional wireless systems.

Following this, the researchers designed a 2 × 2 × 2 MIMO transceiver system for BackCom, featuring two transmit and two receive antennas with different polarizations (such as vertical and horizontal). This setup enhances signal reception, throughput, and efficiency in BackCom. Utilizing a dual-polarized Vivaldi antenna, the team achieved a high gain exceeding 11.5 dBi and effective cross-polarization suppression of 18 dB.

The researchers tested their algorithm and MIMO BackCom system in the 5.725 GHz to 5.875 GHz C-band of the Industrial, Scientific, and Medical band, offering a 150 MHz bandwidth. Their approach achieved a spectral efficiency of 2.0 bps/Hz using 4-QAM modulation, demonstrating effective bandwidth utilization. They also attained an error vector magnitude of 9.35%, indicating high reliability and efficiency in data transmission.

“The combination of accurate circuit modeling, advanced modulation techniques, and polarization diversity, all tested in over-the-air environments, presents a holistic approach to tackling the challenges in ISC and IoT,” says Prof. Kim.

Overall, the proposed system lays the groundwork for a highly reliable and efficient backscatter system for multiple applications, including consumer electronics, healthcare monitoring, smart infrastructure for urban management, environmental sensing, and even radar communication. 





DOI: 10.1109/JIOT.2024.3379854


ORCID ID: 0000-0003-1720-2410


About the institute
Pusan National University, located in Busan, South Korea, was founded in 1946, and is now the no. 1 national university of South Korea in research and educational competency. The multi-campus university also has other smaller campuses in Yangsan, Miryang, and Ami. The university prides itself on the principles of truth, freedom, and service, and has approximately 30,000 students, 1200 professors, and 750 faculty members. The university is composed of 14 colleges (schools) and one independent division, with 103 departments in all.



About the author
Prof. Sangkil Kim graduated magna cum laude with a B.S. from Yonsei University in 2010. He earned his M.S. and Ph.D. from the Georgia Institute of Technology in 2012 and 2014, respectively. From 2015 to 2018, he was the lead engineer at Qualcomm, pioneering the world’s first 5G mmWave AiP module for mobile devices. In 2018, he joined Pusan National University, where his SWARM lab focuses on advanced mmWave phased antenna arrays, machine learning-enhanced backscattering communication, and RF-photonics systems. Dr. Kim has received several accolades, including the IET Premium Award in 2015 and the KIEES Young Researcher Award in 2019. He is also a member of the IEEE MTT-26 RFID, Wireless Sensors, and IoT Committee.

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