BUSAN,
South Korea, July 10,
2024 /PRNewswire/ -- 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.
Reference
Title of original paper: Polarization
Diversity and Transfer Learning Based Modulation Optimization for
High-Speed Dual Channel MIMO Backscatter Communication
Journal: IEEE Internet of Things Journal
DOI: 10.1109/JIOT.2024.3379854
About the institute
Website:
https://www.pusan.ac.kr/eng/Main.do
Contact:
Jae-Eun
Lee
82 51 510 7928
380154@email4pr.com
View original content to download
multimedia:https://www.prnewswire.com/news-releases/pusan-national-university-researchers-propose-backscatter-communication-technique-for-low-power-internet-of-things-communication-302192447.html
SOURCE Pusan National University