TY - JOUR
T1 - Toward AI-Enhanced VLC Systems for Industrial Applications
AU - Da Silva Costa, Wesley
AU - Camporez, Higor
AU - Hinrichs, Malte
AU - Rocha, Helder
AU - Pontes, Maria
AU - Segatto, Marcelo
AU - Paraskevopoulos, Anagnostis
AU - Jungnickel, Volker
AU - Freund, Ronald
AU - Lima Silva, J.A.
PY - 2023/2/15
Y1 - 2023/2/15
N2 - In this paper, artificial intelligence tools are implemented in order to predict trajectory positions, as well as channel performance of an optical wireless communications link. Case studies for industrial scenarios are considered to this aim. In a first stage, system parameters are optimized using a hybrid multi-objective optimization (HMO) procedure based on the grey wolf optimizer and the non-sorting genetic algorithm III with the goal of simultaneously maximizing power and spectral efficiency. In a second stage, we demonstrate that a long short-term memory neural network (LSTM) is able to predict positions, as well as channel gain. In this way, the VLC links can be configured with the optimal parameters provided by the HMO. The success of the proposed LSTM architectures was validated by training and test root-mean square error evaluations below 1%.
AB - In this paper, artificial intelligence tools are implemented in order to predict trajectory positions, as well as channel performance of an optical wireless communications link. Case studies for industrial scenarios are considered to this aim. In a first stage, system parameters are optimized using a hybrid multi-objective optimization (HMO) procedure based on the grey wolf optimizer and the non-sorting genetic algorithm III with the goal of simultaneously maximizing power and spectral efficiency. In a second stage, we demonstrate that a long short-term memory neural network (LSTM) is able to predict positions, as well as channel gain. In this way, the VLC links can be configured with the optimal parameters provided by the HMO. The success of the proposed LSTM architectures was validated by training and test root-mean square error evaluations below 1%.
KW - light emitting diodes
KW - nonlinear optics
KW - optical filters
KW - optical sensors
KW - optical transmitters
KW - optimization
KW - visible light communication
KW - artificial intelligence
KW - lichtgevende diodes
KW - niet-lineaire optica
KW - optische filters
KW - optische sensoren
KW - optische zenders
KW - optimalisatie
KW - zichtbare lichtcommunicatie
KW - kunstmatige intelligentie
UR - https://ieeexplore.ieee.org/document/9998061/
U2 - 10.1109/JLT.2022.3231791
DO - 10.1109/JLT.2022.3231791
M3 - Article
SN - 1558-2213
VL - 41
SP - 1064
EP - 1076
JO - Journal of Lightwave Technology
JF - Journal of Lightwave Technology
IS - 4
M1 - 9998061
ER -