RT info:eu-repo/semantics/article
T1 Adaptive filtering: Issues, challenges, and best-fit solutions using particle swarm optimization variants
A1 Khan, Arooj
A1 Shafi, Imran
A1 Khawaja, Sajid Gul
A1 Torre Díez, Isabel de la
A1 López Flores, Miguel Angel
A1 Castañedo Galvlán, Juan
A1 Ashraf, Imran
K1 Adaptive filters
K1 Filtros adaptativos
K1 Swarm intelligence
K1 Mathematical optimization
K1 Optimización matemática
K1 Artificial intelligence
K1 Bit error rate
K1 Signal processing
K1 Tratamiento de señal
K1 Information technology
K1 Tecnología de la información
K1 1203.04 Inteligencia Artificial
K1 1203.17 Informática
AB Adaptive equalization is crucial in mitigating distortions and compensating for frequency response variations in communication systems. It aims to enhance signal quality by adjusting the characteristics of the received signal. Particle swarm optimization (PSO) algorithms have shown promise in optimizing the tap weights of the equalizer. However, there is a need to enhance the optimization capabilities of PSO further to improve the equalization performance. This paper provides a comprehensive study of the issues and challenges of adaptive filtering by comparing different variants of PSO and analyzing the performance by combining PSO with other optimization algorithms to achieve better convergence, accuracy, and adaptability. Traditional PSO algorithms often suffer from high computational complexity and slow convergence rates, limiting their effectiveness in solving complex optimization problems. To address these limitations, this paper proposes a set of techniques aimed at reducing the complexity and accelerating the convergence of PSO.
PB MDPI
SN 1424-8220
YR 2023
FD 2023
LK https://uvadoc.uva.es/handle/10324/66743
UL https://uvadoc.uva.es/handle/10324/66743
LA eng
NO Sensors, 2023, Vol. 23, Nº. 18, 7710
NO Producción Científica
DS UVaDOC
RD 24-nov-2024