eprintid: 8726 rev_number: 8 eprint_status: archive userid: 2 dir: disk0/00/00/87/26 datestamp: 2023-09-08 23:30:16 lastmod: 2023-09-08 23:30:16 status_changed: 2023-09-08 23:30:16 type: article metadata_visibility: show creators_name: Khan, Arooj creators_name: Shafi, Imran creators_name: Khawaja, Sajid Gul creators_name: de la Torre Díez, Isabel creators_name: López Flores, Miguel Ángel creators_name: Castanedo Galán, Juan creators_name: Ashraf, Imran creators_id: creators_id: creators_id: creators_id: creators_id: miguelangel.lopez@uneatlantico.es creators_id: juan.castanedo@uneatlantico.es creators_id: title: Adaptive Filtering: Issues, Challenges, and Best-Fit Solutions Using Particle Swarm Optimization Variants ispublished: pub subjects: uneat_eng divisions: uneatlantico_produccion_cientifica divisions: uninimx_produccion_cientifica divisions: uninipr_produccion_cientifica divisions: unic_produccion_cientifica full_text_status: public keywords: adaptive filtering; particle swarm optimization; bit error rate; signal quality abstract: 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. date: 2023-09 publication: Sensors volume: 23 number: 18 pagerange: 7710 id_number: doi:10.3390/s23187710 refereed: TRUE issn: 1424-8220 official_url: http://doi.org/10.3390/s23187710 access: open language: en citation: Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica Universidad Internacional Iberoamericana México > Investigación > Producción Científica Universidad Internacional Iberoamericana Puerto Rico > Investigación > Producción Científica Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés 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. metadata Khan, Arooj; Shafi, Imran; Khawaja, Sajid Gul; de la Torre Díez, Isabel; López Flores, Miguel Ángel; Castanedo Galán, Juan y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, miguelangel.lopez@uneatlantico.es, juan.castanedo@uneatlantico.es, SIN ESPECIFICAR (2023) Adaptive Filtering: Issues, Challenges, and Best-Fit Solutions Using Particle Swarm Optimization Variants. Sensors, 23 (18). p. 7710. ISSN 1424-8220 document_url: http://repositorio.unib.org/id/eprint/8726/1/sensors-23-07710-v2.pdf