A PSO-Based Hybrid Adaptive Equalization Algorithm for Asynchronous Cooperative Communications

Wang, Jieling; Zhi, Kaiyu; Zhang, Rui

doi:10.1007/s11277-019-06699-y

Cited by 4 publications

(1 citation statement)

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“…HPSO combines PSO with other optimization algorithms such as the GA or simulated annealing (SA) [ 134 ]. This integration enables leveraging the strengths of different algorithms [ 135 ], allowing efficient navigation of the tap weight space and achieving improved convergence and adaptation in the presence of frequency response variations [ 136 ]. Cooperative PSO introduces cooperation mechanisms among particles, facilitating information exchange and adaptation based on shared knowledge.…”

Section: Pso Techniques For Adaptive Equalizationmentioning

confidence: 99%

Adaptive Filtering: Issues, Challenges, and Best-Fit Solutions Using Particle Swarm Optimization Variants

Khan,

Shafi,

Khawaja

et al. 2023

Sensors

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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.

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Section: Pso Techniques For Adaptive Equalizationmentioning

confidence: 99%