A Particle Swarm Optimization based Training Algorithm for MCMA Blind Adaptive Equalizer

Sahu, Jyoti; Majumder, Saikat

doi:10.1109/esci50559.2021.9396935

Cited by 8 publications

(3 citation statements)

References 10 publications

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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 ].…”

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%

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

Khan,

Shafi,

Khawaja

et al. 2023

Sensors

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“…The output of the equalizer is divided into in-phase and quadrature parts. The cost function can be expressed as follows: 17 J(n)=E{[|Eout,xi(n)false|2−Rx,i2false]2}+E{[|Eout,xq(n)false|2−Rx,q2false]2},where Eout,xi(n) and Eout,xq(n) are the in-phase and quadrature parts of the butterfly filter output signal Eout,x(n), respectively. Rx,i2 and Rx,q2 are the real-valued constant depends on the ideal symbols xsym and given as Rx,i2…”

Section: Principle Of Lite Dspmentioning

confidence: 99%

Low power digital signal processing-lite scheme for short-reach polarization demultiplexing-64 quadrature amplitude modulation coherent optical system

Li,

Ji,

et al. 2023

Opt. Eng.

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Coherent optical communication critically relies on efficient digital signal processing (DSP). We demonstrate a lite DSP scheme to reduce the transmission cost and power consumption, which is mainly realized by reducing the sampling rate and simplifying the DSP algorithm. On the one hand, the baud-rate sampling technique based on the integral circuit can reduce the amount of data processing in DSP. On the other hand, we proposed a simplified joint modified constant modulus algorithm (MCMA) and phase-dependent decision-directed least mean square (DD-LMS) algorithm for adaptive blind polarization demultiplexing and phase recovery in which the butterfly structure is no longer needed for DD-LMS. And we compare the proposed simplified algorithm with the joint traditional CMA and blind phase search and the MCMA, in terms of performance in the 80-Gbaud dual-polarization 64QAM homodyne detection system. The results show that the proposed lite DSP mechanism can effectively reduce the power consumption of DSP by sub-rate sampling and simplifying the corresponding algorithm, which provides an alternative scheme for low-power optical interconnection of data centers.

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“…Therefore, they have become popular research topics in the field of equalizer optimization methods. A modified constant modulus algorithm digital channel equalizer learning algorithm based on PSO is proposed by Sahu (Sahu and Majumder, 2021). The particle swarm algorithm is employed as the training algorithm, resulting in a shorter convergence time and better performance compared to traditional LMS algorithms.…”

Section: Introductionmentioning

confidence: 99%

A simplified decision feedback Chebyshev function link neural network with intelligent initialization for underwater acoustic channel equalization

Zhou,

Zhang,

et al. 2024

Front. Mar. Sci.

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IntroductionIn shallow-water environments, the reliability of underwater communication links is often compromised by significant multipath effects. Some equalization techniques such as decision feedback equalizer, and deep neural network equalizer suffer from slow convergence and high computational complexity.MethodsTo address this challenge, this paper proposes a simplified decision feedback Chebyshev function link neural network equalizer (SDF-CFLNNE). The structure of the SDF-CFLNNE employs Chebyshev polynomial function expansion modules to directly and non-linearly transform the input signals into the output layer, without the inclusion of hidden layers. Additionally, it feeds the decision signal back to the input layer rather than the function expansion module, which significantly reduces computational complexity. Considering that, in the training phase of neural networks, the random initialization of weights and biases can substantially impact the training process and the ultimate performance of the network, this paper proposes a chaotic sparrow search algorithm combining the osprey optimization algorithm and Cauchy mutation (OCCSSA) to optimize the initial weights and thresholds of the proposed equalizer. The OCCSSA utilizes the Piecewise chaotic population initialization and combines the exploration strategy of the ospreywith the Cauchy mutation strategy to enhance both global and local search capabilities. RseultsSimulations were conducted using underwater multipath signals generated by the Bellhop Acoustic Toolbox. The results demonstrate that the performance of the SDFCFLNNE initialized by OCCSSA surpasses that of CFLNN-based and traditional nonlinear equalizers, with a notable improvement of 2-6 dB in terms of signal-to-noise ratio at a bit error rate (BER) of 10−4 and a reduced mean square error (MSE). Furthermore, the effectiveness of the proposed equalizer was validated using the lake experimental data, demonstrating lower BER and MSE with improved stability. DiscussionThis underscores thepromise of employing the SDFCFLNNE initialized by OCCSSA as a promising solution to enhance the robustness of underwater communication in challenging environments.

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A Particle Swarm Optimization based Training Algorithm for MCMA Blind Adaptive Equalizer

Cited by 8 publications

References 10 publications

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

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

Low power digital signal processing-lite scheme for short-reach polarization demultiplexing-64 quadrature amplitude modulation coherent optical system

A simplified decision feedback Chebyshev function link neural network with intelligent initialization for underwater acoustic channel equalization

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