Genetic Grey Wolf Optimizer Based Channel Estimation in Wireless Communication System

Sujitha, J.; Baskaran, K.

doi:10.1007/s11277-017-5161-8

Cited by 16 publications

(6 citation statements)

References 23 publications

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“…This experimental analysis has undergone with the population count as 10 and maximum iterations count as 100 for the proposed channel estimation model. The proposed AIMR‐CSO was compared with other meta‐heuristic algorithms like “Particle Swarm Optimization (PSO), 27 Grey Wolf Optimizer (GWO), 28 Jaya Algorithm (JA), 29 CSO 26 and also with different estimation methods like CNN, 30 NN, 33 DNN, 36 RNN 39 and DELM 42 …”

Section: Resultsmentioning

confidence: 99%

“…In 2011, Nuri and Tas ¸pinar 27 have developed a Particle Swarm Optimization (PSO) for optimizing the power and placement of the comb-type pilot tones, which was helpful for enhancing the performance of the LS channel estimation of the MIMO-OFDM systems. In 2018, Sujitha and Baskaran 28 have implemented a hybrid mechanism by incorporating the genetic algorithms (GAs) and grey wolf optimization (GWO) to estimate the channel that belongs to the MIMO-OFCDM schemes.…”

Section: Related Workmentioning

confidence: 99%

“…At last, these velocity values are required to be updated. The velocities are updated in whole dimensions based on Equation (28).…”

Section: Proposed Si-delm-based Channel Estimationmentioning

confidence: 99%

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Swarm intelligence‐based deep ensemble learning machine for efficient channel estimation in MIMO communication systems

Manasa

Venugopal

2022

Int J Communication

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Swarm intelligence‐based deep ensemble learning machine for efficient channel estimation in MIMO communication systems

Manasa

Venugopal

2022

Int J Communication

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“…In [25]- [28], the GA and PSO algorithms were adopted to optimize pilot allocation for channel estimation in OFDM systems, as well as ABC [29], GWO [30], and firefly algorithm (FA) [31]. Moreover, a few hybrid techniques with various basic algorithms have been proposed to obtain better performance, such as cooperative PSO (CPSO) [32], modified adaptive GA (MAGA) [33], PSO-GA [34], and GWO-GA [35]. However, almost all these studies are based on LS algorithm by minimizing the estimation error of mean square error (MSE) criterion.…”

Section: A Related Workmentioning

confidence: 99%

Joint Compressed Sensing and Enhanced Whale Optimization Algorithm for Pilot Allocation in Underwater Acoustic OFDM Systems

et al. 2019

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In underwater acoustic-orthogonal frequency division multiplexing (UWA-OFDM) systems, the performance of channel estimation is significantly affected by pilot allocation in the framework of compressed sensing (CS). However, for optimizing the pilot allocation, an exhaustive search method over all possible allocations is computationally prohibitive and random search method may not ensure convergence accuracy. In this paper, the meta-heuristic algorithm of the whale optimization algorithm (WOA) is employed to address this issue. For reinforcing the capability of balancing exploration and exploitation, an enhanced variant of WOA termed EWOA is presented with four optimization strategies. After that, a joint algorithm combining CS with EWOA (CS-EWOA) is proposed for pilot allocation in UWA-OFDM systems. Through extensive simulations, the improvement of EWOA is demonstrated on the majority of benchmark functions over other well-known meta-heuristic algorithms. With regard to bit error rate (BER) and mean square error (MSE) for channel estimation, the proposed CS-EWOA algorithm outperforms the equispaced, random, genetic algorithm (GA), particle swarm optimization (PSO), and WOA-based pilot allocation methods. Moreover, it is robust with varying system subcarriers and channel models. Furthermore, the CS-EWOA exhibits superior convergence performance without increasing the computational complexity compared with the GA-, PSO-, and WOA-based methods in the iteration process of pilot allocation optimization. It can be concluded from the simulation results that the proposed CS-EWOA algorithm is competitive to optimize pilot allocation for channel estimation in UWA-OFDM systems.

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“…However, it employs a complex methodology. In terms of successful applications of the GWO, representative application research can be summarized as flow shop scheduling [32], machine learning [33][34][35][36], economic load dispatch [37], robotics and path planning [38,39], channel estimation in wireless communication systems [40], and other applications detailed in References [24,25]. eoretical and practical research has shown the potential of the GWO algorithm in real life.…”

Section: Introductionmentioning

confidence: 99%

Dynamically Dimensioned Search Grey Wolf Optimizer Based on Positional Interaction Information

Jia

Yun

2019

Complexity

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The grey wolf optimizer (GWO) algorithm is a recently developed, novel, population-based optimization technique that is inspired by the hunting mechanism of grey wolves. The GWO algorithm has some distinct advantages, such as few algorithm parameters, strong global optimization ability, and ease of implementation on a computer. However, the paramount challenge is that there are some cases where the GWO is prone to stagnation in local optima. This drawback of the GWO algorithm may be attributed to an insufficiency in its position-updated equation, which disregards the positional interaction information about the three best grey wolves (i.e., the three leaders). This paper proposes an improved version of the GWO algorithm that is based on a dynamically dimensioned search, spiral walking predation technique, and positional interaction information (referred to as the DGWO). In addition, a nonlinear control parameter strategy, i.e., the control parameter that is nonlinearly increased with an increase in iterations, is designed to balance the exploration and exploitation of the GWO algorithm. The experimental results for 23 general benchmark functions and 3 well-known engineering optimization design applications validate the effectiveness and feasibility of the proposed DGWO algorithm. The comparison results for the 23 benchmark functions show that the proposed DGWO algorithm performs significantly better than the GWO and its improved variant for most benchmarks. The DGWO provides the highest solution precision, strongest robustness, and fastest convergence rate among the compared algorithms in almost all cases.

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Genetic Grey Wolf Optimizer Based Channel Estimation in Wireless Communication System

Cited by 16 publications

References 23 publications

Swarm intelligence‐based deep ensemble learning machine for efficient channel estimation in MIMO communication systems

Swarm intelligence‐based deep ensemble learning machine for efficient channel estimation in MIMO communication systems

Joint Compressed Sensing and Enhanced Whale Optimization Algorithm for Pilot Allocation in Underwater Acoustic OFDM Systems

Dynamically Dimensioned Search Grey Wolf Optimizer Based on Positional Interaction Information

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