An improved approximation for the Nakagami-m inverse CDF using artificial bee colony optimization

Kabalcı, Yasin

doi:10.1007/s11276-016-1396-7

Cited by 9 publications

(24 citation statements)

References 19 publications

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“…However, the expression proposed in this paper is just a polynomial containing three simple elementary functions and four parameters which we need to compute. Compared with References [4][5][6], our simulation results prove the strengths of our approximation. (2) We propose the generalized opposition-based learning strategy in quantum space, which is first proposed in the literature.…”

Section: Introductionsupporting

confidence: 60%

“…Mathematical Problems in Engineering correspondingly, such as the genetic algorithm [4], backtracking search optimization algorithm [5], and artificial bee colony optimization [6]. Bilim and Develi [4] proposed an expression consisting of a cubic polynomial and a linear polynomial and then used genetic algorithm to compute its total five coefficients.…”

Section: Related Workmentioning

confidence: 99%

“…Bilim and Develi [4] proposed an expression consisting of a cubic polynomial and a linear polynomial and then used genetic algorithm to compute its total five coefficients. Kabalci [5,6] investigated a more concise expression with four coefficients. Meanwhile, he adopted backtracking search optimization algorithm and artificial bee colony optimization to solve this curve-fitting problem, respectively.…”

Section: Related Workmentioning

confidence: 99%

“…Furthermore, the channel modelled with Nakagami-m distribution can converge to a nonfading AWGN channel, and different parameters even make it a close approximation to the Hoyt and Rice distribution. Another significant feature is that the Nakagami-m distribution is a perfect statistical model to fit scintillating ionospheric radio links, indoor-mobile multipath propagation, and land-mobile multipath propagation [4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

“…However, it is difficult to utilize Nakagami-m quantile function firsthand to operate these calculations because the exact closed-form expression of Nakagami-m quantile function does not exist. In recent years, a new method to approximate the quantile function of Nakagami-m distribution by dint of the curvefitting method has been illustrated, and scholars have utilized several metaheuristic optimization algorithms to compute the coefficients of their expressions correspondingly, such as the genetic algorithm [4], backtracking search optimization algorithm [5], and artificial bee colony optimization [6].…”

Section: Introductionmentioning

confidence: 99%

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An Efficient Approximation for Nakagami‐m Quantile Function Based on Generalized Opposition‐Based Quantum Salp Swarm Algorithm

Gao

Hou

Zhang

et al. 2019

Mathematical Problems in Engineering

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With the further research in communication systems, especially in wireless communication systems, a statistical model called Nakagami-mdistribution appears to have better performance than other distributions, including Rice and Rayleigh, in explaining received faded envelopes. Therefore, the Nakagami-mquantile function plays an important role in numerical calculations and theoretical analyses for wireless communication systems. However, it is quite difficult to operate numerical calculations and theoretical analyses because Nakagami-mquantile function has no exact closed-form expression. In order to obtain the closed-form expression that is able to fit the curve of Nakagami-mquantile function as well as possible, we adopt the method of curve fitting in this paper. An efficient expression for approximating the Nakagami-mquantile function is proposed first and then a novel heuristic optimization algorithm—generalized opposition-based quantum salp swarm algorithm (GO-QSSA)—which contains quantum computation, intelligence inspired by salp swarm and generalized opposition-based learning strategy in quantum space, to compute the coefficients of the proposed expression. Meanwhile, we compare GO-QSSA with three swarm intelligence algorithms: artificial bee colony algorithm (ABC), particle swarm optimization algorithm (PSO), and salp swarm algorithm (SSA). The comparing simulation results reveal that GO-QSSA owns faster convergence speed than PSO, ABC, and SSA. Moreover, GO-QSSA is capable of computing more accurately than traditional algorithms. In addition, the simulation results show that compared with existing curve-fitting-based methods, the proposed expression decreases the fitting error by roughly one order of magnitude in most cases and even higher in some cases. Our approximation is proved to be simple and efficient.

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