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