Fitting method of wireless channel attenuation model based on quantum sea squirt group

Abstract

The present invention relates to a wireless channel attenuation model fitting method based on a quantum ascidian group, specifically: setting parameters of Nakagami-m distribution and obtaining an accurate data set of Nakagami-m inverse cumulative distribution; initializing the quantum position and position of the ascidian group; Evaluate the fitness of all sea squirts, and determine the quantum position and position of the food; update the quantum rotation angle, quantum position and position of the selected sea squirts in sequence according to strategy 1 or strategy 2; update the selected sea squirts in sequence according to strategy 3 Quantum rotation angle, quantum position and position; evaluate the fitness of all sea squirts, and update the quantum position and position of food; the final output food position is the best coefficient of the fitting equation, and Nakagami‑m inverse accumulation can be obtained The best-fit equation for the distribution function. The invention has higher fitting precision, faster fitting speed and wider application range.

Description

technical field [0001] The invention relates to a wireless channel attenuation model fitting method, in particular to a wireless channel attenuation model fitting method based on quantum sea squirt groups, and relates to the field of wireless communication. Background technique [0002] In wireless communication, the attenuation of signal is an important reason that affects the quality of communication system, so the simulation of signal attenuation is an extremely important part in the simulation and theoretical analysis of communication system. Since the reasons affecting signal attenuation are different in different scenarios, the statistical distribution models used to simulate signal attenuation in different scenarios are also different. The commonly used distribution models include unilateral Gaussian distribution, Rice distribution and Ray profit distribution, etc. However, with the in-depth research of many scholars, it is found that the Nakagami-m distribution can ...

AI Technical Summary

Problems solved by technology

At the same time, because the existing fitting methods such as genetic algorithm, artificial bee colony optimization and backtracking search methods have different degrees of slow convergence speed and low optimization accuracy, a new intelligent method of sea squirt swarm intelligence is considered. Combining quantum computing to obtain the dual advantages of the two, greatly improving the optimization speed, so as to solve the Nakagami-m inverse cumulative distribution more efficiently

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