基于遗传算法的认知无线网络虚警概率和检测概率优化

2015 IEEE 2nd International Conference on Recent Trends in Information Systems (ReTIS) Pub Date : 2015-07-09 DOI:10.1109/ReTIS.2015.7232852

S. Bhattacharjee, P. Das, S. Mandal, B. Sardar

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引用次数: 22

摘要

本文利用遗传算法优化认知无线网络的检测概率和虚警概率，以最小化集中式认知无线网络中特定SU的错误概率。我们的目标是最小化误差概率，找出占用检测概率或检测概率和虚警概率的最优值。我们使用遗传算法来解决这个优化问题。将结果与差分进化算法进行了比较，结果表明，差分进化算法能找到更好的解，且求优次数要少得多。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Optimization of probability of false alarm and probability of detection in cognitive radio networks using GA

In this paper, we optimize probability of detection and probability of false alarm in cognitive radio network to minimize probability of error of a particular SU in a centralized cognitive radio network using Genetic algorithm (GA). Our objective is to minimize probability of error and find out optimum values of probability of occupancy detection or probability of detection and probability of false alarm. We use Genetic Algorithm to solve this optimization problem. The result is compared with Differential Evolution algorithm and it is evident from the comparison that DE finds better solution and takes much lesser number of evaluations to find optimum solution.

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来源期刊

2015 IEEE 2nd International Conference on Recent Trends in Information Systems (ReTIS)

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