基于机器学习算法的频谱占用分析
Analysis of Spectrum Occupancy Using Machine Learning Algorithms
摘要：
在本文中，我们使用不同的机器学习技术来分析频谱占用。研究了监督技术（朴素贝叶斯分类器（NBC）、决策树（DT）、支持向量机（SVM）、线性回归（LR））和非监督算法（隐马尔可夫模型（HMM）），以找到具有最高分类精度（CA）的最佳技术。在计算时间和分类精度方面的监督和无监督算法进行了详细的比较。分类的占用状态进一步用于评估未来时隙的次用户中断的概率，其可以由系统设计者用来定义频谱分配和频谱共享策略。实验结果表明，在所有的监督分类器和无监督分类器中，支持向量机是最好的分类器。在此基础上，我们提出了一种新的SVM算法，将其与火飞算法（FFA），这是优于所有其他算法。
英文摘要：
In this paper, we analyze the spectrum occupancy using different machine learning techniques. Both supervised techniques (naive Bayesian classifier (NBC), decision trees (DT), support vector machine (SVM), linear regression (LR)) and unsupervised algorithm (hidden markov model (HMM)) are studied to find the best technique with the highest classification accuracy (CA). A detailed comparison of the supervised and unsupervised algorithms in terms of the computational time and classification accuracy is performed. The classified occupancy status is further utilized to evaluate the probability of secondary user outage for the future time slots, which can be used by system designers to define spectrum allocation and spectrum sharing policies. Numerical results show that SVM is the best algorithm among all the supervised and unsupervised classifiers. Based on this, we proposed a new SVM algorithm by combining it with fire fly algorithm (FFA), which is shown to outperform all other algorithms.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
