Analysis of Spectrum Occupancy Using Machine Learning Algorithms
Analysis of Spectrum Occupancy Using Machine Learning Algorithms
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DOI：
10.1109/tvt.2015.2487047
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发表时间：
2015-03
影响因子：
6.8
通讯作者：
Freeha Azmat;Yunfei Chen;N. Stocks
中科院分区：
文献类型：
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作者：
Freeha Azmat;Yunfei Chen;N. Stocks
本文利用不同的机器学习技术分析了认知无线电网络(CRN)的频谱占用情况。研究了有监督技术[朴素贝叶斯分类器(NBC)、决策树(DT)、支持向量机(SVM)、线性回归(LR)]和无监督算法[隐马尔可夫模型(HMM)]，以寻找具有最高分类精度的最佳技术。详细比较了监督算法和非监督算法在计算时间和CA方面的优劣。进一步利用分类的占用状态来评估次级用户对未来时隙的阻塞概率，系统设计者可以利用该概率来定义频谱分配和频谱共享策略。数值结果表明，在所有的监督和非监督分类器中，支持向量机是最好的算法。在此基础上，将支持向量机与萤火虫算法相结合，提出了一种新的支持向量机算法(FFA)，该算法的性能优于所有其他算法。
In this paper, we analyze the spectrum occupancy in cognitive radio networks (CRNs) using different machine learning techniques. Both supervised techniques [naive Bayesian classifier (NBC), decision trees (DT), support vector machine (SVM), linear regression (LR)] and unsupervised algorithms [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 the CA is performed. The classified occupancy status is further utilized to evaluate the blocking probability of secondary user for 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 a firefly algorithm (FFA), which is shown to outperform all the other algorithms.