| SVM自适应波束成形 |
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| 引用本文： | 葛红艳,邢光龙,李燕,王伟超.SVM自适应波束成形[J].东北重型机械学院学报,2009(4):332-335. |
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| 作者姓名： | 葛红艳 邢光龙 李燕 王伟超 |
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| 作者单位： | 燕山大学信息科学与工程学院,河北秦皇岛066004 |
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| 摘 要： | 支持向量机（SupportVectorMachine，SVM）是机器学习领域的最新成果，它有较强的泛化能力，收敛快以及低复杂度等优点。本文通过对训练样本进行数据格式的转化，继而转化为libsvm和lssvm分类所要求的数据格式。然后在上行波束成形中使用SVM算法，提高空域滤波的分辨率，仿真结果显示：与LMS（LeastMeanSquares，最小均方值，又叫随机梯度下降法）、MMSE（MinimumMean—SquareError，最小均方误差）经典算法相比，误码率有了明显改善。
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| 关 键 词： | SVM LMS MMSE 误码率 波束成形 天线阵列 |
Adaptive beamforming using SVM method |
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| Authors: | GE Hong-yan XING Guang-long LI Yan WANG Wei-chao |
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| Affiliation: | (College of Information Science and Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China) |
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| Abstract: | With a number of prominent features, SVM is the latest technique in the area of machine learning. In this paper, a way is proposed by transforming the data format of training samples, and so data format can be used in libsvm and lssvm. And then SVM algorithm is used in uplink beamforming, improving airspace filter differentiation power. Simulation results show that the proposed algorithm achieves an improved BER performance in comparison to LMS and MMSE algorithms. |
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| Keywords: | SVM LMS MMSE BER beamforming antenna array |
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