东北大学学报（自然科学版）

东北大学学报（自然科学版） ›› 2022, Vol. 43 ›› Issue (2): 176-182.DOI: 10.12068/j.issn.1005-3026.2022.02.004

• 信息与控制 • 上一篇    下一篇

基于KNN-TSVR算法的MIMO-OFDM系统信道估计

李朔1,2， 雷为民1， 张伟1   

  1. (1. 东北大学 计算机科学与工程学院， 辽宁 沈阳110169； 2. 辽宁科技大学 电子与信息工程学院， 辽宁 鞍山114051)
  • 修回日期:2020-03-25 接受日期:2020-03-25 发布日期:2022-02-28
  • 通讯作者: 李朔
  • 作者简介:李朔(1976-)，女，辽宁沈阳人，东北大学博士研究生； 雷为民(1972-)，男，辽宁沈阳人，东北大学教授，博士生导师.
  • 基金资助:
    国家重点研究发展计划项目(2018YFB1702000)； 辽宁省自然科学基金资助项目(20180551007).

Channel Estimation for MIMO-OFDM System Based on KNN-TSVR Algorithm

LI Shuo1,2， LEI Wei-min1， ZHANG Wei1   

  1. 1. School of Computer Science & Engineering， Northeastern University， Shenyang 110169， China; 2. School of Electronic and Information Engineering， University of Science and Technology， Anshan 114000， China.
  • Revised:2020-03-25 Accepted:2020-03-25 Published:2022-02-28
  • Contact: LI Shuo
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摘要： 为了提高多输入多输出正交频分复用(MIMO-OFDM)系统的信道估计性能，提出了一种基于K近邻加权孪生支持向量回归(KNN-TSVR)的信道频率响应估计算法.该算法的工作过程是首先用最小二乘算法对导频位置的信道参数进行估计，获取训练样本，然后用K近邻(KNN)算法对训练样本进行预处理，得到赋予各样本的权重，最后由加权TSVR对MIMO-OFDM系统所有位置的信道参数进行插值估计.本文提出的改进的加权TSVR信道估计方法不仅具有TSVR对非线性关系回归的优势，同时引入KNN算法对TSVR进行改进，使得该算法与传统TSVR相比，具有更好的回归性能和抗噪声能力.对非线性MIMO-OFDM信道进行估计的仿真实验结果证实了这一结论.

关键词: 信道估计；K最近邻(KNN)算法；多进多出(MIMO)系统；正交频分复用(OFDM)；孪生支持向量回归(TSVR)

Abstract: In order to improve the channel estimation performance of MIMO-OFDM system， a channel frequency response estimation algorithm based on K-nearest neighbor twin support vector regression(KNN-TSVR)is proposed. The working process of the algorithm is that the least square algorithm is used to estimate the channel parameters of the pilot position， and the training samples are obtained. Then the KNN algorithm is used to preprocess the training samples， and the weights given to each sample are obtained. Finally， the weighted TSVR is used to interpolate the channel parameters of all the positions in the MIMO-OFDM system. The improved weighted TSVR channel estimation method proposed not only has the advantages of TSVR in nonlinear regression， but also has better regression performance and anti-noise ability compared with traditional TSVR due to the improvement of TSVR based on KNN. The simulation results of nonlinear MIMO-OFDM channel estimation confirm this conclusion.

Key words: channel estimation; KNN(K-nearest neighbor); MIMO(multiple-input multiple-out); OFDM(orthogonal frequency division multiplexing); TSVR(twin support vector regression)

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