正交频分复用(Orthogonal Frequency Division Multiplexing，OFDM)在现代通信系统中发挥着重要的作用，而信道估计是OFDM系统接收机的关键环节。针对传统导频信道估计算法需要大量的导频开销，降低信道带宽利用率，以及已有的盲信道估计算法估计性能差且算法复杂度高等不足，该文提出了一种基于改进聚类算法的盲信道估计算法。该算法在系统接收端把接收到信号看作一幅时频二维平面，通过使用时频窗口以迭代方式遍历该平面，在遍历过程中用改进的K-means算法对时频窗口内的符号数据做聚类分析，并根据符号先验信息以及信道时频相关性实现信道估计与均衡的目的。通过计算机仿真实验结果表明，改进K-means的盲信道估计算法的误比特率整体上低于基于简单线性预编码的盲估计算法，当信噪比大于10 dB时，改进K-means的盲信道估计算法的误比特率比子空间方法更低。在高信噪比情况下估计性能与最小均方误差(Minimum Mean Square Error，MMSE)估计算法相当。因此，相较于已有的盲信道估计算法，改进K-means的盲信道估计算法提高了估计精度，降低了算法复杂度。
Research and Optimization of Blind Channel Estimation Algorithm Based on Clustering
The Orthogonal Frequency Division Multiplexing plays an important role in the modern communication system,and the channel estimation is the key link of the OFDM system receiver.The traditional pilot channel estimation algorithm needs a lot of pilot overhead,which reduces the channel bandwidth utilization,and the existing blind channel estimation algorithms have poor estimation performance and high algorithm complexity.In view of these deficiencies,a blind channel estimation algorithm based on improved clustering algorithm is proposed.In this algorithm,the received signal is regarded as a time-frequency two-dimensional plane at the receiving end of the system,and the plane is traversed in an iterative manner by using a time-frequency window.In the traversal process,the symbol data in the time-frequency window are clustered by an improved K-means algorithm,and the channel estimation and equalization are realized according to the symbol prior information and the channel time-frequency correlation.The computer simulation results show that the bit error rate of the improved K-means blind channel estimation algorithm is lower than that of the blind channel estimation algorithm based on simple linear precoding.And the bit error rate of the improved K-means blind channel estimation algorithm is lower than that of the subspace method when the signal-to-noise ratio is greater than 10 dB.The estimation performance is equivalent to the Minimum Mean Square Error estimation algorithm in the case of high SNR.Therefore,compared with the existing blind channel estimation algorithms,the improved K-means blind channel estimation algorithm improves the estimation accuracy and reduces the complexity of the algorithm.
orthogonal frequency division multiplexingblind channel estimationchannel equalizationcluster analysisK-means
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