基于星座图和相似性度量的调制方式识别

Classification of Modulation Techniques Using Constellation Shape and Similarity Measure

讨论了存在相位误差情况下的调制识别问题,改进了以星座图形状为特征的识别算法.首先利用基于样本与核的相似性度量,对接收信号观测点动态聚类,得到重构星座图.然后,将重构星座图和预期星座图进行匹配,利用所提出的最大似然准则,完成星座图分类.该准则等效于最小距离分类准则,匹配方法简单,避免了以往基于星座图形状识别算法中,为得到重构星座图顶点统计特性所需的训练阶段.考虑到噪声对相位估计的影响,仿真表明,在已知和未知信号调制状态数情况下,SNR分别为10 dB和15 dB时,对所涉及的调制集可获得90%以上的识别率.

In this paper, we study the problem of modulation classification in the presence of phase error and improve modulation classification algorithm which uses constellation shape as classification signature. First, dynamic clustering algorithm is utilized to recover unknown constellation based on similarity between samples and kernels. Then recovery constellation is matched with constellations of different modulated signals and classified by a proposed max-likelihood rule, which is simple and equivalent to minimum distance classification rule. This rule avoids a training phase which is necessary for spatial statistics of recovery constellation vertices in previous algorithms. Considering the effect of additive noise on phase estimation, simulations show that the correct rate is above 90 percent when SNR is 10 dB and 15 dB respectively under the condition of known and unknown number of modulation states.