基于EM算法的联合调制识别与参数估计

针对低信噪比情况下单节点接收调制识别率低的问题，提出了一种基于多节点接收与混合最大似然的调制识别算法。多节点同步接收后将基带信号发送到融合中心，使用混合最大似然实现全局分类判决，通过空间分集提高低信噪比下调制识别的性能。为了解决联合似然函数中多维未知参数难以直接求解的问题，将未知发送符号序列表示成隐藏变量后采用EM算法实现未知参数的最大似然估计。给出的算法在平坦衰落信道下实现了BPSK、QPSK、8PSK、16QAM信号的调制识别与参数估计。与基于矩的算法相比，基于EM迭代的最大似然估计提高了未知参数的估计精度。仿真实验结果表明，当采用四节点同步接收，在信噪比大于-2dB时，平均正确识别率能够达到95%以上。

Joint Modulation Classification and Parameter Estimation Based on EM Algorithm

The performance of modulation classification in single radio is sensitive to signal-to-noise ratio (SNR). A hybrid maximum likelihood (ML) modulation classification algorithm using multiple radios was proposed. All received baseband signal from different radios working on synchronous mode were fused at a fusion center to make the global classification decision by using the hybrid ML. Due to the spatial diversity, the performance of modulation classification in low SNR regimes was improved. The joint likelihood function contained multiple dimensional unknown parameters. In order to alleviate the computational complexity associated with the ML estimates of the unknown parameters, the Expectation Maximization (EM) algorithm was adopted, in which the constellation symbols were represented unobserved data. The proposed algorithm completed the classification of BPSK, QPSK, 8PSK and 16QAM, as well as the unknown parameters estimation. Compared to the algorithm based on moments, the unknown parameters estimate based on EM estimation provided superior performance in precision. The simulation results showed that when the number of radios was four and SNR of signal was more than -2dB, the average probability of correct classification was more than 95%.