In this paper, we consider the problem of automatic modulation classification with multiple sensors in the presence of unknown time offset, phase offset and received signal amplitude. We develop a novel hybrid maximum likelihood (HML) classification scheme based on a generalized expectation maximization (GEM) algorithm. GEM is capable of finding ML estimates numerically that are extremely hard to obtain otherwise. Assuming a good initialization technique is available for GEM, we show that the classification performance can be greatly improved with multiple sensors compared to that with a single sensor, especially when the signal-to-noise ratio (SNR) is low. We further demonstrate the superior performance of our approach when simulated annealing (SA) with uniform as well as nonuniform grids is employed for initialization of GEM in low SNR regions. The proposed GEM based approach employs only a small number of samples (in the order of hundreds) at a given sensor node to perform both time and phase synchronization, signal power estimation, followed by modulation classification. We provide simulation results to show the computational efficiency and effectiveness of the proposed algorithm.
翻译:在本文中,我们考虑了在未知时间抵消、相抵和接收信号振幅的情况下对多个传感器进行自动调控的问题。我们根据普遍预期最大化算法制定了新的混合最大可能性(HML)分类办法。GEM能够从数字上找到极难获得的ML估计值。假设GEM具备良好的初始化技术,我们表明,与单一传感器相比,多传感器的分类性能可以大大改进,特别是在信号对噪音比率低的情况下。我们进一步表明,在模拟统一和非统一电网的情况下,在低标准NR区域启动GEM时,我们的方法表现优异。拟议的GEM方法在给定传感器节点仅使用少量样本(大约几百个),以进行时间和阶段同步、信号功率估计,然后进行调制分类。我们提供了模拟结果,以显示拟议算法的效率和效力。