arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1905.04876eess.SP
收藏

AWGN信道下的M-QAM信号调制识别算法

An M-QAM Signal Modulation Recognition Algorithm in AWGN Channel

Ahmed K. Ali, Ergun Erçelebi

摘要:

调制分类是一个模式识别问题,自动调制分类(AMC)方法的复杂性之一是在分类之前从输入数据中提取特征。虽然针对不同信道下的多电平正交幅度调制(M-QAM)算法进行了详细的介绍。通过对文献的检索发现,对高阶M-QAM调制方案如128-QAM、256-QAM、512-QAM和1024-QAM的分类研究很少。本论文的工作主要是研究自然对数特性的强大功能以及从原始数据中提取高阶累积量特征的可能性。在加性高斯白噪声(AWGN)信道下提取HOC信号,定义了四个有效参数来区分调制类型:4-QAM~1024-QAM。该方法提高了识别器的智能化程度,提高了分类的成功率。在噪声信道统计模型下的仿真结果表明,识别算法是在M-QAM信道下进行识别的,而且大多数结果都是有希望的,表明对数分类器在AWGN和不同衰落信道下都能很好地工作,即使在较低的信噪比下也能达到可靠的识别率(小于零),它可以看作是一个综合自动调制分类(AMC)系统,为了识别高阶M-QAM信号,采用了独特的对数分类器,具有更高的通用性,因此它在自动调制识别系统中的性能优于以往的工作

英文摘要:

Computing the distinct features from input data, before the classification, is a part of complexity to the methods of Automatic Modulation Classification (AMC) which deals with modulation classification was a pattern recognition problem. Although the algorithms that focus on MultiLevel Quadrature Amplitude Modulation (M-QAM) which underneath different channel scenarios was well detailed. A search of the literature revealed indicates that few studies were done on the classification of high order M-QAM modulation schemes like128-QAM, 256-QAM, 512-QAM and1024-QAM. This work is focusing on the investigation of the powerful capability of the natural logarithmic properties and the possibility of extracting Higher-Order Cumulant's (HOC) features from input data received raw. The HOC signals were extracted under Additive White Gaussian Noise (AWGN) channel with four effective parameters which were defined to distinguished the types of modulation from the set; 4-QAM~1024-QAM. This approach makes the recognizer more intelligent and improves the success rate of classification. From simulation results, which was achieved under statistical models for noisy channels, manifest that recognized algorithm executes was recognizing in M-QAM, furthermore, most results were promising and showed that the logarithmic classifier works well over both AWGN and different fading channels, as well as it can achieve a reliable recognition rate even at a lower signal-to-noise ratio (less than zero), it can be considered as an Integrated Automatic Modulation Classification (AMC) system in order to identify high order of M-QAM signals that applied a unique logarithmic classifier, to represents higher versatility, hence it has a superior performance via all previous works in automatic modulation identification system

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑