通信工程文献翻译——与分类性能和在心里计算的复杂性，我们提出一个新的优化分布抽样测试（ODST）分类器的 M-QAM 信号的自动分类。在 ODST，信号累积分布进行采样，在预先确定的地点。实际的采样过程转化为简单的计数任务，降低计算复杂度。采样位置的优化是基于在各种信道条件下获得的理论信号模型。采用遗传算法（GA）优化距离度量的采样分布参数之间的分布测试信号。最终的决定是基于测试信号和候选调制之间的距离。通过使用多个取样位置信号的累积分布，分类器的鲁棒性增强可能信号的方差或信号模型失配。AWGN信道和频率偏移，相位偏移，是评估该算法的性能。实验结果表明，该方法具有优势，在分类精度和计算复杂度在大多数现有的分类。

自动调制分类（AMC）多年来一直是一个研究课题。AMC 最初的应用主要是在军事电子对抗、监视和威胁分析[ 1 ]。AMC 的主要目的是自动分类的截获信号的调制类型，它可以正确地解调。许多论文，如[ 2 - 6 ]，已公布建议不同的解决这个问题。最近，作为智能无线通信系统出现在现代民用通信应用，AMC，这是自适应调制模块的重要组成部分，吸引了来自认知无线电的关注（CR）和软件无线电（SDR）的开发商，如 7–[ 9 ]。AMC 的根本任务是相同的，但在目前的 CR 和 SDR 开发环境中出现的新的挑战。一个明显的困难来自不同的调制类型被使用。近年来，信号调制的使用已经对正交幅度调制（QAM）由于其高效的大容量数据传输能力。QAM 调制的普及可以用在许多现代无线通信标准的存在容易验证。在 IEEE 802.11a 的[ 10 ]，BPSK，4-QAM，64-QAM 和作为许多无线通信应用的调制。在数字视频广播（DVB-T）[ 11 ]，4-QAM，64-QAM 和进行数字电视广播的普遍选择。

在本文中，4-QAM，64-QAM 和已选择的分类器的发展。然而，修改后可以很容易适应其他 QAM 或更广泛地选择调制。这个 QAM 调制的分类有其独特的挑战，大部分的信号特征非常相似，不同的多进制 QAM 的调制（M-QAM）。AMC 的另一个挑战是在不同的信道条件下进行准确的分类性能的需求。除了信道效应，短的处理时间的需求也是一个兴趣的不同的应用程序，需要实时重新配置的通信系统。总之，目标是开发一个简单的 AMC 分类器，得到了精确、可靠的分类性能。

With the classification performance and computational complexity in mind, we propose a new optimized distribution sampling test (ODST) classifier for automatic classification of M-QAM signals. In ODST, signal cumulative distributions are sampled at pre-established locations. The actual sampling process is transformed into simple counting task for reduced computational complexity. The ptimization of sampling locations is based on theoretical signal models derived under various channel conditions. Genetic Algorithm (GA) is employed to optimize distance metrics using sampled istribution parameters for distribution test between signals. The final decision is made based on distances between tested signal and candidate modulations. By using multiple sampling locations on signal cumulative distributions, the classifier's robustness is enhanced for possible signal statistical variance or signal model mismatching. AWGN channel, phase offset, and frequency offset are considered to evaluate the performance of the proposed algorithm. Experimental results show that the proposed method has advantages in both classification accuracy and computational complexity over most existing classifiers.

Keywords:Automatic modulation classification ,Cognitive radio,Distribution test,Genetic algorithm,AWGN,Flat fading channel 

1.Introduction

Automatic Modulation Classification (AMC) has been an established research topic for many years. The initial application of AMC was mostly in military electronic warfare, surveillance and threat analysis [1]. The main purpose of AMC is to classify automatically the modulation type of the intercepted signal so that it can be correctly demodulated. Many papers, e.g. [2–6], have been blished

suggesting different solutions for this problem. Recently, as intelligent radio communication systems emerges in modern civilian communication applications, AMC, which is an important component in the adaptive modulation module, has attracted much attention from Cognitive Radio (CR) and Software Defined Radio (SDR) developers, e.g. [7–9]. The fundamental task of AMC remains the same, though new challenges arise in the current CR and SDR development environments. One obvious difficulty comes from the different modulation types being used. In recent years,the use of signal modulations has migrated towards Quadrature Amplitude......