FSK、PSK、QAM信号直接波形合成的实现方法

利用波形存储来直接合成电话线路通讯中所用的FSK,PSK,QAM调制波形,利用软件来实现,一改传统的硬件合成方法,提高了可靠性,实现起来相当简单,特别是对合成高波牲率的QAM有着极其显著的优越性.     

16QAM调制解调技术分析仿真

根据QAM调制解调的基本原理,以Matlab为开发平台,设计了16QAM数字调制解调系统并进行仿真分析,并在信噪比变化条件下,得到了不同进制QAM系统的误码率。仿真结果表明,QAM调制相对PSK调制具有较好的性能。     

QAM调制技术及其MATLAB仿真

文章首先概述了QAM调制技术的原理,然后重点介绍了QAM调制技术在有线电视系统中的作用和意义,并且对QAM的频道设置和输出进行了说明。文章还利用MATLAB对基于QAM的数字通信系统进行仿真,得到了良好效果的接收星座图和补偿后的星座图、信道估计图、系统BER分析图。仿真结果表明这种通过MATLAB实现的数字通信系统具有较强的可实现性,为实际应用和科学合理地设计正交幅度调制系统,提供了高效的仿真平台。     

一种改进的BP神经网络调制分类器

本文致力于基于神经网络的通信信号调制类型识别器设计研究。论文提出了一种改进的BP神经网络分类器,它采用7个特征参数,可以对CW、2FSK、4FSK、8FSK、2PSK、4PSK、8PSK、8QAM、16QAM、4ASK、8ASK共11种调制类型实现正确分类识别。论文讨论了方案设计,给出了仿真试验结果,并将其与其他神经网络分类器进行了性能比较。     

一种改进的BP神经网络调制分类器

本文致力于基于神经网络的通信信号调制类型识别器设计研究.论文提出了一种改进的BP神经网络分类器,它采用7个特征参数,可以对CW、2FSK、4FSK、8FSK、2PSK、4PSK、8PSK、8QAM、16QAM、4ASK、8ASK共11种调制类型实现正确分类识别.论文讨论了方案设计,给出了仿真试验结果,并将其与其他神经网络分类器进行了性能比较.     

OFDM系统16QAM和QPSK调制仿真分析

以研究OFDM系统中使用的最佳数字调制方式为目的,分析了OFDM系统的原理、信号处理流程,以及一般使用的数字调制方式,利用matlab对系统进行了仿真,对比了系统分别采用16QAM和QPSK进行调制时的误码率,根据仿真曲线分析了二者的性能,得出系统在设定参数下特定信噪比时使用QPSK调制比16QAM调制误码率更低。     

基于高阶累积量的数字调制信号识别方法

通信信号调制方式自动识别在信号检测、威胁分析、频谱监测等领域有着重要的地位,是非合作通信关注的关键技术.针对单一累积量调制信号识别有限且识别率低等问题,利用信号的二、四、六阶累积量特征所构造的矢量集,实现了MASK、MPSK、MFSK、MQAM四类信号的类间识别,以及2ASK、4ASK、8ASK,2PSK、4PSK、8PSK,2FSK、4FSK、8FSK,4QAM、16QAM、64QAM的类内识别.在Matlab环境下进行了仿真实验,实验结果表明,该方法在信噪比大于5 dB时可以达到90％以上的识别率.     

基于IT++的QAM调制解调算法的实现

信息传输的核心问题是有效性和可靠性,正交幅度调制(QAM)作为一种新的调制技术因其具有很高的频带利用率而得到广泛应用。IT++是基于C++语言的信号处理和通信方面的仿真库函数工具。本文分析和研究了M-QAM调制在IT++平台中的实现方法,并比较了不同M值的QAM的性能。仿真结果与理论分析一致,与MATLAB相比极大地提高了仿真速度。     

基于数字调制技术误码率分析的计算机设计及实现

文章论述了在物理层上采用的数字信号调制技术——QPSK与QAM调制，并对各种调制技术的误码性能做了详细的分析。     

QAM解调及频偏估计联合算法

针对在水声传输环境下,载波频率偏移会导致较大的传输误码率的问题,提出一种能够同时进行载波频率偏移估计以及QAM解调的联合算法。在频域将接收信号样点与标准正弦信号进行最小二乘逼近,通过估计接收信号的频率实现载波频偏估计;同时得到接收信号的幅度和相位,从而得到QAM调制信号的I分量和Q分量系数。仿真证明,该算法降低了QAM调制解调算法的误码率。     

16QAM调制算法及其DSP实现

正交振幅调制技术（QAM）由于其高的频带利用率和相对低的误码率而被定为很多数字通信系统的数字传输标准.该论文讨论了16QAM调制原理,用软件产生16QAM信号的算法及其基于DSP软件编程实现.     

正交幅度调制（QAM）信号的解调技术与实现

QAM调制方式广泛应用于有线和无线传输领域。本文阐述了一个QAM的解调系统并且用芯片实现。该系统涉及下变频模块,A/D模块,码元时钟恢复模块,载波恢复模块和自适应均衡模块,前端纠错模块等。     

一种基于盲均衡的多径环境下调制识别算法

针对频率选择性多径衰落信道下MPSK和MQAM信号的调制分类问题,提出了一种基于盲均衡算法的自动识别算法。将接收到的码元星座图通过一组并行的自适应盲均衡器,当盲均衡器与星座图匹配时其代价函数收敛到最小。所以直接利用盲均衡的代价函数作为调制识别特征,当代价函数收敛后,将具有最小代价函数值的均衡器所对应的信号判为识别结果。仿真结果表明,该算法可以有效识别频率选择性多径衰落信道下的BPSK,QPSK,8PSK,16QAM,32QAM,64QAM信号。     

DSP-based hierarchical neural network modulation signal classification

This paper discusses a real-time digital signal processor (DSP)-based hierarchical neural network classifier capable of classifying both analog and digital modulation signals. A high-performance DSP processor, namely the TMS320C6701, is utilized to implement different kinds of classifiers including a hierarchical neural network classifier. A total of 31 statistical signal features are extracted and used to classify 11 modulation signals plus white noise. The modulation signals include CW, AM, FM, SSB, FSK2, FSK4, PSK2, PSK4, OOK, QAM16, and QAM32. A classification hierarchy is introduced and the genetic algorithm is employed to obtain the most effective set of features at each level of the hierarchy. The classification results and the number of operations on the DSP processor indicate the effectiveness of the introduced hierarchical neural network classifier in terms of both classification rate and processing time.     

Blind signal modulation recognition through clustering analysis of constellation signature

Automatic recognition of digital modulation schemes is becoming an active research area in many covert operations. It has many military applications where surveillance and electronic warfare requires a type of modulation in intercepted signal to prepare jamming signals. Most of the approaches are based on modulated signal's component, but the modulation type can be best identified with the use of constellation diagram. The proposed technique is able to recognize M-QAM, M-ASK, and M-PSK modulation scheme in Additive White Gaussian Noise (AWGN) environment. As the constellation points form clusters in the I-Q plane, the order of the modulation can be obtained by estimating the correct number of clusters, which is calculated by OPTICS algorithm. The least square error has been calculated using linear regression from the obtained constellation points, to identify either ASK or PSK and QAM. The error is least for ASK which differentiates ASK from PSK and QAM. To identify between the PSK and QAM, k-means clustering is employed to find the number of centroids equal to order of modulation estimated by OPTICS. With the difference in maximum and minimum absolute value of the centroids, PSK or QAM is recognized. The proposed method shows an improvement in the classification accuracy which reaches 100% using 1024 symbols at 20 dB compared to 98.89%, 98.05%, and 98% when using more complex classifiers like Support Vector Machine, Naive Bayes Classifier, KNN respectively. The method used is unsupervised whereas most of the methods in the literature require training phase to set the thresholds or weights for final model to detect modulation type. This algorithm is also implemented in LabVIEW, and tested on real-time signals. An intelligent system is made which does not require any knowledge of symbol rate, carrier frequency, and any training phase to set thresholds, and detects the type of modulation blindly in real time. Modulated RF signals are generated by NI PXIe-5673 (RF transmitter). NI PXI 5600 is used to downconvert RF signal and NI PXI-5142 (100 MS/s OSP digitizer) is used to sample the downverted signal.     

基于CPLD的连续相位QAM调制器

本文以CPLD器件为核心设计连续相位QAM调制器,将绝大部分功能模块由大规模CPLD内部资源来实现,这样既可以提高通信系统的稳定性和灵活性,又便于系统的集成化和小型化。由于连续相位QAM调制独特的相位变化,调制器中采用了双通道设计,成功实现了过渡区相位与主要区间相位的交替产生。     

蒙特卡罗法在调制解调中的应用

在加性高斯噪声干扰条件下的2FSK和16QAM调制解调方法基础上,根据蒙特卡罗仿真方法基本思想,建立在加性噪声干扰下的相关检测系统蒙特卡罗仿真模型。模型通过Matlab软件方法实现,最终仿真结果与对应系统的理论误码率基本符合。