TBIF1210-03-01

说明：  BCH码的Berklak-Massey算法，编程比较经典。(BCH code Berklak-Massey algorithm, compare the classic programming.)

相关指导及说明，可帮助新手了解28035。(Related instructions and instructions can help novices understand 28035)

ofdm的编码过程，用evm和比特率进行对比(The encoding process of OFDM is compared with EVM and bit rate)

说明：  m序列的生成，包括在有限域GF(p)中的基于不同p取值，不同级数条件下的本原多项式多种选择条件下的m序列生成。(m sequence generation, including in GF (p), the p value based on different, different levels under the conditions of primitive polynomial number of options under the conditions of m-sequences generated.)

本文分析了多级维纳滤波器的特性，在加性噪声和二维天线阵列如均匀圆阵、均匀面阵、十字阵等条件和背景下，对信源个数和信源参数估计问题进行了研究，提出了基于多级维纳滤波器前向分解特性的快速参数估计方法，同时提出了基于多级维纳滤波器的二维ESPRIT参数估计方法，该类方法无需协方差矩阵的估计运算及分解运算，计算复杂度较低。另外，还提出了对信源个数的估计算法。(This paper analyzes the characteristics of multistage Wiener filter, in the additive noise and the two-dimensional antenna array such as a uniform circular array, the uniform array, cross array conditions and background, the source number and source parameter estimation problem undertook study, put forward based on Multistage Wiener filter prior to the decomposition characteristics of fast parameter estimation method, at the same time based on Multistage Wiener filter 2D ESPRIT parameter estimation method, the method does not need to estimate of covariance matrix calculation and decomposition, relatively low computational complexity. In addition, also put forward for source number estimation algorithm.)

主要研究无线通信系统中，调制信号的载波同步问题，利用锁相环技术实现同步的matlab仿真(This paper mainly studies the carrier synchronization of modulated signal in wireless communication system, and realizes the MATLAB simulation of synchronization by using phase-locked loop technology.)

OFDMA next stage of LTE performance analysis

提供了几种利用jakes模型建立瑞利信道的matlab代码。(Provides several jakes model building using Rayleigh channel matlab code.)

第一次作业_基于分类算法的雷达状态识别 对于本数据集中的雷达状态识别，数据降维前使用朴素贝叶斯、支持向量机、神经网络的分类算法对于识别的准确率无太大影响；数据降维后使用神经网络算法最优，支持向量机算法其次，朴素贝叶斯算法较差。此外，训练样本越多，分类准确率有小幅度提高。(First Operation Radar State Recognition Based on Classification Algorithms For radar state recognition in this data set, the classification algorithm of Naive Bayesian, Support Vector Machine and Neural Network before data dimension reduction has little effect on the accuracy of recognition; Neural Network algorithm is the best after data dimension reduction, Support Vector Machine algorithm is the second, Naive Bayesian algorithm is the worst. In addition, the more training samples, the smaller the classification accuracy.)

说明：  仿真了ofdm的LS和MMSE的信道估计程序，并进行了比较，能调通运行(Ofdm simulation of the LS and MMSE channel estimation procedures, and compared to pass transfer operation)