跳频仿真

MIMO技术中瑞利衰落信道的MATLAB源程序的仿真(rayleigh fading channel generator)

该文档提供ASK,BPSK,QPSK,4QAM,16QAM等多种调制格式的LMS时域均衡算法，最初结构由MathWorks提供。(This document provides LMS time domain equalization algorithms in ASK, BPSK, QPSK, 4QAM, 16QAM and other modulation formats. The original structure is provided by MathWorks.)

此程序用于OFDM与FBMC的对比，详细描述了UFMC系统的发送和接收的仿真实现过程，并对比相同系统参数下的OFDM系统，发现FBMC系统的优势。(This program is used for OFDM and FBMC contrast, describing the detail of UFMC transmitter and receiver simulation process, and comparing the the OFDM system under the same system parameters to verify the advantages of FBMC system.)

说明：  mimo各个信道具体源码，包括高斯、瑞利、多径衰弱等信道模型，本人将其调试、整理并附上运行结果。(MIMO channel specific source code, including Gaussian, Rayleigh, multipath fading and other channel models, I will debug, collate and attach the operation results.)

该文档为一个实现ofdm子载波调制和解调的代码，利用QPSK和16QAM两种方式实现，并附有运行结果。(This document is an implementation of ofdm subcarrier modulation and demodulation of the code, the use of QPSK and 16QAM two ways, along with operating results.)

OFDM上下行的链路仿真，很简单，解释的也很清楚，易懂(simulink ofdm,easy)

说明：  论文——基于特征分解的GFDM实现无ICI、ISI干扰和导频插入用于信道估计的仿真代码(Thesis: the implementation of gfdm based on feature decomposition without ICI, ISI interference and pilot insertion simulation code for channel estimation)

接受分集中采用选择式合并的函数，输出误码率和信噪比曲线(Acceptance of centralized using selective merge function, the output is bit error rate and SNR curve)

opnet与matlab联调的步骤，过程详细，使用方便(Steps of OPNET and MATLAB debugging, detailed process, easy to use)

说明：  求解双层规划问题常用的算法有极点算法、直接搜索法、下降法和非数值优化方法(如模拟退火算法、遗传算法等)，遗传算法的求解思路是：首先对上层的决策变量编码，代人下层规划模型，通过求解下层模型的决策变量值，代入上层模型计算适应度值，然后进行交叉、变异、选择操作，最后求出最优解。(Solving Bilevel Programming Problems with pole algorithm commonly used algorithms, direct search method, descent and non-numerical optimization methods (such as simulated annealing, genetic algorithms, etc.), genetic algorithm for solving approaches: the first on the top of the decision variable coding, on behalf of the were lower programming model, by solving the lower model of decision variables values into the upper model on behalf of the fitness value, and then crossover and mutation, select Options, and finally get the best solution.)