fm-modulation

本资料是关于MST_Kruskal算法的matlab实现程序(MST_Kruskal matlab)

RS编码的simulink 仿真，在ＡＮＧＷ条件下１(RS coding simulink simulation conditions in ANGW 1)

半正定矩阵cholesky方法将其分解为两个上下三角矩阵相乘的形式。matlab自带的函数只能分解正定矩阵。并且给出例子。(Semi-definite matrix cholesky method be decomposed into two upper and lower triangular matrices form. matlab built-in function can only be positive definite matrix decomposition.)

数字预失真的很好的算法，从原理到算法都给出比较详细的说明，对初学者有很大的帮助。(A good digital pre-distortion algorithm to the algorithm are given from the principle of a more detailed explanation, very helpful for beginners.)

the code simulates the MIMO-OFDM spatial multiplexing with successive interference cancellation

基于 matlab 的三角分解法，推荐(based on Matlab 1.3 decomposition method, recommended!)

验证傅里叶切片定理，可减少傅里叶变换的计算量(fourier slice)

自适应信号处理方面的RLS自适应线性预测算法程序(Adaptive signal processing for adaptive linear prediction algorithm RLS procedure)

In a multiple transmit antenna, single antenna per receiver downlink channel with limited channel state feedback, we consider the following question: given a constraint on the total system-wide feedback load, is it referable to get low-rate/coarse channel feedback from a large number of receivers or high-rate/high-quality feedback from a smaller number of receivers? Acquiring feedback from many receivers allows multi-user diversity to be exploited, while high-rate feedback allows for very precise selection of beamforming directions. We show that there is a strong preference for obtaining high-quality feedback, and that obtaining near-perfect channel information from as many receivers as possible provides a significantly larger sum rate than collecting a few feedback bits from a large number of users.

-u"+u^2=x, 0<x<1 u(0)=0, u(1)=0