代码搜索：Variance
找到约 2,271 项符合「Variance」的源代码
代码结果 2,271
www.eeworm.com/read/394381/8228021
m randn.m
%R=randn(m,n) 生成标准正态分布的m行n列随机矩阵
%RANDN Normally distributed random numbers.
% RANDN(N) is an N-by-N matrix with random entries, chosen from
% a normal distribution with mean zero and variance
www.eeworm.com/read/171988/9727625
m channel.m
function y = channel(sig2, Mt, Mr, x, H, N);
% function y = channel(sig2, Mt, Mr, x, H, N)
%
% Channel transmission simulator
%
% inputs:
% sig2 - noise variance
% Mt - number of Tx antennas
% M
www.eeworm.com/read/413420/11156783
m channel.m
function y = channel(sig2, Mt, Mr, x, H, N);
% function y = channel(sig2, Mt, Mr, x, H, N)
%
% Channel transmission simulator
%
% inputs:
% sig2 - noise variance
% Mt - number of Tx antennas
% M
www.eeworm.com/read/267448/11178472
m gensig.m
function [Y,t] = gensig(T,N,B,tstep)
% [Y,t] = gensig(T,N,B)
% generates N low-passed zero-mean unit-variance Gaussian noise waveforms,
% each T milliseconds long.
%
% T = duration of inpu
www.eeworm.com/read/412650/11189954
m channel.m
function y = channel(sig2, Mt, Mr, x, H, N);
% function y = channel(sig2, Mt, Mr, x, H, N)
%
% Channel transmission simulator
%
% inputs:
% sig2 - noise variance
% Mt - number of Tx antennas
% M
www.eeworm.com/read/334860/12568424
m randn.m
%R=randn(m,n) 生成标准正态分布的m行n列随机矩阵
%RANDN Normally distributed random numbers.
% RANDN(N) is an N-by-N matrix with random entries, chosen from
% a normal distribution with mean zero and variance
www.eeworm.com/read/203091/15365506
m channel.m
function y = channel(sig2, Mt, Mr, x, H, N);
% function y = channel(sig2, Mt, Mr, x, H, N)
%
% Channel transmission simulator
%
% inputs:
% sig2 - noise variance
% Mt - number of Tx antennas
% M
www.eeworm.com/read/389823/8496993
m ovsfspread.m
function [mux,codes] = OVSFSpread(i_input,q_input,logsf,ovsfn)
Len = length(i_input);
if rem(Len,2) ~= 0
error('source variance invalid!');
end
H=ovsfgen(logsf);
codes=H(ovsfn,:);
muxi = kron(i_i
www.eeworm.com/read/428608/8856257
m arwin.m
function [a,V,FPE]=arwin(x,p)
% All-Pole (AR) modeling using Full-Windowing
% Linear least-squares
% Model parameters [1 a1...ap V]
% V=input signal variance.
% FPE=Akaike's final prediction erro
www.eeworm.com/read/282317/9102229
m arwin.m
function [a,V,FPE]=arwin(x,p)
% All-Pole (AR) modeling using Full-Windowing
% Linear least-squares
% Model parameters [1 a1...ap V]
% V=input signal variance.
% FPE=Akaike's final prediction erro