Avetis Ioannisyan

avetis@60ateight.com

Last Updated: 11/30/05

LMS Channel Adaptation





reset randomizers

randn( state ,sum(100*clock))

rand( state ,sum(100*clock))



numPoints = 5000

numTaps = 10 　　　　 channel order

Mu = 0.001:0.001:0.01 iteration step size



input is guassian

x = randn(numPoints,1) + j*randn(numPoints,1)

choose channel to be random uniform

h = rand(numTaps, 1) + i*rand(numTaps, 1)

h = [1 0 0 0 1] testing only

h = h/max(h) normalize channel

convolve channel with the input

d = filter(h, 1, x)



initialize variables

w = []

y = []

in = []

e = [] error, f-　Avetis Ioannisyan

　avetis@60ateight.com

　Last Updated: 11/30/05

　LMS Channel Adaptation





　reset randomizers

randn( state ,sum(100*clock))　

rand( state ,sum(100*clock))　



numPoints = 5000

numTaps = 10 　　　　　channel order

Mu = 0.001:0.001:0.01　　　　　　 iteration step size



　input is guassian

x = randn(numPoints,1) + j*randn(numPoints,1)　

　choose channel to be random uniform

h = rand(numTaps, 1)　 + i*rand(numTaps, 1)

h = [1 0 0 0 1]　　 testing only

h = h/max(h)　　 normalize channel

　convolve channel with the input

d = filter(h, 1, x)



　initialize variables

w = []

y = []

in = []　

e = []　　error, f