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Description: 这是我的课程设计，Kaiser窗FIR滤波器的Matlab实现，内附对结果的分析-This is my course design, Kaiser window FIR filter Matlab implementation, analysis of results included
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Size: 230400 |
Author: Amin |
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Description:
reduction to papr in ofdm with Peak Windowing:
The basic idea of peak windowing is to multiply the envelope of OFDM
signal with a weighting function . Therefore,
~xE (t)= xE (t) f (t)
where
XE(t) =[x(t)]
The weighting function given by:
f (t)= 1-Σ α .w(t- t )
t-t
w(t) : is the window function.
t : denotes the position of a local maximum of the envelop xE(t) .
α : attenuation constant .
When the amplitude of envelop amplitude of the OFDM signal exceeds a threshold, a window function is applied to the envelop of the OFDM signal to eliminate the peak amplitude. windowing results in a smooth signal.
Hanning window function will be used for w(t)
As a matter of fact , other windows such as cosine, hamming and Kaiser may be employed.
-
reduction to papr in ofdm with Peak Windowing:
The basic idea of peak windowing is to multiply the envelope of OFDM
signal with a weighting function . Therefore,
~xE (t)= xE (t) f (t)
where
XE(t) =[x(t)]
The weighting function given by:
f (t)= 1-Σ α .w(t- t )
t-t
w(t) : is the window function.
t : denotes the position of a local maximum of the envelop xE(t) .
α : attenuation constant .
When the amplitude of envelop amplitude of the OFDM signal exceeds a threshold, a window function is applied to the envelop of the OFDM signal to eliminate the peak amplitude. windowing results in a smooth signal.
Hanning window function will be used for w(t)
As a matter of fact , other windows such as cosine, hamming and Kaiser may be employed.
Platform: |
Size: 2048 |
Author: asmaa |
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Description: empirical formula with kaiser
clc
clear all
fs=1000
fc=250
df=50
r=0.001
f=fc/fs
dw=2*pi*(df/fs)
a=-20*log(r)
n=floor(((a-8)/(2.285*dw))+1)
if a>50
b=0.1102*(a-8.7)
elseif a>=21 && a<=50
b=0.5842*((a-21)^0.4)+0.07886*(a-21)
elseif a<21
b=0.0
end
w=kaiser(n,b)
for i=1:n
if i~=(n-1)/2
hd(i)= (2*f*sin((i-((n-1)/2))*2*pi*f))/((i-((n-1)/2))*2*pi*f)
elseif i==(n-1)/2
hd(i)=2*f
end
end
for j=1:n
h(j)=w(j)*hd(j)
end
subplot(3,1,1), plot(w)
subplot(3,1,2), plot(h)
subplot(3,1,3), plot(h,n)
- empirical formula with kaiser
clc
clear all
fs=1000
fc=250
df=50
r=0.001
f=fc/fs
dw=2*pi*(df/fs)
a=-20*log(r)
n=floor(((a-8)/(2.285*dw))+1)
if a>50
b=0.1102*(a-8.7)
elseif a>=21 && a<=50
b=0.5842*((a-21)^0.4)+0.07886*(a-21)
elseif a<21
b=0.0
end
w=kaiser(n,b)
for i=1:n
if i~=(n-1)/2
hd(i)= (2*f*sin((i-((n-1)/2))*2*pi*f))/((i-((n-1)/2))*2*pi*f)
elseif i==(n-1)/2
hd(i)=2*f
end
end
for j=1:n
h(j)=w(j)*hd(j)
end
subplot(3,1,1), plot(w)
subplot(3,1,2), plot(h)
subplot(3,1,3), plot(h,n)
Platform: |
Size: 81920 |
Author: rezwan |
Hits:
Description: empirical formula with kaiser
clc
clear all
fs=1000
fc=250
df=50
r=0.001
f=fc/fs
dw=2*pi*(df/fs)
a=-20*log(r)
n=floor(((a-8)/(2.285*dw))+1)
if a>50
b=0.1102*(a-8.7)
elseif a>=21 && a<=50
b=0.5842*((a-21)^0.4)+0.07886*(a-21)
elseif a<21
b=0.0
end
w=kaiser(n,b)
for i=1:n
if i~=(n-1)/2
hd(i)= (2*f*sin((i-((n-1)/2))*2*pi*f))/((i-((n-1)/2))*2*pi*f)
elseif i==(n-1)/2
hd(i)=2*f
end
end
for j=1:n
h(j)=w(j)*hd(j)
end
subplot(3,1,1), plot(w)
subplot(3,1,2), plot(h)
subplot(3,1,3), plot(h,n)
- empirical formula with kaiser
clc
clear all
fs=1000
fc=250
df=50
r=0.001
f=fc/fs
dw=2*pi*(df/fs)
a=-20*log(r)
n=floor(((a-8)/(2.285*dw))+1)
if a>50
b=0.1102*(a-8.7)
elseif a>=21 && a<=50
b=0.5842*((a-21)^0.4)+0.07886*(a-21)
elseif a<21
b=0.0
end
w=kaiser(n,b)
for i=1:n
if i~=(n-1)/2
hd(i)= (2*f*sin((i-((n-1)/2))*2*pi*f))/((i-((n-1)/2))*2*pi*f)
elseif i==(n-1)/2
hd(i)=2*f
end
end
for j=1:n
h(j)=w(j)*hd(j)
end
subplot(3,1,1), plot(w)
subplot(3,1,2), plot(h)
subplot(3,1,3), plot(h,n)
Platform: |
Size: 541696 |
Author: rezwan |
Hits: