anfis based data rate prediction for cognitive radio
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Transcript of anfis based data rate prediction for cognitive radio
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FINAL YEAR P3 PROJECT PRESENTATION
ADAPTIVE NEURO- FUZZY INFERENCE SYSTEM BASED
DATA RATE PREDICTIONFOR
COGNITIVE RADIO
Presented By
As!"t# Y#d#$ %&'&(3)*
G#+r#$ J#"s,# %&'&(./*
M#y#n0 S"n12# %&'&(&*
N"s2#nt S"n2# %&'&(4'*
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Contents
Project Layout
Design Procedure
- CR Design procedure
- ANFIS Design procedure for data rate prediction
Simulation Model
- CR Simulation model
- ANFIS ased data rate prediction simulation model Results
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Project Layout
!"e #or$ proposed in our project summari%es t"e follo#ing
points&
- 'arious stages of Cogniti(e Radio) its emergent e"a(ior
and standards) and its applications*
- Implementation of Cogniti(e Radio Net#or$*
- ANFIS ased learning sc"eme and its use for data rate
prediction for Cogniti(e Radio*
- Discussing performance of ANFIS met"ods li$e RMS+)prediction accuracy etc*
- Simulation of ANFIS model along #it" results*
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CR Design Procedure
Figure 1: ,loc$ Diagram for simulation of cogniti(e radio implementation
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ANFIS Design procedurefor data rate prediction
Figure 2& ANFIS design procedure for
data rate prediction
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CR Simulation Model
No* of primary users in t"e net#or$ &
Do you #ant to enter first primary user ./N & .
Do you #ant to enter second primary user ./N & .
Do you #ant to enter t"ird primary user ./N & . 00
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Contd
Carrier Frequency
Fc121333 4%
Sampling Frequency
Fs215333 4%
Message signal &
61 2 78cos958pi81333*8t:Modulated Signal :
y1 2 ammod961)Fc1)Fs:
Figure 3: 1stmodulated signal
Figure 4: 5ndmodulated signal
Carrier Frequency
Fc525333 4%Sampling Frequency
Fs215333 4%
Message signal &
61 2 78cos958pi81333*8t:
Modulated Signal :
y5 2 ammod961)Fc5)Fs:
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Contd
Figure 5 : rdmodulated signal
Carrier Frequency
Fc2333 4%
Sampling Frequency
Fs215333 4%
Message signal &
61 2 78cos958pi81333*8t:Modulated Signal :
y 2 ammod961)Fc)Fs:
#"ere ammod96) Fc)Fs: is a matla function #"ic" uses message
signal 6 to modulate a carrier signal #it" fre;uency Fc 94%: using
amplitude modulation* Carrier signal and 6 "a(e sampling
fre;uency Fs94%:*
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Contd
Figure 6: Adder
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Contd
Figure 7 :Po#er Spectral Density (ia Periodogram 9all
primary users are present:
4ere periodogram is an inuilt MA!LA, function
#"ic" returns po#er spectral density 9psd: of a signal
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Contd
Allocated spectrum ands used y
primary users
Figure 8 : Po#er spectral Density #"en all
users are present
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Contd
1stand rdprimary users
are present 5ndprimary user asent
Spectral "ole left
Allocation of t"is (acant slot to
secondary user
Figure : Po#er spectral Density
#"en only 5nd user is asent
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ANFIS !ased data rate predictionsimulation model
!ime Series "ata :
!"e data is generated from t"e Mac$ey >lass time delay differential e;uation
#"ic" is defined y&
dx (t)/dt = 0.2x (t tau)/ (1+x (t tau) ^10) 0.1x (t)
?"en 6 93: 2 1*5 and tau 2 1@) #e "a(e a non-periodic and non-con(ergent time
series t"at is (ery sensiti(e to initial conditions* 9?e assume 6 9t: 2 3 #"en t 3*:
Figure 1#: Plot of generation of Mac$ey->lass time series data
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Contd
$reprocessing t%e "ata :
No# #e #ant to uild an ANFIS t"at can predict 6 9t=B: from t"e past
(alues of t"is time series) t"at is) 6 9t-1:) 6 9t-15:) 6 9t-B:) and 6 9t:* !"erefore
t"e training data format is
6 9t 1:) 6 9t 15:) 6 9t B:) 6 9t:) 6 9t=B:E
From t 2 11 to 111@) #e collect 1333 data pairs of t"e ao(e format* !"e
first 33 are used for training #"ile t"e ot"ers are used for c"ec$ing* !"e plot
s"o#s t"e segment of t"e time series #"ere data pairs #ere e6tracted from*
!"e first 133 data points are ignored to a(oid t"e transient portion of t"e data*
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Contd
Figure 11: Plot of preprocessing t"e time series data
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Contd
&rror Cur'es :
!"is plot displays error cur(es for ot" training and c"ec$ing data* Note
t"at t"e training error is "ig"er t"an t"e c"ec$ing error* !"is p"enomenon
is not uncommon in ANFIS learning or nonlinear regression in generalG it
could indicate t"at t"e training process is not close to finis"ed yet*
Figure 12 :+rror cur(es plot
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Contd
Comparison:
!"is plot s"o#s t"e original time series and t"e one predicted y ANFIS* !"e
difference is so tiny t"at it is impossile to tell one from anot"er y eye
inspection* !"at is #"y you proaly see only t"e ANFIS prediction cur(e*
!"e prediction errors must e (ie#ed on anot"er scale*
Figure 13 :Plot et#een original time series and t"e one predicted y
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Contd
$rediction &rrors o( )*F+S:
Prediction error of ANFIS is s"o#n "ere* Note t"at t"e scale is aout a
"undredt" of t"e scale of t"e pre(ious plot*
Rememer t"at #e "a(e only 13 epoc"s of training in t"is caseG etter
performance is e6pected if #e "a(e e6tensi(e training*
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Contd
Figure 14 & Plot of ANFIS prediction errors
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Results
4ence t"e original time data series and t"e one predicted y ANFIS
are nearly t"e same* !"e difference is so tiny t"at it is impossile to
tell one from anot"er y eye inspection*
!"e ANFIS ased tec"ni;ue #as successfully implemented to
predict data rate* Con(entional ANFIS #or$s etter in accuracy and RMS+ error
compared to neural net#or$ met"od* ,ut it generated "uge rule
#"en numer inputs #ere increased and #"ic" could not e "andled
y simulation en(ironment*