anfis based data rate prediction for cognitive radio

7/25/2019 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*

anfis based data rate prediction for cognitive radio

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