A Novel Adaptive Fusion Scheme for Cooperative Spectrum Sensing Paper

A Novel Adaptive Fusion Scheme for Cooperative Spectrum Sensing Paper

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This Paper is implemented for RLS and NLMS.In cognitive radio systems, the accuracy of spectrum
sensing depends on the received primary signal strength at the secondary user (SU). In fact, a single node sensing would be compromised if the received signal strength is not high enough to be detected by this node. In this paper, we propose a cooperative decision fusion rule based on adaptive linear combiner. The weights which correspond to confidence levels affected to SUs, are determined adaptively using the Normalized Least Mean Squares (NLMS) and the Recursive Mean Squares (RLS) algorithms. The proposed algorithms combine the SUs decisions with the adaptive confidence levels to track the surrounding environment.

Proposal Work:

To implement the paper by NLMS, LMS RLS, KRLS, Kalman Filter, EKF Filter.

Figure (3): Mean of the confidence level vector versus time (K = 3):

a)     NLMS algorithm

b)    KLMS algorithm

c)     LMS algorithm

d)    RLS algorithm

e)     KRLS Algorithm

f)     Kalman Filter

g)    EKF Algorithm


Fig. 4. MSD versus. for

a)     NLMS algorithm

b)    KLMS

c)     LMS

d)    RLS

e)     KRLS

f)     Kalman Filter

g)    EKF

Fig. 5. Performance of the proposed approach:

a)     Qd and versus time (K = 10) for (NLMS, LMS, KLMS, RLS, KRLS, Kalman, EKF, OR Rule)

b)     Qf versus time (K = 10). for (NLMS, LMS, KLMS, RLS, KRLS, Kalman, EKF, OR Rule

c)     Qd versus time (K = 10).  for (NLMS, LMS, KLMS, RLS, KRLS, Kalman, EKF, AND Rule.

d)    Qf versus time (K = 10).  for (NLMS, LMS, KLMS, RLS, KRLS, Kalman, EKF, AND Rule.

e)     Qd and versus time (K = 10) for (NLMS, RLS, LMS, KRLS, Kalman, EKF, Majority Rule)

f)     Qf versus time (K = 10). For (NLMS, KLMS, LMS, RLS, KRLS, Kalman, EKF, Majority Rule)

Fig. 6. ROC curves (K = 10 and Pf = 0.1). For all adaptive proposed schemes.

Fig. 7. Received SNR at the 1st SU changes (K = 3 and Pf = 0.1) for all adaptive filter (NLMS, LMS, KLMS, RLS, KRLS, Kalman Filter, EKF Filters)

Fig. 8. PU status changes (K = 10 and Pf = 0.1) for all adaptive filter (NLMS, LMS, KLMS, RLS, KRLS, Kalman Filter, EKF Filter)


Matlab Code


clc
clear all
close all
%%

klen=10;
len_time=100;
ori_signal=[];
noise_data=[];
signal_data=[];
for snr_db=[10];
   
    snr = 10.^(snr_db./10); % SNR in linear scale
    mess_data=randi([0 1],klen,len_time);
    mod_data=(2.*(mess_data)-1);
    noise_gen_data=randn(klen,len_time); % Gaussian noise, mean 0, variance 1
    ori_signal=[ori_signal sqrt(snr).*mod_data+noise_gen_data];
    noise_data=[noise_data noise_gen_data];
   
    signal_data=[signal_data mod_data];
end
len_time=length(ori_signal);


%% NLMS
srtleg{1}='NLMS';
propval=0.1;
ind=1;
for muval=0.1:0.1:2
   
threshold_value1=qfuncinv(0.1);

for prop_fals=propval
    weight_value=ones(klen,len_time);
    threshold_value=qfuncinv(prop_fals);
    for kiter=1:len_time
        ddata=ori_signal(:,kiter);
        Weigth1n_value(kiter)=mean(weight_value(:,kiter));
        ddata1=(abs(ddata).^2)>threshold_value;
        ddatax=2*ddata1-1;
        sn=weight_value(:,kiter)'*ddatax;
        res=ddata1(1);
        for k3=2:length(ddata1)
            res=bitor(ddata1(k3),res);
        end
        desrval=res*2-1;
        en=desrval-sn;
        weight_value(:,kiter+1)=weight_value(:,kiter)+muval*en*(ddatax/(ddatax'*ddatax));
       
    end
   
    
msdval(ind)=10*log10(mean((Weigth1n_value-mean(Weigth1n_value)).^2));
muvalfinal(ind)=muval;
ind=ind+1;

end

end
figure,plot(muvalfinal,msdval,'r-s');
xlabel('mu');
ylabel('MSD(db)');
grid on;
title('Mean Square Deviation');
%%