I will add you my codes just please continue from my codes step by step just from figure 11
- Please Could You Help Me To Continue My Codes I Need Just To Add Adaptive Lms Method And Without Any Filter From Figur 1 (293.58 KiB) Viewed 37 times
clear; clc;
%% variables
SNR_dB=0:1:30; % SNR (dB)
Npath = 4; % number of paths
Np = 256; % Length of message
p = 0.01; % impulse percentage
B = 0; % ratio of impulsive to AWGN
%B = 0;
cpfx = 4; % cyclic prefix
Np2 = Np*(1 + 1/cpfx);
%cpfx = 1/16; % cyclic prefix
%Np2 = Np*(1 + cpfx);
Nav=1000; % Number of runs
%% Constants
M=4;
C=[1+1j -1+1j -1-1j 1-1j];
SNR=10.^(SNR_dB/10); % SNR (lin)
Es=mean(abs(C).^2); % Symbol energy
Eb=Es/log2(M); % Bit energy
%sgm_AWGN =sqrt(0.5*Eb./SNR); % standard deviation
sgm=(0.5*Eb./SNR); % standard deviation
N_snr=length(SNR_dB);
sgm_AWGN = sqrt(sgm); %%%%%%% eqn1* %%%%%%%%
% sgm_imp = sqrt(B*sgm/((1+B)*p));
sgm_imp = sqrt(B*(sgm_AWGN.^2)/p); %%%%%%% eqn2 %%%%%%%%
N_snr=length(SNR_dB);
%% AWGN with fading
b_est = zeros(1,Np);
d_est = zeros(1,Np);
for k=1:N_snr
total_errors=0; % Reset errors for each SNR
for m=1:Nav
%% Ray channel
h = 1/sqrt(2)*(randn(1,Npath)+1i*randn(1,Npath));
H = fft(h,Np);
% Data
b = randi([0 M-1],1,Np);
d = C(b+1);
s1 = ifft(d,Np)*sqrt(Np);
% cyclic prefix
s2 = [s1(Np-Np/cpfx+1:end) s1];
% AWGN channel
noise_AWGN = sgm_AWGN(k)*(randn(1,Np2)+1j*randn(1,Np2));
% Impulsive noise
im_location=binornd(1,p*ones(1,length(s2)));%binary vector according to Bernoulli trials
im_hight1=sgm_imp(k)*randn(1,length(s2));
im_hight2=sgm_imp(k)*randn(1,length(s2));
im_noise=im_location.*(im_hight1 + 1j*im_hight2);% complex impulsive noise vector
x = filter(h,1,s2) + noise_AWGN + im_noise;
% x = filter(h,1,s2) + noise_AWGN ;
% Removing cyclic prefix
x1 = x(Np/cpfx+1:end);
% reciever
% x2 = fft(x1,Np)/sqrt(Np)./hf;
x2 = fft(x1,Np)/sqrt(Np);
x3 = x2./H;
% x4 = ifft(x3,Np);
for n = 1:Np
[~,index]=min(abs(C-x3(n)));
b_est(n)=index-1;
d_est(n)=C(index);
end
new_errors = biterr(b,b_est);
total_errors = total_errors + new_errors;
end
BER1(k)=total_errors/(2*Np*Nav);
disp(sprintf('SNR1 = %.2f dB, Bit Errors = %d',SNR_dB(k),total_errors))
end
%% Plot
figure(1)
semilogy(SNR_dB,BER1,'r*-');
grid on;
hold on
xlabel('SNR (dB)'); ylabel ('BER')
axis([0 30 1e-4 1])
- Please Could You Help Me To Continue My Codes I Need Just To Add Adaptive Lms Method And Without Any Filter From Figur 2 (293.58 KiB) Viewed 37 times
- Please Could You Help Me To Continue My Codes I Need Just To Add Adaptive Lms Method And Without Any Filter From Figur 3 (293.58 KiB) Viewed 37 times
- Please Could You Help Me To Continue My Codes I Need Just To Add Adaptive Lms Method And Without Any Filter From Figur 4 (293.58 KiB) Viewed 37 times
- Please Could You Help Me To Continue My Codes I Need Just To Add Adaptive Lms Method And Without Any Filter From Figur 5 (293.58 KiB) Viewed 37 times
- Please Could You Help Me To Continue My Codes I Need Just To Add Adaptive Lms Method And Without Any Filter From Figur 6 (293.58 KiB) Viewed 37 times
- Please Could You Help Me To Continue My Codes I Need Just To Add Adaptive Lms Method And Without Any Filter From Figur 7 (293.58 KiB) Viewed 37 times
IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | PISSN: 2321-7308 data. The bit error rate (BER) is the percentage of bits that have errors relative to the total number of bits received. [6]. Y.C.Kim, J.Y.Kim," Novel noise reduction scheme for power line communication systems with smart grid applications" in Proc. IEEE.ICCE, pp 791-792, Jan 2011 [7]. Ondraka J, Oravec R, K adlec J, Cochereova E, " Simulation Of RLS And LMS Algorithm For Adaptive Noise Cancellation In MATLAB", Department Of Radioelectronics, Bratislava, Slovak Republic [8]. Mario Bogdanovic, "Computer based simulation model realization of OFDM communication over power lines", 20th TELFOR, pp 249-252, Nov. 2012. Fig 11 shows the BER of the PLC system under periodic impulsive noise. Red colour represents without any filtering, light green colour represents with filtering using notch filter and blue colour represents with filtering using adaptive LMS filtering Simulation results shows that BER rate of the PLC system with noise mitigation technique is decreased than BER of the PLC system without noise mitigation algorithm. BER of PLC system using adaptive LMS filtering is decreased than BER of PLC system using notch filter. Thus these filtering techniques can be used to mitigate the periodic impulsive noise from the power line communication system. The proposed algorithm is a simple and effective method for mitigating periodic impulsive noise from the power line communication channel and thus it improves the performance of the PLC system BIOGRAPHIES Sumi Mathew was born in Kerala, India, in 1990. She received the B.Tech degree in electronics and communication Engineering from CUSAT university, India, in 2012 and is currently pursuing M.Tech degree in applied electronics and communication system at Nehru College of Engineering and Research Centre, Kerala, India. Her 6. CONCLUSIONS research interests include power line communication, modulation techniques, and wireless communication. The main objective of the paper is to reduce the effect of periodic impulsive noise in the Power Line Communication channel. First the presence of periodic impulsive noise is detected and then an adaptive notch filter is designed to mitigate the impulsive noise which is interfered with the OFDM data. Then an adaptive LMS filter is designed and suppress the noise. Simulation results shows that proposed algorithm gives better performance than conventional PLC system. i.e, BER of PLC system with filtering is decreased. than BER of PLC system without filtering. Adaptive LMS algorithm can be used for effectively remove the periodic impulsive noise. Prasanth Murukan received B.Tech Degree in electronics and communication engineering from University of Kerala, India in 2008 and M.E degree in applied electronics from Sardar Raja College of Engineering, Tirunelveli, Tamilnadu, India in 2012. Currently, he is professor in electronics and communication system at Nehru College of Engineering and Research Centre, Thrissur, Kerala, India. ACKNOWLEDGEMENTS I would like to thank all faculty members of ECE department, Nehru College of Engineering and Research Centre, Thrissur, Kerala for the guidance and support throughout the project work. REFERENCES [1]. M. Zimmermann and K. Dosert, "Analysis and modelling of impulsive noise in broadband power line communications, "IEEE Trans. Electromagn.Compat., Vol 44,pp 249-258,February 2002. [2]. G.Ndo, P.Sihon, and M.H Hamon, "Adaptive noise mitigation in impulsive environment Application to power line communication,"IEEE Transactions on Power Delivery. Vol 25, pp647-656, April 2010. [3]. A.Mengi, A.J Han Vink, "Successive impulsive noise suppression in OFDM" in Proc. IEEE.ISPLC, pp 33-37 March 2010. [4]. H.Meng, Y.L.Guan and S.Chen, Modelling and analysis of noise effects on broadband power line communications, IEEE Transactions on Power Delivery, pp 14-21, April 2006. [5]. Gaofeng Ren, Shushan Qiao, Huidong Zhao, Chundyang Li and Yong Hei, "Mitigation of periodic impulsive noise in OFDM based power line communications, IEEE Transactions on Power Delivery, Vol 28,825-834, April 2013. Volume: 03 Issue: 05 | May-2014, Available @ http://www.ijret.org 522
IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | PISSN: 2321-7308 data. The bit error rate (BER) is the percentage of bits that have errors relative to the total number of bits received. [6]. Y.C.Kim, J.Y.Kim," Novel noise reduction scheme for power line communication systems with smart grid applications" in Proc. IEEE.ICCE, pp 791-792, Jan 2011 [7]. Ondraka J, Oravec R, K adlec J, Cochereova E, " Simulation Of RLS And LMS Algorithm For Adaptive Noise Cancellation In MATLAB", Department Of Radioelectronics, Bratislava, Slovak Republic [8]. Mario Bogdanovic, "Computer based simulation model realization of OFDM communication over power lines", 20th TELFOR, pp 249-252, Nov. 2012. Fig 11 shows the BER of the PLC system under periodic impulsive noise. Red colour represents without any filtering, light green colour represents with filtering using notch filter and blue colour represents with filtering using adaptive LMS filtering Simulation results shows that BER rate of the PLC system with noise mitigation technique is decreased than BER of the PLC system without noise mitigation algorithm. BER of PLC system using adaptive LMS filtering is decreased than BER of PLC system using notch filter. Thus these filtering techniques can be used to mitigate the periodic impulsive noise from the power line communication system. The proposed algorithm is a simple and effective method for mitigating periodic impulsive noise from the power line communication channel and thus it improves the performance of the PLC system BIOGRAPHIES Sumi Mathew was born in Kerala, India, in 1990. She received the B.Tech degree in electronics and communication Engineering from CUSAT university, India, in 2012 and is currently pursuing M.Tech degree in applied electronics and communication system at Nehru College of Engineering and Research Centre, Kerala, India. Her 6. CONCLUSIONS research interests include power line communication, modulation techniques, and wireless communication. The main objective of the paper is to reduce the effect of periodic impulsive noise in the Power Line Communication channel. First the presence of periodic impulsive noise is detected and then an adaptive notch filter is designed to mitigate the impulsive noise which is interfered with the OFDM data. Then an adaptive LMS filter is designed and suppress the noise. Simulation results shows that proposed algorithm gives better performance than conventional PLC system. i.e, BER of PLC system with filtering is decreased. than BER of PLC system without filtering. Adaptive LMS algorithm can be used for effectively remove the periodic impulsive noise. Prasanth Murukan received B.Tech Degree in electronics and communication engineering from University of Kerala, India in 2008 and M.E degree in applied electronics from Sardar Raja College of Engineering, Tirunelveli, Tamilnadu, India in 2012. Currently, he is professor in electronics and communication system at Nehru College of Engineering and Research Centre, Thrissur, Kerala, India. ACKNOWLEDGEMENTS I would like to thank all faculty members of ECE department, Nehru College of Engineering and Research Centre, Thrissur, Kerala for the guidance and support throughout the project work. REFERENCES [1]. M. Zimmermann and K. Dosert, "Analysis and modelling of impulsive noise in broadband power line communications, "IEEE Trans. Electromagn.Compat., Vol 44,pp 249-258,February 2002. [2]. G.Ndo, P.Sihon, and M.H Hamon, "Adaptive noise mitigation in impulsive environment Application to power line communication,"IEEE Transactions on Power Delivery. Vol 25, pp647-656, April 2010. [3]. A.Mengi, A.J Han Vink, "Successive impulsive noise suppression in OFDM" in Proc. IEEE.ISPLC, pp 33-37 March 2010. [4]. H.Meng, Y.L.Guan and S.Chen, Modelling and analysis of noise effects on broadband power line communications, IEEE Transactions on Power Delivery, pp 14-21, April 2006. [5]. Gaofeng Ren, Shushan Qiao, Huidong Zhao, Chundyang Li and Yong Hei, "Mitigation of periodic impulsive noise in OFDM based power line communications, IEEE Transactions on Power Delivery, Vol 28,825-834, April 2013. Volume: 03 Issue: 05 | May-2014, Available @ http://www.ijret.org 522
IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | PISSN: 2321-7308 data. The bit error rate (BER) is the percentage of bits that have errors relative to the total number of bits received. [6]. Y.C.Kim, J.Y.Kim," Novel noise reduction scheme for power line communication systems with smart grid applications" in Proc. IEEE.ICCE, pp 791-792, Jan 2011 [7]. Ondraka J, Oravec R, K adlec J, Cochereova E, " Simulation Of RLS And LMS Algorithm For Adaptive Noise Cancellation In MATLAB", Department Of Radioelectronics, Bratislava, Slovak Republic [8]. Mario Bogdanovic, "Computer based simulation model realization of OFDM communication over power lines", 20th TELFOR, pp 249-252, Nov. 2012. Fig 11 shows the BER of the PLC system under periodic impulsive noise. Red colour represents without any filtering, light green colour represents with filtering using notch filter and blue colour represents with filtering using adaptive LMS filtering Simulation results shows that BER rate of the PLC system with noise mitigation technique is decreased than BER of the PLC system without noise mitigation algorithm. BER of PLC system using adaptive LMS filtering is decreased than BER of PLC system using notch filter. Thus these filtering techniques can be used to mitigate the periodic impulsive noise from the power line communication system. The proposed algorithm is a simple and effective method for mitigating periodic impulsive noise from the power line communication channel and thus it improves the performance of the PLC system BIOGRAPHIES Sumi Mathew was born in Kerala, India, in 1990. She received the B.Tech degree in electronics and communication Engineering from CUSAT university, India, in 2012 and is currently pursuing M.Tech degree in applied electronics and communication system at Nehru College of Engineering and Research Centre, Kerala, India. Her 6. CONCLUSIONS research interests include power line communication, modulation techniques, and wireless communication. The main objective of the paper is to reduce the effect of periodic impulsive noise in the Power Line Communication channel. First the presence of periodic impulsive noise is detected and then an adaptive notch filter is designed to mitigate the impulsive noise which is interfered with the OFDM data. Then an adaptive LMS filter is designed and suppress the noise. Simulation results shows that proposed algorithm gives better performance than conventional PLC system. i.e, BER of PLC system with filtering is decreased. than BER of PLC system without filtering. Adaptive LMS algorithm can be used for effectively remove the periodic impulsive noise. Prasanth Murukan received B.Tech Degree in electronics and communication engineering from University of Kerala, India in 2008 and M.E degree in applied electronics from Sardar Raja College of Engineering, Tirunelveli, Tamilnadu, India in 2012. Currently, he is professor in electronics and communication system at Nehru College of Engineering and Research Centre, Thrissur, Kerala, India. ACKNOWLEDGEMENTS I would like to thank all faculty members of ECE department, Nehru College of Engineering and Research Centre, Thrissur, Kerala for the guidance and support throughout the project work. REFERENCES [1]. M. Zimmermann and K. Dosert, "Analysis and modelling of impulsive noise in broadband power line communications, "IEEE Trans. Electromagn.Compat., Vol 44,pp 249-258,February 2002. [2]. G.Ndo, P.Sihon, and M.H Hamon, "Adaptive noise mitigation in impulsive environment Application to power line communication,"IEEE Transactions on Power Delivery. Vol 25, pp647-656, April 2010. [3]. A.Mengi, A.J Han Vink, "Successive impulsive noise suppression in OFDM" in Proc. IEEE.ISPLC, pp 33-37 March 2010. [4]. H.Meng, Y.L.Guan and S.Chen, Modelling and analysis of noise effects on broadband power line communications, IEEE Transactions on Power Delivery, pp 14-21, April 2006. [5]. Gaofeng Ren, Shushan Qiao, Huidong Zhao, Chundyang Li and Yong Hei, "Mitigation of periodic impulsive noise in OFDM based power line communications, IEEE Transactions on Power Delivery, Vol 28,825-834, April 2013. Volume: 03 Issue: 05 | May-2014, Available @ http://www.ijret.org 522
IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | PISSN: 2321-7308 data. The bit error rate (BER) is the percentage of bits that have errors relative to the total number of bits received. [6]. Y.C.Kim, J.Y.Kim," Novel noise reduction scheme for power line communication systems with smart grid applications" in Proc. IEEE.ICCE, pp 791-792, Jan 2011 [7]. Ondraka J, Oravec R, K adlec J, Cochereova E, " Simulation Of RLS And LMS Algorithm For Adaptive Noise Cancellation In MATLAB", Department Of Radioelectronics, Bratislava, Slovak Republic [8]. Mario Bogdanovic, "Computer based simulation model realization of OFDM communication over power lines", 20th TELFOR, pp 249-252, Nov. 2012. Fig 11 shows the BER of the PLC system under periodic impulsive noise. Red colour represents without any filtering, light green colour represents with filtering using notch filter and blue colour represents with filtering using adaptive LMS filtering Simulation results shows that BER rate of the PLC system with noise mitigation technique is decreased than BER of the PLC system without noise mitigation algorithm. BER of PLC system using adaptive LMS filtering is decreased than BER of PLC system using notch filter. Thus these filtering techniques can be used to mitigate the periodic impulsive noise from the power line communication system. The proposed algorithm is a simple and effective method for mitigating periodic impulsive noise from the power line communication channel and thus it improves the performance of the PLC system BIOGRAPHIES Sumi Mathew was born in Kerala, India, in 1990. She received the B.Tech degree in electronics and communication Engineering from CUSAT university, India, in 2012 and is currently pursuing M.Tech degree in applied electronics and communication system at Nehru College of Engineering and Research Centre, Kerala, India. Her 6. CONCLUSIONS research interests include power line communication, modulation techniques, and wireless communication. The main objective of the paper is to reduce the effect of periodic impulsive noise in the Power Line Communication channel. First the presence of periodic impulsive noise is detected and then an adaptive notch filter is designed to mitigate the impulsive noise which is interfered with the OFDM data. Then an adaptive LMS filter is designed and suppress the noise. Simulation results shows that proposed algorithm gives better performance than conventional PLC system. i.e, BER of PLC system with filtering is decreased. than BER of PLC system without filtering. Adaptive LMS algorithm can be used for effectively remove the periodic impulsive noise. Prasanth Murukan received B.Tech Degree in electronics and communication engineering from University of Kerala, India in 2008 and M.E degree in applied electronics from Sardar Raja College of Engineering, Tirunelveli, Tamilnadu, India in 2012. Currently, he is professor in electronics and communication system at Nehru College of Engineering and Research Centre, Thrissur, Kerala, India. ACKNOWLEDGEMENTS I would like to thank all faculty members of ECE department, Nehru College of Engineering and Research Centre, Thrissur, Kerala for the guidance and support throughout the project work. REFERENCES [1]. M. Zimmermann and K. Dosert, "Analysis and modelling of impulsive noise in broadband power line communications, "IEEE Trans. Electromagn.Compat., Vol 44,pp 249-258,February 2002. [2]. G.Ndo, P.Sihon, and M.H Hamon, "Adaptive noise mitigation in impulsive environment Application to power line communication,"IEEE Transactions on Power Delivery. Vol 25, pp647-656, April 2010. [3]. A.Mengi, A.J Han Vink, "Successive impulsive noise suppression in OFDM" in Proc. IEEE.ISPLC, pp 33-37 March 2010. [4]. H.Meng, Y.L.Guan and S.Chen, Modelling and analysis of noise effects on broadband power line communications, IEEE Transactions on Power Delivery, pp 14-21, April 2006. [5]. Gaofeng Ren, Shushan Qiao, Huidong Zhao, Chundyang Li and Yong Hei, "Mitigation of periodic impulsive noise in OFDM based power line communications, IEEE Transactions on Power Delivery, Vol 28,825-834, April 2013. Volume: 03 Issue: 05 | May-2014, Available @ http://www.ijret.org 522
IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | PISSN: 2321-7308 data. The bit error rate (BER) is the percentage of bits that have errors relative to the total number of bits received. [6]. Y.C.Kim, J.Y.Kim," Novel noise reduction scheme for power line communication systems with smart grid applications" in Proc. IEEE.ICCE, pp 791-792, Jan 2011 [7]. Ondraka J, Oravec R, K adlec J, Cochereova E, " Simulation Of RLS And LMS Algorithm For Adaptive Noise Cancellation In MATLAB", Department Of Radioelectronics, Bratislava, Slovak Republic [8]. Mario Bogdanovic, "Computer based simulation model realization of OFDM communication over power lines", 20th TELFOR, pp 249-252, Nov. 2012. Fig 11 shows the BER of the PLC system under periodic impulsive noise. Red colour represents without any filtering, light green colour represents with filtering using notch filter and blue colour represents with filtering using adaptive LMS filtering Simulation results shows that BER rate of the PLC system with noise mitigation technique is decreased than BER of the PLC system without noise mitigation algorithm. BER of PLC system using adaptive LMS filtering is decreased than BER of PLC system using notch filter. Thus these filtering techniques can be used to mitigate the periodic impulsive noise from the power line communication system. The proposed algorithm is a simple and effective method for mitigating periodic impulsive noise from the power line communication channel and thus it improves the performance of the PLC system BIOGRAPHIES Sumi Mathew was born in Kerala, India, in 1990. She received the B.Tech degree in electronics and communication Engineering from CUSAT university, India, in 2012 and is currently pursuing M.Tech degree in applied electronics and communication system at Nehru College of Engineering and Research Centre, Kerala, India. Her 6. CONCLUSIONS research interests include power line communication, modulation techniques, and wireless communication. The main objective of the paper is to reduce the effect of periodic impulsive noise in the Power Line Communication channel. First the presence of periodic impulsive noise is detected and then an adaptive notch filter is designed to mitigate the impulsive noise which is interfered with the OFDM data. Then an adaptive LMS filter is designed and suppress the noise. Simulation results shows that proposed algorithm gives better performance than conventional PLC system. i.e, BER of PLC system with filtering is decreased. than BER of PLC system without filtering. Adaptive LMS algorithm can be used for effectively remove the periodic impulsive noise. Prasanth Murukan received B.Tech Degree in electronics and communication engineering from University of Kerala, India in 2008 and M.E degree in applied electronics from Sardar Raja College of Engineering, Tirunelveli, Tamilnadu, India in 2012. Currently, he is professor in electronics and communication system at Nehru College of Engineering and Research Centre, Thrissur, Kerala, India. ACKNOWLEDGEMENTS I would like to thank all faculty members of ECE department, Nehru College of Engineering and Research Centre, Thrissur, Kerala for the guidance and support throughout the project work. REFERENCES [1]. M. Zimmermann and K. Dosert, "Analysis and modelling of impulsive noise in broadband power line communications, "IEEE Trans. Electromagn.Compat., Vol 44,pp 249-258,February 2002. [2]. G.Ndo, P.Sihon, and M.H Hamon, "Adaptive noise mitigation in impulsive environment Application to power line communication,"IEEE Transactions on Power Delivery. Vol 25, pp647-656, April 2010. [3]. A.Mengi, A.J Han Vink, "Successive impulsive noise suppression in OFDM" in Proc. IEEE.ISPLC, pp 33-37 March 2010. [4]. H.Meng, Y.L.Guan and S.Chen, Modelling and analysis of noise effects on broadband power line communications, IEEE Transactions on Power Delivery, pp 14-21, April 2006. [5]. Gaofeng Ren, Shushan Qiao, Huidong Zhao, Chundyang Li and Yong Hei, "Mitigation of periodic impulsive noise in OFDM based power line communications, IEEE Transactions on Power Delivery, Vol 28,825-834, April 2013. Volume: 03 Issue: 05 | May-2014, Available @ http://www.ijret.org 522
IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | PISSN: 2321-7308 data. The bit error rate (BER) is the percentage of bits that have errors relative to the total number of bits received. [6]. Y.C.Kim, J.Y.Kim," Novel noise reduction scheme for power line communication systems with smart grid applications" in Proc. IEEE.ICCE, pp 791-792, Jan 2011 [7]. Ondraka J, Oravec R, K adlec J, Cochereova E, " Simulation Of RLS And LMS Algorithm For Adaptive Noise Cancellation In MATLAB", Department Of Radioelectronics, Bratislava, Slovak Republic [8]. Mario Bogdanovic, "Computer based simulation model realization of OFDM communication over power lines", 20th TELFOR, pp 249-252, Nov. 2012. Fig 11 shows the BER of the PLC system under periodic impulsive noise. Red colour represents without any filtering, light green colour represents with filtering using notch filter and blue colour represents with filtering using adaptive LMS filtering Simulation results shows that BER rate of the PLC system with noise mitigation technique is decreased than BER of the PLC system without noise mitigation algorithm. BER of PLC system using adaptive LMS filtering is decreased than BER of PLC system using notch filter. Thus these filtering techniques can be used to mitigate the periodic impulsive noise from the power line communication system. The proposed algorithm is a simple and effective method for mitigating periodic impulsive noise from the power line communication channel and thus it improves the performance of the PLC system BIOGRAPHIES Sumi Mathew was born in Kerala, India, in 1990. She received the B.Tech degree in electronics and communication Engineering from CUSAT university, India, in 2012 and is currently pursuing M.Tech degree in applied electronics and communication system at Nehru College of Engineering and Research Centre, Kerala, India. Her 6. CONCLUSIONS research interests include power line communication, modulation techniques, and wireless communication. The main objective of the paper is to reduce the effect of periodic impulsive noise in the Power Line Communication channel. First the presence of periodic impulsive noise is detected and then an adaptive notch filter is designed to mitigate the impulsive noise which is interfered with the OFDM data. Then an adaptive LMS filter is designed and suppress the noise. Simulation results shows that proposed algorithm gives better performance than conventional PLC system. i.e, BER of PLC system with filtering is decreased. than BER of PLC system without filtering. Adaptive LMS algorithm can be used for effectively remove the periodic impulsive noise. Prasanth Murukan received B.Tech Degree in electronics and communication engineering from University of Kerala, India in 2008 and M.E degree in applied electronics from Sardar Raja College of Engineering, Tirunelveli, Tamilnadu, India in 2012. Currently, he is professor in electronics and communication system at Nehru College of Engineering and Research Centre, Thrissur, Kerala, India. ACKNOWLEDGEMENTS I would like to thank all faculty members of ECE department, Nehru College of Engineering and Research Centre, Thrissur, Kerala for the guidance and support throughout the project work. REFERENCES [1]. M. Zimmermann and K. Dosert, "Analysis and modelling of impulsive noise in broadband power line communications, "IEEE Trans. Electromagn.Compat., Vol 44,pp 249-258,February 2002. [2]. G.Ndo, P.Sihon, and M.H Hamon, "Adaptive noise mitigation in impulsive environment Application to power line communication,"IEEE Transactions on Power Delivery. Vol 25, pp647-656, April 2010. [3]. A.Mengi, A.J Han Vink, "Successive impulsive noise suppression in OFDM" in Proc. IEEE.ISPLC, pp 33-37 March 2010. [4]. H.Meng, Y.L.Guan and S.Chen, Modelling and analysis of noise effects on broadband power line communications, IEEE Transactions on Power Delivery, pp 14-21, April 2006. [5]. Gaofeng Ren, Shushan Qiao, Huidong Zhao, Chundyang Li and Yong Hei, "Mitigation of periodic impulsive noise in OFDM based power line communications, IEEE Transactions on Power Delivery, Vol 28,825-834, April 2013. Volume: 03 Issue: 05 | May-2014, Available @ http://www.ijret.org 522