IJCA Template - International Journal of Computer Applications - IJCA

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International Journal of Computer Applications (0975 – 8887)
Volume 7– No.7, October 2010
In Fig.11, we present a comparison between our simulation results
for (1533, 495) product code and results published in [12] for
convolutional turbo code of rate 1/3 using 16 states. We can
observe that product code have worse performance at low SNR
compared to convolutional code, whereas at SNR> 4.3 dB, the
product code are better.
10 -1
10 -2
10 0 SNR
10 -1 SNR (dB)
(4545,2025) Lucas
(4545,2025) 20iter
10 -3
10 -2
10 -3
10 -4
10 -5
iteration-1
iteration-2
iteration-4
iteration-8
iteration-16
iteration-32
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
10 -4
10 -5
Fig. 9 : Performance of iterative decoding of the (1533, 495)
product code on AWGN channel
10 0 SNR
10 -6
1 1.5 2 2.5
Fig. 7 : Performance of iterative decoding of the PCB(4545,
2025). The BER of the Lucas et al.[3] algorithm is given as
reference.
10 -1
10 -2
10 -3
10 0 SNR (dB)
PCB(17633,8595) R=0.48
Shannon Capatity Limit R=0.48
10 -4
10 -1
10 -2
10 -5
(1253, 495)-iter-8
(4545, 2025)-iter-8
(5329, 2025)-iter-8
10 -6
1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 2.8 3
10 -3
10 -4
Fig. 10 : Performance of iterative decoding of the
PC(5329,2025),PCB(4545,2025) and PCB(1253,495) codes
10 -5
10 -6
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
Fig. 8 : Performance of the (17633,8595) PCB code compared
to Shannon capacity limit
Fig. 11: Performance of iterative decoding of the (1533, 495)
product code on Rayleigh fading channel
17