Source and Channel Coding Strategies for Wireless Sensor Networks Page: 23
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dundancy pc,
2 2 2cAUB 24pc _-1
(24) DecA AI7 (1 2 1
4 A
When pc = Pc,min =0, Dec reaches maximum, Dec,max
reaches minimum, Dec,min =0.when 0 < pc < Pcmax.
A .'B When Pc - Pc,max Dec3.3. Simulations
I modeled the input as two zero mean Gaussian sources and studied the performance
of the proposed MDC scheme on erasure channels. The two MDC schemes with real and
complex transform matrices are simulated in MATLAB. For this simulation, I set 7A to 1 and
UB to 0.5. I also define a parameter (SNR) as,
2
(25) SNR 101ogo102
n
2 "21 + "2
where 7 n 2
n 2
3.3.1. Rate-Redundancy Efficiency
0 35
Simulated D
0 3 . . Simulated D
Theoretical D
Theoretical D
0.25
02
0.15
0
0.1
0 05
0 01 0.2 0.3 0.4 05 06 0 7 0.8
P
Figure 3.3. Rate redundancy distortion curves for the proposed complex MDC
transform compared to the real MDC transform under different values of p
Fig. 3.3 shows the plots corresponding to the the theoretical and simulated rate-
redundancy distortion of complex orthogonal transform described in (12) relative to the real23
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Li, Li. Source and Channel Coding Strategies for Wireless Sensor Networks, dissertation, December 2012; Denton, Texas. (https://digital.library.unt.edu/ark:/67531/metadc177226/m1/33/: accessed April 23, 2024), University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu; .