2017 15th Canadian Workshop on Information Theory (CWIT) 2017
DOI: 10.1109/cwit.2017.7994822
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On the design of good LDPC codes with joint genetic algorithm and linear programming optimization

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“…. (43) For all channel states , ̅ ( , , π(1)) can be approximated as ̅ ( , , π(1)) = ̅ ,0 −1 ( , , π(1)) [40], where ̅ ,0 is a constant and independent on the channel state, ( , , π(1)) represents the theoretical achievable rate of user under channel matrix . In the following, we derive ( , , π(1)).…”
Section: Figure 7 the Relationship Between The Output And Input Ei Fmentioning
confidence: 99%
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“…. (43) For all channel states , ̅ ( , , π(1)) can be approximated as ̅ ( , , π(1)) = ̅ ,0 −1 ( , , π(1)) [40], where ̅ ,0 is a constant and independent on the channel state, ( , , π(1)) represents the theoretical achievable rate of user under channel matrix . In the following, we derive ( , , π(1)).…”
Section: Figure 7 the Relationship Between The Output And Input Ei Fmentioning
confidence: 99%
“…For the first aspect, firstly consider the complexity for the optimization of the LDPC compression code, i.e., the problem (28). The complexity is ( ) , wherein ( ) is the complexity of LP [43] (usually a polynomial of the variable number ), q denotes the number of the discrete values of ̅ for exhaustive searching, and Q is the number of channel states (since (28) should be solved for each channel state). Secondly, consider the degree profile optimization for Raptor code, i.e., problem (53).…”
Section: Complexity Analysismentioning
confidence: 99%