Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Recently, a new decoding rule called jar decoding was proposed, under which the decoder first forms a set of suitable size, called a jar, consisting of sequences from the channel input alphabet considered to be closely related to y n , and then takes any codeword from the jar as the estimate of the transmitted codeword. ,  show that under jar decoding, the analysis of low density parity check (LDPC) codes is much easier compared to maximum a posteriori (MAP) or maximum likelihood (ML) and Belief Propagation (BP) decoding, and new general LDPC coding theorems can be established. Specifically, it is proved that LDPC codes can approach the mutual information, with diminishing bit error probability, of any binary input memoryless channel with uniform input distribution when the average variable node degree is large. Moreover, simulation shows an interesting connection beten jar decoding and BP decoding, i.e., BP decoding can be regarded as one of many ways to pick up a codeword from the jar for LDPC codes when it succeeds in outputting a codeword.
['En-hui Yang', 'Jin Meng']
I am writing a research paper on a idea Jar decoding: LDPC coding theorems for binary input memoryless channels
[{"from": "human", "value": "I am writing a research paper on a idea Jar decoding: LDPC coding theorems for binary input memoryless channels"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Recently, a new decoding rule called jar decoding was proposed, under which the decoder first forms a set of suitable size, called a jar, consisting of sequences from the channel input alphabet considered to be closely related to y n , and then takes any codeword from the jar as the estimate of the transmitted codeword. ,  show that under jar decoding, the analysis of low density parity check (LDPC) codes is much easier compared to maximum a posteriori (MAP) or maximum likelihood (ML) and Belief Propagation (BP) decoding, and new general LDPC coding theorems can be established. Specifically, it is proved that LDPC codes can approach the mutual information, with diminishing bit error probability, of any binary input memoryless channel with uniform input distribution when the average variable node degree is large. Moreover, simulation shows an interesting connection beten jar decoding and BP decoding, i.e., BP decoding can be regarded as one of many ways to pick up a codeword from the jar for LDPC codes when it succeeds in outputting a codeword."}]