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Novel blind identification of LDPC codes using average LLR of syndrome a posteriori probability
Blind signal processing methods have been very popular recently since they can play crucial roles in the prevalent cognitive radio research. Blind encoder identification has drawn immense research interest lately. In this paper, we make the first-ever attempt to tackle the blind low-density parity-check (LDPC) encoder identification for binary phase-shift keying (BPSK) signals. We propose a novel blind identification system which consists of three components, namely EM (expectation-maximization) estimator for signal amplitude and noise variance, LLR (log-likelihood ratio) estimator for syndrome a posteriori probabilities, and maximum average LLR detector. Monte Carlo simulation results demonstrate that our proposed new blind LDPC encoder identification scheme is very promising even for harsh channel environments with low signal-to-noise ratios.
Novel blind identification of LDPC codes using average LLR of syndrome a posteriori probability
Blind signal processing methods have been very popular recently since they can play crucial roles in the prevalent cognitive radio research. Blind encoder identification has drawn immense research interest lately. In this paper, we make the first-ever attempt to tackle the blind low-density parity-check (LDPC) encoder identification for binary phase-shift keying (BPSK) signals. We propose a novel blind identification system which consists of three components, namely EM (expectation-maximization) estimator for signal amplitude and noise variance, LLR (log-likelihood ratio) estimator for syndrome a posteriori probabilities, and maximum average LLR detector. Monte Carlo simulation results demonstrate that our proposed new blind LDPC encoder identification scheme is very promising even for harsh channel environments with low signal-to-noise ratios.
Novel blind identification of LDPC codes using average LLR of syndrome a posteriori probability
Tian Xia, (author) / Wu, Hsiao-Chun (author)
2012-11-01
1049663 byte
Conference paper
Electronic Resource
English
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