Blind Identification of Nonbinary LDPC Codes Using Average LLR of Syndrome a Posteriori Probability

• Published in: IEEE communications letters Vol. 17; no. 7; pp. 1301 - 1304
• Main Authors: Xia, Tian, Wu, Hsiao-Chun
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.07.2013; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Applied sciences; Blind encoder identification; Coding, codes; Detection, estimation, filtering, equalization, prediction; Encoding; Exact sciences and technology; Frequency modulation; Information, signal and communications theory; log-likelihood ratio; Modulation, demodulation; nonbinary low-density parity-check codes; Parity check codes; Radiocommunications; Receivers; Signal and communications theory; Signal, noise; Telecommunications; Telecommunications and information theory; Transceivers; Transmitters; Transmitters. Receivers; Performance evaluation; log-likelihood ratio; Finite field; Monte Carlo method; Blind encoder identification; Quadrature amplitude modulation; Logarithmic function; Coding circuit; Likelihood ratio; Blind identification; nonbinary low-density parity-check codes; Adaptive coding; Transceiver; Posterior probability; Numerical simulation; Error correcting code; Parity check codes; System identification; Adaptive modulation; Modulation coding; Parity check
• ISSN: 1089-7798; 1558-2558
• DOI: 10.1109/LCOMM.2013.051313.130462

Prevalent adaptive modulation and coding (AMC) techniques can facilitate the flexible strategies subject to the dynamic channel quality. It would be quite intriguing for one to build a blind encoder identification technique without spectrum-efficiency sacrifice for AMC transceivers. In this paper, we make the first-ever attempt to tackle the blind nonbinary low-density parity-check (LDPC) encoder identification given a predefined encoder candidate set over the Galois field GF(q) for q-ary quadrature amplitude modulation (q-QAM) signals. Our proposed method establishes the log-likelihood ratios (LLRs) of syndrome a posteriori probabilities (APPs), which specify the potential correctness of the underlying parity-check relations, and identifies the nonbinary LDPC encoder leading to the maximum average LLR over the candidate set. Monte Carlo simulation results verify the effectiveness of our proposed new scheme.