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Low-complexity LDPC decoding algorithms for ultra-high-order modulated signals. | LitMetric

AI Article Synopsis

  • Low-complexity decoding algorithms and ultra-high-order modulation formats are essential for achieving data rates over 1 Tbps, and the information bottleneck algorithm has recently improved the performance of LDPC decoding to match advanced techniques.* -
  • The study introduces a new LDPC decoding method that combines multivariate covariates with an information bottleneck framework specifically for decoding 1024-QAM signals, which enhances the efficiency of higher-order modulation formats with LDPC codes.* -
  • By using a bidirectional recursive network, the proposed technique significantly reduces table lookup operations from a square to a linear scale, resulting in faster decoding times while only sacrificing a small amount of performance (0.2 to 0.3 dB)

Article Abstract

Low-complexity decoding algorithms and ultra-high-order modulation formats are necessary to meet data rate requirements in excess of 1 Tbps. The information bottleneck algorithm has been effectively applied to the LDPC decoding algorithm in recent years, and its performance is comparable to that of the double-precision information propagation technique. However, the application of information bottleneck decoding algorithms in ultra-high order modulation forms has received little attention. Furthermore, the number of table lookups required for a single decoding loop is square to the node degree, which is undesirable for optical communications. We present a low-complexity LDPC decoding technique for ultra-high-order modulated signals in this study. First, the algorithm employs multivariate covariates to build an information bottleneck framework, which introduces the processing required for applying the information bottleneck algorithm to 1024-QAM signals and the requirement of combining higher-order modulation formats with LDPC codes. The technique makes use of a bidirectional recursive network and the symmetry of quantized information to reuse the same set of tables, considerably reducing the number of table lookup operations required in the decoding process. Constructing a coherent optical communication system with 1024-QAM signals proves that the proposed algorithm can operate effectively. The performance sacrifice of 0.2 ∼ 0.3 dB reduces the number of table lookup operations required for decoding from square to linear magnitude, which greatly reduces the decoding time required in optical communication.

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Source
http://dx.doi.org/10.1364/OE.507292DOI Listing

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