GRADE-AO: Towards Near-Optimal Spatially-Coupled Codes with High Memories

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Date

2021-07-12

Author

Yang, Siyi
Hareedy, Ahmed
Venkatasubramanian, Shyam
Calderbank, Robert
Dolecek, Lara

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Spatially-coupled (SC) codes, known for their threshold saturation phenomenon and low-latency windowed decoding algorithms, are ideal for streaming applications and data storage systems. SC codes are constructed by partitioning an underlying block code, followed by rearranging and concatenating the partitioned components in a 'convolutional' manner. The number of partitioned components determines the 'memory' of SC codes. While adopting higher memories results in improved SC code performance, obtaining optimal SC codes with high memory is known to be hard. In this paper, we investigate the relation between the performance of SC codes and the density distribution of partitioning matrices. We propose a probabilistic framework that obtains (locally) optimal density distributions via gradient descent. Starting from random partitioning matrices abiding by the obtained distribution, we perform low complexity optimization algorithms over the cycle properties to construct high memory, high performance quasi-cyclic SC codes. Simulation results show that codes obtained through our proposed method notably outperform state-of-the-art SC codes with the same constraint length and codes with uniform partitioning.

Subject Keywords

Simulation, Memory management, Performance gain, Probabilistic logic, Partitioning algorithms, Decoding, Complexity theory

URI

https://hdl.handle.net/11511/98513

DOI

https://doi.org/10.1109/isit45174.2021.9517931

Conference Name

2021 IEEE International Symposium on Information Theory, ISIT 2021

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

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Citation Formats

S. Yang, A. Hareedy, S. Venkatasubramanian, R. Calderbank, and L. Dolecek, “GRADE-AO: Towards Near-Optimal Spatially-Coupled Codes with High Memories,” Virtual, Melbourne, Avustralya, 2021, vol. 2021-July, Accessed: 00, 2022. [Online]. Available: https://hdl.handle.net/11511/98513.