|| GRADE-AO: Towards Near-Optimal Spatially-Coupled Codes With High Memories
||Siyi Yang, University of California, Los Angeles, United States; Ahmed Hareedy, Duke University, United States; Shyam Venkatasubramanian, University of California, Los Angeles, United States; Robert Calderbank, Duke University, United States; Lara Dolecek, University of California, Los Angeles, United States|
||D2-S2-T2: Spatial Coupling & Protograph Codes
||Tuesday, 13 July, 22:20 - 22:40
||Tuesday, 13 July, 22:40 - 23:00
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.