English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Spatially-coupled (SC) codes, known for their threshold saturation phenomenonand low-latency windowed decoding algorithms, are ideal for streamingapplications and data storage systems. SC codes are constructed by partitioningan underlying block code, followed by rearranging and concatenating thepartitioned components in a convolutional manner. The number of partitionedcomponents determines the memory of SC codes. In this paper, we investigate therelation between the performance of SC codes and the density distribution ofpartitioning matrices. While adopting higher memories results in improved SCcode performance, obtaining finite-length, high-performance SC codes with highmemory is known to be computationally challenging. We break this computationalbottleneck by developing a novel probabilistic framework that obtains (locally)optimal density distributions via gradient descent. Starting from randompartitioning matrices abiding by the obtained distribution, we performlow-complexity optimization algorithms that minimize the number of detrimentalobjects to construct high-memory, high-performance quasi-cyclic SC codes. Weapply our framework to various objects of interests, from the simplest shortcycles, to more sophisticated objects such as concatenated cycles aiming atfiner-grained optimization. Simulation results show that codes obtained throughour proposed method notably outperform state-of-the-art SC codes with the sameconstraint length and optimized SC codes with uniform partitioning. Theperformance gain is shown to be universal over a variety of channels, fromcanonical channels such as additive white Gaussian noise and binary symmetricchannels, to practical channels underlying flash memory and magnetic recordingsystems.

Breaking the Computational Bottleneck: Design of Near-Optimal High-Memory Spatially-Coupled Codes