{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T15:15:07Z","timestamp":1770909307063,"version":"3.50.1"},"reference-count":22,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T00:00:00Z","timestamp":1770854400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"SSP Task Order","award":["24-001"],"award-info":[{"award-number":["24-001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>We propose a reinforcement learning (RL)-based decoding framework for high-throughput parallel decoding of low-density parity-check (LDPC) codes using clustered scheduling. Parallel LDPC decoders must balance error-correction performance and decoding latency while avoiding memory conflicts. To address this trade-off, we construct clusters of check nodes that satisfy a two-edge independence property, which enables conflict-free row-parallel belief propagation. An RL agent is trained offline to assign Q-values to clusters and to prioritize their update order during decoding. To overcome the exponential storage requirements of existing RL-based scheduling methods, we introduce the Q-Sum method, which approximates cluster-level Q-values as the sum of Q-values of individual check nodes, reducing storage complexity from exponential to linear in the number of check nodes. We further propose an On-the-Fly clustering strategy that enforces two-edge independence dynamically during decoding and provides additional flexibility when static clustering is not feasible. Simulation results for array-based LDPC codes over additive white Gaussian noise (AWGN) channels show that the proposed methods improve the latency-versus-performance trade-off of parallel LDPC decoders, achieving lower decoding latency and higher throughput while maintaining error rates comparable to state-of-the-art decoding methods.<\/jats:p>","DOI":"10.3390\/e28020215","type":"journal-article","created":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T14:06:16Z","timestamp":1770905176000},"page":"215","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["RL-Based Parallel LDPC Decoding with Clustered Scheduling"],"prefix":"10.3390","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9529-9866","authenticated-orcid":false,"given":"Yusuf","family":"Ozkan","sequence":"first","affiliation":[{"name":"Helen and John C. Hartmann Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3030-2336","authenticated-orcid":false,"given":"Yauhen","family":"Yakimenka","sequence":"additional","affiliation":[{"name":"Helen and John C. Hartmann Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0942-8006","authenticated-orcid":false,"given":"J\u00f6rg","family":"Kliewer","sequence":"additional","affiliation":[{"name":"Helen and John C. Hartmann Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,12]]},"reference":[{"key":"ref_1","unstructured":"(2025, August 13). 3rd Generation Partnership Project (3GPP). Release 18. Available online: https:\/\/www.3gpp.org\/specifications-technologies\/releases."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"5661","DOI":"10.1109\/TCOMM.2023.3296621","article-title":"Reldec: Reinforcement learning-based decoding of moderate length ldpc codes","volume":"71","author":"Habib","year":"2023","journal-title":"IEEE Trans. Commun."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Bocharova, I.E., Kudryashov, B.D., Skachek, V., and Yakimenka, Y. (2017). Distance properties of short LDPC codes and their impact on the BP, ML and near-ML decoding performance. 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