RL-Based Parallel LDPC Decoding with Clustered Scheduling

TL;DR

This paper introduces a reinforcement learning framework for efficient parallel decoding of LDPC codes using clustered scheduling to improve performance and reduce latency.

Contribution

The novel contribution is an RL-based decoding framework with Q-Sum and On-the-Fly clustering to reduce storage complexity and improve decoding efficiency.

Findings

The proposed RL framework achieves lower decoding latency and higher throughput.

The Q-Sum method reduces storage complexity from exponential to linear.

On-the-Fly clustering enhances flexibility and maintains error rates comparable to state-of-the-art methods.

Abstract

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.

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