RELDEC: Reinforcement Learning-Based Decoding of Moderate Length LDPC Codes

Kavli Affiliate: Salman Habib

| First 5 Authors: Salman Habib, Allison Beemer, Joerg Kliewer, ,

| Summary:

In this work we propose RELDEC, a novel approach for sequential decoding of
moderate length low-density parity-check (LDPC) codes. The main idea behind
RELDEC is that an optimized decoding policy is subsequently obtained via
reinforcement learning based on a Markov decision process (MDP). In contrast to
our previous work, where an agent learns to schedule only a single check node
(CN) within a group (cluster) of CNs per iteration, in this work we train the
agent to schedule all CNs in a cluster, and all clusters in every iteration.
That is, in each learning step of RELDEC an agent learns to schedule CN
clusters sequentially depending on a reward associated with the outcome of
scheduling a particular cluster. We also modify the state space representation
of the MDP, enabling RELDEC to be suitable for larger block length LDPC codes
than those studied in our previous work. Furthermore, to address decoding under
varying channel conditions, we propose two related schemes, namely, agile
meta-RELDEC (AM-RELDEC) and meta-RELDEC (M-RELDEC), both of which employ
meta-reinforcement learning. The proposed RELDEC scheme significantly
outperforms standard flooding and random sequential decoding for a variety of
LDPC codes, including codes designed for 5G new radio.

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