Kavli Affiliate: Salman Habib
| First 5 Authors: Salman Habib, David G. M. Mitchell, , ,
| Summary:
In this work, we propose reinforcement learning (RL) for sequential decoding
of moderate length generalized low-density parity-check (GLDPC) codes. Here,
sequential decoding refers to scheduling all the generalized constraint nodes
(GCNs) and single parity-check nodes (SPCNs) of a GLDPC code serially in each
iteration. A GLDPC decoding environment is modeled as a finite Markov decision
process (MDP) in which the state-space comprises of all possible sequences of
hard-decision values of the variables nodes (VNs) connected to the scheduled
GCN or SPCN, and the action-space of the MDP consists of all possible actions
(GCN and SPCN scheduling). The goal of RL is to determine an optimized
scheduling policy, i.e., one that results in a decoded codeword by minimizing
the complexity of the belief propagation (BP) decoder. For training, we
consider the proportion of correct bits at the output of the GCN or SPCN as a
reward once it is scheduled. The expected rewards for scheduling all the
GCNs/SPCNs in the code’s Tanner graph are earned via BP decoding during the RL
phase. The proposed RL-based decoding scheme is shown to significantly
outperform the standard BP flooding decoder, as well as a sequential decoder in
which the GCNs/SPCNs are scheduled randomly.
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