We show in this work that reinforcement learning can be successfully applied to decoding short to moderate length sparse graph-based channel codes. Specifically, we focus on low-density parity check (LDPC) codes, which for example have been standardized in the context of 5G cellular communication systems due to their excellent error correcting performance. These codes are typically decoded via belief propagation iterative decoding on the corresponding bipartite (Tanner) graph of the code via flooding, i.e., all check and variable nodes in the Tanner graph are updated at once. In contrast, in this paper we utilize a sequential update policy which selects the optimum check node (CN) scheduling in order to improve decoding performance. In particular, we model the CN update process as a multi-armed bandit process with dependent arms and employ a Q-learning scheme for optimizing the CN scheduling policy. In order to reduce the learning complexity, we propose a novel graph-induced CN clustering approach to partition the state space in such a way that dependencies between clusters are minimized. Our results show that compared to other decoding approaches from the literature, the proposed reinforcement learning scheme not only significantly improves the decoding performance, but also reduces the decoding complexity dramatically once the scheduling policy is learned.

Strengths: LDPC code is an indispensable building block for LTE/5G communication systems, a more efficient and accurate decoding algorithm is impactful for current communication systems. Node-wise scheduling (NS) is known to improve decoding efficiency, yet incurs more complexity. Using Q-learning Table the computation complexity improves, which makes the NS-based method become viable. The long block length nature of LDPC code, makes the number of state exponential. The author uses clustered based method to reduce the number of potential state.

This paper proposes application of reinforcement learning, in particular Q-learning, to determine the check-node (CN) scheduling policy in BP decoding of short LDPC codes. It is in contrast to other works in the broad area of machine learning applications to coding which focus on finding coding schemes or "deep unfolding" of iterative decoders. Discretization of state space and clustering of CNs are introduced to avoid explosion of the state space size and learning complexity. The reviewers rated this paper favorably, especially with emphasis on the novelty. They are also satisfied with the author response.

We show in this work that reinforcement learning can be successfully applied to decoding short to moderate length sparse graph-based channel codes. Specifically, we focus on low-density parity check (LDPC) codes, which for example have been standardized in the context of 5G cellular communication systems due to their excellent error correcting performance. These codes are typically decoded via belief propagation iterative decoding on the corresponding bipartite (Tanner) graph of the code via flooding, i.e., all check and variable nodes in the Tanner graph are updated at once. In contrast, in this paper we utilize a sequential update policy which selects the optimum check node (CN) scheduling in order to improve decoding performance. In particular, we model the CN update process as a multi-armed bandit process with dependent arms and employ a Q-learning scheme for optimizing the CN scheduling policy.