2020 IEEE International Symposium
on Information Theory
21-26 June 2020 • Los Angeles, California, USA
| Paper ID | L.12.3 | ||
| Paper Title | Learned Scheduling of LDPC Decoders Based on Multi-armed Bandits | ||
| Authors | Salman Habib, Allison Beemer, Joerg Kliewer, New Jersey Institute of Tech, United States | ||
| Session | L.12: Multi-Arm Bandits | ||
| Presentation | Lecture | ||
| Track | Statistics and Learning Theory | ||
| Manuscript | Click here to download the manuscript | ||
| Virtual Presentation | Click here to watch in the Virtual Symposium | ||
| Abstract | The multi-armed bandit (MAB) problem refers to the dilemma encountered by a gambler when deciding which arm of a multi-armed slot machine to pull in order to maximize the total reward earned in a sequence of pulls. In this paper, we model the scheduling of a node-wise sequential LDPC decoder as a Markov decision process, where the underlying Tanner graph is viewed as a slot machine with multiple arms corresponding to the check nodes. A fictitious gambler decides which check node to pull (schedule) next by observing a reward associated with each pull. This interaction enables the gambler to discover an optimized scheduling policy that aims to reach a codeword output by propagating the fewest possible messages. Based on this policy, we contrive a novel MAB-based node-wise scheduling (MAB-NS) algorithm to perform sequential decoding of LDPC codes. Simulation results show that the MAB-NS scheme, aided by an appropriate scheduling policy, outperforms traditional scheduling schemes in terms of complexity and bit error probability. | ||
Plan Ahead
2021 IEEE International Symposium on Information Theory
11-16 July 2021 | Melbourne, Victoria, Australia
