Testing Error Correcting Codes by Multicanonical Sampling of Rare Events

TL;DR

This paper introduces a multicanonical sampling method to efficiently estimate the performance of error-correcting codes, especially their rare error events, demonstrated with a convolutional code.

Contribution

It applies multicanonical Monte Carlo to error-correcting codes for the first time, improving rare event probability estimation accuracy.

Findings

Efficient estimation of tail distributions of bit error rate.

Successful application to a convolutional code.

Enhanced understanding of error performance in coding systems.

Abstract

The idea of rare event sampling is applied to the estimation of the performance of error-correcting codes. The essence of the idea is importance sampling of the pattern of noises in the channel by Multicanonical Monte Carlo, which enables efficient estimation of tails of the distribution of bit error rate. The idea is successfully tested with a convolutional code.