The Optimization of Random Tree Codes for Limited Computational Resources

TL;DR

This paper develops an achievability bound for random tree codes under limited decoding resources and demonstrates how optimizing their structure can nearly match maximum likelihood decoding performance.

Contribution

It introduces an achievability bound for random tree codes with resource constraints and optimizes their structure to improve decoding performance.

Findings

Achievability bound approaches ML decoding performance.

Optimized tree code ensembles outperform non-optimized ones.

Resource-limited decoding can be nearly as effective as ML decoding.

Abstract

In this paper, we introduce an achievability bound on the frame error rate of random tree code ensembles under a sequential decoding algorithm with a hard computational limit and consider the optimization of the random tree code ensembles over their branching structures/profiles and the decoding measure. Through numerical examples, we show that the achievability bound for the optimizated random tree codes can approach the maximum likelihood (ML) decoding performance of pure random codes.