Variable-length Convolutional Coding for Short Blocklengths with Decision Feedback

TL;DR

This paper introduces a variable-length decision-feedback coding scheme using tail-biting convolutional codes and ROVA, demonstrating superior throughput for short blocklengths on BSC and AWGN channels compared to traditional methods.

Contribution

It proposes a novel ROVA-based decision-feedback scheme that surpasses the finite-blocklength bounds for short blocklengths, with practical decoding strategies and reliability-based stopping rules.

Findings

ROVA-based decision feedback exceeds random-coding bounds at blocklengths <100 symbols.

Performance with limited decoding increments is comparable to full decoding.

ROVA-based approach outperforms CRC-based retransmission decisions in short blocklength scenarios.

Abstract

This paper presents a variable-length decision-feedback scheme that uses tail-biting convolutional codes and the tail-biting Reliability-Output Viterbi Algoritm (ROVA). Comparing with recent results in finite-blocklength information theory, simulation results for both the BSC and the AWGN channel show that the decision-feedback scheme using ROVA can surpass the random-coding lower bound on throughput for feedback codes at average blocklengths less than 100 symbols. This paper explores ROVA-based decision feedback both with decoding after every symbol and with decoding limited to a small number of increments. The performance of the reliability-based stopping rule with the ROVA is compared to retransmission decisions based on CRCs. For short blocklengths where the latency overhead of the CRC bits is severe, the ROVA-based approach delivers superior rates.