CRC-Aided List Decoding of Convolutional Codes in the Short Blocklength Regime

TL;DR

This paper explores CRC-aided convolutional codes with list decoding for short blocklengths, proposing optimal CRC design and demonstrating near-theoretical performance with manageable complexity.

Contribution

It introduces a distance-spectrum optimal CRC polynomial design and analyzes list decoding complexity, achieving near-ideal performance in short blocklength regimes.

Findings

CRC-ZTCC approaches RCU bound within 0.4 dB at 10^{-4} error probability

CRC-TBCCs outperform RCU bound at moderate SNRs

Complexity of list decoding converges to 1 at high SNR

Abstract

We consider the concatenation of a convolutional code (CC) with an optimized cyclic redundancy check (CRC) code as a promising paradigm for good short blocklength codes. The resulting CRC-aided convolutional code naturally permits the use of serial list Viterbi decoding (SLVD) to achieve maximum-likelihood decoding. The convolutional encoder of interest is of rate-$1/\omega$ and the convolutional code is either zero-terminated (ZT) or tail-biting (TB). The resulting CRC-aided convolutional code is called a CRC-ZTCC or a CRC-TBCC. To design a good CRC-aided convolutional code, we propose the distance-spectrum optimal (DSO) CRC polynomial. A DSO CRC search algorithm for the TBCC is provided. Our analysis reveals that the complexity of SLVD is governed by the expected list rank which converges to $1$ at high SNR. This allows a good performance to be achieved with a small increase in complexity. In this paper, we focus on transmitting $64$ information bits with a rate-$1/2$ convolutional encoder. For a target error probability $10^{-4}$, simulations show that the best CRC-ZTCC approaches the random-coding union (RCU) bound within $0.4$ dB. Several CRC-TBCCs outperform the RCU bound at moderate SNR values.