Would Learning Help? Adaptive CRC-QC-LDPC Selection for Integrity in 5G-NR V2X

2026-04-05

AI Summary

This work addresses the challenge of undetected errors in 5G-NR V2X communications under high mobility and short-packet transmission, which compromises physical-layer integrity. The authors propose the first formulation of joint CRC polynomial and QC-LDPC code rate selection as a lightweight contextual bandit problem, enabling online configuration optimization via a discounted LinUCB strategy based on coarse-grained receiver feedback. Using a standard-compliant simulation platform built on Sionna, the method is evaluated over time-correlated Rayleigh fading channels with a two-state Markovian interference model. Experimental results demonstrate that, in low-to-moderate mobility scenarios and at low SNRs (–5 to 5 dB), the proposed approach reduces the probability of undetected errors by up to 50%–70% compared to a greedy policy, thereby delineating the performance boundaries of online learning across varying mobility conditions.

Abstract

Vehicle-to-everything (V2X) communications impose stringent physical-layer integrity requirements, particularly under short-packet transmission and mobility-induced channel variation. This paper studies whether standard-compliant online selection of Cyclic Redundancy Check (CRC) polynomials and Quasi-Cyclic Low-Density Parity-Check (QC-LDPC) coding rates can reduce silent (undetected) errors in 5G New Radio (5G-NR) V2X links. The joint configuration problem is formulated as a lightweight Contextual Bandit (CB) with a small, discrete action space, and a discounted LinUCB policy is evaluated against greedy online adaptation and a conservative fixed baseline. A 5G-NR-compliant physical-layer simulation is developed using Sionna, modeling mobility through time-correlated Rayleigh fading, where vehicle speed governs channel correlation, and non-stationary interference via a two-state Markov process. The learning agent operates on coarse receiver feedback, including a noisy Signal-to-Noise Ratio (SNR) estimate and indicators of burst interference and deep fades, and targets minimization of the Undetected Error Probability ((P{UE})) while accounting for the Detected Error Probability ((P{DE})). Overall, our objective is to delineate the mobility regimes in which learning-assisted CRC-QC-LDPC configuration improves physical-layer integrity in 5G-NR V2X systems. Our results indicate that learning-assisted adaptation is most effective at low to moderate mobility, reducing (P_UE) by up to 50-70% relative to greedy selection in the low-SNR regime ((-5) to 5~dB) and approaching the best fixed configuration at higher (E_b/N_0). At high mobility (>= 180~km/h), fast channel decorrelation weakens temporal predictability, limiting the effectiveness of online learning and reducing performance differences across policies.

Problem

• V2X
• CRC-QC-LDPC
• undetected error
• 5G-NR
• mobility

Innovation

• Contextual Bandit
• Adaptive Coding
• 5G-NR V2X
• Undetected Error Probability
• CRC-QC-LDPC