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.

翻译：车辆到一切（V2X）通信对物理层完整性提出了严苛要求，尤其在短包传输与移动性引起信道变化的场景下。本文研究遵循标准的循环冗余校验（CRC）多项式与准循环低密度奇偶校验码（QC-LDPC）编码速率在线选择能否降低5G新空口（5G-NR）V2X链路中的静默（未检测）错误。该联合配置问题被建模为具有小离散动作空间的轻量级上下文强盗（CB）问题，并采用折扣LinUCB策略与贪婪在线自适应及保守固定基线进行对比评估。本文基于Sionna开发符合5G-NR标准的物理层仿真，通过时间相关瑞利衰落建模移动性（车辆速度决定信道相关性），并采用两状态马尔可夫过程模拟非平稳干扰。学习代理基于粗粒度接收机反馈运行，包括带噪声的信噪比（SNR）估计、突发干扰与深度衰落指示，目标为在考虑检测错误概率（(P{DE})）的同时最小化未检测错误概率（(P{UE})）。总体而言，本文旨在刻画学习辅助的CRC-QC-LDPC配置可提升5G-NR V2X系统物理层完整性的移动性区间。结果表明：学习辅助自适应在低至中等移动性场景下最为有效，在低信噪比区域（(-5)~5~dB）中，相对贪婪选择可将(P_UE)降低50-70%，并在更高(E_b/N_0)下逼近最优固定配置。在高移动性场景（(>= 180~km/h)）中，快速信道去相关削弱了时间可预测性，限制了在线学习效果，并使各策略间的性能差异缩小。