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

Sarah Al-Shareeda, Gulcihan Özdemir, Arouj Fatima, Madalin-Dorin Pop, Bander A. Jabr, Yasser Bin Salamah, Jacques Demerjian

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.

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

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.

Paper Structure

This paper contains 17 sections, 19 equations, 6 figures, 3 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model and Learning-Assisted Configuration Framework
  3. Transmitter-Side Physical-Layer Encoding Model
  4. Mobility-Affected Channel and Interference Model
  5. Receiver-Side Physical-Layer Decoding
  6. Learning-based CRC-LDPC Configuration Framework
  7. Context Definition under Partial Observability
  8. Configuration Space and Action Definition
  9. Integrity-Aware Reward Model
  10. Simulation and Results Discussion
  11. Integrity Metrics and Rare-Event Evaluation
  12. Policies Compared: Online Adaptation vs Conservative Fixing
  13. Online Adaptation Dynamics Under Mobility
  14. Decoder Quality Check via $P_{DE}$
  15. $P_{UE}$ vs. $E_b/N_0$ Across Mobility Regimes
  16. ...and 2 more sections

Figures (6)

  • Figure 1: 5G-NR V2X protocol stack supporting both the direct PC5 sidelink and the Uu air interface, highlighting PHY-layer CRC-based error detection and QC-LDPC-based error correction mechanisms considered in this work.
  • Figure 2: Literature landscape and Addressed Research Question.
  • Figure 3: Our Learning-Assisted standard-compliant 5G-NR physical-layer processing chain.
  • Figure 4: Online learning dynamics under mobility-induced channel variation.
  • Figure 5: Rare-event evaluation of $P_{UE}$ vs. $E_b/N_0$ under different mobility regimes.
  • ...and 1 more figures