发表机构
Pohang University of Science and Technology (POSTECH); Seoul National University（浦项科技大学; 首尔大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对V2X安全消息解码失败，提出利用预测分布生成比特级概率先验LLR，在CRC失败后结合信道观测进行二次LDPC解码，在0.75dB下恢复87.27%的失败消息。
AI 中文摘要
周期性基本安全消息（BSM）随着车辆运动随时间连续演化而表现出时间相关性。这种时间结构提供了关于当前BSM的预测信息，并促使在解码失败后进行预测辅助恢复。对于低密度奇偶校验（LDPC）编码传输，一种直接的方法是将预测的车辆状态映射为候选比特序列，并将其用作解码器的先验信息。然而，点预测无法捕捉预测的不确定性。由此产生的硬点先验为每个预测比特分配固定的置信度，并且当预测不正确时可能引入强烈的错误证据。本文提出了一种用于车联网（V2X）安全消息的不确定性感知的预测辅助LDPC恢复框架。所提出的方法不使用确定性点预测，而是通过预测分布来表示当前车辆状态。该分布通过BSM字段量化和序列化进行传播，以获得比特级概率，这些概率被转换为先验对数似然比（LLR），其幅度反映预测可靠性。在初始循环冗余校验（CRC）失败后，这些概率先验与原始信道观测相结合，并在第二次LDPC解码过程中使用。所提出的方法保留了标准化的消息表示和信道编码过程。仿真结果表明，所提出的概率先验在不同的运动预测器上均优于硬点先验。在$E_b/N_0=0.75$~dB时，所提出的方法恢复了初始解码尝试失败的87.27%的消息。
英文摘要
Periodic basic safety messages (BSMs) exhibit temporal correlation as vehicle motion evolves continuously over time. This temporal structure provides predictive information about the current BSM and motivates prediction-aided recovery after a decoding failure. For low-density parity-check (LDPC)-coded transmission, a straightforward approach maps a predicted vehicle state to a candidate bit sequence and uses it as decoder prior information. However, a point prediction does not capture the uncertainty of the prediction. The resulting hard point prior assigns fixed confidence to each predicted bit and may introduce strong erroneous evidence when the prediction is incorrect. This paper proposes an uncertainty-aware prediction-aided LDPC recovery framework for vehicle-to-everything (V2X) safety messages. Instead of using a deterministic point prediction, the proposed method represents the current vehicle state by a predictive distribution. The distribution is propagated through BSM field quantization and serialization to obtain bit-level probabilities, which are converted into prior log-likelihood ratios (LLRs) whose magnitudes reflect prediction reliability. After an initial cyclic redundancy check (CRC) failure, these probabilistic priors are combined with the original channel observations and used in a second LDPC decoding pass. The proposed method preserves the standardized message representation and channel-coding procedure. Simulation results show that the proposed probabilistic priors outperform the hard point prior across different motion predictors. At $E_b/N_0=0.75$~dB, the proposed method recovers 87.27% of the messages that fail the initial decoding attempt.
