基于关键集辅助的极化码简化盲SCL识别
Critical-Set-Aided Simplified Blind SCL Recognition of Polar Codes
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中文总结 AI 辅助
研究从噪声观测中盲识别极化码的问题，提出关键集辅助的简化盲连续消除列表（SBSCL）识别方法，通过在选定关键集位置进行双假设路径扩展降低复杂度，利用基于密度进化的边界完善性能分析，该方法识别成功率高且关键集大小随信噪比减小。
中文摘要 AI 辅助
从噪声观测中对极化码进行盲识别是非合作信号处理中的关键问题。现有盲连续消除列表（BSCL）识别虽利用信道软信息，但在每个源比特位置进行双假设路径扩展，导致复杂度高。本文首先分析盲连续消除（BSC）识别中的首次识别错误位置，发现其与现有基于巴氏参数的上界中的相应贡献项密切相关。基于此，提出关键集辅助的简化盲连续消除列表（SBSCL）识别方法，仅在选定的关键集位置进行双假设路径扩展，其余位置保持BSC识别，从而降低复杂度。为提高关键集选择的可靠性并完善性能分析，进一步开发基于密度进化（DE）的边界。在理想SC一致条件下，利用密度进化得到的合成对数似然比（LLR）分布计算上界的优化切尔诺夫系数和下界的重叠系数。仿真结果表明，基于DE的边界比基于巴氏参数的边界更紧。在考虑的设置中，在识别错误概率为10^-2左右时，DE上下界之间的差距在1dB以内。此外，SBSCL实现了与BSCL几乎相同的识别成功率，且关键集大小随信噪比（SNR）增加而迅速减小。
英文摘要
Blind recognition of polar codes from noisy observations is a key problem in non-cooperative signal processing. Although existing blind successive cancellation list (BSCL) recognition exploits channel soft information, it performs two-hypothesis path expansion at every source-bit position, resulting in high complexity. In this paper, we first analyze the first recognition-error positions in the blind successive cancellation (BSC) recognition and observe that they are closely related to the corresponding contribution terms in the existing Bhattacharyya-parameter-based upper bounds. Based on this observation, a critical-set-aided simplified blind successive cancellation list (SBSCL) recognition method is proposed. SBSCL performs two-hypothesis path expansion only at the selected critical-set positions and keeps BSC recognition at the remaining positions, thereby reducing complexity. To improve the reliability of critical-set selection and refine the performance analysis, density-evolution (DE)-based bounds are further developed. Under the ideal SC-consistent condition, the synthetic log-likelihood-ratio (LLR) distributions obtained from density evolution are used to compute the optimized Chernoff coefficient for the upper bound and the overlap coefficient for the lower bound. Simulation results show that the DE-based bounds are tighter than the Bhattacharyya-parameter-based bounds. In the considered settings, the gap between the DE upper and lower bounds is within $1$ dB around a recognition-error probability of $10^{-2}$. Furthermore, SBSCL achieves nearly the same recognition success rate as BSCL, and the size of critical set decreases rapidly as the signal-to-noise ratio (SNR) increases.
