通过部分高斯消元降低ORBGRAND延迟
Reducing ORBGRAND Latency via Partial Gaussian Elimination
AI总结：
本文提出一种结合部分高斯消元与RMRE位排名的ORBGRAND改进方案，有效降低解码延迟并保持纠错性能。
摘要：
猜测随机加性噪声解码（GRAND）是一种通用框架，通过测试候选错误模式（EPs）来解码所有分组码。有序可靠性位GRAND（ORBGRAND）通过利用对数似然比（LLR）排名来促进GRAND的并行实现，但在不利信道条件下仍遭受高尾延迟，限制了其在实时系统中的应用。我们提出了一种辅助消除的ORBGRAND方案，通过将最可靠的错误位的排名（RMRE）位与部分高斯消元（GE）过滤机制相结合，以减少解码延迟。该方案将共享相同RMRE的EPs分组并联合验证，一旦识别出有效的EP，ORBGRAND搜索即可恢复。通过利用先前GE步骤来过滤掉不必要的猜测，该方法显著减少了需要测试的EPs数量，从而降低了平均和最坏情况下的延迟，同时保持纠错性能。仿真结果表明，与原始ORBGRAND相比，辅助消除的ORBGRAND过滤掉了超过50%的EPs，并相应地减少了总体计算复杂度，所有这些都无损于分组错误率。这证明了该方法适用于超可靠低延迟通信场景。
英文摘要：
Guessing Random Additive Noise Decoding (GRAND) is a universal framework for decoding all block codes by testing candidate error patterns (EPs). Ordered Reliability Bits GRAND (ORBGRAND) facilitates parallel implementation of GRAND by exploiting log-likelihood ratio (LLR) rankings but still suffers from high tail latency under unfavorable channel conditions, limiting its use in real-time systems. We propose an elimination-aided ORBGRAND scheme that reduces decoding latency by integrating the Rank of the Most Reliable Erroneous (RMRE) bit with a partial Gaussian-elimination (GE) filtering mechanism. The scheme groups and jointly verifies EPs that share the same RMRE, and once a valid EP is identified, the ORBGRAND search is resumed. By leveraging prior GE steps to filter out unnecessary guesses, this approach significantly reduces the number of EPs to be tested, thereby lowering both average and worst-case latency while maintaining error-correction performance. Simulation results show that compared to the original ORBGRAND, the elimination-aided ORBGRAND filters out more than 50\% of EPs and correspondingly reduce overall computational complexity, all with no loss in block error rate. This demonstrates that this approach is suitable for ultra-reliable low-latency communication scenarios.
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