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.

翻译：猜测随机加性噪声解码（GRAND）是一种通过测试候选错误模式（EP）来解码所有分组码的通用框架。有序可靠性比特GRAND（ORBGRAND）通过利用对数似然比（LLR）排序实现了GRAND的并行化，但在不利信道条件下仍面临较高的尾部延迟，限制了其在实时系统中的应用。本文提出一种消元辅助的ORBGRAND方案，通过将最可靠错误比特秩（RMRE）与部分高斯消元（GE）过滤机制相结合来降低解码延迟。该方案对共享相同RMRE的EP进行分组和联合验证，一旦识别出有效EP，即恢复ORBGRAND搜索过程。通过利用先前的GE步骤过滤不必要的猜测，该方法显著减少了待测试EP的数量，从而在保持纠错性能的同时降低了平均和最坏情况下的延迟。仿真结果表明，相较于原始ORBGRAND，消元辅助ORBGRAND能过滤超过50%的EP，并相应降低整体计算复杂度，且未造成误块率损失。这证明该方法适用于超可靠低延迟通信场景。