Reducing ORBGRAND Latency via Partial Gaussian Elimination

2026-02-01

AI Summary

This work addresses the high tail latency of ORBGRAND under harsh channel conditions, which impedes its applicability to ultra-reliable low-latency communication (URLLC). To overcome this limitation, the authors propose a novel joint verification mechanism that, for the first time, integrates partial Gaussian elimination (GE) with a grouping strategy based on the most reliable error bits (RMRE) to efficiently prune redundant error patterns. The proposed approach significantly reduces the number of candidate error patterns requiring testing—by over 50%—while preserving block error rate performance. Consequently, both average and worst-case decoding latencies are markedly lowered, and computational complexity is substantially reduced, making the scheme well-suited for latency-sensitive real-time communication scenarios.

Technology Category

• Machine Learning: Hardware-aware ML
• Reasoning under Uncertainty: Stochastic Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Security and Privacy: Large-scale security measurements
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

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.

Problem

• ORBGRAND
• latency
• tail latency
• real-time systems
• block codes

Innovation

• Partial Gaussian Elimination
• ORBGRAND
• RMRE
• Latency Reduction
• Error Pattern Filtering