Develops unified AGP framework for ORB-type GRAND, derives exact BLER and stopping-time expressions for random ensembles plus code-dependent bounds, and shows RS-ORBGRAND within 0.1 dB of ML benchmark at 10^{-6} BLER for BCH(127,113).
ORBGRAND Is Exactly Capacity-achieving via Rank Companding
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abstract
Within the family of guessing-based decoding algorithms, ordered reliability bits GRAND (ORBGRAND) has attracted considerable attention due to its efficient use of soft information and suitability for hardware implementation. It has also been shown that ORBGRAND achieves a rate very close to the capacity of an additive white Gaussian noise channel under antipodal signaling. In this work, it is further established that, for general binary-input memoryless channels under symmetric input distribution, via suitably companding the ranks in ORBGRAND according to the inverse cumulative distribution function (CDF) of channel reliability, the resulting CDF-ORBGRAND algorithm exactly achieves the mutual information, i.e., the symmetric capacity. This result is then applied to bit-interleaved coded modulation (BICM) systems to handle high-order input constellations. Via considering the effects of mismatched decoding due to both BICM and ORBGRAND, it is shown that CDF-ORBGRAND is capable of achieving the BICM capacity, which was initially derived in the literature by treating BICM as a set of independent parallel channels.
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cs.IT 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
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Performance Analysis and Optimal Design of ORB-Type GRAND Algorithms
Develops unified AGP framework for ORB-type GRAND, derives exact BLER and stopping-time expressions for random ensembles plus code-dependent bounds, and shows RS-ORBGRAND within 0.1 dB of ML benchmark at 10^{-6} BLER for BCH(127,113).