9 papers
Performance Analysis and Optimal Design of ORB-Type GRAND Algorithms
Li Wan, Wenyi Zhang
Guessing Random Additive Noise Decoding (GRAND) performs decoding by sequentially guessing channel error patterns (EPs). Ordered Reliability Bits GRAND (ORBGRAND) is a notable inst…
ORBGRAND Is Exactly Capacity-achieving via Rank Companding
Zhuang Li, Wenyi Zhang
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…
A Parallelization Strategy for GRAND with Optimality Guarantee by Exploiting Error Pattern Tree Representation
Li Wan, Huarui Yin, Wenyi Zhang
Parallelism has become a central concern in modern decoding frameworks aiming to meet stringent throughput and latency requirements. Guessing Random Additive Noise Decoding (GRAND)…
GRAND for Gaussian Intersymbol Interference Channels
Zhuang Li, Wenyi Zhang
Channel decoding is a challenging task in communication channels exhibiting memory effects. In this work, we apply the recently proposed decoding paradigm of guessing random additi…
A Finite-Blocklength Analysis for ORBGRAND
Zhuang Li, Wenyi Zhang
Within the Guessing Random Additive Noise Decoding (GRAND) family, ordered reliability bits GRAND (ORBGRAND) has received considerable attention for its hardware-friendly exploitat…
Reducing ORBGRAND Latency via Partial Gaussian Elimination
Li Wan, Wenyi Zhang
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 (ORB…