From the 1 of 7 linked papers with an AI index.
7 papers
Generalized Segmented GRAND for Guesswork Reduction in Turbo Product Decoding
Lukas Rapp, Jiewei Feng, Muriel Médard +1
The paper proposes GSegGRAND, a generalized version of Segmented GRAND decoding that works with a broader class of codes, incorporates more parity‑check constraints, and provides s…
SOGRAND decoding of LDPC codes
Ken R. Duffy, Jiewei Feng, Lukas Rapp +1
Long forward error correction codes are typically constructed by concatenating shorter component codes that are then decoded through iterative Soft-Input Soft-Output (SISO) of thei…
The Linear Reliability Channel
Alexander Mariona, Ken R. Duffy, Muriel Médard
We introduce and analyze a discrete soft-decision channel called the linear reliability channel (LRC) in which the soft information is the rank ordering of the received symbol reli…
Joint Error Correction and Fading Channel Estimation Enhancement Leveraging GRAND
Charles Wiame, Ken R. Duffy, Muriel Médard
We present a novel method for error correction in the presence of fading channel estimation errors (CEE). When such errors are significant, considerable performance losses can be o…
SOGRAND Assisted Guesswork Reduction
Lukas Rapp, Muriel Médard, Ken R. Duffy
Proposals have been made to reduce the guesswork of Guessing Random Additive Noise Decoding (GRAND) for binary linear codes by leveraging codebook structure at the expense of degra…
A Balanced Tree Transformation to Reduce GRAND Queries
Lukas Rapp, Jiewei Feng, Muriel Médard +1
Guessing Random Additive Noise Decoding (GRAND) and its variants, known for their near-maximum likelihood performance, have been introduced in recent years. One such variant, Segme…