3 citations · 5 across the 6 of their papers we have counts for
7 papers
Sensing-Aided Ordered Reliability Bits Guessing Random Additive Noise Decoding
Yu Ge, Lukas Rapp, Ken R. Duffy +1
Integrated sensing and communication (ISAC) is a key enabler for future wireless systems, providing environmental information that can support tasks beyond conventional data transm…
Generalized Segmented GRAND for Guesswork Reduction in Turbo Product Decoding
Lukas Rapp, Jiewei Feng, Muriel Médard +1
Guessing random additive noise decoding (GRAND) can efficiently decode any moderately redundant code with near maximum likelihood (ML) performance via noise effect guessing. For bi…
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…
Efficient Soft-Output Guessing for Enhanced Quantum Tanner Code Decoding
Lukas Rapp, Muriel Médard, Eugene Tang +1
We introduce a generalized low-density parity-check decoding framework for quantum Tanner codes utilizing soft-output guessing random additive noise decoding (SOGRAND). By soft-out…
Group Probability Decoding of Turbo Product Codes over Higher-Order Fields
Lukas Rapp, Muriel Médard, Ken R. Duffy
Binary turbo product codes (TPCs) are powerful error-correcting codes constructed from short component codes. Traditionally, turbo product decoding passes log likelihood ratios (LL…
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…