14 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…
NOVA: Fundamental Limits of Knowledge Discovery Through AI
Salman Avestimehr, Ken Duffy, Muriel Médard +1
Can AI systems discover new knowledge through iterative self-improvement, and at what cost? We introduce NOVA, which models the ``generate, verify, accumulate, retrain'' loop as an…
The Benefit of Decoder-Provided Pilots in Highly Dynamic Channels
Duschia Bodet, Muriel Médard, Muralidhar Rangaswamy +1
Communications in highly dynamic channels relying on training-based channel estimation experience a trade-off between increasing channel measurement accuracy by sending more freque…
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…
Improving the decoding performance of CA-polar codes
Jiewei Feng, Peihong Yuan, Ken R. Duffy +1
We investigate the use of modern code-agnostic decoders to convert CA-SCL from an incomplete decoder to a complete one. When CA-SCL fails to identify a codeword that passes the CRC…