13 citations · 19 across the 15 of their papers we have counts for
19 papers
Conditional Correctness in List Decoding: How a Late Second Codeword Can Rescue Confidence in the First
Conrad Struss, Muriel Medard, Ken R. Duffy
In error correction, the difference between a codeword and a received word, a candidate noise sequence, carries information on the confidence of the codeword as a proposed decoding…
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…
NOVA: Fundamental Limits of Knowledge Discovery Through AI
Salman Avestimehr, Ken Duffy, Muriel Médard
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…
Guessing Decoding of Short Blocklength Codes
Qianfan Wang, Jifan Liang, Peihong Yuan +3
Future beyond-5G and 6G systems demand ultra-reliable, low-latency communication with short blocklengths, motivating the development of universal decoding algorithms. Guessing deco…
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…
Enhanced GCD through ORBGRAND-AI: Exploiting Partial and Total Correlation in Noise
Jiewei Feng, Ken R. Duffy, Muriel Médard
There have been significant advances in recent years in the development of forward error correction decoders that can decode codes of any structure, including practical realization…
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…
Leveraging Code Structure to Improve Soft Output for GRAND, GCD, OSD, and SCL
Jiewei Feng, Ken R. Duffy, Muriel Médard
In addition to a codeword, error correction decoders that provide blockwise soft output (SO) return an estimate of the likelihood that the decoding is correct. Following Forney, su…
Soft-Output Successive Cancellation List Decoding
Peihong Yuan, Ken R. Duffy, Muriel Médard
We introduce an algorithm for approximating the codebook probability that is compatible with all successive cancellation (SC)-based decoding algorithms, including SC list (SCL) dec…
Error correction in interference-limited wireless systems
Charles Wiame, Ken R. Duffy, Muriel Médard
We introduce a novel approach to error correction decoding in the presence of additive alpha-stable noise, which serves as a model of interference-limited wireless systems. In the…
Leveraging AES Padding: dBs for Nothing and FEC for Free in IoT Systems
Jongchan Woo, Vipindev Adat Vasudevan, Benjamin D. Kim +5
The Internet of Things (IoT) represents a significant advancement in digital technology, with its rapidly growing network of interconnected devices. This expansion, however, brings…