Soft decoding without soft demapping with ORBGRAND
For spectral efficiency, higher order modulation symbols confer information on more than
one bit. As soft detection forward error correction decoders assume the availability of …
one bit. As soft detection forward error correction decoders assume the availability of …
On the role of quantization of soft information in GRAND
In this work, we investigate guessing random additive noise decoding (GRAND) with
quantized soft input. First, we analyze the achievable rate of ordered reliability bits GRAND …
quantized soft input. First, we analyze the achievable rate of ordered reliability bits GRAND …
[引用][C] Reduced-complexity soft-ln-soft-out decoding of variable-length codes
C Weidmann - IEEE International Symposium on Information …, 2003 - ieeexplore.ieee.org
We present approaches to complexity reduction for a recently introduced soft-in-soft-out
(SISO) decoder for variable-length codes (VLC). One method is based on a new trellis …
(SISO) decoder for variable-length codes (VLC). One method is based on a new trellis …
DemodNet: Learning soft demodulation from hard information using convolutional neural network
S Zheng, X Zhou, S Chen, P Qi, C Lou… - ICC 2022-IEEE …, 2022 - ieeexplore.ieee.org
Soft demodulation is a basic module of traditional communication receivers. It converts
received symbols into soft bits, that is, log likelihood ratios (LLRs). However, in the non-ideal …
received symbols into soft bits, that is, log likelihood ratios (LLRs). However, in the non-ideal …
Energy-efficient soft-assisted product decoders
Energy-Efficient Soft-Assisted Product Decoders Page 1 W3H.6.pdf OFC 2019 © OSA 2019
Energy-Efficient Soft-Assisted Product Decoders Christoffer Fougstedt1, Alireza Sheikh2 …
Energy-Efficient Soft-Assisted Product Decoders Christoffer Fougstedt1, Alireza Sheikh2 …
CMDNet: Learning a probabilistic relaxation of discrete variables for soft detection with low complexity
E Beck, C Bockelmann… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Following the great success of Machine Learning (ML), especially Deep Neural Networks
(DNNs), in many research domains in 2010s, several ML-based approaches were proposed …
(DNNs), in many research domains in 2010s, several ML-based approaches were proposed …
Iterative soft-input soft-output decoding with ordered reliability bits GRAND
C Condo - 2022 IEEE Globecom Workshops (GC Wkshps), 2022 - ieeexplore.ieee.org
Guessing Random Additive Noise Decoding (GRAND) is a universal decoding algorithm
that can be used to perform maximum likelihood decoding. It attempts to find the errors …
that can be used to perform maximum likelihood decoding. It attempts to find the errors …
GRAND for fading channels using pseudo-soft information
H Sarieddeen, M Médard… - GLOBECOM 2022-2022 …, 2022 - ieeexplore.ieee.org
Guessing random additive noise decoding (GRAND) is a universal maximum-likelihood
decoder that recovers codewords by guessing rank-ordered putative noise sequences and …
decoder that recovers codewords by guessing rank-ordered putative noise sequences and …
Algebraic soft-decision decoding of Reed–Solomon codes using bit-level soft information
J Jiang, KR Narayanan - IEEE transactions on information …, 2008 - ieeexplore.ieee.org
The performance of algebraic soft-decision decoding of Reed-Solomon codes using bit-level
soft information is investigated. Optimal multiplicity assignment strategies for algebraic soft …
soft information is investigated. Optimal multiplicity assignment strategies for algebraic soft …
Reed-Muller subcodes: Machine learning-aided design of efficient soft recursive decoding
Reed-Muller (RM) codes are conjectured to achieve the capacity of any binary-input
memoryless symmetric (BMS) channel, and are observed to have a comparable …
memoryless symmetric (BMS) channel, and are observed to have a comparable …