6 research outputs found
Quantized Guessing Random Additive Noise Decoding
We introduce a soft-detection variant of Guessing Random Additive Noise
Decoding (GRAND) called Quantized GRAND (QGRAND) that can efficiently decode
any moderate redundancy block-code of any length in an algorithm that is
suitable for highly parallelized implementation in hardware. QGRAND can avail
of any level of quantized soft information, is established to be almost
capacity achieving, and is shown to provide near maximum likelihood decoding
performance when provided with five or more bits of soft information per
received bit
Segmented GRAND: Combining Sub-patterns in Near-ML Order
The recently introduced maximum-likelihood (ML) decoding scheme called
guessing random additive noise decoding (GRAND) has demonstrated a remarkably
low time complexity in high signal-to-noise ratio (SNR) regimes. However, the
complexity is not as low at low SNR regimes and low code rates. To mitigate
this concern, we propose a scheme for a near-ML variant of GRAND called ordered
reliability bits GRAND (or ORBGRAND), which divides codewords into segments
based on the properties of the underlying code, generates sub-patterns for each
segment consistent with the syndrome (thus reducing the number of inconsistent
error patterns generated), and combines them in a near-ML order using two-level
integer partitions of logistic weight. The numerical evaluation demonstrates
that the proposed scheme, called segmented ORBGRAND, significantly reduces the
average number of queries at any SNR regime. Moreover, the segmented ORBGRAND
with abandonment also improves the error correction performance
Iterative Soft-Input Soft-Output Decoding with Ordered Reliability Bits GRAND
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 introduced by the channel by generating a sequence of
possible error vectors in order of likelihood of occurrence and applying them
to the received vector. Ordered reliability bits GRAND (ORBGRAND) integrates
soft information received from the channel to refine the error vector sequence.
In this work, ORBGRAND is modified to produce a soft output, to enable its use
as an iterative soft-input soft-output (SISO) decoder. Three techniques
specific to iterative GRAND-based decoding are then proposed to improve the
error-correction performance and decrease computational complexity and latency.
Using the OFEC code as a case study, the proposed techniques are evaluated,
yielding substantial performance gain and astounding complexity reduction of
48\% to 85\% with respect to the baseline SISO ORBGRAND.Comment: Submitted to Globecom 202
Guessing random additive noise decoding with soft detection symbol reliability information - SGRAND
We recently introduced a noise-centric algorithm,
Guessing Random Additive Noise Decoding (GRAND), that
identifies a Maximum Likelihood (ML) decoding for arbitrary
code-books. GRAND has the unusual property that its complexity
decreases as code-book rate increases. Here we provide an
extension to GRAND, soft-GRAND (SGRAND), that incorporates
soft detection symbol reliability information and identifies a
ML decoding in that context. In particular, we assume symbols
received from the channel are declared to be error free or
to have been potentially subject to additive noise. SGRAND
inherits desirable properties of GRAND, including being capacity
achieving when used with random code-books, and having a
complexity that reduces as the code-rate increases