Feb 20, 2018 · Abstract:We introduce a new algorithm for realizing Maximum Likelihood (ML) decoding in discrete channels with or without memory.
Mar 22, 2019 · Abstract. We introduce a new algorithm for realizing Maximum Likelihood (ML) decoding in discrete channels with or without memory.
Guessing Random Additive Noise Decoding (GRAND) is a methodology based on an innovative paradigm. It is agnostic to the code structure as it aims to identify ...
An approximate ML decoding scheme where the receiver abandons the search after a fixed number of queries is introduced, an approach that is also ...
Guessing random additive noise decoding with soft detection symbol ...
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Abstract: We recently introduced a noise-centric algorithm, Guessing Random Additive Noise Decoding (GRAND), that identifies a Maximum Likelihood (ML) ...
Duration: 1:08:36
Posted: Sep 6, 2020
Posted: Sep 6, 2020
For common additive noise channels, we establish that the algorithm is capacity-achieving for uniformly selected code-books, providing an intuitive alternate ...
Feb 20, 2019 · PDF | We introduce a new algorithm for realizing Maximum Likelihood (ML) decoding in discrete channels with or without memory.
The algorithm is extended to high-order modulations by guessing symbol noises, void- ing de-mappers and achieving additional decoding gains, especially with the ...
This chapter introduces GRAND, a universal maximum likelihood decoding technique for linear block codes of short code-length and high code-rates.