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Difference between revisions of "Forward error-correction ASIC using GRAND"

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Latest revision as of 17:16, 27 May 2022

Example of a block diagram of a detection scheme based on GRAND error correction decoding.

Short Description

Guessing random additive noise decoding (GRAND) is an emerging maximum likelihood (ML) decoding technique. The idea of GRAND is to efficiently guess the noise that corrupted a transmitted codeword. The goal of this project is to develop a more effective GRAND algorithm that improves the guessing accuracy, implement an architecture, and test it on an FPGA.


Status: Available

Looking for 1-2 Semester/Master students
Contact: Darja Nonaca

Prerequisites

Matlab or Python
VLSI I-II
Familiarity with the basics of digital communication is recommended but not strictly required

Character

50% MATLAB simulation
50% VLSI Implementation and Verification

Professor

Christoph Studer

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