Difference between revisions of "Forward error-correction ASIC using GRAND"
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Revision as of 17:09, 27 May 2022
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.
- Looking for 1-2 Semester/Master students
- Contact: Darja Nonaca
- Matlab or Python
- VLSI I-II
- Familiarity with the basics of digital communication is recommended but not strictly required
- 50% MATLAB simulation
- 50% VLSI Implementation and Verification