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Forward error-correction ASIC using GRAND

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A MIMO basestation mitigates an ongoing jamming attack while continuing to serve the legitimate user equipments. The signal processing for this takes place in a custom ASIC.

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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Detailed Task Description

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