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(Created page with "thumb ==Short Description== Abstract of the project ===Status: Available === : Looking for 1-2 Semester/Master students : Contact: :User:Mlu...")
 
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[[File:Variation Tolerant.jpg|thumb]]
 
[[File:Variation Tolerant.jpg|thumb]]
 
==Short Description==
 
==Short Description==
Abstract of the project
+
 
 +
Data transmission over frequency-selective channels using orthogonal frequency-division multiplexing (OFDM) suffers from high peak-to-average power ratio (PAR). The high dynamic range of OFDM signals is further aggravated in multi-antenna or massive MIMO systems where hundreds of parallel OFDM stream must be transmitted wirelessly. This project aims at investigating new methods that reduce the PAR in massive MIMO-OFDM systems. The project can be either fully theoretical, half theory and half implementation, or purely VLSI implementation based. The tools to be learned in this project are numerical (convex) optimization and deep unfolding, a recent paradigm to tune algorithm parameters using deep learning frameworks.
  
 
===Status: Available ===
 
===Status: Available ===
 
: Looking for 1-2 Semester/Master students
 
: Looking for 1-2 Semester/Master students
: Contact: [[:User:Mluisier | Mathieu Luisier]]
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: Contact: [[:User:studer | Christoph Studer]]
 
===Prerequisites===
 
===Prerequisites===
 
: VLSI I
 
: VLSI I
 
: VLSI II (''recommended'')
 
: VLSI II (''recommended'')
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: Communication Systems (''recommended'')
 
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===Status: Completed ===
 
===Status: Completed ===

Revision as of 15:19, 19 August 2020

Variation Tolerant.jpg

Short Description

Data transmission over frequency-selective channels using orthogonal frequency-division multiplexing (OFDM) suffers from high peak-to-average power ratio (PAR). The high dynamic range of OFDM signals is further aggravated in multi-antenna or massive MIMO systems where hundreds of parallel OFDM stream must be transmitted wirelessly. This project aims at investigating new methods that reduce the PAR in massive MIMO-OFDM systems. The project can be either fully theoretical, half theory and half implementation, or purely VLSI implementation based. The tools to be learned in this project are numerical (convex) optimization and deep unfolding, a recent paradigm to tune algorithm parameters using deep learning frameworks.

Status: Available

Looking for 1-2 Semester/Master students
Contact: Christoph Studer

Prerequisites

VLSI I
VLSI II (recommended)
Communication Systems (recommended)

Character

20% Theory
40% ASIC Design
40% EDA tools

Professor

Christoph Studer

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Detailed Task Description

Goals

Practical Details

Results

Links

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