Difference between revisions of "Peak-to-average power Reduction"
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==Short Description== | ==Short Description== | ||
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+ | 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: | + | : Contact: [[:User:studer | Christoph Studer]] |
===Prerequisites=== | ===Prerequisites=== | ||
: VLSI I | : VLSI I | ||
: VLSI II (''recommended'') | : VLSI II (''recommended'') | ||
+ | : Communication Systems (''recommended'') | ||
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===Status: Completed === | ===Status: Completed === |
Revision as of 15:19, 19 August 2020
Contents
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