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[[File:Low_complexity_mimo_bs6-crop.png|450px|thumb|High performance low-complexity iterative MIMO receiver.]]
[[File:Low_complexity_mimo_bs6-crop.png|500px|thumb|High performance low-complexity iterative MIMO receiver.]]
==Short Description==
==Short Description==

Latest revision as of 12:54, 30 May 2022

High performance low-complexity iterative MIMO receiver.

Short Description

Iterative detection and decoding (IDD) is a performant, near-capacity achieving, MIMO detection and decoding scheme. However, due to multiple iterations, the complexity of the soft-input soft-output MIMO detector is of high relevance to outperform non-iterative methods. This project addresses the performance/complexity trade-off of state-of-the-art MIMO detection algorithms, with a special focus on GPU-based implementations. The objective of this project is to implement a low complexity MIMO detection algorithm and to optimize its performance with state-of-the-art machine learning methods. Therefore, a novel 5G-compliant link-level simulation and machine learning framework will be applied to implement performant GPU-based simulations.

Status: Available

Looking for 1-2 Semester/Bachelor/Master students
Contact: Reinhard Wiesmayr


Basic programming skills (e.g. MATLAB, or Python/Numpy/Tensorflow)
Wireless Communications


70% Programming (e.g. with MATLAB, Python, Tensorflow)
20% Theory
10% Literature research


Christoph Studer

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Detailed Task Description


Practical Details



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