Personal tools

Difference between revisions of "Low-Complexity MIMO Detection"

From iis-projects

Jump to: navigation, search
Line 1: Line 1:
[[File:Low_complexity_mimo_detection.png|450px|thumb|High performance low-complexity iterative MIMO receiver.]]
+
[[File:Low_complexity_mimo_bs6-crop.png|450px|thumb|High performance low-complexity iterative MIMO receiver.]]
 
==Short Description==
 
==Short Description==
  

Revision as of 12:53, 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

Prerequisites

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

Character

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

Professor

Christoph Studer

↑ top

Detailed Task Description

Goals

Practical Details

Results

Links

↑ top