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(Created page with "File:jammer_mitigation_asic.png|380px|thumb|A MIMO basestation mitigates an ongoing jamming attack while continuing to serve the legitimate user equipments. The signal proce...")
 
 
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[[File:jammer_mitigation_asic.png|380px|thumb|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.]]
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[[File:Low_complexity_mimo_bs6-crop.png|500px|thumb|High performance low-complexity iterative MIMO receiver.]]
 
==Short Description==
 
==Short Description==
  
Jamming attacks pose a critical threat to wireless communication systems. Multi-antenna (MIMO) wireless systems have the potential to mitigate such jamming attacks through signal processing. Methods for jammer mitigation are thus currently a hot research topic in wireless communication. Many different linear [1], [2] and non-linear (e.g., deep learning based [3]) jammer mitigation algorithms have been proposed. To be practically viable, such methods will ultimately have to be implemented in hardware (using FPGAs or, more likely, ASICs), since sofware-based signal processing will never support the data rates of modern wireless systems. To this date, however, there are no hardware implementations of jammer mitigation algorithms.  
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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.
 
 
The goal of this project is to develop the first ASIC implementation of a jammer-mitigating signal processing algorithm. For this, the student will take a state-of-the art jammer mitigation algorithm and adapt it as an efficient VLSI implementation. The student will then synthesize this design and tape out a chip using CMOS technology.
 
 
 
 
 
[1] Q. Yan, H. Zeng, T. Jiang, M. Li, W. Lou, and Y. T. Hou "Jamming resilient communication using MIMO interference cancellation." IEEE Transactions on Information Forensics and Security 11(7), 2016, 1486-1499.
 
 
 
[2] H. Akhlaghpasand, E. Björnson, and S. Mohammad Razavizadeh. "Jamming suppression in massive MIMO systems." IEEE Transactions on Circuits and Systems II: Express Briefs 67(1), 2019, 182-186.
 
 
 
[3] T. Erpek,  Y. E. Sagduyu, and Y. Shi. "Deep learning for launching and mitigating wireless jamming attacks." IEEE Transactions on Cognitive Communications and Networking 5(1), 2018, 2-14.
 
 
 
  
 
===Status: Available ===
 
===Status: Available ===
: Looking for 1-2 Semester/Master students
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: Looking for 1-2 Semester/Bachelor/Master students
 
: Contact: [https://iip.ethz.ch/people/profiles.MzAxMjAz.TGlzdC80MTExLDEwNjY3Mjg3NDU=.html Reinhard Wiesmayr]
 
: Contact: [https://iip.ethz.ch/people/profiles.MzAxMjAz.TGlzdC80MTExLDEwNjY3Mjg3NDU=.html Reinhard Wiesmayr]
  
 
===Prerequisites===
 
===Prerequisites===
: Verilog or VHDL
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: Basic programming skills (e.g. MATLAB, or Python/Numpy/Tensorflow)
: VLSI II
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: Wireless Communications
: Familiarity with the basics of digital communication is recommended but not strictly required
 
 
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===Status: Completed ===
 
===Status: Completed ===
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===Character===
 
===Character===
: 80% VLSI Implementation and Verification
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: 70% Programming (e.g. with MATLAB, Python, Tensorflow)
: 20% MATLAB simulation
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: 20% Theory
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: 10% Literature research
 
===Professor===
 
===Professor===
 
<!-- : [http://www.iis.ee.ethz.ch/people/person-detail.html?persid=194234 Luca Benini] --->
 
<!-- : [http://www.iis.ee.ethz.ch/people/person-detail.html?persid=194234 Luca Benini] --->

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

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

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Detailed Task Description

Goals

Practical Details

Results

Links

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