1. IEEE Signal Processing Magazine
2. Signal Processing Digital Library*
3. Inside Signal Processing Newsletter
4. SPS Resource Center
5. Career advancement & recognition
6. Discounts on conferences and publications
7. Professional networking
8. Communities for students, young professionals, and women
9. Volunteer opportunities
10. Coming soon! PDH/CEU credits
Click here to learn more.
10 years of news and resources for members of the IEEE Signal Processing Society
Topic: Learning the MMSE Channel Estimator
Date: 24 June 2021
Time: 11:00 AM ET (New York time)
Duration: 1 Hour
Presenters: Dr. David Neumann, Dr. Thomas Wiese, Dr. Wolfgang Utschick
Based on the IEEE Xplore® article: Learning the MMSE Channel Estimator
Published: IEEE Transactions on Signal Processing, January 2018
Download: Original article will be made freely available for download on the day of the webinar, on IEEE Xplore®
The IEEE Signal Processing Society would like to express our concern and support for the members of our global community and all affected by the current COVID-19 pandemic. We appreciate your continued patience and support as we work together to navigate these unforeseen and uncertain circumstances. We hope that you, your families, and your communities are safe!
This webinar will discuss the MMSE channel estimator for a simple SIMO system model, without knowledge of the required channel statistics. Although the derived MMSE estimator is computationally intractable in the general form, its structure can be used to motivate a neural network architecture with lower complexity. The complexity reduction is based on a set of assumptions on the system model that simplify the MMSE estimator. The performance of the simplified MMSE estimator degrades significantly when those assumptions are not met. In contrast, a neural network based on the simplified MMSE estimator can compensate the mismatch by learning from data of the actual channel model. The presenters will also show how to extend the simple SIMO model to other practically relevant scenarios. Finally, they will demonstrate the performance when the neural network is learned based on actual channel measurements as compared to simulated data.
Dr. David Neumann received the Dipl.-Ing. degree in electrical engineering from Technische Universität München (TUM), München, Germany, in 2011. He received the doctoral degree from the Professorship for Signal Processing at TUM in 2020 for work on transceiver design for large-scale communication systems.
Dr. Neumann was working at Intel on the development of a next generation cellular modem from 2018 to 2019. In 2019, he joined Apple in Munich.
Dr. Thomas Wiese received the Dipl.-Ing. degree in electrical engineering and the Dipl.-Math. degree in mathematics from Technische Universität München (TUM) in 2011 and 2012, respectively.
From 2012 through 2018 he worked at the Professorship for Signal Processing at TUM with Prof.
Utschick. In his dissertation, he analyzed the complexity of the channel estimation problem in millimeter wave communication systems and studied the applicability of low-complexity algorithms to that problem.
Dr. Wiese joined Cadami GmbH in Munich in 2018 and has since helped bringing the coded caching technology from research to application.
Dr. Wolfgang Utschick (Fellow, IEEE) completed several years of industrial training programs before he received the diploma and doctoral degrees in Electrical Engineering (both with distinction), with a dissertation on machine learning, from the Technische Universität München (TUM), München, Germany, in 1993 and 1998, respectively. Since 2002, he has been a Professor with the TUM, where he is chairing a Signal Processing group. He teaches courses on Signal Processing, Stochastic Processes, Optimization Theory, and Machine Learning in the field of Wireless Communications and various application areas of Signal Processing. Since 2011, he has been a regular Guest Professor with Singapore’s new autonomous university, Singapore Institute of Technology, Singapore, and since 2017, he has been the Dean of the Department of Electrical and Computer Engineering, TUM.
He holds several patents in the field of Multiantenna Signal Processing and has authored and coauthored a large number of technical articles in international journals and conference proceedings. He edited several books and is Founder and Editor of the Springer book series Foundations in Signal Processing, Communications and Networking. He was the recipient of a couple of best paper awards. He has been a Principal Investigator in multiple research projects funded by the German Research Fund (DFG) and coordinated a German DFG priority program on Communications Over Interference Limited Networks.
Dr. Utschick is a member of the VDE, and therein a member of the Expert Group 5.1 for Information and System Theory of the German Information Technology Society. He is currently chairing the German Signal Processing Section. He was also an Associate Editor for the IEEE Transactions on Signal Processing and was a member of the IEEE Signal Processing Society Technical Committee on Signal Processing for Communications and Networking.
|Call for Nominations: IEEE Technical Field Awards||15 January 2022|
|Call for Officer Nominations: Vice President-Education and Vice President-Membership||21 February 2022|
|Nominate an IEEE Fellow today!||1 March 2022|
|Call for Nominations for Editor-in-Chief, IEEE Signal Processing Letters||4 April 2022|
|Call for Nominations: IEEE Medals & Recognitions||15 June 2022|
© Copyright 2022 IEEE – All rights reserved. Use of this website signifies your agreement to the IEEE Terms and Conditions.
A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.