Deep learning for massive MIMO hybrid beamforming - Hybrid event

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Deep learning for massive MIMO hybrid beamforming


The millimeter-wave (mm-Wave) massive multiple-input multiple-output (MIMO) communications employ hybrid analog-digital beamforming architectures to reduce the cost-power-size-hardware overheads arising from the use of extremely large arrays at this band. Lately, there is also a gradual push to move from the millimeter-wave (mmWave) to Terahertz (THz) frequencies for short-range communications and radar applications to exploit very wide THz bandwidths. At THz, ultramassive MIMO array is an enabling technology to exploit ultrawide bandwidth while employing thousands of antennas. The design of the hybrid beamforming techniques requires the solution to difficult nonconvex optimization problems that involve a common performance metric as a cost function and several constraints related to the employed communication regime and the adopted architecture of the hybrid system(s). There is no standard methodology for solving such problems and usually, the derivation of an efficient solution is a very challenging task. Since optimization-based approaches suffer from high computational complexity and their performance strongly relies on the perfect channel condition, we introduce deep learning (DL) techniques that provide robust performance while designing a hybrid beamformer. In this talk, the audience will learn about applying DL to various aspects of hybrid beamforming including channel estimation, antenna selection, wideband beamforming, and spatial modulation. In addition, we will examine these concepts in the context of joint radar-communications and intelligent-surfaces-aided architectures.



  Date and Time

  Location

  Hosts

  Registration



  • Date: 20 Jul 2023
  • Time: 06:00 PM to 08:00 PM
  • All times are (UTC-05:00) Eastern Time (US & Canada)
  • Add_To_Calendar_icon Add Event to Calendar
  • 101 Crawfords Corner Road
  • Holmdel, New Jersey
  • United States 07733
  • Building: Bell Works
  • Room Number: Township meeting room

  • Contact Event Hosts
  • This will be hybrid event - in person at Bell works and also online.

    For Webex details - please register and details will be emailed to you.

  • Co-sponsored by Prasad Atluri, Chair Communication Soc
  • Starts 18 May 2023 08:19 AM
  • Ends 20 July 2023 06:00 PM
  • All times are (UTC-05:00) Eastern Time (US & Canada)
  • No Admission Charge


  Speakers

Dr. Mishra

Topic:

Deep learning for massive MIMO hybrid beamforming - Hybrid event

The millimeter-wave (mm-Wave) massive multiple-input multiple-output (MIMO) communications employ hybrid analog-digital beamforming architectures to reduce the cost-power-size-hardware overheads arising from the use of extremely large arrays at this band. Lately, there is also a gradual push to move from the millimeter-wave (mmWave) to Terahertz (THz) frequencies for short-range communications and radar applications to exploit very wide THz bandwidths. At THz, ultramassive MIMO array is an enabling technology to exploit ultrawide bandwidth while employing thousands of antennas. The design of the hybrid beamforming techniques requires the solution to difficult nonconvex optimization problems that involve a common performance metric as a cost function and several constraints related to the employed communication regime and the adopted architecture of the hybrid system(s). There is no standard methodology for solving such problems and usually, the derivation of an efficient solution is a very challenging task. Since optimization-based approaches suffer from high computational complexity and their performance strongly relies on the perfect channel condition, we introduce deep learning (DL) techniques that provide robust performance while designing a hybrid beamformer. In this talk, the audience will learn about applying DL to various aspects of hybrid beamforming including channel estimation, antenna selection, wideband beamforming, and spatial modulation. In addition, we will examine these concepts in the context of joint radar-communications and intelligent-surfaces-aided architectures.

 

Distinguished talk "Deep learning for massive MIMO hybrid beamforming"
Thursday, July 20, 2023
6:00 PM  |  (UTC-04:00) Eastern Time (US & Canada)  |  2 hrs
 
Join WebEx meeting


https://ieeemeetings.webex.com/ieeemeetings/j.php?MTID=m776fe215adfd6bd224ec6f42b76383fa

Meeting number: 2538 459 6318
Meeting password: qRZuGXkZ555

 

 

Join from a video system or application

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To dial from an IEEE Video Conference System: *1 2538 459 6318

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Biography:

Kumar Vijay Mishra (S’08-M’15-SM’18) obtained a Ph.D. in electrical engineering and M.S. in
mathematics from The University of Iowa in 2015, and M.S. in electrical engineering from
Colorado State University in 2012, while working on NASA’s Global Precipitation Mission Ground
Validation (GPM-GV) weather radars. He received his B. Tech. summa cum laude (Gold Medal,
Honors) in electronics and communication engineering from the National Institute of Technology,
Hamirpur (NITH), India in 2003. He is currently Senior Fellow at the United States Army Research
Laboratory (ARL), Adelphi; Technical Adviser to Singapore-based automotive radar start-up
Hertzwell and Boston-based imaging radar startup Aura Intelligent Systems; and honorary
Research Fellow at SnT - Interdisciplinary Centre for Security, Reliability and Trust, University of
Luxembourg. Previously, he had research appointments at Electronics and Radar Development
Establishment (LRDE), Defence Research and Development Organisation (DRDO) Bengaluru; IIHR
- Hydroscience & Engineering, Iowa City, IA; Mitsubishi Electric Research Labs, Cambridge, MA;
Qualcomm, San Jose; and Technion - Israel Institute of Technology.
Dr. Mishra is the Distinguished Lecturer of the IEEE Communications Society (2023-2024), IEEE
Aerospace and Electronic Systems Society (AESS) (2023-2024), IEEE Vehicular Technology Society
(2023-2024), and IEEE Future Networks Initiative (2022). He is the recipient of the IET Premium
Best Paper Prize (2021), U. S. National Academies Harry Diamond Distinguished Fellowship (2018-
2021), American Geophysical Union Editors' Citation for Excellence (2019), Royal Meteorological
Society Quarterly Journal Editor's Prize (2017), Viterbi Postdoctoral Fellowship (2015, 2016), Lady
Davis Postdoctoral Fellowship (2017), DRDO LRDE Scientist of the Year Award (2006), NITH
Director’s Gold Medal (2003), and NITH Best Student Award (2003). He has received Best Paper
Awards at IEEE MLSP 2019 and IEEE ACES Symposium 2019.
Dr. Mishra is Chair (2023-present) of the Synthetic Apertures Technical Working Group of the
IEEE Signal Processing Society (SPS) and Vice-Chair (2021-present) of the IEEE Synthetic Aperture
Standards Committee, which is the first SPS standards committee. He is the Vice Chair (2021-
2023) and Chair-designate (2023-2026) of the International Union of Radio Science (URSI)
Commission C. He has been an elected member of three technical committees of IEEE SPS:
SPCOM, SAM, and ASPS, and IEEE AESS Radar Systems Panel. Since 2020, he has been Associate
Editor of IEEE Transactions on Aerospace and Electronic Systems, where he was awarded
Outstanding Editor recognition in 2021. He has been a lead/guest editor of several special issues
in journals such as IEEE Signal Processing Magazine, IEEE Journal of Selected Topics in Signal
Processing, and IEEE Journal on Selected Areas in Communications. He is the lead co-editor of
three upcoming books on radar: Signal Processing for Joint Radar-Communications (Wiley-IEEE
Press), Next-Generation Cognitive Radar Systems (IET Press Radar, Electromagnetics & Signal
Processing Technologies Series), and Advances in Weather Radar Volumes 1, 2 & 3 (IET Press
Radar, Electromagnetics & Signal Processing Technologies Series). His research interests include
radar systems, signal processing, remote sensing, and electromagnetics.

Address:US Army Research Laboratory, 2800 Powder Mill Rd, Adelphi, United States, 20783





Agenda

Arrive in township meeting room (within Holmdel library) by 5.45pm

Set up talk in the meeting room and also set up Webex session 5:50pm

Begin talk (6PM): Deep learning for massive MIMO hybrid beamforming

Conclude talk (7PM) and begin Q & A session

7:30PM close of event - all those who attend "in person" will join speaker for dinner at local restaurant

----

Distinguished talk "Deep learning for massive MIMO hybrid beamforming"
Thursday, July 20, 2023
6:00 PM  |  (UTC-04:00) Eastern Time (US & Canada)  |  2 hrs

 

Join WebEx meeting


https://ieeemeetings.webex.com/ieeemeetings/j.php?MTID=m776fe215adfd6bd224ec6f42b76383fa

Meeting number: 2538 459 6318
Meeting password: qRZuGXkZ555

 

 

Join from a video system or application

Dial 25384596318@ieeemeetings.webex.com
You can also dial 173.243.2.68 and enter your meeting number.

To dial from an IEEE Video Conference System: *1 2538 459 6318

Tap to join from a mobile device (attendees only)

+1-415-655-0002,,25384596318## United States Toll
1-855-282-6330,,25384596318## United States Toll Free


To attend"in person", please arrive at Bell works, 101 Crawfords Corner Rd, Holmdel NJ 07733 by 5.45pm