IEEE Wireless Communications and Networking Conference
10–13 April 2022 // Austin, TX, USA
Boosting Verticals into Wireless Orbit

Program

PLANNED FORMAT OF THE WORKSHOP

The workshop is in a hybrid mode for a full day.  Its tentative program is as follows (exact time and duration may be adjusted to agree with the final conference program):

[10:00 – 12:00]: IN-PERSON WORKSHOP SESSION SESSION

[10:00-10:10] Session Opening & Welcome,

Tim O’Shea (Virginia Tech & DeepSig)

 

[10:10-10:40] Opening Talk: Machine Learning Based Sensing Aided Wireless Communications,

Ahmed Alkhateeb (Arizona State University)

 

[10:40-11:00] Deep Learning-based Channel State Information Prediction with Incomplete History

Ezgi Tekgul, Jie Chen, Jun Tan, Fred Vook, Serdar Ozen, Akshay Jajoo (Department of Electrical and Computer Engineering, The University of Texas at Austin and Nokia of America Corporation, Standardization Research, Naperville)

 

[11:00-11:20] Model-driven Machine Learning Approach for Mobility Classification in Intelligent 5G Network

Tiexing Wang, Yeqing Hu, Yang Li, Junmo Sung, Rui Wang, Jianzhong (Charlie) Zhang (Samsung Research America)

 

[11:20-11:40] Score-Based Generative Models for Robust Channel Estimation

Marius Arvinte, Jonathan I. Tamir (Department of Electrical and Computer Engineering, The University of Texas at Austin)

 

[11:40-12:00] Session Closing Discussions & Networking

 

[ON DEMAND] REMOTE SESSION (15 PAPER PRESENTATIONS, 15 MINUTES EACH)

Learning-Based Symbol Level Precoding: A Memory-Efficient Unsupervised Learning Approach

Abdullahi Mohammad, Christos Masouros, Yiannis Andreopoulos (University College London)

Opening the Black Box of Deep Neural Networks in Physical Layer Communication

Jun Liu, Haitao Zhao, Dongtang Ma, Kai Mei, Ji-Bo Wei (National University of Defense Technology, China)

Model-driven Machine Learning Approach for Mobility Classification in Intelligent 5G Network

Tiexing Want, Yeqing Hu, Yang Li, Junmo Sung, Rui Wang, Jingzhong Zhang (Samsung Research America, University of Texas at Austin)

Learning the Optimal LLR under Carrier Frequency Offset

Jiankun Zhang, Hao Wang (Huawei Technologies, China)

Deep Reinforcement Learning Based Dynamic Power and Beamforming Design for Time-Varying Wireless Downlink Channel

Mengfan Liu, Rui Wang, Zhe Xing, Ismael Soto (Imperial College Longon, Tongji University, China, University of Santiago, Chile)

Deep Learning-based Channel State Information Prediction with Incomplete History

Ezgi Tekgul, Jie Chen, Jun Tan, Frederick W. Vook, Serdar Ozen, Akshay Jajoo (University of Texas at Austin, Nokia of America Corporation, Nokia Networks)

Score-Based Generative Models for Robust Channel Estimation

Marius Avinte, Jonathan I Tamir (University of Texas at Austin)

Deep Reinforcement Learning based Joint Active and Passive Beamforming Design for RIS-Assisted MISO Systems

Yuqian Zhu, Zhu Bo, Ming Li, Yang Liu, Qian Liu, Zheng Chang, Yulin Hu (Dalian University of Technology, China, University of Jyväskylä, Finland, RWTH Aachen University, Germany)

Scalable Wireless Anomaly Detection with Generative-LSTMs on RF Post-Detection Metadata

Blake A Barnes-Cook, Tim O’Shea (Virginia Tech, DeepSig)

Deep Reinforcement Learning-based Power Allocation in Uplink Cell-Free Massive MIMO

Mostafa Rahmani, Manijeh Bashar, Mohammad Javad Dehghani, Pei Xiao, Rahim Tafazolli, Merouane Debbah (Shiraz University of Technology, UK, Shiraz University of Technology, Iran, University of Surrey, UK, Huawei, Frange)

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