MA6b2.8

Reinforcement Learning-Based Weak Signal Detection from Compressed Measurements in Massive MIMO Systems

Md Saidur Rahman Pavel, Yimin Zhang, Temple University, United States
Session:
MA6b2: Multiuser and Massive MIMO Poster
Track:
MIMO Communications and Signal Processing
Location:
Kiln
Presentation Time:
Mon, 30 Oct, 10:15 - 11:55 PT (UTC -8)
Session Chair:
Canan Cebeci, University of California, Santa Barbara
Presentation
Discussion
Resources
No resources available.
Session MA6b2
MA6b2.1: DFT-Based Channel Estimation for Holographic MIMO
Antonio Alberto D'Amico, Giacomo Bacci, Luca Sanguinetti, University of Pisa, Italy
MA6b2.2: Enhanced Low-Complexity FDD System Feedback with Variable Bit Lengths via Generative Modeling
Nurettin Turan, Benedikt Fesl, Wolfgang Utschick, Technical University of Munich, Germany
MA6b2.3: Device Detection and Channel Estimation in MTC with Correlated Activity Pattern
Hamza Djelouat, Mikko Sillanpää, Markku Juntti, University of Oulu, Finland
MA6b2.4: PAPR Reduction Utilizing Difference in Singular Values Among Spatial Channels in Uplink Eigenmode Massive MIMO Transmission
Yuta Abekura, Kenichi Higuchi, Tokyo University of Science, Japan
MA6b2.5: The Impact of SAR-ADC Mismatch on Quantized Massive MU-MIMO Systems
Jérémy Guichemerre, Christoph Studer, ETH Zurich, Switzerland
MA6b2.6: Enhanced Data Detection for Massive MIMO with 1-Bit ADCs
Amin Radbord, Italo Atzeni, Antti Tölli, University of Oulu, Finland
MA6b2.7: A Fourier Analysis of Digital Beamforming with Severely Quantized mmWave Arrays
Canan Cebeci, Upamanyu Madhow, University of California, Santa Barbara, United States
MA6b2.8: Reinforcement Learning-Based Weak Signal Detection from Compressed Measurements in Massive MIMO Systems
Md Saidur Rahman Pavel, Yimin Zhang, Temple University, United States
Contacts