Deep Learning for 1-Bit Compressed Sensing-based Superimposed CSI Feedback

no code implementations • 13 Mar 2022 • Chaojin Qing, Qing Ye, Bin Cai, Wenhui Liu, Jiafan Wang

In frequency-division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems, 1-bit compressed sensing (CS)-based superimposed channel state information (CSI) feedback has shown many advantages, while still faces many challenges, such as low accuracy of the downlink CSI recovery and large processing delays.

Region Specific Optimization (RSO)-based Deep Interactive Registration

no code implementations • 8 Mar 2022 • Ti Bai, MuHan Lin, Xiao Liang, Biling Wang, Michael Dohopolski, Bin Cai, Dan Nguyen, Steve Jiang

A test time optimization (TTO) technique was proposed to further improve the DL models' performance.

Image Registration Medical Image Registration

ELM-based Frame Synchronization in Burst-Mode Communication Systems with Nonlinear Distortion

no code implementations • 14 Feb 2020 • Chaojin Qing, Wang Yu, Bin Cai, Jiafan Wang, Chuan Huang

In burst-mode communication systems, the quality of frame synchronization (FS) at receivers significantly impacts the overall system performance.

Deep Learning for CSI Feedback Based on Superimposed Coding

no code implementations • 27 Jul 2019 • Chaojin Qing, Bin Cai, Qingyao Yang, Jiafan Wang, Chuan Huang

Massive multiple-input multiple-output (MIMO) with frequency division duplex (FDD) mode is a promising approach to increasing system capacity and link robustness for the fifth generation (5G) wireless cellular systems.