Detecting Abrupt Change of Channel Covariance Matrix in IRS-Assisted Communication

no code implementations • 26 Oct 2023 • Runnan Liu, Liang Liu, Yin Xu, Dazhi He, Wenjun Zhang, Chang Wen Chen

We first categorize two types of channel covariance matrix changes based on their impact on system design: Type I change, which denotes the change in the BS receive covariance matrix, and Type II change, which denotes the change in the IRS transmit/receive covariance matrix.

Detecting Abrupt Changes in Channel Covariance Matrix for MIMO Communication

no code implementations • 5 Jul 2022 • Runnan Liu, Liang Liu, Dazhi He, Wenjun Zhang, Erik G. Larsson

Our paper aims to adopt the classic change detection theory to detect the change in the channel covariance matrix as accurately and quickly as possible such that the new covariance matrix can be re-estimated in time.

Change Detection

Task Decoupled Framework for Reference-Based Super-Resolution

no code implementations • CVPR 2022 • Yixuan Huang, Xiaoyun Zhang, Yu Fu, Siheng Chen, Ya zhang, Yan-Feng Wang, Dazhi He

Those methods conduct the super-resolution task of the input low-resolution(LR) image and the texture transfer task from the reference image together in one module, easily introducing the interference between LR and reference features.

Image Super-Resolution Reference-based Super-Resolution

Detection of Abrupt Change in Channel Covariance Matrix for Multi-Antenna Communication

1 code implementation • 9 Sep 2021 • Runnan Liu, Liang Liu, Dazhi He, Wenjun Zhang, Erik G. Larsson

This result verifies the possibility to detect the channel covariance change both accurately and quickly in practice.

Change Detection