.png){kind=icon}
.png){kind=icon}
Return
Randomized Low-Rank Approximation Based Massive MIMO CSI Compression
.png){kind=icon}
PRE
AI
DOI：10.1109/LCOMM.2021.3065751.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Massive multiple-input multiple-output (MIMO) is regarded as one enabling technique to improve channel capacity and energy/spectrum efficiency of 5G communications. To attain such potential benefits, accurate channel information is critical to the transmitter, which yet remains a challenging task for frequency division duplexing (FDD) systems, i.e., the channel state information (CSI) feedback tends to be resource-demanding especially for massive MIMO communications. In this work, we propose a novel CSI feedback method with low complexity and high precision, which is inspired by randomized matrix approximation. Our approach exploits the inherent low-rank characteristic of a large channel matrix, and approximates it by small sub-matrices which are then reported to transmitter to recover a CSI matrix. Theoretical bounds of the recovered CSI in both error-free and error cases are derived. Simulation results demonstrate our method could recover CSI accurately via an extremely low complexity and yet achieve a largely reduced compression ratio (or feedback overhead), compared to other schemes. It thus has the great potential in the emerging massive MIMO FDD communications.
Keywords:
Massive MIMO
channel state information (CSI) feedback
randomized matrix approximation
low rank
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
4.4
Papers:
1.3W
Citations:
2.2W
Organization
No organization information available