.png){kind=icon}
Return
Source Coding Based Millimeter-Wave Channel Estimation With Deep Learning Based Decoding
.png){kind=icon}
OA
AI
DOI：10.1109/TCOMM.2021.3072999.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The speed at which millimeter-Wave (mmWave) channel estimation can be carried out is critical for the adoption of mmWave technologies. This is particularly crucial because mmWave transceivers are equipped with large antenna arrays to combat severe path losses, which consequently creates large channel matrices, whose estimation may incur significant overhead. This paper focuses on the mmWave channel estimation problem. Our objective is to reduce the number of measurements required to reliably estimate the channel. Specifically, channel estimation is posed as a source compression problem in which measurements mimic an encoded (compressed) version of the channel. Decoding the observed measurements, a task which is traditionally computationally intensive, is performed using a deep-learning-based approach, facilitating a high-performance channel discovery. Our solution not only outperforms state-of-the-art compressed sensing methods, but it also determines the lower bound on the number of measurements required for reliable channel discovery.
Keywords:
Millimeter-wave
channel estimation
path discovery
sparse recovery
source coding
machine learning
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
8.3
Papers:
1.2W
Citations:
3.6W
Organization
Cited Papers
ShareSave
ShareSaveEphrin Bs are essential components of the Reelin pathway to regulate neuronal migration.png)
Nature
NatureIF0
PREAI
ShareSaveShareSaveDeep Learning for Wireless Physical Layer: Opportunities and ChallengesCHINA COMMUNICATIONSOAAI
CHINA COMMUNICATIONSIF3.1
OAAIShareSave
View more