Spatial CSI prediction experiments on simulated data show a VAE outperforms GPT-2, Transformer, and MLP, but the claimed benefit of fusing environmental features is not tested against a no-feature baseline.
Overcoming the Channel Estimation Barrier in Massive MIMO Communication Systems
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
A new wave of wireless services, including virtual reality, autonomous driving and internet of things, is driving the design of new generations of wireless systems to deliver ultra-high data rates, massive number of connected devices and ultra low latency. Massive multiple-input multiple-output (MIMO) is one of the critical underlying technologies that allow future wireless networks to meet these service needs. This article discusses the application of deep learning (DL) for massive MIMO channel estimation in wireless networks by integrating the underlying characteristics of channels in future high-speed cellular deployment. We develop important insights derived from the physical radio frequency (RF) channel properties and present a comprehensive overview on the application of DL for accurately estimating channel state information (CSI) with low overhead. We provide examples of successful DL application in CSI estimation for massive MIMO wireless systems and highlight several promising directions for future research.
fields
cs.LG 1years
2024 1verdicts
REJECT 1representative citing papers
citing papers explorer
-
Large Models Enabled Ubiquitous Wireless Sensing
Spatial CSI prediction experiments on simulated data show a VAE outperforms GPT-2, Transformer, and MLP, but the claimed benefit of fusing environmental features is not tested against a no-feature baseline.