Massive MIMO channel estimation taking into account spherical waves
November 14, 2018 Β· Declared Dead Β· π International Workshop on Signal Processing Advances in Wireless Communications
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Antoine Le Calvez, Luc Le Magoarou, StΓ©phane Paquelet
arXiv ID
1811.05669
Category
cs.NI: Networking & Internet
Citations
33
Venue
International Workshop on Signal Processing Advances in Wireless Communications
Last Checked
6 months ago
Abstract
Together with millimiter waves (mmWaves), massive multiple-input multiple-output (MIMO) systems are key technological components of fifth generation (5G) wireless communication systems. In such a context, geometric considerations show that the largely adopted plane wave model (PWM) of the channel potentially loses its validity. An alternative is to consider the more accurate but more complex spherical wave model (SWM). This paper introduces an intermediate parabolic wave model (ParWM), more accurate than the PWM while less complex than the SWM. The validity domains of those three physical models are assessed in a novel way. Finally, estimation algorithms for the SWM and ParWM are proposed and compared with classical algorithms, showing a promising performance complexity trade-off.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Networking & Internet
R.I.P.
π»
Ghosted
π
π
The Cartographer
Federated Learning in Mobile Edge Networks: A Comprehensive Survey
π
π
The Cartographer
A Survey of Indoor Localization Systems and Technologies
R.I.P.
π»
Ghosted
Survey of Important Issues in UAV Communication Networks
π
π
The Cartographer
Network Function Virtualization: State-of-the-art and Research Challenges
π
π
The Cartographer
Applications of Deep Reinforcement Learning in Communications and Networking: A Survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted