Clock synchronization over networks using sawtooth models
October 21, 2019 Β· Declared Dead Β· π IEEE International Conference on Acoustics, Speech, and Signal Processing
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Pol del Aguila Pla, Lissy Pellaco, Satyam Dwivedi, Peter HΓ€ndel, Joakim JaldΓ©n
arXiv ID
1910.09374
Category
eess.SP: Signal Processing
Cross-listed
cs.DB,
eess.SY
Citations
2
Venue
IEEE International Conference on Acoustics, Speech, and Signal Processing
Last Checked
4 months ago
Abstract
Clock synchronization and ranging over a wireless network with low communication overhead is a challenging goal with tremendous impact. In this paper, we study the use of time-to-digital converters in wireless sensors, which provides clock synchronization and ranging at negligible communication overhead through a sawtooth signal model for round trip times between two nodes. In particular, we derive CramΓ©r-Rao lower bounds for a linearitzation of the sawtooth signal model, and we thoroughly evaluate simple estimation techniques by simulation, giving clear and concise performance references for this technology.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Signal Processing
R.I.P.
π»
Ghosted
π
π
The Cartographer
1D Convolutional Neural Networks and Applications: A Survey
R.I.P.
π»
Ghosted
Wireless Communications with Reconfigurable Intelligent Surface: Path Loss Modeling and Experimental Measurement
π
π
The Cartographer
Accessing From The Sky: A Tutorial on UAV Communications for 5G and Beyond
R.I.P.
π»
Ghosted
6G Wireless Systems: Vision, Requirements, Challenges, Insights, and Opportunities
R.I.P.
π»
Ghosted
A New Wireless Communication Paradigm through Software-controlled Metasurfaces
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