1,362 research outputs found
Machine Learning Applied in 2D Parasitic Extraction
With the scale of interconnect number grows to billions, parasitic capacitance extraction speed is an important issue for fast turn-around time for designers.
In this thesis, we propose to build a regression model for the input interconnect geometry to predict the parasitic capacitance based on machine learning. A simplification algorithm is proposed to reduce the number of conductors for quicker and easier regression modeling and the regression models can improve by machine learning technique.
Experimental results show that the proposed method is significantly faster than existing method and provides satisfactory accuracy
An Iterative 5G Positioning and Synchronization Algorithm in NLOS Environments with Multi-Bounce Paths
5G positioning is a very promising area that presents many opportunities and
challenges. Many existing techniques rely on multiple anchor nodes and
line-of-sight (LOS) paths, or single reference node and single-bounce non-LOS
(NLOS) paths. However, in dense multipath environments, identifying the LOS or
single-bounce assumptions is challenging. The multi-bounce paths will make the
positioning accuracy deteriorate significantly. We propose a robust 5G
positioning algorithm in NLOS multipath environments. The corresponding
positioning problem is formulated as an iterative and weighted least squares
problem, and different weights are utilized to mitigate the effects of
multi-bounce paths. Numerical simulations are carried out to evaluate the
performance of the proposed algorithm. Compared with the benchmark positioning
algorithms only using the single-bounce paths, similar positioning accuracy is
achieved for the proposed algorithm
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