Modelling of non-stationary mobile radio channels using two-dimensional brownian motion processes

2013 ◽  
Author(s):  
Alireza Borhani ◽  
Matthias Patzold
Keyword(s):  
Brownian Motion ◽  
Mobile Radio ◽  
Two Dimensional ◽  
Radio Channels

New adaptive Viterbi detector for fast-fading mobile-radio channels

1990 ◽  
Vol 26 (19) ◽  
pp. 1572 ◽  
Author(s):  
E. Dahlman
Keyword(s):  
Mobile Radio ◽  
Radio Channels ◽  
Fast Fading ◽  
Viterbi Detector

Enabling Code Diversity for Mobile Radio Channels using Long-Range Fading Prediction

2012 ◽  
Vol 11 (12) ◽  
pp. 4362-4371 ◽  
Author(s):  
Yiyue Wu ◽  
Tao Jia ◽  
Robert Calderbank ◽  
Alexandra Duel-Hallen ◽  
Hans Hallen
Keyword(s):  
Long Range ◽  
Mobile Radio ◽  
Radio Channels

scholarly journals Two-dimensional stochastic modeling for predicting bankruptcy for manufacturing companies

2021 ◽  
Vol 54 (2) ◽  
pp. 123-129
Author(s):  
James C. Fu ◽  
Winnie H. W. Fu
Keyword(s):  
Brownian Motion ◽  
Real Data ◽  
Bankruptcy Prediction ◽  
Boundary Crossing ◽  
Time Interval ◽  
Two Dimensional ◽  
Manufacturing Companies ◽  
Boundary Crossing Probability ◽  
Data Set ◽  
Crossing Probability

Increasing accuracy of the model prediction on business bankruptcy helps reduce substantial losses for owners, creditors, investors and workers, and, further, minimize an economic and social problem frequently. In this study, we propose a stochastic model of financial working capital and cashflow as a two-dimensional Brownian motion X(t) = (X1(t),X2(t)) on the business bankruptcy prediction. The probability of bankruptcy occurring in a time interval [0,T] is defined by the boundary crossing probability of the two-dimensional Brownian motion entering a predetermined threshold domain. Mathematically, we extend the result in Fu and Wu (2016) on the boundary crossing probability of a high dimensional Brownian motion to an unbounded convex hull. The proposed model is applied to a real data set of companies in US and the numerical results show the proposed method performs well.

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