Lower Bounds for Noisy Wireless Networks using Sampling Algorithms

Author(s):  
Chinmoy Dutta ◽  
Jaikumar Radhakrishnan
1995 ◽  
Vol 53 (1) ◽  
pp. 17-25 ◽  
Author(s):  
Ran Canetti ◽  
Guy Even ◽  
Oded Goldreich

2016 ◽  
Vol 48 (4) ◽  
pp. 1061-1094 ◽  
Author(s):  
Christian Hirsch ◽  
Benedikt Jahnel ◽  
Paul Keeler ◽  
Robert I. A. Patterson

AbstractWe study large deviation principles for a model of wireless networks consisting of Poisson point processes of transmitters and receivers. To each transmitter we associate a family of connectable receivers whose signal-to-interference-and-noise ratio is larger than a certain connectivity threshold. First, we show a large deviation principle for the empirical measure of connectable receivers associated with transmitters in large boxes. Second, making use of the observation that the receivers connectable to the origin form a Cox point process, we derive a large deviation principle for the rescaled process of these receivers as the connection threshold tends to 0. Finally, we show how these results can be used to develop importance sampling algorithms that substantially reduce the variance for the estimation of probabilities of certain rare events such as users being unable to connect.


2010 ◽  
Vol 18 (5) ◽  
pp. 1624-1636 ◽  
Author(s):  
Giusi Alfano ◽  
Michele Garetto ◽  
Emilio Leonardi ◽  
Valentina Martina

Sign in / Sign up

Export Citation Format

Share Document