A Universal Splitting Estimator for the Performance Evaluation of Wireless Communications Systems

We propose a unified rare-event estimator for the performance evaluation of wireless communication systems. The estimator is derived from the well-known multilevel splitting algorithm. In its original form, the splitting algorithm cannot be applied to the simulation and estimation of time-independen...

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Bibliographic Details
Published in:IEEE transactions on wireless communications Vol. 19; no. 7; pp. 4353 - 4362
Main Authors: Rached, Nadhir Ben, Mackinlay, Daniel, Botev, Zdravko, Tempone, Raul, Alouini, Mohamed-Slim
Format: Journal Article
Language:English
Published: New York IEEE 01.07.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Computer simulation
Distribution functions
Diversity reception
Estimation
Fading channels
Heuristic algorithms
Markov analysis
Markov processes
multilevel splitting algorithm
Performance evaluation
Random variables
Rare event
Signal to noise ratio
Splitting
Sums
variance reduction
Wireless communication
Wireless communication systems
Wireless communications
ISSN:1536-1276, 1558-2248
Online Access:Get full text
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Summary:We propose a unified rare-event estimator for the performance evaluation of wireless communication systems. The estimator is derived from the well-known multilevel splitting algorithm. In its original form, the splitting algorithm cannot be applied to the simulation and estimation of time-independent problems, because splitting requires an underlying continuous-time Markov process whose trajectories can be split. We tackle this problem by embedding the static problem of interest within a continuous-time Markov process, so that the target time-independent distribution becomes the distribution of the Markov process at a given time instant. The main feature of the proposed multilevel splitting algorithm is its large scope of applicability. For illustration, we show how the same algorithm can be applied to the problem of estimating the cumulative distribution function (CDF) of sums of random variables (RVs), the CDF of partial sums of ordered RVs, the CDF of ratios of RVs, and the CDF of weighted sums of Poisson RVs. We investigate the computational efficiency of the proposed estimator via a number of simulation studies and find that it compares favorably with existing estimators.
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ISSN:1536-1276
1558-2248
DOI:10.1109/TWC.2020.2982649