Empirical Statistical Model for LTE Downlink Channel Occupancy

Empirical Statistical Model for LTE Downlink Channel Occupancy This paper develops an empirical statistical channel occupancy model for downlink long-term evolution (LTE) cellular systems. The model is based on statistical distributions mixtures for the holding times of the channels. Moreover, statistical distribution of the time when the channels are free is also considered. The data is obtained through an extensive measurement campaign performed in Stockholm, Sweden. Two types of mixtures are considered, namely, exponential and log-normal distributions to fit the measurement findings. The log-likelihood of both mixtures is used as a quantitative measure of the goodness of fit. Moreover, finding the optimal number of linearly combined distributions using the Akaike information criterion is investigated. The results show that good fitting can be obtained by using either exponential or log-normal distributions mixture. Even though, the fitting is done for a representative case with a tempo-spatial consideration, the model is yet applicable in general for LTE and other cellular systems in a wider sense. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Wireless Personal Communications Springer Journals

Empirical Statistical Model for LTE Downlink Channel Occupancy

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Publisher
Springer US
Copyright
Copyright © 2017 by The Author(s)
Subject
Engineering; Communications Engineering, Networks; Signal,Image and Speech Processing; Computer Communication Networks
ISSN
0929-6212
eISSN
1572-834X
D.O.I.
10.1007/s11277-017-4205-4
Publisher site
See Article on Publisher Site

Abstract

This paper develops an empirical statistical channel occupancy model for downlink long-term evolution (LTE) cellular systems. The model is based on statistical distributions mixtures for the holding times of the channels. Moreover, statistical distribution of the time when the channels are free is also considered. The data is obtained through an extensive measurement campaign performed in Stockholm, Sweden. Two types of mixtures are considered, namely, exponential and log-normal distributions to fit the measurement findings. The log-likelihood of both mixtures is used as a quantitative measure of the goodness of fit. Moreover, finding the optimal number of linearly combined distributions using the Akaike information criterion is investigated. The results show that good fitting can be obtained by using either exponential or log-normal distributions mixture. Even though, the fitting is done for a representative case with a tempo-spatial consideration, the model is yet applicable in general for LTE and other cellular systems in a wider sense.

Journal

Wireless Personal CommunicationsSpringer Journals

Published: May 10, 2017

References

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