A Predictive Resource Allocation Algorithm in the LTE Uplink for Event Based M2M Applications

Brown, J; Khan, JY

A Predictive Resource Allocation Algorithm in the LTE Uplink for Event Based M2M Applications

Brown, J

Khan, JY

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Mobile Computing, 2015, 14 (12), pp. 2433 - 2446
Issue Date:: 2015-12-01

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Field	Value	Language
dc.contributor.author	Brown, J https://orcid.org/0000-0002-0698-5758	en_US
dc.contributor.author	Khan, JY	en_US
dc.date.issued	2015-12-01	en_US
dc.identifier.citation	IEEE Transactions on Mobile Computing, 2015, 14 (12), pp. 2433 - 2446	en_US
dc.identifier.issn	1536-1233	en_US
dc.identifier.uri	http://hdl.handle.net/10453/119439
dc.description.abstract	© 2002-2012 IEEE. Some M2M applications such as event monitoring involve a group of devices in a vicinity that act in a co-ordinated manner. An LTE network can exploit the correlated traffic characteristics of such devices by proactively assigning resources to devices based upon the activity of neighboring devices in the same group. This can reduce latency compared to waiting for each device in the group to request resources reactively per the standard LTE protocol. In this paper, we specify a new low complexity predictive resource allocation algorithm, known as the one way algorithm, for use with delay sensitive event based M2M applications in the LTE uplink. This algorithm requires minimal incremental processing power and memory resources at the eNodeB, yet can reduce the mean uplink latency below the minimum possible value for a non-predictive resource allocation algorithm. We develop mathematical models for the probability of a prediction, the probability of a successful prediction, the probability of an unsuccessful prediction, resource usage/wastage probabilities, and mean uplink latency. The validity of these models is demonstrated by comparison with the results from a simulation. The models can be used offline by network operators or online in real time by the eNodeB scheduler to optimize performance.	en_US
dc.relation.ispartof	IEEE Transactions on Mobile Computing	en_US
dc.relation.isbasedon	10.1109/TMC.2015.2398447	en_US
dc.subject.classification	Networking & Telecommunications	en_US
dc.title	A Predictive Resource Allocation Algorithm in the LTE Uplink for Event Based M2M Applications	en_US
dc.type	Journal Article
utslib.citation.volume	12	en_US
utslib.citation.volume	14	en_US
utslib.for	0805 Distributed Computing	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	1005 Communications Technologies	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.issue	12	en_US
pubs.publication-status	Published	en_US
pubs.volume	14	en_US

Abstract:

© 2002-2012 IEEE. Some M2M applications such as event monitoring involve a group of devices in a vicinity that act in a co-ordinated manner. An LTE network can exploit the correlated traffic characteristics of such devices by proactively assigning resources to devices based upon the activity of neighboring devices in the same group. This can reduce latency compared to waiting for each device in the group to request resources reactively per the standard LTE protocol. In this paper, we specify a new low complexity predictive resource allocation algorithm, known as the one way algorithm, for use with delay sensitive event based M2M applications in the LTE uplink. This algorithm requires minimal incremental processing power and memory resources at the eNodeB, yet can reduce the mean uplink latency below the minimum possible value for a non-predictive resource allocation algorithm. We develop mathematical models for the probability of a prediction, the probability of a successful prediction, the probability of an unsuccessful prediction, resource usage/wastage probabilities, and mean uplink latency. The validity of these models is demonstrated by comparison with the results from a simulation. The models can be used offline by network operators or online in real time by the eNodeB scheduler to optimize performance.

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http://hdl.handle.net/10453/119439