A sparse sampling algorithm for self-optimisation of coverage in LTE networks

Ajay K Thampi; Dritan Kaleshi; Peter Randall; Walter  Featherstone; Simon M D Armour

A sparse sampling algorithm for self-optimisation of coverage in LTE networks

Ajay K Thampi, Dritan Kaleshi, Peter Randall, Walter Featherstone, Simon M D Armour

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

15 Citations (Scopus)

Abstract

Coverage optimisation is an important self-organising capability that operators would like to have in LTE networks. This paper applies a Reinforcement Learning (RL) based Sparse Sampling algorithm for the self-optimisation of coverage through antenna tilting. This algorithm is better than supervised learning and Q-learning based algorithms as it has the ability to adapt to network environments without prior knowledge, handle large state spaces, perform self-healing and potentially focus on multiple coverage problems.

Original language	English
Title of host publication	International Symposium on Wireless Communication Systems
Pages	909-913
Number of pages	5
ISBN (Electronic)	978-1-4673-0760-4
Publication status	Published - 28 Aug 2012

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@inproceedings{dd48d7e1ba1e40899794693dea871428,

title = "A sparse sampling algorithm for self-optimisation of coverage in LTE networks",

abstract = "Coverage optimisation is an important self-organising capability that operators would like to have in LTE networks. This paper applies a Reinforcement Learning (RL) based Sparse Sampling algorithm for the self-optimisation of coverage through antenna tilting. This algorithm is better than supervised learning and Q-learning based algorithms as it has the ability to adapt to network environments without prior knowledge, handle large state spaces, perform self-healing and potentially focus on multiple coverage problems.",

author = "Thampi, \{Ajay K\} and Dritan Kaleshi and Peter Randall and Walter Featherstone and Armour, \{Simon M D\}",

year = "2012",

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AU - Thampi, Ajay K

AU - Kaleshi, Dritan

AU - Randall, Peter

AU - Featherstone, Walter

AU - Armour, Simon M D

PY - 2012/8/28

Y1 - 2012/8/28

N2 - Coverage optimisation is an important self-organising capability that operators would like to have in LTE networks. This paper applies a Reinforcement Learning (RL) based Sparse Sampling algorithm for the self-optimisation of coverage through antenna tilting. This algorithm is better than supervised learning and Q-learning based algorithms as it has the ability to adapt to network environments without prior knowledge, handle large state spaces, perform self-healing and potentially focus on multiple coverage problems.

AB - Coverage optimisation is an important self-organising capability that operators would like to have in LTE networks. This paper applies a Reinforcement Learning (RL) based Sparse Sampling algorithm for the self-optimisation of coverage through antenna tilting. This algorithm is better than supervised learning and Q-learning based algorithms as it has the ability to adapt to network environments without prior knowledge, handle large state spaces, perform self-healing and potentially focus on multiple coverage problems.

M3 - Conference Contribution (Conference Proceeding)

SN - 978-1-4673-0761-1

SP - 909

EP - 913

BT - International Symposium on Wireless Communication Systems

ER -

A sparse sampling algorithm for self-optimisation of coverage in LTE networks

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