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 proceedingConference Contribution (Conference Proceeding)

12 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 languageEnglish
Title of host publicationInternational Symposium on Wireless Communication Systems
Pages909-913
Number of pages5
ISBN (Electronic)978-1-4673-0760-4
Publication statusPublished - 28 Aug 2012

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