Generalized global bandit and its application in cellular coverage optimization
Author
Shen, C.
Zhou, R.
Tekin, C.
Schaar, M. V. D.
Date
2018Source Title
IEEE Journal on Selected Topics in Signal Processing
Print ISSN
1932-4553
Publisher
Institute of Electrical and Electronics Engineers
Volume
12
Issue
1
Pages
218 - 232
Language
English
Type
ArticleItem Usage Stats
136
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149
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Abstract
Motivated by the engineering problem of cellular coverage optimization, we propose a novel multiarmed bandit model called generalized global bandit. We develop a series of greedy algorithms that have the capability to handle nonmonotonic but decomposable reward functions, multidimensional global parameters, and switching costs. The proposed algorithms are rigorously analyzed under the multiarmed bandit framework, where we show that they achieve bounded regret, and hence, they are guaranteed to converge to the optimal arm in finite time. The algorithms are then applied to the cellular coverage optimization problem to achieve the optimal tradeoff between sufficient small cell coverage and limited macroleakage without prior knowledge of the deployment environment. The performance advantage of the new algorithms over existing bandits solutions is revealed analytically and further confirmed via numerical simulations. The key element behind the performance improvement is a more efficient 'trial and error' mechanism, in which any trial will help improve the knowledge of all candidate power levels.