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. Index Terms-Multi-armed bandit, online learning, regret analysis, coverage optimization.
In REF , the authors design a generalized global bandit algorithm to control the transmit power in the cellular coverage optimization problem.
Generalized Global Bandit and Its Application in Cellular Coverage Optimization
IEEE Journal of Selected Topics in Signal Processing
IEEE Journal of Selected Topics in Signal Processing
Computer Science