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Small Cell Power Assignment with Unimodal Continuum-Armed Bandit Learning

机译：小型电池功率分配与单峰连续武装匪盗学习

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摘要

Judiciously setting the base station transmit power that matches its deployment environment is a key problem in ultra dense networks and heterogeneous in-building cellular deployments. A unique characteristic of this problem is the tradeoff between sufficient indoor coverage and limited outdoor leakage, which has to be met without explicit knowledge of the environment. In this paper, we address the small base station(SBS) transmit power assignment problem based on stochastic bandit learning with a continuous set of arms to avoid the constant performance loss or heavy workload on initialization caused by crude or excessive sampling in the previous strategies. With the aim of minimizing the expected cumulative performance loss, we capture the unimodality of the performance function which efficiently accelerates the search for the globally optimal power value. Simulations mimicking practical deployments are performed for both single and multiple SBS scenarios, and the resulting power settings are compared to the state-of-the-art solutions. Significant performance gains of the proposed algorithms are observed.
机译：明智地设置与其部署环境匹配的基站传输功率是超密集网络和非均匀建立蜂窝部署中的关键问题。这个问题的独特特征是足够的室内覆盖和户外泄漏有限的权衡，这必须满足而不明确地了解环境。在本文中，我们通过连续的一组臂基于随机匪徒学习来解决小型基站（SBS）发射功率分配问题，以避免在先前策略中原油或过度采样引起的持续性能损失或繁重工作量。目的是最小化预期的累积性能损失，我们捕获了绩效函数的单位，这有效地加速了全局最佳功率值的搜索。为单个和多个SBS场景执行模拟模拟实际部署，并将产生的电源设置与最先进的解决方案进行比较。观察到所提出的算法的显着性能提升。

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