Self-optimizing energy management in heterogeneous cellular networks

Majid Ghaderi, Mohammad Naghibi
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Abstract

In this paper, we develop and evaluate a distributed algorithm to efficiently balance the trade-off between network throughput and energy consumption in a heterogeneous cellular network. We formulate the problem as a joint optimization of base station activation, power control and user association. To solve the problem, which is a non-convex optimization problem, we design a self-optimizing algorithm based on Gibbs sampling in which each base station individually optimizes its configuration without the involvement of any central controller. In our algorithm, base stations only need to exchange information in a locally defined neighborhood, yet the network state eventually converges to the global optimal. Simulation results are also provided, which show that, i) the proposed algorithm indeed converges to a state that is close to optimal, and ii) by dynamically activating base stations, we see about 10% reduction in network energy consumption without penalizing the network throughput.
异构蜂窝网络中的自优化能量管理
在本文中,我们开发并评估了一种分布式算法,以有效地平衡异构蜂窝网络中网络吞吐量和能量消耗之间的权衡。我们将该问题表述为基站激活、功率控制和用户关联的联合优化。为了解决这一非凸优化问题,我们设计了一种基于Gibbs抽样的自优化算法,其中每个基站在没有任何中央控制器参与的情况下单独优化其配置。在我们的算法中,基站只需要在局部定义的邻域中交换信息,但网络状态最终收敛到全局最优。仿真结果表明,该算法确实收敛到接近最优的状态,并且通过动态激活基站,在不影响网络吞吐量的情况下,我们看到网络能耗降低了约10%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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