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arXiv 1806.03451cs.ITmath.IT
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负载平衡HetNet中面向学习的交叉熵用户关联方法

Learning Oriented Cross-Entropy Approach to User Association in Load-Balanced HetNet

Xietian Huang, Wei Xu, Guo Xie, Shi Jin, Xiaohu You

摘要:

这封信考虑通过效用最大化优化异构网络中的用户关联,这是一个组合优化问题,由于整数约束。不同于现有的解决方案的基础上凸优化,我们提出了一个交叉熵(CE)为基础的算法的启发采样方法在机器学习。采用概率模型,我们首先将原问题转化为CE最小化问题,其目的是学习最优关联中变量的概率分布。提出了一种基于随机抽样的学习算法。该算法直接处理整数约束,对网络部署和算法参数选择具有鲁棒性。仿真结果表明,所提出的CE方法实现了接近最优的性能相当有效。

英文摘要:

This letter considers optimizing user association in a heterogeneous network via utility maximization, which is a combinatorial optimization problem due to integer constraints. Different from existing solutions based on convex optimization, we alternatively propose a cross-entropy (CE)-based algorithm inspired by a sampling approach developed in machine learning. Adopting a probabilistic model, we first reformulate the original problem as a CE minimization problem which aims to learn the probability distribution of variables in the optimal association. An efficient solution by stochastic sampling is introduced to solve the learning problem. The integer constraint is directly handled by the proposed algorithm, which is robust to network deployment and algorithm parameter choices. Simulations verify that the proposed CE approach achieves near-optimal performance quite efficiently.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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