In this letter, we propose a novel offloading learning approach to compromise energy consumption and latency in a multi-tier network with mobile edge computing. In order to solve this integer programming problem, instead of using conventional optimization tools, we apply a cross entropy approach with iterative learning of the probability of elite solution samples. Compared to existing methods, the proposed one in this network permits a parallel computing architecture and is verified to be computationally very efficient. Specifically, it achieves performance close to the optimal and performs well with different choices of the values of hyperparameters in the proposed learning approach.

中文翻译：

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一种在移动边缘计算中卸载学习的新型交叉熵方法

在这封信中，我们提出了一种新颖的卸载学习方法，以在具有移动边缘计算的多层网络中降低能耗和延迟。为了解决这个整数规划问题，我们不使用传统的优化工具，而是应用交叉熵方法，迭代学习精英解样本的概率。与现有方法相比，该网络中提出的方法允许并行计算架构，并被验证在计算上非常有效。具体来说，它实现了接近最优的性能，并且在所提出的学习方法中选择不同的超参数值时表现良好。