Effective resource management plays a pivotal role in wireless networks, which, unfortunately, typically results in challenging mixed-integer nonlinear programming (MINLP) problems. Machine learning-based methods have recently emerged as a disruptive way to obtain near-optimal performance for MINLPs with affordable computational complexity. There have been some attempts in applying such methods to resource management in wireless networks, but these attempts require huge amounts of training samples and lack the capability to handle constrained problems. Furthermore, they suffer from severe performance deterioration when the network parameters change, which commonly happens and is referred to as the task mismatch problem. In this paper, to reduce the sample complexity and address the feasibility issue, we propose a framework of Learning to Optimize for Resource Management (LORM). In contrast to the end-to-end learning approach adopted in previous studies, LORM learns the optimal pruning policy in the branch-and-bound algorithm for MINLPs via a sample-efficient method, namely, imitation learning. To further address the task mismatch problem, we develop a transfer learning method via self-imitation in LORM, named LORM-TL, which can quickly adapt a pre-trained machine learning model to the new task with only a few additional unlabeled training samples. Numerical simulations demonstrate that LORM outperforms specialized state-of-the-art algorithms and achieves near-optimal performance, while providing significant speedup compared with the branch-and-bound algorithm. Moreover, LORM-TL, by relying on a few unlabeled samples, achieves comparable performance with the model trained from scratch with sufficient labeled samples.

中文翻译：

---

LORM：学习用很少的训练样本优化无线网络中的资源管理

有效的资源管理在无线网络中起着关键作用，不幸的是，这通常会导致具有挑战性的混合整数非线性规划 (MINLP) 问题。最近，基于机器学习的方法已成为一种破坏性方式，可以以负担得起的计算复杂性为 MINLP 获得近乎最佳的性能。已经有一些尝试将这种方法应用于无线网络中的资源管理，但是这些尝试需要大量的训练样本并且缺乏处理受限问题的能力。此外，当网络参数发生变化时，它们会遭受严重的性能下降，这种情况通常发生并且被称为任务不匹配问题。在本文中，为了降低样本复杂度并解决可行性问题，我们提出了一个学习优化资源管理（LORM）的框架。与之前研究中采用的端到端学习方法相比，LORM 通过一种有效样本的方法，即模仿学习，在 MINLP 的分支定界算法中学习最佳剪枝策略。为了进一步解决任务不匹配问题，我们通过 LORM 中的自模仿开发了一种迁移学习方法，称为 LORM-TL，它可以快速地将预训练的机器学习模型适应新的任务，只需几个额外的未标记训练样本。数值模拟表明，LORM 优于专门的最先进算法并实现了接近最佳的性能，同时与分支定界算法相比提供了显着的加速。此外，LORM-TL 依靠一些未标记的样本，