LORM: Learning to Optimize for Resource Management in Wireless Networks With Few Training Samples

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...

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Vydané v:IEEE transactions on wireless communications Ročník 19; číslo 1; s. 665 - 679
Hlavní autori: Shen, Yifei, Shi, Yuanming, Zhang, Jun, Letaief, Khaled B.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.01.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithms
Complexity
Computer simulation
few-shot learning
Heuristic algorithms
Machine learning
Machine learning algorithms
Mathematical models
mixed-integer nonlinear programming
Nonlinear programming
Optimization
Performance degradation
Resource allocation
Resource management
Training
transfer learning
wireless communications
Wireless networks
ISSN:1536-1276, 1558-2248
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Shrnutí: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.
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ISSN:1536-1276
1558-2248
DOI:10.1109/TWC.2019.2947591