Offline Contextual Bandits for Wireless Network Optimization
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Abstract (arXiv)
The explosion in mobile data traffic together with the ever-increasing expectations for higher quality of service call for the development of AI algorithms for wireless network optimization. In this paper, we investigate how to learn policies that can automatically adjust the configuration parameters of every cell in the network in response to the changes in the user demand. Our solution combines existent methods for offline learning and adapts them in a principled way to overcome crucial challenges arising in this context. Empirical results suggest that our proposed method will achieve important performance gains when deployed in the real network while satisfying practical constrains on computational efficiency.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 8 of its 22 references that are in the map. First posted on arXiv 11 Nov 2021.
Builds on
8 of 22 references.
References · 8
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
- 2020Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problemscited by 1,873
- 2019When to Trust Your Model: Model-Based Policy Optimizationcited by 753
- 2019Conditioning by adaptive sampling for robust designcited by 110
- 2019Learning Counterfactual Representations for Estimating Individual Dose-Response Curvescited by 90
- 2020Off-policy Bandits with Deficient Supportcited by 60
- 2018Perfect Match: A Simple Method for Learning Representations For Counterfactual Inference With Neural Networkscited by 55
- 2014Counterfactual Estimation and Optimization of Click Metrics for Search Enginescited by 5
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Citing papers per year: 2022: 1.
1 paper.