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Machine Learning Enabled Spectrum Sharing in Dense LTE-U/Wi-Fi Coexistence Scenarios

  • Published:2020 Issue: Volume:1 Page:173-189
  • ISSN:2644-1330
  • Container-title:IEEE Open Journal of Vehicular Technology
  • language:
  • Short-container-title:IEEE Open J. Veh. Technol.

Author:

Dziedzic Adam,Sathya VanlinORCID,Rochman Muhammad Iqbal,Ghosh Monisha,Krishnan Sanjay

Funder

National Science Foundation

Center For Unstoppable Computing

University of Chicago

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Pharmacology (medical),Complementary and alternative medicine,Pharmaceutical Science

Cited by 19 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. An ML-based Spectrum Sharing Technique for Time-Sensitive Applications in Industrial Scenarios;2024 International Wireless Communications and Mobile Computing (IWCMC);2024-05-27

2. An Intelligent Approach to Spectrum Sharing Process for Coexistence Between Cellular and Wi-Fi Technologies;2024 6th International Conference on Electrical Engineering and Information & Communication Technology (ICEEICT);2024-05-02

3. A novel scheduling algorithm for LTE on unlicensed bands to ensure fair coexistence with Wi-Fi;Computer Networks;2024-03

4. Fair and Efficiency Coexistence Between NR-U and WiGig Networks Enabled by a Matching-Based Framework With Forbidden Pairs;IEEE Transactions on Vehicular Technology;2023-09

5. An Efficient and Fair Wireless Resource Allocation Scheme for LTE and Wi-Fi over Unlicensed Bands;2023 32nd International Conference on Computer Communications and Networks (ICCCN);2023-07
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