Journal of Platform Technology

ICT Platform Society (ICT플랫폼학회)
권호 표지

Research Trends in Wi-Fi Performance Improvement in Coexistence Networks with Machine Learning

기계학습을 활용한 이종망에서의 Wi-Fi 성능 개선 연구 동향 분석

  • 강영명 (성결대학교 컴퓨터공학과)
  • Received : 2022.06.07
  • Accepted : 2022.08.10
  • Published : 2022.09.30
Download PDF
⟨ Previous Next ⟩

Abstract

Machine learning, which has recently innovatively developed, has become an important technology that can solve various optimization problems. In this paper, we introduce the latest research papers that solve the problem of channel sharing in heterogeneous networks using machine learning, analyze the characteristics of mainstream approaches, and present a guide to future research directions. Existing studies have generally adopted Q-learning since it supports fast learning both on online and offline environment. On the contrary, conventional studies have either not considered various coexistence scenarios or lacked consideration for the location of machine learning controllers that can have a significant impact on network performance. One of the powerful ways to overcome these disadvantages is to selectively use a machine learning algorithm according to changes in network environment based on the logical network architecture for machine learning proposed by ITU.

최근 혁신적으로 발전하고 있는 기계학습은 다양한 최적화 문제를 해결할 수 있는 중요한 기술이 되었다. 본 논문에서는 기계학습을 활용하여 이종망의 채널 공용화 문제를 해결하는 최신 연구 논문들을 소개하고 주된 기술의 특성을 분석하여 향후 연구 방향에 대해 가이드를 제시한다. 기존 연구들은 대체로 온라인 및 오프라인으로 빠른 학습이 가능한 Q-learning을 활용하는 경우가 많았다. 반면 다양한 공존 시나리오를 고려하지 않거나 망 성능에 큰 영향을 줄 수 있는 기계학습 컨트롤러의 위치에 대한 고려는 제한적이었다. 이런 단점을 극복할 수 있는 유력한 방안으로는 ITU에서 제안한 기계학습용 논리적 망구조를 기반으로 망 환경 변화에 따라 기계학습 알고리즘을 선택적으로 사용할 수 있는 방법이 있다.

Keywords

References

  1. E. Chai, K. Sundaresan, M. A. Khojastepour, and R. Sampath, "LTE in Unlicensed Spectrum: Are We There Yet?" in Proc. of 22nd ACM International Conference on Mobile Computing and Networking (MobiCom 2016), New York City, NY: ACM, Oct. 2016, pp. 135-148.
  2. F. M. Abinader, E. P. L. Almeida, F. S. Chaves, A. M. Cavalcante, R. D. Vieira, R. C. D. Paiva, A. M. Sobrinho, S. Choudhury, E. Tuomaala, K. Doppler, and V. A. Sousa, "Enabling the Coexistence of LTE and Wi-Fi in Unlicensed Bands," IEEE Communications Magazine (COMMAG), Vol. 52, No. 11, pp. 54-61, Nov. 2014.
  3. G. Gur, "Expansive networks: Exploiting spectrum sharing for capacity boost and 6G vision," Journal of Communications and Networks, Vol. 22, No. 6, pp. 444-454, Dec. 2020. https://doi.org/10.23919/JCN.2020.000037
  4. I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. MIT Press, 2016, http://www.deeplearningbook.org.
  5. R. S. Sutton and A. G. Barto, Reinforcement Learning, second. A Bradford Book, 2018.
  6. S. Szott, K. Kosek-Szott, P. Gawlowicz, J. Torres Gomez, B. Bellalta, A. Zubow and F. Dressler, "Wi-Fi Meets ML: A Survey on Improving IEEE 802.11 Performance with Machine Learning," IEEE Communications Surveys & Tutorials, 2022. (online first)
  7. S. Zinno, G. D. Stasi, S. Avallone, and G. Ventre, "On a fair coexistence of LTE and Wi-Fi in the unlicensed spectrum: A Survey," Elsevier Computer Communications, vol. 115, pp. 35-50, 2018. https://doi.org/10.1016/j.comcom.2017.10.019
  8. P. Tian, "Traffic-Aware Resource Allocation and Spectrum Share for LTE-U and Wi-fi," in International Conference on Mechatronics and Intelligent Robotics, Springer, 2019, pp. 837-843.
  9. H. Kushwaha, V. J. Kotagi, and C. S. R. Murthy, "A Novel Reinforcement Learning Based Adaptive Optimization of LTE-TDD Configurations for LTE-U/WiFi Coexistence," in 2019 IEEE 30th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), IEEE, 2019, pp. 1-7.
  10. K. Naveen and C. Amballa, "Coexistence of LTE-Unlicensed and WiFi: A Reinforcement Learning Framework," in 2021 International Conference on COMmunication Systems & NETworkS (COMSNETS), IEEE, 2021, pp. 308-316.
  11. K. Zhou, A. Li, C. Dong, L. Zhang, and J. Sun, "LWCQ: An Adaptive Algorithm for LAA and WiFi Coexistence Based on Q-learning," in 2019 IEEE 19th International Conference on Communication Technology (ICCT), IEEE, 2019, pp. 556-560.
  12. L. Wang, M. Zeng, J. Guo, Q. Cui, and Z. Fei, "Joint Bandwidth and Transmission Opportunity Allocation for the Coexistence Between NR-U and WiFi Systems in the Unlicensed Band," IEEE Transactions on Vehicular Technology, vol. 70, no. 11, pp. 11 881-11 893, 2021. https://doi.org/10.1109/TVT.2021.3116378
  13. K. Kosek-Szott, A. Lo Valvo, S. Szott, P. Gallo, and I. Tinnirello, "Downlink channel access performance of NR-U: Impact of numerology and mini-slots on coexistence with Wi-Fi in the 5 GHz band," Computer Networks, vol. 195, p. 108 188, 2021. https://doi.org/10.1016/j.comnet.2021.108188
  14. I. Tinnirello, A. Lo Valvo, S. Szott, and K. Kosek-Szott, "No Reservations Required: Achieving Fairness between Wi-Fi and NR-U with Self-Deferral Only," in Proceedings of the 24th International ACM Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems, 2021, pp. 115-124.
  15. A. Galanopoulos, F. Foukalas, and T. A. Tsiftsis, "Efficient coexistence of LTE with WiFi in the licensed and unlicensed spectrum aggregation," IEEE Transactions on Cognitive Communications and Networking, vol. 2, no. 2, pp. 129-140, 2016. https://doi.org/10.1109/TCCN.2016.2594780
  16. M. Girmay, A. Shahid, V. Maglogiannis, D. Naudts, and I. Moerman, "Machine Learning Enabled Wi-Fi Saturation Sensing for Fair Coexistence in Unlicensed Spectrum," IEEE Access, vol. 9, pp. 42959-42974, 2021. https://doi.org/10.1109/ACCESS.2021.3066052
  17. A. M. Alenezi and K. A. Hamdi, "Reinforcement Learning Approach for Hybrid WiFi-VLC Networks," in 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring), IEEE, 2020, pp. 1-5.
  18. ITU-T, "Architectural framework for machine learning in future networks including IMT-2020," Recommendation ITU-T Y.3172, 2019.