Collaborative Research: SWIFT: Coexistence and Interference Mitigation in the Mid-Band Spectrum: Analysis, Protocol Design, and Experimentation

Team Members

  • Dr. Marwan Krunz (PI, University of Arizona)

  • Dr. Monisha Ghosh (PI, Univ. of Notre Dame)

  • Zhengguang Zhang (Ph.D. Student, Univ. of Arizona)

  • Rabiul Hossain (Ph.D. Student, Univ. of Arizona)

  • Dr. Seda Tusha (post-doc, Univ. of Notre Dame)

  • Dr. Armed Tusha (post-doc, Univ. of Notre Dame)

  • Hossein Nasiri (Ph.D. Student, Univ. of Notre Dame)

Summary

Rapid proliferation of mobile technologies, manifested by a boom in smart phones and mobile video, along with the emergence of new verticals such as industrial IoT and autonomous vehicles, placed high demand on the communications infrastructure and motivated the introduction of 5G cellular systems. Compared to its 4G/LTE predecessor, 5G systems promise to deliver orders of magnitude higher data rates, significantly lower end-to-end latency, much denser connectivity, seamless coverage, high reliability, and longer battery lifetime. Facilitating these performance gains are advances on multiple fronts, most notably access to new swaths of the radio frequency (RF) spectrum. In addition to opening new spectrum above 24 GHz for 5G systems, the FCC and other regulatory bodies also created new opportunities at mid-bands below 7 GHz (“beachfront spectrum”). These bands exhibit favorable propagation characteristics that enable wide-area coverage at high data rates.

CBRS_1
CBRS band

This project focuses on addressing coexistence challenges over three recently available mid-bands: the C-band (3.7-3.98 GHz), the CBRS band (3.55-3.7 GHz), and the recently unlicensed 6 GHz bands (5.925-7.125 GHz). These bands have been only recently allocated for commercial wireless systems, which will have to coexist on these bands with a wide variety of incumbents, including Navy radar in 3.55–3.7 GHz CBRS band, point-to-point microwave links and passive radioastronomy receivers (e.g., the Methanol line) in the 6 GHz bands, and adjacent radar altimeters in the 4.2–4.4 GHz band. The proposed research agenda includes three thrusts. In the first thrust, the PIs will focus on coexistence and interference mitigation in the C-band. Despite extensive media coverage of the potential of 5G interference onto radar altimeters, there is a lack of rigorous, unbiased analyses and measurement of such interference in realistic operational scenarios. The PIs will accurately characterize C-band interference and establish the conditions under which it may occur. Novel machine learning (ML) techniques will be developed for real-time detection of interference and mitigation of its impact by adapting the 5G transmission parameters. The second thrust focuses on coexistence and interference mitigation in the CBRS band, particularly secondary coexistence between Tier 3 (GAA) users, which is quickly emerging as a bottleneck to efficient use of the band. Protocol-based and ML-based solutions will be explored. The third thrust focuses on coexistence in the unlicensed 6 GHz bands, where ML-based protocol classification designs will be explored to allow a given device to detect the underlying protocols of other devices and adapt its parameters accordingly. The proposed solutions will be evaluated via simulations and field measurements. These solutions are expected to have broader applicability beyond the specific studied bands. Research outcomes from this project will be shared with industry and will be provided to the FCC as case studies to facilitate subsequent policy making in other bands. The proposed research will be fully integrated into the educational plan by incorporating its outcomes in newly designed and existing courses as well as training undergraduate and graduate students via mentoring, participation in test-bed development, and special projects. The project contains a detailed plan to broaden participation of underrepresented groups via various channels established in the past years.

Broader Impacts

  1. Comments on Client-to-Client Communication in 6 GHz, FCC OET Docket 18-295, March 27, 2024, https://www.fcc.gov/ecfs/document/10328785722855/1
  2. Ex-parte submission on 6 GHz measurements to FCC OET Docket 18-295, Unlicensed Use of the 6 GHz Band, July 21, 2023, https://www.fcc.gov/ecfs/document/107211592305290/1

Related Publications

  1. Zhengguang Zhang and Marwan Krunz, "Preamble forgery and injection in Wi-Fi networks: Attacks and defenses," accepted to appear in the IEEE Transactions on Mobile Computing (TMC), March 2024.
  2. M. I. Rochman, W. Ye, Z.-L. Zhang and M. Ghosh, "A comprehensive real-world evaluation of 5G improvements over 4G in low and mid-bands", accepted to DySPAN 2024.
  3. A. Tusha, S. Tusha, H. Nasiri, M. I. Rochman, and M. Ghosh, "A comprehensive analysis of secondary coexistence in a real-world CBRS deployment," accepted to DySPAN 2024.
  4. Wenhan Zhang, Marwan Krunz, and Md Rabiul Hossain, "CyPA: A cyclic prefix assisted DNN for protocol classification in shared spectrum," Proc. of the International Conference on Computing, Networking and Communications (ICNC) - AI and Machine Learning for Communications and Networking Symposium, Feb. 2024.
  5. S. Dogan-Tusha, A. Tusha, H. Nasiri, M. I. Rochman and M. Ghosh, "Indoor and outdoor measurement campaign for unlicensed 6 GHz operation with Wi-Fi 6E," Proc. of the 26th International Symposium on Wireless Personal Multimedia Communications (WPMC), Tampa, FL, USA, 2023, pp. 1-6.
  6. S. Dogan-Tusha, M. I. Rochman, A. Tusha, H. Nasiri, J. Helzerman, and M. Ghosh, "Evaluating the interference potential in 6 GHz: An extensive measurement campaign of a dense indoor Wi-Fi 6E network," Proc. of the 17th ACM Workshop on Wireless Network Testbeds, Experimental evaluation & Characterization (WiNTECH), October 6, 2023.
  7. M. I. Rochman, V. Sathya, B. Payne, M. Yavuz and M. Ghosh, "A measurement study of the impact of adjacent channel interference between C-band and CBRS," Proc. of the IEEE 34th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), Toronto, ON, Canada, 2023, pp. 1-7.
  8. Zhengguang Zhang and Marwan Krunz, "Detection and classification of smart jamming in Wi-Fi networks using machine learning," Proc. of the IEEE MILCOM Conference (TRACK 5 – Machine Learning for Communications and Networking), Oct. 2023.
  9. Zhengguang Zhang, Hanif Rahbari, and Marwan Krunz, “Adaptive preamble embedding with MIMO to support user-defined functionalities in WLANs,” IEEE Transactions on Mobile Computing, vol. 22, no. 2, pp. 691-707, 1 Feb. 2023.
  10. Zhengguang Zhang and Marwan Krunz, "SIGTAM: A tampering attack on Wi-Fi preamble signaling and countermeasures," Proc. of the IEEE Conference on Communications and Network Security (CNS), Austin, Texas, Oct. 2022.

This project is in collaboration with the University of Notre Dame, and is supported by NSF under grant no. CNS-2229386 and CNS-2229387. Any opinions, findings, conclusions, or recommendations expressed in this page are those of the investigators and do not necessarily reflect the views of NSF.