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An AI-based incumbent protection system for collaborative intelligent radio networks

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Abstract
Since the early days of wireless communication, wireless spectrum has been allocated according to a static frequency plan, whereby most of the spectrum is licensed for exclusive use by specific services or radio technologies. While some spectrum bands are overcrowded, many other bands are heavily underutilized. As a result, there is a shortage of available spectrum to deploy emerging technologies that require high demands on data like 5G. Several global efforts address this problem by providing multi-tier spectrum sharing frameworks, for example, the Citizens Broadband Radio Service (CBRS) and Licensed Shared Access (LSA) models, to increase spectrum reuse. In these frameworks, the incumbent (i.e., the technology that used the spectrum exclusively in the past) has to be protected against service disruptions caused by the transmissions of the new technologies that start using the same spectrum. However, these approaches suffer from two main problems. First, spectrum re-allocation to new uses is a slow process that may take years. Second, they do not scale fast since it requires a centralized infrastructure to protect the incumbent and coordinate and grant access to the shared spectrum. As a solution, the Spectrum Collaboration Challenge (SC2) has shown that the collaborative intelligent radio networks (CIRNs) -- artificial intelligence (AI)-based autonomous wireless networks that collaborate -- can share and reuse spectrum efficiently without any coordination and with the guarantee of incumbent protection. In this article, we present the architectural design and the experimental validation of an incumbent protection system for the next generation of spectrum sharing frameworks. The proposed system is a two-step AI-based algorithm that recognizes, learns, and proactively predicts the incumbent's transmission pattern with an accuracy above 95 percent in near real time (less than 300 ms). The proposed algorithm was validated in Colosseum, the RF channel emulator built for the SC2 competition, using up to two incumbents simultaneously with different transmission patterns and sharing spectrum with up to five additional CIRNs.
Keywords
PREDICTION

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Citation

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MLA
Camelo, Miguel, et al. “An AI-Based Incumbent Protection System for Collaborative Intelligent Radio Networks.” IEEE WIRELESS COMMUNICATIONS, vol. 27, no. 5, 2020, pp. 16–23, doi:10.1109/MWC.001.2000032.
APA
Camelo, M., Mennes, R., Shahid, A., Struye, J., Donato, C., Jabandžić, I., … Latré, S. (2020). An AI-based incumbent protection system for collaborative intelligent radio networks. IEEE WIRELESS COMMUNICATIONS, 27(5), 16–23. https://doi.org/10.1109/MWC.001.2000032
Chicago author-date
Camelo, Miguel, Ruben Mennes, Adnan Shahid, Jakob Struye, Carlos Donato, Irfan Jabandžić, Spilios Giannoulis, et al. 2020. “An AI-Based Incumbent Protection System for Collaborative Intelligent Radio Networks.” IEEE WIRELESS COMMUNICATIONS 27 (5): 16–23. https://doi.org/10.1109/MWC.001.2000032.
Chicago author-date (all authors)
Camelo, Miguel, Ruben Mennes, Adnan Shahid, Jakob Struye, Carlos Donato, Irfan Jabandžić, Spilios Giannoulis, Farouk Mahfoudhi, Prasanthi Maddala, Ivan Seskar, Ingrid Moerman, and Steven Latré. 2020. “An AI-Based Incumbent Protection System for Collaborative Intelligent Radio Networks.” IEEE WIRELESS COMMUNICATIONS 27 (5): 16–23. doi:10.1109/MWC.001.2000032.
Vancouver
1.
Camelo M, Mennes R, Shahid A, Struye J, Donato C, Jabandžić I, et al. An AI-based incumbent protection system for collaborative intelligent radio networks. IEEE WIRELESS COMMUNICATIONS. 2020;27(5):16–23.
IEEE
[1]
M. Camelo et al., “An AI-based incumbent protection system for collaborative intelligent radio networks,” IEEE WIRELESS COMMUNICATIONS, vol. 27, no. 5, pp. 16–23, 2020.
@article{8682790,
  abstract     = {{Since the early days of wireless communication, wireless spectrum has been allocated according to a static frequency plan, whereby most of the spectrum is licensed for exclusive use by specific services or radio technologies. While some spectrum bands are overcrowded, many other bands are heavily underutilized. As a result, there is a shortage of available spectrum to deploy emerging technologies that require high demands on data like 5G. Several global efforts address this problem by providing multi-tier spectrum sharing frameworks, for example, the Citizens Broadband Radio Service (CBRS) and Licensed Shared Access (LSA) models, to increase spectrum reuse. In these frameworks, the incumbent (i.e., the technology that used the spectrum exclusively in the past) has to be protected against service disruptions caused by the transmissions of the new technologies that start using the same spectrum. However, these approaches suffer from two main problems. First, spectrum re-allocation to new uses is a slow process that may take years. Second, they do not scale fast since it requires a centralized infrastructure to protect the incumbent and coordinate and grant access to the shared spectrum. As a solution, the Spectrum Collaboration Challenge (SC2) has shown that the collaborative intelligent radio networks (CIRNs) -- artificial intelligence (AI)-based autonomous wireless networks that collaborate -- can share and reuse spectrum efficiently without any coordination and with the guarantee of incumbent protection. In this article, we present the architectural design and the experimental validation of an incumbent protection system for the next generation of spectrum sharing frameworks. The proposed system is a two-step AI-based algorithm that recognizes, learns, and proactively predicts the incumbent's transmission pattern with an accuracy above 95 percent in near real time (less than 300 ms). The proposed algorithm was validated in Colosseum, the RF channel emulator built for the SC2 competition, using up to two incumbents simultaneously with different transmission patterns and sharing spectrum with up to five additional CIRNs.}},
  author       = {{Camelo, Miguel and Mennes, Ruben and Shahid, Adnan and Struye, Jakob and Donato, Carlos and Jabandžić, Irfan and Giannoulis, Spilios and Mahfoudhi, Farouk and Maddala, Prasanthi and Seskar, Ivan and Moerman, Ingrid and Latré, Steven}},
  issn         = {{1536-1284}},
  journal      = {{IEEE WIRELESS COMMUNICATIONS}},
  keywords     = {{PREDICTION}},
  language     = {{eng}},
  number       = {{5}},
  pages        = {{16--23}},
  title        = {{An AI-based incumbent protection system for collaborative intelligent radio networks}},
  url          = {{http://dx.doi.org/10.1109/MWC.001.2000032}},
  volume       = {{27}},
  year         = {{2020}},
}

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