The Future is Unlicensed: Coexistence in the Unlicensed Spectrum for 5G
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Abstract (arXiv)
5G has to fulfill the requirements of ultra-dense, scalable, and customizable networks such as IoT while increasing spectrum and energy efficiency. Given the diversity of envisaged applications and scenarios, one crucial property for 5G New Radio (NR) is flexibility: flexible UL/DL allocation, bandwidths, or scalable transmission time interval, and most importantly operation at different frequency bands. In particular, 5G should exploit the spectral opportunities in the unlicensed spectrum for expanding network capacity when and where needed. However, unlicensed bands pose the challenge of "coexisting networks", which mostly lack the means of communication for negotiation and coordination. This deficiency is further exacerbated by the heterogeneity, massive connectivity, and ubiquity of IoT systems and applications. Therefore, 5G needs to provide mechanisms to coexist and even converge in the unlicensed bands. In that regard, WiFi, as the most prominent wireless technology in the unlicensed bands, is both a key enabler for boosting 5G capacity and competitor of 5G cellular networks for the shared unlicensed spectrum. In this work, we describe spectrum sharing in 5G and present key coexistence solutions, mostly in the context of WiFi. We also highlight the role of machine learning which is envisaged to be critical for reaching coexistence and convergence goals by providing the necessary intelligence and adaptation mechanisms.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 15 Jan 2018.
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Citing papers per year: 2021: 3.
3 papers.
- 2021Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challengescited by 12
- 2021Bayesian Nonparametric Reinforcement Learning in LTE and Wi-Fi Coexistencecited by 0
- 2021Bayesian Nonparametric Modelling for Model-Free Reinforcement Learning in LTE-LAA and Wi-Fi Coexistencecited by 0
- 2021Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challenges14 Nov 2021
- 2021Bayesian Nonparametric Modelling for Model-Free Reinforcement Learning in LTE-LAA and Wi-Fi Coexistence6 Jul 2021
- 2021Bayesian Nonparametric Reinforcement Learning in LTE and Wi-Fi Coexistence25 May 2021