# Spectrum Sharing for Internet of Things: A Survey

arXiv: https://arxiv.org/abs/1810.04408 · HTML: https://mlsanity.com/paper/1810.04408/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Oct 2018.

## Built on by (most cited first, 5 of 5)

- [Large Language Models for UAVs: Current State and Pathways to the Future](https://mlsanity.com/paper/2405.01745/) (2024)
- [When Machine Learning Meets Spectrum Sharing Security: Methodologies and Challenges](https://mlsanity.com/paper/2201.04677/) (2022)
- [Socially-Optimal Mechanism Design for Incentivized Online Learning](https://mlsanity.com/paper/2112.14338/) (2021)
- [Machine Learning Framework for Sensing and Modeling Interference in IoT Frequency Bands](https://mlsanity.com/paper/2106.06010/) (2021)
- [QoS Improvement in Multi User Cellular-Symbiotic Radio Network Assisted by Active-STAR-RIS](https://mlsanity.com/paper/2401.08301/) (2024)

## Abstract (arXiv)

The Internet of Things (IoT) is a promising paradigm to accommodate massive device connections in 5G and beyond. To pave the way for future IoT, the spectrum should be planed in advance. Spectrum sharing is a preferable solution for IoT due to the scarcity of available spectrum resource. In particular, mobile operators are inclined to exploit the existing standards and infrastructures of current cellular networks and deploy IoT within licensed cellular spectrum. Yet, proprietary companies prefer to deploy IoT within unlicensed spectrum to avoid any licence fee. In this paper, we provide a survey on prevalent IoT technologies deployed within licensed cellular spectrum and unlicensed spectrum. Notably, emphasis will be on the spectrum sharing solutions including the shared spectrum, interference model, and interference management. To this end, we discuss both advantages and disadvantages of different IoT technologies. Finally, we identify challenges for future IoT and suggest potential research directions.