# Machine Learning Framework for Sensing and Modeling Interference in IoT Frequency Bands

arXiv: https://arxiv.org/abs/2106.06010 · HTML: https://mlsanity.com/paper/2106.06010/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 40 references that are in the map.
- First posted on arXiv 10 Jun 2021.

## Builds on (most cited first, 3 of 3)

- [Over the Air Deep Learning Based Radio Signal Classification](https://mlsanity.com/paper/1712.04578/) (2017)
- [Understanding the limits of LoRaWAN](https://mlsanity.com/paper/1607.08011/) (2016)
- [Spectrum Sharing for Internet of Things: A Survey](https://mlsanity.com/paper/1810.04408/) (2018)

## Built on by (most cited first, 1 of 1)

- [Artificial Intelligence Techniques for Next-Generation Mega Satellite Networks](https://mlsanity.com/paper/2207.00414/) (2022)

## Abstract (arXiv)

Spectrum scarcity has surfaced as a prominent concern in wireless radio communications with the emergence of new technologies over the past few years. As a result, there is growing need for better understanding of the spectrum occupancy with newly emerging access technologies supporting the Internet of Things. In this paper, we present a framework to capture and model the traffic behavior of short-time spectrum occupancy for IoT applications in the shared bands to determine the existing interference. The proposed capturing method utilizes a software defined radio to monitor the short bursts of IoT transmissions by capturing the time series data which is converted to power spectral density to extract the observed occupancy. Furthermore, we propose the use of an unsupervised machine learning technique to enhance conventionally implemented energy detection methods. Our experimental results show that the temporal and frequency behavior of the spectrum can be well-captured using the combination of two models, namely, semi-Markov chains and a Poisson-distribution arrival rate. We conduct an extensive measurement campaign in different urban environments and incorporate the spatial effect on the IoT shared spectrum.