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

TL;DR

This paper introduces a machine learning framework utilizing semi-Markov chains and Poisson models to analyze and model interference in IoT spectrum bands, based on extensive real-world measurements.

Contribution

It presents a novel framework combining spectrum sensing with unsupervised machine learning to better understand IoT interference patterns in shared frequency bands.

Findings

Spectrum occupancy can be effectively modeled using semi-Markov chains and Poisson processes.

The framework improves interference detection accuracy over traditional energy detection methods.

Urban environment measurements reveal spatial effects on IoT spectrum sharing.

Abstract

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.