ML-Based Classification of Device Environment Using Wi-Fi and Cellular Signal Measurements

Arun Ramamurthy,Muhammad Iqbal Rochman,Vanlin Sathya,Monisha Ghosh

doi:10.1109/access.2022.3158056

Abstract

Future spectrum sharing rules very likely will be based on device environment: indoors or outdoors. For example, the 6 GHz rules created different power regimes for unlicensed devices to protect incumbents: “indoor” devices, subject to lower transmit powers but not required to access an Automatic Frequency Control database to obtain permission to use a channel, and “outdoor” devices, allowed to transmit at higher power but required to do so to determine channel availability. However, since there are no reliable means of determining if a wireless device is indoors or outdoors, other restrictions were mandated: reduced power for client devices and indoor access points that cannot be battery powered, have detachable antennas or be weatherized. These constraints lead to sub-optimal spectrum usage and potential for misuse. Hence, there is a need for robust identification of device environments to enable spectrum sharing. In this paper we study automatic indoor/outdoor classification based on the radio frequency (RF) environment experienced by a device. Using a custom Android app, we first create a labeled data set of a number of parameters of Wi-Fi and cellular signals in various indoor and outdoor environments, and then evaluate the classification performance of various machine learning (ML) models on this data set. We find that tree-based ensemble ML models can achieve greater than 99% test accuracy and F1-Score, thus allowing devices to self-identify their environment and adapt their transmit power accordingly.

Highlights

AND MOTIVATIONAs the current generation of cellular (5G) and Wi-Fi (802.11ax) networks begin to be widely deployed, it is becoming increasingly clear that the generation of wireless systems will largely be deployed in spectrum that is shared, between cellular and Wi-Fi and with various incumbents such as federal radar systems, fixed microwave links, satellite providers, weather satellites and broadcast auxiliary services (BAS)
We posit that such a data-set, collected in labeled indoor and outdoor environments, across a wide variety of frequency bands and signal types, can be used to train Machine Learning (ML) models that can perform robust indoor/outdoor classification, leading to improved spectrum usage, incumbent protection and resilience, in 6 GHz, and in future bands such as the 12 GHz satellite band where sharing with indoor devices is being considered [4]
The contributions of the paper are as follows: (i) we developed an Android app and collected a large, labeled data-set of Wi-Fi, 4G LTE, and 5G NR measurements in various indoor and outdoor environments: such a data-set does not exist today and will be made openly available to other researchers; (ii) we have evaluated various machine learning (ML) algorithms on this data set and shown classification accuracy of 99%; and (iii) we evaluated the ML models which were already trained on real data collected from smartphone

Summary

INTRODUCTION

As the current generation of cellular (5G) and Wi-Fi (802.11ax) networks begin to be widely deployed, it is becoming increasingly clear that the generation of wireless systems will largely be deployed in spectrum that is shared, between cellular and Wi-Fi and with various incumbents such as federal radar systems, fixed microwave links, satellite providers, weather satellites and broadcast auxiliary services (BAS). It is possible to extract detailed information on both signal strength and number of Wi-Fi APs and cellular BSs received by a smartphone, over frequency bands from the unlicensed 2.4 GHz and 5 GHz bands to the low (< 1 GHz), mid (1 GHz - 6 GHz) and high (> 24 GHz) cellular bands, directly, using apps We posit that such a data-set, collected in labeled indoor and outdoor environments, across a wide variety of frequency bands and signal types, can be used to train Machine Learning (ML) models that can perform robust indoor/outdoor classification, leading to improved spectrum usage, incumbent protection and resilience, in 6 GHz, and in future bands such as the 12 GHz satellite band where sharing with indoor devices is being considered [4].

ML ALGORITHM EVALUATION

ML ALGORITHMS

TEST SCENARIOS

Findings

CONCLUSIONS AND FUTURE WORK