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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.
Ramamurthy et al. (Sat,) studied this question.