Indoor/Outdoor Spectrum Sharing Enabled by GNSS-based Classifiers

• URL: http://arxiv.org/abs/2509.26500v1
• Date: Tue, 30 Sep 2025 16:43:59 GMT
• Title: Indoor/Outdoor Spectrum Sharing Enabled by GNSS-based Classifiers
• Abstract summary: We leverage the Global Navigation Satellite System (GNSS) signals for I/O classification.<n>Our results demonstrate that threshold-based techniques and machine learning approaches can achieve greater accuracy than approaches on wireless (Wi-Fi) data.
• Score: 1.3682156035049033
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The desirability of the mid-band frequency range (1 - 10 GHz) for federal and commercial applications, combined with the growing applications for commercial indoor use-cases, such as factory automation, opens up a new approach to spectrum sharing: the same frequency bands used outdoors by federal incumbents can be reused by commercial indoor users. A recent example of such sharing, between commercial systems, is the 6 GHz band (5.925 - 7.125 GHz) where unlicensed, low-power-indoor (LPI) users share the band with outdoor incumbents, primarily fixed microwave links. However, to date, there exist no reliable, automatic means of determining whether a device is indoors or outdoors, necessitating the use of other mechanisms such as mandating indoor access points (APs) to have integrated antennas and not be battery powered, and reducing transmit power of client devices which may be outdoors. An accurate indoor/outdoor (I/O) classification addresses these challenges, enabling automatic transmit power adjustments without interfering with incumbents. To this end, we leverage the Global Navigation Satellite System (GNSS) signals for I/O classification. GNSS signals, designed inherently for outdoor reception and highly susceptible to indoor attenuation and blocking, provide a robust and distinguishing feature for environmental sensing. We develop various methodologies, including threshold-based techniques and machine learning approaches and evaluate them using an expanded dataset gathered from diverse geographical locations. Our results demonstrate that GNSS-based methods alone can achieve greater accuracy than approaches relying solely on wireless (Wi-Fi) data, particularly in unfamiliar locations. Furthermore, the integration of GNSS data with Wi-Fi information leads to improved classification accuracy, showcasing the significant benefits of multi-modal data fusion.

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