Indoor/Outdoor Spectrum Sharing Enabled by GNSS-based Classifiers

2025-09-30

AI Summary

Current spectrum sharing relies on physical constraints (e.g., antenna type, power supply) and conservative power control due to the lack of reliable, automated indoor/outdoor (I/O) environment identification, limiting mid-band spectrum utilization efficiency. To address this, we present the first systematic validation of GNSS signal efficacy for I/O classification and propose a multimodal approach integrating GNSS signal strength, threshold-based decision rules, machine learning, and complementary Wi-Fi features. Evaluated on a geographically diverse real-world dataset, results show that GNSS-only classification already outperforms Wi-Fi-only methods—especially in unseen locations, demonstrating superior robustness. The GNSS–Wi-Fi fusion model further improves accuracy. Our method enables dynamic transmit power adaptation and coexistence of unlicensed devices, breaking from conventional detection paradigms. It provides a deployable, practical pathway toward intelligent, context-aware spectrum sharing.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Application Domains: Internet of Things, Sensor Networks & Smart Cities

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

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.

Problem

• Developing reliable indoor/outdoor classification using GNSS signals
• Enabling automatic transmit power control for spectrum sharing systems
• Addressing spectrum sharing challenges between federal and commercial users

Innovation

• Uses GNSS signals for indoor/outdoor classification
• Combines threshold-based and machine learning approaches
• Integrates GNSS with Wi-Fi for multi-modal fusion