Hossein Nasiri

Joshua Roy Palathinkal, Hardani Ismu Nabil, Muhammad Iqbal Rochman et al.

The recently deployed 6 GHz spectrum in the U.S. utilizes distinct power categories, with the latest proposed "Geofenced Variable Power" (GVP) category permitting indoor and outdoor operations without continuous Automated Frequency Coordination (AFC) by relying instead on local databases of exclusion zones. Consequently, the safe operation of GVP devices depends entirely on reliable GNSS localization to respect these geofences. However, GNSS accuracy is highly variable and significantly degrades in environments like urban canyons or indoors. This paper presents the first comprehensive empirical study evaluating GNSS reliability specifically for GVP compliance. Utilizing the SigCap Android application, we document and compare GNSS accuracy across an extensive array of real-world conditions, encompassing urban versus suburban landscapes, varying mobility states (stationary, walking, driving), and indoor versus outdoor settings. The results demonstrate that while device hardware causes variations in GNSS accuracy, the operational environment is the primary driver of error. Indoor settings and dense urban areas consistently degrade localization. Moreover, outdoor positions adjacent to buildings often surprisingly produce significant inaccuracies, even near low-elevation structures. We further analyze the contribution of different GNSS constellations to device positioning and show that satellites from non-U.S.-licensed constellations-although currently used in a substantial portion of location fixes-are not permitted for regulatory geolocation under FCC requirements.

Hossein Nasiri, Seda Dogan-Tusha, Muhammad Iqbal Rochman et al.

Robust classification of the operational environment of wireless devices is becoming increasingly important for wireless network optimization, particularly in a shared spectrum environment. Distinguishing between indoor and outdoor devices can enhance reliability and improve coexistence with existing, outdoor, incumbents. For instance, the unlicensed but shared 6 GHz band (5.925 - 7.125 GHz) enables sharing by imposing lower transmit power for indoor unlicensed devices and a spectrum coordination requirement for outdoor devices. Further, indoor devices are prohibited from using battery power, external antennas, and weatherization to prevent outdoor operations. As these rules may be circumvented, we propose a robust indoor/outdoor classification method by leveraging the fact that the radio-frequency environment faced by a device are quite different indoors and outdoors. We first collect signal strength data from all cellular and Wi-Fi bands that can be received by a smartphone in various environments (indoor interior, indoor near windows, and outdoors), along with GPS accuracy, and then evaluate three machine learning (ML) methods: deep neural network (DNN), decision tree, and random forest to perform classification into these three categories. Our results indicate that the DNN model performs the best, particularly in minimizing the most important classification error, that of classifying outdoor devices as indoor interior devices.

Hossein Nasiri, Muhammad Iqbal Rochman, Monisha Ghosh

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.