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Hossein Nasiri

Publications and source records attributed to Hossein Nasiri.

7 recordsLinked to original sources

Wi-Fi Self-Coexistence in the 6 GHz Band: An ns-3 Evaluation of LPI and SP Usage

The U.S. has adopted four power regimes for opera tion in the shared unlicensed 6 GHz band -- standard power (SP), low-power indoor (LPI), geofenced variable power (GVP), and very low power (VLP) -- with maximum permitted EIRP levels of 36 dBm, 30 dBm, 24 dBm, and 14 dBm, respectively. Although these regimes are primarily intended to protect incumbent services, their heterogeneous transmit power levels also introduce additional coexistence challenges within 6 GHz Wi-Fi networks. In this paper, we develop an ns-3 Wi-Fi 6E/802.11ax coexistence testbed to study coexistence under heterogeneous power regimes and to provide a reproducible simulation methodology. To the best of our knowledge, prior work has not specifically examined self-coexistence issues within 6 GHz Wi-Fi networks. We evaluate two coexistence scenarios: one in which both the LPI AP and the SP AP are indoors, and another in which the LPI AP is indoors while the SP AP is outdoors. Results are compared against an indoor LPI--LPI baseline when applicable. Our findings show that: (i) the presence of an indoor SP AP can significantly degrade the goodput of an LPI AP; (ii) channel bandwidth is a key factor in determining the extent of SP-to-LPI impact, with the degradation being most severe at 20 MHz and partially alleviated at 160 MHz; (iii) physical blockage between outdoor SP and LPI APs improves fairness; and (iv) BSS coloring does not necessarily improve fairness in mixed-regime deployments. The simulation framework can be extended to study coexistence between Wi-Fi and cellular systems, as recently proposed by Ofcom in the U.K.

eess.SP

Evaluating Smartphone GNSS Accuracy for Geofenced 6 GHz Operations

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.

eess.SP

Indoor/Outdoor Spectrum Sharing Enabled by GNSS-based Classifiers

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.

eess.SP

Evaluation of Indoor/Outdoor Sharing in the Unlicensed 6 GHz Band

Standard Power (SP) Wi-Fi 6E in the U.S. is just beginning to be deployed outdoors in the shared but unlicensed 6 GHz band under the control of an Automated Frequency Coordination (AFC) system to protect incumbents, while low-power-indoor (LPI) usage has been steadily increasing over the past 2 years. In this paper, we present the first comprehensive measurements and analyses of a SP Wi-Fi 6E deployment at the University of Notre Dame's football stadium, with 902 access points and a seating capacity of 80,000, coexisting with LPI deployments in adjacent buildings. Measurement campaigns were conducted during and after games, outdoors and indoors to fully characterize the performance of SP Wi-Fi 6E, interactions between SP and LPI and potential for interference to incumbents. Our main conclusions are: (i) in a very short time of about 2 months, the percentage of Wi-Fi 6E client connections is already 14% indicating rapid adoption, (ii) dense SP operation outdoors can negatively impact LPI deployments indoors, depending on building loss, indicating the need to carefully consider hybrid indoor-outdoor sharing deployments, and (iii) spectrum analyzer results indicate an aggregate signal level increase of approximately 10 dB in a Wi-Fi channel during peak usage which could potentially lead to interference since the AFC does not consider aggregate interference when allocating permitted power levels. These results from real-world deployments can inform spectrum policy in other bands where similar sharing mechanisms are being considered, such as 7.125 - 8.4 GHz.

cs.NI

Data Driven Environmental Awareness Using Wireless Signals

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.

cs.NI

A Comprehensive Analysis of Secondary Coexistence in a Real-World CBRS Deployment

The Federal Communications Commission (FCC) in the U.S. has made the Citizens Broadband Radio Service (CBRS) band (3.55 - 3.7 GHz) available for commercial wireless usage under a shared approach using a three-tier hierarchical architecture, where the federal incumbent is the highest priority Tier 1 user, Priority Access License (PAL) holders, who have paid for licenses, are Tier 2 users and Tier 3 users operate under General Authorized Access (GAA), without license fees or protection from higher priority users. The Spectrum Access System (SAS) ensures that higher priority users are protected from interference from lower priority users. However, the lowest priority GAA users are not given any protection from each other by the SAS and are expected to not cause any harmful interference to Tier 1 and Tier 2 users. As the deployments of GAA devices grow, the potential for secondary interference between GAA users increases, especially since the SAS architecture does not allow dynamic channel switching when faced with interference. In this paper, we present a first-of-its-kind extensive measurement campaign of a commercial CBRS network deployed in the city of South Bend, IN, that quantifies both co-channel interference (CCI) and adjacent channel interference (ACI) caused by competing GAA devices and C-band 5G, respectively. We (i) identify a particular CCI scenario and improve performance by changing the frequency allocation based on our study of other allocations in the vicinity and (ii) quantify ACI from 5G in C-band (3.7 GHz) on CBRS throughput. We conclude that (i) CCI and ACI for GAA users is not handled well by the SAS, (ii) proper frequency allocation for GAA requires additional analysis of interference from other GAA users followed by dynamical channel selection, and (iii) utilization of immediate adjacent channels by high power 5G deployments limits the performance of CBRS.

cs.NI

Evaluating The Interference Potential in 6 GHz: An Extensive Measurement Campaign of A Dense Indoor Wi-Fi 6E Network

The Federal Communications Commission (FCC) has allocated the 6 GHz band (5.925 - 7.125 GHz) for unlicensed, shared use in the US. Incumbents in the band are protected via Low Power Indoor (LPI) rules that do not require the use of an Automatic Frequency Control (AFC) mechanism and Standard Power (SP) rules which do. As the deployment of Wi-Fi 6E APs implementing LPI rules have been increasing, there is limited research examining the real-world interference potential of dense LPI deployments to fixed links, which remains a concern for incumbents. We have conducted a first-of-its-kind extensive measurement campaign of a dense indoor Wi-Fi 6E network at the University of Michigan, which includes walking, driving, and drone measurements to assess outdoor beacon Received Signal Strength Indicator (RSSI), building entry loss (BEL), channel utilization, and appropriate enabling signal level for a proposed client-to-client (C2C) mode in 6 GHz. Our detailed measurements under various conditions show median outdoor RSSI between -75 dBm and -85 dBm, BEL between 12 dB and 16 dB through double-pane low-emission windows, and only 5% of indoor Basic Service Set Identifiers (BSSIDs) observed outdoors. Our overall conclusion is that the probability of interference to incumbent fixed links is low, but more research is required to determine the appropriate signal level for the C2C enabling signal.

cs.NI