Indoor environments present significant challenges for wireless connectivity. Public Mobile Network Operators (MNOs) utilizing outdoor macro base stations (BSs) to serve indoor customers suffer from poor signal penetration while indoor Wi-Fi networks may face reliability issues due to spectrum contention. Shared spectrum models, particularly the Citizens Broadband Radio Service (CBRS) band utilized by private 4G/5G networks, have emerged as a promising alternative to providing reliable indoor service. Moreover, these private networks are equipped with the neutral-host (NH) model, seamlessly offloading indoor MNOs’ traffic to the private CBRS network. This paper presents a comprehensive, real-world performance evaluation of three co-located technologies utilizing mid-band spectrum (1–6 GHz): a CBRS-based NH network, public MNO macro net-works, and a Wi-Fi 6 network, inside a large, big-box retail store characterized by significant building loss. While the evaluation is site-specific, it is a real-world representation of a highly dense class of indoor deployments. Our analysis demonstrates: (i) the NH network provides superior indoor coverage compared to MNO macro, requiring only six CBRS devices (CBSDs) versus 65 Access Points (APs) for enterprise Wi-Fi to achieve full coverage, with a median building loss of 26.6 dB ensuring interference-free coexistence with outdoor federal incumbents; (ii) the NH network achieves substantial indoor throughput gains, with per-channel normalized throughput improvements of 1.44× and 1.62× in downlink (DL), and 4.33× and 13× in uplink (UL), compared to 4G and 5G macro deployments, respectively; (iii) the NH deployment achieves a median indoor aggregated physical (PHY)-layer DL throughput gain of 2.08× over 5G macro deployments indoors, despite utilizing only 40 MHz of aggregated bandwidth compared to 225 MHz for 5G macro; and (iv) the NH deployment also outperforms Wi-Fi in application-layer HTTP DL performance by 5.05×. The findings offer critical insights by presenting: (i) a framework to leverage real-world deployments as testbeds for studying indoor shared-spectrum networks; and (ii) measurement-based evidence supporting the use of NH to improve indoor cellular coverage.

The measurement of network speed, specifically broadband speed/throughput as measured by tools like iPerf, has long been used as a key performance indicator for home broadband. Unfortunately, home users rarely have the capability to conduct reliable wired tests, instead being only able to measure using Wi-Fi. Hence, home wireless is often viewed as an unreliable indicator of network speed, leaving home users with little recourse to challenge the quality of broadband speed being delivered. To that end, we seek to answer the extent to which such tests are unreliable and, more importantly, to understand if one can accurately determine if the result was indicative of broadband as a bottleneck or if the measurement was limited by Wi-Fi. In our paper, we demonstrate that such a determination is eminently possible and, moreover, such a determination can be done drawing only on features and groups of features already reported by iPerf. We show through extensive experiments that one can capture the goodness (test was indicative of broadband speeds) or badness (test was not indicative of broadband speeds) with a 92. 5% accuracy, drawing only on the median throughput and interquartile range with second-by-second windowing reported by iPerf. Finally, we show that there is a negative correlation between the ratio of Wi-Fi throughput to Ethernet (broadband) throughput and the interquartile range normalized with its corresponding Wi-Fi throughput.

Understanding the real-world end-to-end performance of residential Wi-Fi and broadband networks is essential for consumers, service providers, and policy makers in an increasingly connected society to determine how connectivity can be improved, through better technology or appropriate spectrum decisions. While previous research has studied such performance, the focus has primarily been on the wired Internet, pointing to Wi-Fi as a bottleneck without investigating the reasons thereof. We believe that the preliminary results presented in this paper, from in-depth studies of residential Wi-Fi deployments in a small Midwestern town, offer a first look into how the Wi-Fi environment may impact the user experience. Over a period of six months, we deployed single-board computers to measure the throughput and latency of Wi-Fi and the backhaul broadband network in a number of different residential environments, as well as capture the Wi-Fi signal environment. Our analyses show: (i) interoperability issues between AP and client supporting different Wi-Fi amendments (e.g., 802.11ac, 802.11ax); (ii) a lack of smart interference management and Dynamic Frequency Selection (DFS) support, resulting in over-usage of channels in the U-NII-1 and U-NII-3 bands when other, less congested bands may be available.

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.

The 3.7 - 3.98 GHz frequency band (also known as C-band) was recently allocated in the US for the deployment of 5G cellular services. Prior to this, the lower adjacent band, 3.55 - 3.7 GHz, had been allocated to Citizens Broadband Radio Service (CBRS), where the entire 150 MHz can be used for free by Tier 3 General Authorized Access (GAA) users, under strict authorization of the Spectrum Access System (SAS). However, adjacent channel interference may reduce the performance of GAA and C-band systems due to lack of guard bands and no Time Division Duplexing (TDD) synchronization, i.e., uplink/downlink configurations are not synchronized. In this paper, we quantify the effect of this mutual interference by performing experiments with a real-world deployment. Significant downlink throughput reductions on both systems are observed when two devices are in close proximity to each other, with one is transmitting uplink while the other is transmitting downlink: 60% for 4G CBRS and 43% for 5G C-band. We believe that this is the first paper to demonstrate this in a real deployment. This throughput degradation was reduced when the CBRS base-station (i.e., CBSD) changed its channel and operated 20 MHz away from C-band, essentially creating a guard band between the channels. We also demonstrate the improvement in latency under adjacent channel interference by implementing MicroSlicing at the CBSD. Our results indicate that addressing adjacent channel interference due to the lack of guard bands and TDD configuration mismatch is crucial to improving the performance of both CBRS and C-band systems.

5G-NR is beginning to be widely deployed in the mmWave frequencies in urban areas in the US and around the world. Due to the directional nature of mmWave signal propagation, improving performance of such deployments heavily relies on beam management and deployment configurations. We perform detailed measurements of mmWave 5G deployments by two major commercial 5G operators in the US in two diverse environments: an open field with a baseball park (BP) and a downtown urban canyon region (DT), using smartphone-based tools that collect detailed measurements across several layers (PHY, MAC and up) such as beam-specific metrics like signal strength, beam switch times, and throughput per beam. Our measurement analysis shows that the parameters of the two deployments differ in a number of aspects: number of beams used, number of channels aggregated, and density of deployments, which reflect on the throughput performance. Our measurement-driven propagation analysis demonstrates that narrower beams experience a lower path-loss exponent than wider beams, which combined with up to eight frequency channels aggregated on up to eight beams can deliver a peak throughput of 1.2 Gbps at distances greater than 100m.