Accurately measuring propagation loss is crucial for Citizens Broadband Radio Service (CBRS) network planning, deployment, and successful spectrum sharing among CBRS users. The currently used propagation models, such as the Irregular Terrain Model (ITM) and Extended Hata (eHata) in the CBRS network, disregard significant environmental factors like foliage or clutter data, making the loss prediction unreliable. Additionally, current experimental studies on CBRS propagation are limited, with methodologies often relying on expensive measurement tools. To address these limitations, this paper proposes a CBRS network propagation framework, which includes a signal measurement system made of affordable, commercial off-the shelf electronic devices and advanced empirical data analysis. To validate the framework, an extensive measurement campaign is conducted in a live CBRS network in Buffalo, NY. The empirical path loss results have been compared with existing analytical models, with the alpha-beta (𝛼 − 𝛽) model giving the best path loss prediction. The methodology and framework presented in this paper can be applied to other network environments, helping researchers and engineers estimate network performance during the design phase, thus saving resources.

The release of the Citizens Broadband Radio Service (CBRS) spectrum has enabled private network deployments, but signal strength in this band is highly sensitive to weather conditions. While traditional signal propagation models are often constrained by their assumptions and lack adaptability to varying environments, Machine Learning (ML) models offer a flexible alternative for predicting signal strength under complex conditions. In this work, we develop a Neural Network (NN) based predictive model for CBRS signal strength, leveraging a comprehensive set of weather parameters, collected over a 24month period from a live CBRS network in Buffalo, NY. Our model demonstrates a Mean Absolute Error (MAE) of 1.83 dB in predicting hourly signal strength, and we further evaluate its generalization capability by applying a transfer learning approach to another CBRS network in Elmira, NY, achieving a MAE of 2.13 dB. This study highlights the potential of ML methods in modeling weather-induced signal variability and their applicability across different network environments.

Weather variations can significantly impact the performance of high-frequency wireless networks. Although much research has been conducted on these impacts, there has been limited focus specifically on Citizens Broadband Radio Service (CBRS) networks, which operate at 3.5 GHz. Analyzing these effects is crucial for CBRS-based private networks, where reliability and high performance are critical. This paper explores how different weather conditions-temperature, relative humidity, absolute humidity, rainfall, and snowfall-affect the signal strength in the CBRS spectrum. Through an extensive experimental campaign within a CBRS-based private LTE network in Buffalo, NY, we collected signal strength data from 32 data collection points over 21 months. Using weather data from Oikolab, our data-driven analyses indicate that relative humidity is a more reliable predictor of signal strength fluctuations compared to temperature and absolute humidity. We also investigate how various types of precipitation affect signal strength, with rainfall causing immediate decreases and snowfall leading to more prolonged impacts. These insights are vital for enhancing the understanding of weather impacts on the CBRS spectrum, a topic not extensively covered in the existing literature.

The release of the 3.5 GHz Citizens Broadband Radio Service (CBRS) band has unlocked significant opportunities for a diverse range of stakeholders, particularly enabling communities and municipalities to establish private wireless networks. Successful deployment of these networks and effective spectrum sharing among CBRS users require accurate and reliable measurements of propagation loss. Often, private stakeholders depend on expensive commercial software that uses analytical models, which may not accurately reflect specific network conditions, thus potentially leading to measurement inaccuracies. Additionally, CBRS network propagation is complicated by its high sensitivity to weather and foliage, which makes network planning, coverage, and interference assessment more challenging. To this end, this paper presents a framework and methodology for characterizing CBRS network propagation, incorporating an extensive measurement campaign within a live CBRS private network in Buffalo, NY. Our study focuses on experimentally measuring path loss, comparing it with theoretical models, and assessing the impacts of weather and foliage on network performance. The methodologies and insights detailed in this paper can guide researchers and engineers in improving network design and operational efficiency across various network environments.

Smart grid applications heavily rely on communication infrastructures that offer flexibility, scalability, and cost-effectiveness to enable bi-directional information exchange across geographically distributed grid elements. Wireless cellular networks, such as LTE cat-M, provide extensive coverage at a reduced cost, both in terms of installation and power consumption. This paper presents a multi-node testbed to assess the suitability of LTE cat-M technology for a variety of smart grid applications, with distributed nodes collecting and transmitting data at a variable rate, from 0.25 to 100 frames per second (fps). Based on field experiments, an extensive performance analysis is presented with Key Performance Indicators (KPIs), such as delay, jitter, and frame loss. The impact of the number of smart grid nodes and propagation quality are also considered in the analysis. Finally, a calibration method to optimize the packet transmission time is presented with a 33.91% delay reduction under different conditions.