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In the United States, government and commercial entities have begun sharing the 3550–3650 MHz military Radar band. The key to ensuring successful sharing is robust interference prediction based on accurate and reliable propagation models. This paper presents the results of a comprehensive propagation measurement and modeling campaign for the 3.5 GH...
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Citations
... The authors in [10] utilized a single-slope regression-based model with simple diffraction and 7 GIS clutter categories and demonstrated up to a 2.0 dB reduction in uncertainty from a regression-only model, and 10 dB uncertainty reduction from the ITM or eHata predictions. ...
Classical propagation models currently utilized in spectrum management have a limited incorporation of foliage and clutter effects that result in inefficient spectrum utilization in modern spectrum-sharing scenarios. Recent propagation modeling efforts have increasingly utilized machine-learning approaches that demonstrate significantly improved performance. Unfortunately, pure machine learning models have limitations inherent to all data-driven techniques in addition to a lack of theoretical grounding in the physics of wave propagation.
This manuscript presents an alternative approach, augmenting a physics-based baseline propagation model with machine learning predictions of clutter losses. To develop the model, we utilized a dataset of approximately 368,000 propagation measurements recorded in nine diverse locations across the United States. Features input to several machine learning algorithms were derived from publicly available geographic information system datasets. Our hybrid model results in RMS difference between measurements and model ranging from 3.7 -- 4.95 dB depending on the choice of baseline propagation model and learning technique, a vast improvement from the individual baseline model predictions. Finally, cross-validation was utilized to demonstrate the generalizability of our approach to a wide variety of environments.
The advances made in wireless communication technology have led to efforts to improve the quality of reception, prevent poor connections and avoid disconnections between wireless and cellular devices. One of the most important steps toward preventing communication failures is to correctly estimate the received signal strength indicator (RSSI) of a wireless device. RSSI prediction is important for addressing various challenges such as localization, power control, link quality estimation , terminal connectivity estimation, and handover decisions. In this study, we compare different machine learning (ML) techniques that can be used to predict the received signal strength values of a device, given the received signal strength values of other devices in the region. We consider various ML methods, such as multi-layer ANN, K nearest neighbors, decision trees, random forest, and the K-means based method, for the prediction challenge. We checked the accuracy level of the learning process using a real dataset provided by a major national cellular operator. Our results show that the weighted K nearest neighbors algorithm, for K=3 neighbors, achieved, on average, the most accurate RSSI predictions. We conclude that in environments where the size of data is relatively small, and data of close geographical points is available, a method that predicts the coverage of a point using the coverage near geographical points can be more successful and more accurate compared with other ML methods.
Through a combination of continued demand, technological advancement and regulatory change, mobile and wireless networking is entering an expected period of rapid and broad innovation and change. This landscape presents mobile and wireless researchers with unique and unparalleled opportunities, but the inherent complexity of the wireless ecosystem also presents unique challenges. Specifically, the envisioned broad advancement of the science of wireless networking requires experimentation at scale in real environments, with control and visibility from the lowest layers of the radio up to the top of the application stack. This paper provides an overview of the Platform for Open Wireless Data-driven Experimental Research (Powder). Powder is a city-scale, remotely accessible, end-to-end software defined platform being designed and built to address this need. Compared to other mobile and wireless testbeds Powder provides advances in scale, realism, diversity, flexibility, and access. We describe the Powder architecture, building blocks, control framework and experimental workflow. We also describe example research efforts being supported by the platform and its current deployment status.
