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4G, 5G, and smart city networks often rely on microwave and millimeter-wave x-haul links. A major challenge associated with these high frequency links is their susceptibility to weather conditions. In particular, precipitation may cause severe signal attenuation, which significantly degrades the network performance. In this paper, we develop a Predictive Network Reconfiguration (PNR) framework that uses historical data to predict the future condition of each link and then prepares the network ahead of time for imminent disturbances. The PNR framework has two components: (i) an Attenuation Prediction (AP) mechanism; and (ii) a Multi-Step Network Reconfiguration (MSNR) algorithm. The AP mechanism employs an encoder-decoder Long Short-Term Memory (LSTM) model to predict the sequence of future attenuation levels of each link. The MSNR algorithm leverages these predictions to dynamically optimize routing and admission control decisions aiming to maximize network utilization, while preserving max-min fairness among the nodes using the network (e.g., base-stations) and preventing transient congestion that may be caused by switching routes. We train, validate, and evaluate the PNR framework using a dataset containing over 2 million measurements collected from a real-world city-scale backhaul network. The results show that the framework: (i) predicts attenuation with high accuracy, with an RMSE of less than 0.4 dB for a prediction horizon of 50 seconds; and (ii) can improve the instantaneous network utilization by more than 200% when compared to reactive network reconfiguration algorithms that cannot leverage information about future disturbances. 

This work proposes an Adaptive Fuzzy Prediction (AFP) method for the attenuation time series in Commercial Microwave links (CMLs). Time-series forecasting models regularly rely on the assumption that the entire data set follows the same Data Generating Process (DGP). However, the signals in wireless microwave links are severely affected by the varying weather conditions in the channel. Consequently, the attenuation time series might change its characteristics significantly at different periods. We suggest an adaptive framework to better employ the training data by grouping sequences with related temporal patterns to consider the non-stationary nature of the signals. The focus in this work is two-folded. The first is to explore the integration of static data of the CMLs as exogenous variables for the attenuation time series models to adopt diverse link characteristics. This extension allows to include various attenuation datasets obtained from additional CMLs in the training process and dramatically increasing available training data. The second is to develop an adaptive framework for short-term attenuation forecasting by employing an unsupervised fuzzy clustering procedure and supervised learning models. We empirically analyzed our framework for model and data-driven approaches with Recurrent Neural Network (RNN) and Autoregressive Integrated Moving Average (ARIMA) variations. We evaluate the proposed extensions on real-world measurements collected from 4G backhaul networks, considering dataset availability and the accuracy for 60 seconds prediction. We show that our framework can significantly improve conventional models’ accuracy and that incorporating data from various CMLs is essential to the AFP framework. The proposed methods have been shown to enhance the forecasting model’s performance by 30 − 40%, depending on the specific model and the data availability. 

The signals of microwave links used for wireless communications are prone to attenuation that can be significant due to rain. This attenuation may limit the capacity of the communication channel and cause irreversible damage. Accurate prediction of the attenuation opens the possibility to take appropriate actions to minimize such damage. In this paper, we present the use of the Long Short Time Memory (LSTM) machine learning method for short term prediction of the attenuation in commercial microwave links (CMLs), where only past measurements of the attenuation in a given link are used to predict future attenuation, with no side information. We demonstrate the operation of the proposed method on real-data signal level measurements of CMLs during rain events in Sweden. Moreover, this method is compared to a widely used statistical method for time series forecasting, the Auto-Regression Moving Average (ARIMA). The results show that learning patterns from previous attenuation values during rain events in a given CM 

Modern infrastructures support smart-city operations, which are based on short millimeter-waves wireless links connected by a dense network. These links are sensitive to hydrometeors, and their signals attenuated by rain. In this study, we demonstrate that standard signal-level measurements being collected by the network can be used to estimate the movement of an ongoing storm. Parameters characterizing the movements of the frontal rain cell, as its velocity and direction, can be accurately estimated. We first estimate the differential time of arrival of the attenuated signals between pairs of links, from which we extract the parameters of interest. We demonstrate our results using actual measurements from an operating system in the city of Rehovot, Israel 

This paper focuses on the K-12 educational activities of COSMOS-Cloud enhanced Open Software defined MObile wireless testbed for city-Scale deployment. The COSMOS wireless reasearch testbed is being deployed in West Harlem (New York City) as part of the NSF Platforms for Advanced Wireless Research (PAWR) program. COSMOS' approach for K-12 education is twofold: (i) create an innovative and concrete set of methods/tools that allow teaching STEM subjects using live experiments related to wireless networks/IoT/cloud, and (ii) enhance the professional development (PD) of K-12 teachers and collaborate with them to create hands-on educational material for the students. The COSMOS team has already conducted successful pilot summer programs for middle and high school STEM teachers, where the team worked with the teachers and jointly developed innovative real-world experiments that were organized as automated and repeatable math, science, and computer science labs to be used in the classroom. The labs run on the COSMOS Educational Toolkit, a hardware and software system that offers a large variety of pre-orchestrated K-12 educational labs. The software executes and manages the experiments in the same operational philosophy as the COSMOS testbed. Specifically, since it is designed for use by non-technical middle and high school teachers/students, it adds easy-to-use enhancements to the experiments' execution and the results visualization. The labs are also supported by Next Generation Science Standards (NGSS)-compliant teacher/student material. This paper describes the teachers' PD program, the NGSS lessons created and the hardware and software system developed to support the initiative. Additionally, it provides an evaluation of the PD approach as well as the expected impact to K-12 STEM education. Current limitations and future work are also included as part of the discussion section.