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1. Rain Estimation from Smart City’s E-band Links

   R. Janco, J. Ostrometzky (January 2022, 2022 IEEE 14th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP))

   Smart cities around the world are supported by high-capacity wireless communication networks, which are based on millimeter-waves links. The propagating waves are sensitive to hydrometeors, and their signal level is attenuated by rain. However, most of the links in such networks are shorter than 1 km, imposing large errors on the rain estimation results. In this paper we demonstrate, using actual measurements from the city of Rehovot, Israel, how high-resolution rain maps can be generated from the received signal level measurements collected by these links. We first propose a method for reducing the errors in converting signal attenuation to rainfall estimates in short, incity links. The proposed method requires calibration of model parameters using side information from either a rain gauge or a long link in the vicinity of the network. We empirically analyze the results of the calibrating method using either auxiliary measurements and show that the performance is satisfactory for both. Then, we apply a spatial interpolation method on the rainfall resulting estimates, and demonstrate the construction of an high-resolution 2-D map of the accumulated rain in a city, a product with great potential for improving well-being of life in urban areas. 

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2. Short-Term Prediction of the Attenuation in a Commercial Microwave Link Using LSTM-based RNN

   https://doi.org/10.23919/Eusipco47968.2020.9287835  

   Jacoby, Dror; Ostrometzky, Jonatan; Messer, Hagit (January 2021, 2020 28th European Signal Processing Conference (EUSIPCO))

   null (Ed.)

   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 

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3. Switching in the Rain: Predictive Wireless x-haul Network Reconfiguration

   https://doi.org/10.1145/3570616  

   Kadota, Igor; Jacoby, Dror; Messer, Hagit; Zussman, Gil; Ostrometzky, Jonatan (December 2022, Proceedings of the ACM on Measurement and Analysis of Computing Systems)

   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. 

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4. Cordon control with spatially-varying metering rates: a Reinforcement Learning approach

   https://doi.org/https://doi.org/10.1016/j.trc.2018.12.007  

   Ni, W. and (January 2019, Transportation research. Part C, Emerging technologies)

   The work explores how Reinforcement Learning can be used to re-time traffic signals around cordoned neighborhoods. An RL-based controller is developed by representing traffic states as graph-structured data and customizing corresponding neural network architectures to handle those data. The customizations enable the controller to: (i) model neighborhood-wide traffic based on directed-graph representations; (ii) use the representations to identify patterns in real-time traffic measurements; and (iii) capture those patterns to a spatial representation needed for selecting optimal cordon-metering rates. Input to the selection process also includes a total inflow to be admitted through a cordon. The rate is optimized in a separate process that is not part of the present work. Our RL-controller distributes that separately-optimized rate across the signalized street links that feed traffic through the cordon. The resulting metering rates vary from one feeder link to the next. The selection process can reoccur at short time intervals in response to changing traffic patterns. Once trained on a few cordons, the RL-controller can be deployed on cordons elsewhere in a city without additional training. This portability feature is confirmed via simulations of traffic on an idealized street network. The tests also indicate that the controller can reduce the network’s vehicle hours traveled well beyond what can be achieved via spatially-uniform cordon metering. The extra reductions in VHT are found to grow larger when traffic exhibits greater in-homogeneities over the network. 

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5. Centrality-based lane interventions in road networks for improved level of service: the case of downtown Boise, Idaho

   https://doi.org/10.1007/s41109-023-00532-z  

   Ahmed, Md Ashraf; Kays, H. M.; Sadri, Arif Mohaimin (December 2023, Applied Network Science)

   Abstract Recent advancements in network science showed that the topological credentials of the elements (i.e., links) in a network carry important implications. Likewise, roadway segments (i.e., links) in a road network should be assessed based on their network position along with traffic conditions at a given geographic scale. The goal of this study is to present a framework that can identify and select critical links in a road network based on their topological importance such as centrality, and the effects of systematic interventions conducted on such links in improving overall system performance (vehicle delay, travel time) to provide an adequate level of service (LOS). A real-world road network (Boise downtown) is investigated by applying lane interventions on roadways experiencing high congestion. Microscopic traffic simulation and analyses are conducted to estimate the traffic flow parameters hence the performance of the road segments. The findings of this study show that interventions applied to critical and congested road segments improve the serviceability from LOS F to LOS E as well as from LOS D to LOS C. Besides, reduced travel time and vehicular delay (after applying intervention on critical components) are also observed for high demand OD pairs of the road network. As such the proposed framework has the potential to incorporate the topological credentials with traffic flow parameters and improve the performance of the road network. This systematic approach will help traffic managers and practitioners to develop strategies that enhance road network performance. 

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