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1. Switching in the rain: Predictive wireless x-haul network reconfiguration

   I. Kadota, D. Jacoby ( June 2022 , ArXivorg)

   Wireless x-haul networks rely on microwave and millimeter-wave links between 4G and/or 5G base-stations to support ultra-high data rate and ultra-low latency. 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 encoderdecoder 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 base-stations sharing the network and preventing transient congestion that may be caused by re-routing. 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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2. DyCroNO: Dynamic Cross-layer Network Orchestration and Real-time Deep Learning-based Network Load Prediction

   Karanam, Venkat Sai ; Ramamurthy, Byrav ( May 2023 , 2023 International Conference on Optical Network Design and Modeling (ONDM))

   In this paper, we present Dynamic Cross-layer Network Orchestration (DyCroNO), a dynamic service provisioning and load balancing mechanism for IP over optical networks. DyCroNO comprises of the following components: i) an end-end (E2E) service provisioning and virtual path allocation algorithm, ii) a lightweight dynamic bandwidth adjustment strategy that leverages the extended duration statistics to ensure optimal network utilization and guarantee the quality-of-service (QoS), and iii) a load distribution mechanism to optimize the network load distribution at runtime. As another contribution, we design a real-time deep learning technique to predict the network load distribution. We implemented a Long Short-Term Memory-based (LSTM) method with a sliding window technique to dynamically (at runtime) predict network load distributions at various lead times. Simulations were performed over three topologies: NSFNet, Cost266 and Eurolarge using real-world traffic traces to model the traffic patterns. Results show that our approach lowers the mean link load and total resources significantly while improving the resource utilization when compared to existing approaches. Additionally, our deep learning-based method showed promising results in load distribution prediction with low root mean squared error (RMSE) and ∼90% accuracy. URL: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10144888&isnumber=10144840 

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3. Simplicial closure and higher-order link prediction

   Benson, Austin R ; Abebe, Rediet ; Schaub, Michael T ; Jadbabaie, Ali ; Kleinberg, Jon. ( April 2018 , arXiv.org)

   Networks provide a powerful formalism for modeling complex sys- tems, by representing the underlying set of pairwise interactions. But much of the structure within these systems involves interac- tions that take place among more than two nodes at once — for example, communication within a group rather than person-to- person, collaboration among a team rather than a pair of co-authors, or biological interaction between a set of molecules rather than just two. We refer to these type of simultaneous interactions on sets of more than two nodes as higher-order interactions; they are ubiquitous, but the empirical study of them has lacked a general framework for evaluating higher-order models. Here we introduce such a framework, based on link prediction, a fundamental prob- lem in network analysis. The traditional link prediction problem seeks to predict the appearance of new links in a network, and here we adapt it to predict which (larger) sets of elements will have fu- ture interactions. We study the temporal evolution of 19 datasets from a variety of domains, and use our higher-order formulation of link prediction to assess the types of structural features that are most predictive of new multi-way interactions. Among our results, we find that different domains vary considerably in their distri- bution of higher-order structural parameters, and that the higher- order link prediction problem exhibits some fundamental differ- ences from traditional pairwise link prediction, with a greater role for local rather than long-range information in predicting the ap- pearance of new interactions. 

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4. Stacking models for nearly optimal link prediction in complex networks

   https://doi.org/10.1073/pnas.1914950117  

   Ghasemian, Amir ; Hosseinmardi, Homa ; Galstyan, Aram ; Airoldi, Edoardo M. ; Clauset, Aaron ( September 2020 , Proceedings of the National Academy of Sciences)

   Most real-world networks are incompletely observed. Algorithms that can accurately predict which links are missing can dramatically speed up network data collection and improve network model validation. Many algorithms now exist for predicting missing links, given a partially observed network, but it has remained unknown whether a single best predictor exists, how link predictability varies across methods and networks from different domains, and how close to optimality current methods are. We answer these questions by systematically evaluating 203 individual link predictor algorithms, representing three popular families of methods, applied to a large corpus of 550 structurally diverse networks from six scientific domains. We first show that individual algorithms exhibit a broad diversity of prediction errors, such that no one predictor or family is best, or worst, across all realistic inputs. We then exploit this diversity using network-based metalearning to construct a series of “stacked” models that combine predictors into a single algorithm. Applied to a broad range of synthetic networks, for which we may analytically calculate optimal performance, these stacked models achieve optimal or nearly optimal levels of accuracy. Applied to real-world networks, stacked models are superior, but their accuracy varies strongly by domain, suggesting that link prediction may be fundamentally easier in social networks than in biological or technological networks. These results indicate that the state of the art for link prediction comes from combining individual algorithms, which can achieve nearly optimal predictions. We close with a brief discussion of limitations and opportunities for further improvements.

    

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5. B-AWARE: Blockage Aware RSU Scheduling for 5G Enabled Autonomous Vehicles

   https://doi.org/10.1145/3609133  

   Szeto, Matthew ; Andert, Edward ; Shrivastava, Aviral ; Reisslein, Martin ; Lin, Chung-Wei ; Richmond, Christ ( October 2023 , ACM Transactions on Embedded Computing Systems)

   5G Millimeter Wave (mmWave) technology holds great promise for Connected Autonomous Vehicles (CAVs) due to its ability to achieve data rates in the Gbps range. However, mmWave suffers from a high beamforming overhead and requirement of line of sight (LOS) to maintain a strong connection. For Vehicle-to-Infrastructure (V2I) scenarios, where CAVs connect to roadside units (RSUs), these drawbacks become apparent. Because vehicles are dynamic, there is a large potential for link blockages. These blockages are detrimental to the connected applications running on the vehicle, such as cooperative perception and remote driver takeover. Existing RSU selection schemes base their decisions on signal strength and vehicle trajectory alone, which is not enough to prevent the blockage of links. Many modern CAVs motion planning algorithms routinely use other vehicle’s near-future path plans, either by explicit communication among vehicles, or by prediction. In this paper, we make use of the knowledge of other vehicle’s near future path plans to further improve the RSU association mechanism for CAVs. We solve the RSU association algorithm by converting it to a shortest path problem with the objective to maximize the total communication bandwidth. We evaluate our approach, titled B-AWARE, in simulation using Simulation of Urban Mobility (SUMO) and Digital twin for self-dRiving Intelligent VEhicles (DRIVE) on 12 highway and city street scenarios with varying traffic density and RSU placements. Simulations show B-AWARE results in a 1.05× improvement of the potential datarate in the average case and 1.28× in the best case vs. the state-of-the-art. But more impressively, B-AWARE reduces the time spent with no connection by 42% in the average case and 60% in the best case as compared to the state-of-the-art methods. This is a result of B-AWARE reducing nearly 100% of blockage occurrences.

    

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