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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. 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. WiMove: Toward Infrastructure Mobility in mmWave WiFi

   https://doi.org/10.1145/3416012.3424625  

   Jian, Yubing; Agarwal, Mohit; Venkateswaran, Shyam Krishnan; Liu, Yuchen; Blough, Douglas M.; Sivakumar, Raghupathy (November 2020, ACM Symposium on Mobility Management and Wireless Access)

   Line-of-sight (LOS) is a critical requirement for mmWave wireless communications. In this work, we explore the use of access point (AP) infrastructure mobility to optimize indoor mmWave WiFi network performance based on the discovery of LOS connectivity to stations (STAs).We consider a ceiling-mounted mobile (CMM) AP as the infrastructure mobility framework. Within this framework, we present a LOS prediction algorithm based on machine learning (ML) that addresses the LOS discovery problem. The algorithm relies on the available network state information (e.g., LOS connectivity between STAs and the AP) to predict the unknown LOS connectivity status between the reachable AP locations and target STAs. We show that the proposed algorithm can predict LOS connectivity between the AP and target STAs with an accuracy up to 91%. Based on the LOS prediction algorithm, we then propose a systematic solution WiMove, which can decide if and where the AP should move to for optimizing network performance. Using both ns-3 based simulation and experimental prototype implementation, we show that the throughput and fairness performance of WiMove is up to 119% and 15% better compared with single static AP and brute force search. 

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4. 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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5. Bursting the Filter Bubble: Fairness-Aware Network Link Prediction

   https://doi.org/10.1609/aaai.v34i01.5429  

   Masrour, Farzan; Wilson, Tyler; Yan, Heng; Tan, Pang-Ning; Esfahanian, Abdol (June 2020, Proceedings of the AAAI Conference on Artificial Intelligence)

   null (Ed.)

   Link prediction is an important task in online social networking as it can be used to infer new or previously unknown relationships of a network. However, due to the homophily principle, current algorithms are susceptible to promoting links that may lead to increase segregation of the network—an effect known as filter bubble. In this study, we examine the filter bubble problem from the perspective of algorithm fairness and introduce a dyadic-level fairness criterion based on network modularity measure. We show how the criterion can be utilized as a postprocessing step to generate more heterogeneous links in order to overcome the filter bubble problem. In addition, we also present a novel framework that combines adversarial network representation learning with supervised link prediction to alleviate the filter bubble problem. Experimental results conducted on several real-world datasets showed the effectiveness of the proposed methods compared to other baseline approaches, which include conventional link prediction and fairness-aware methods for i.i.d data. 

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