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1. Towards Deep Learning Augmented Robust D-Band Millimeter-Wave Picocell Deployment

   Regmi, Hem; Sur, Sanjib (January 2022, Performance evaluation review)

   D-band millimeter-wave, a key wireless technology for beyond 5G networks, promises extremely high data rate, ultra-low latency, and enables new Internet of Things applications. However, massive signal attenuation, complex response to building structures, and frequent non-availability of the Line-Of-Sight path make D-band picocell deployment challenging. To address this challenge, we propose a deep learning-based tool, that allows a network deployer to quickly scan the environment from a few random locations and predict Signal Reflection Profiles everywhere, which is essential to determine the optimal locations for picocell deployment. 

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2. Interference Analysis of Coexisting 5G Networks and NGSO FSS Receivers in the 12 GHz Band

   https://doi.org/10.1109/LWC.2023.3281769  

   Niloy, Ta-seen Reaz; Hassan, Zoheb; Stephenson, Nathan; Shah, Vijay K. (May 2023, IEEE Wireless Communications Letters)

   Despite the promising attributes of the 12 GHz band for expanding terrestrial 5G network’s capacity and coverage, interference between coexisting networks remains a major issue. This paper develops a simulation-based evaluation framework and investigates the harmful interference between the 5G radio links and incumbent fixed non-geostationary satellite orbit (NGSO) fixed satellite services (FSS) receivers of the 12 GHz band. A variety of features including actual deployment locations of 5G base stations (BSs) and fixed NGSO FSS receivers, industry standardized beamforming at BSs, directional signal reception at FSS receivers, realistic propagation channels with obstruction from buildings, and channel scheduling at 5G BSs are incorporated in the interference study. Simulation results conducted in a realistic urban-micro deployment scenario confirm that the terrestrial 5G networks with directional BSs can coexist in the 12GHz band by suitably selecting exclusion zone’s radius around the FSS receiver. Simulation results also show that interference in the coexisting network can be notably reduced by appropriately activating BSs in the 12 GHz band based on their locations and surroundings. 

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3. Analyzing Mutual Influences of Conventional and Distributed FACTS via Stochastic Co-optimization

   https://doi.org/10.1109/PMAPS.2018.8440367  

   Sang, Yuanrui; Sahraei-Ardakani, Mostafa (June 2018, 2018 IEEE International Conference on Probabilistic Methods Applied to Power Systems (PMAPS))

   Distributed flexible AC transmission systems (D-FACTS) is an attractive power flow control technology, featuring low cost and flexibility for re-deployment. Optimal allocation of D-FACTS and the mutual influence between existing FACTS and newly planned D-FACTS are challenging but important issues that need to be addressed. This paper proposes a co-optimization model of FACTS and D-FACTS based on stochastic optimization, considering the uncertainties caused by fluctuating load and renewable energy generation. Using this model, the location and set points of FACTS and D-FACTS can be co-optimized; in a system with existing FACTS, the locations of FACTS can be predetermined and the locations of D-FACTS can be optimized. The study shows that existing FACTS affects the optimal locations of D-FACTS and adding D-FACTS into the system affects the optimal set points of existing FACTS. Thus, it is essential to co-optimize the two technologies to maximize their economic benefits. 

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4. First Light Multi-Frequency Observations with a G-band radar

   Lamer, K.; Oue, M.; Battaglia, A.; Roy, R. J.; Cooper, K. B.; Dhillon, R.; and Kollias, P. (December 2020, Atmospheric measurement techniques)

   Kneifel, Stefan (Ed.)

   Observations collected during the 25-February-2020 deployment of the Vapor In-Cloud Profiling Radar at the Stony Brook Radar Observatory clearly demonstrate the potential of G-band radars for cloud and precipitation research, something that until now was only discussed in theory. The field experiment, which coordinated an X-, Ka, W- and G-band radar, revealed that the Ka-G pairing can generate differential reflectivity signal several decibels larger than the traditional Ka-W pairing underpinning an increased sensitivity to smaller amounts of liquid and ice water mass and sizes. The observations also showed that G-band signals experience non-Rayleigh scattering in regions where Ka- and W-band signal don’t, thus demonstrating the potential of G-band radars for sizing sub-millimeter ice crystals and droplets. Observed peculiar radar reflectivity patterns also suggest that G-band radars could be used to gain insight into the melting behavior of small ice crystals. G-band signal interpretation is challenging because attenuation and non-Rayleigh effects are typically intertwined. An ideal liquid-free period allowed us to use triple frequency Ka-W-G observations to test existing ice scattering libraries and the results raise questions on their comprehensiveness. Overall, this work reinforces the importance of deploying radars with 1) sensitivity sufficient to detect small Rayleigh scatters at cloud top in order to derive estimates of path integrated hydrometeor attenuation, a key constraint for microphysical retrievals, 2) sensitivity sufficient to overcome liquid attenuation, to reveal the larger differential signals generated from using G-band as part of a multifrequency deployment, and 3) capable of monitoring atmospheric gases to reduce related uncertainty 

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5. Rethinking Wireless Network Management Through Sensor-driven Contextual Analysis

   https://doi.org/10.1145/3376897.3377863  

   Irshad, Shazal; Rozner, Eric; Bhartia, Apurv; Chen, Bo (March 2020, HotMobile '20: Proceedings of the 21st International Workshop on Mobile Computing Systems and Applications)

   null (Ed.)

   Wireless network management is important to ensure the performance, utilization, allocation, and robustness of the network is optimized. Until now, wireless network management has typically been dictated by in-band information, such as wireless measurements, client locations, or even device state. This position paper explores fundamental new ways to manage the network by utilizing out-of-band data provided by a rich deployment of sensors. Out-of-band data can capture information about the users, objects, or environments associated with a network device, meaning that richer contextual policies can be implemented in the network. We propose an architecture called SenseNet, which builds upon three recent trends: (1) the massive deployment of sensors today, (2) the existence of deep-learning algorithms to glean meaningful insights from the sensory data, and (3) the provisioning of edge computing resources to provide real-time actuation of new sensor-based policies. A brief evaluation shows the feasibility and motivates SenseNet and afterwards challenges towards practical deployment are discussed. 

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