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A Survey on Verification and Validation, Testing and Evaluations of Neurosymbolic Artificial Intelligence

https://doi.org/10.1109/TAI.2024.3351798

Renkhoff, Justus; Feng, Ke; Meier-Doernberg, Marc; Velasquez, Alvaro; Song, Houbing Herbert (January 2024, IEEE Transactions on Artificial Intelligence)

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A Graph-Analytic Approach to Dynamic Airspace Configuration

https://doi.org/10.1109/IRI58017.2023.00048

Feng, Ke; Liu, Dahai; Yongxin Liu; Liu, Hong; Song, Houbin (August 2023, IEEE Computer Society Conference Publishing Services (CPS) http://www.computer.org/cps)
Cristina Ceballos (Ed.)
The current National Airspace System (NAS) is reaching capacity due to increased air traffic, and is based on outdated pre-tactical planning. This study proposes a more dynamic airspace configuration (DAC) approach that could increase throughput and accommodate fluctuating traffic, ideal for emergencies. The proposed approach constructs the airspace as a constraints-embedded graph, compresses its dimensions, and applies a spectral clustering-enabled adaptive algorithm to generate collaborative airport groups and evenly distribute workloads among them. Under various traffic conditions, our experiments demonstrate a 50% reduction in workload imbalances. This research could ultimately form the basis for a recommendation system for optimized airspace configuration. Code available at https://github.com/KeFenge2022/GraphDAC.git
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Joint Spatial-Propagation Modeling of Cellular Networks Based on the Directional Radii of Poisson Voronoi Cells

https://doi.org/10.1109/TWC.2020.3048646

Feng, Ke; Haenggi, Martin (May 2021, IEEE Transactions on Wireless Communications)
null (Ed.)
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The SINR Meta Distribution in Poisson Cellular Networks

https://doi.org/10.1109/LWC.2021.3068321

Feng, Ke; Haenggi, Martin (April 2021, IEEE Wireless Communications Letters)
null (Ed.)
This work studies the signal-to-interference-plus-noise ratio (SINR) meta distribution (MD) in cellular networks with a focus on the Poisson model. Firstly, we show that for stationary base station point processes, arbitrary fading, and power-law path loss with exponent α, the base station density λ and the noise power σ2 impact the SINR MD only through ηλα/2/σ2, termed the network signal-to-noise ratio (NSNR). Next, we show that for Poisson cellular networks, the SINR MD can be written as g(x)θ-2/α when the target SINR θ and the target reliability x jointly satisfy a constraint. We derive this constraint and the integral of g(x). Lastly, we discuss several extensions of the results to more general models and architectures.
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