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1. Non-LTE radiative transfer with Turbospectrum

   https://doi.org/10.1051/0004-6361/202243673  

   Gerber, Jeffrey M.; Magg, Ekaterina; Plez, Bertrand; Bergemann, Maria; Heiter, Ulrike; Olander, Terese; Hoppe, Richard (January 2023, Astronomy & Astrophysics)

   Physically realistic models of stellar spectra are needed in a variety of astronomical studies, from the analysis of fundamental stellar parameters, to studies of exoplanets and stellar populations in galaxies. Here we present a new version of the widely used radiative transfer code Turbospectrum, which we update so that it is able to perform spectrum synthesis for lines of multiple chemical elements in non-local thermodynamic equilibrium (NLTE). We use the code in the analysis of metallicites and abundances of the Gaia FGK benchmark stars, using 1D MARCS atmospheric models and the averages of 3D radiation-hydrodynamics simulations of stellar surface convection. We show that the new more physically realistic models offer a better description of the observed data, and we make the program and the associated microphysics data publicly available, including grids of NLTE departure coefficients for H, O, Na, Mg, Si, Ca, Ti, Mn, Fe, Co, Ni, Sr, and Ba. 

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2. Energy-efficient LTE/Wi-Fi Coexistence

   https://doi.org/10.1109/ICC40277.2020.9149346  

   Han, Xiao; Samy, Islam; Lazos, Loukas (June 2020, IEEE International Conference on Communications)
3. Price Competition with LTE-U and WiFi

   https://doi.org/10.1109/INFOCOM.2019.8737549  

   Wang, Xu; Berry, Randall A. (April 2019, IEEE INFOCOM)

   LTE-U is an extension of the Long Term Evolution (LTE) standard for operation in unlicensed spectrum. LTE-U differs from WiFi, the predominant technology used in unlicensed spectrum in that it utilizes a duty cycle mode for accessing the spectrum and allows for a more seamless integration with LTE deployments in licensed spectrum. There have been a number of technical studies on the co-existence of LTE-U and WiFi in unlicensed spectrum In this paper, we instead investigate the impact of such a technology from an economic perspective. We consider a model in which an incumbent service provider (SP) deploys a duty cycle-based technology like LTE-U in an unlicensed band along with operating in a licensed band and competes with one or more entrants that only operate in the unlicensed band using a different technology like WiFi. We characterize the impact of a technology like LTE-U on the market outcome and show that the welfare impacts of this technology are subtle, depending in part on the amount of unlicensed spectrum and number of entrants. We also investigate the impact of the duty cycle and the portion of unlicensed spectrum used by the technology. 

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4. Market Competition Between LTE-U and WiFi

   https://doi.org/10.1109/TNSE.2021.3052168  

   Wang, Xu; A. Berry, Randall (January 2021, IEEE Transactions on Network Science and Engineering)

   null (Ed.)
5. Machine Learning Enhanced Channel Selection for Unlicensed LTE

   https://doi.org/10.1109/DySPAN.2019.8935859  

   Tonnemacher, Matthew; Tarver, Chance; Cavallar, Joseph; Camp, Joseph (November 2019, 2019 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN))

   We propose a mechanism for unlicensed LTE channel selection that not only takes into account interference to and from Wi-Fi access points but also considers other LTE operators in the unlicensed band. By collecting channel utilization statistics and sharing this information periodically with other unlicensed LTE eNBs, each eNB can improve their channel selection given their limited knowledge of the full topology. While comparing our algorithm to existing solutions, we find that the similarity between sensed Wi-Fi occupation at neighboring eNBs greatly impacts the performance of channel selection algorithms. To achieve better performance across diverse scenarios, we expand on our statistical channel selection formulation to include reinforcement learning, thereby balancing the shared contextual information with historical performance. We simulate operation in the unlicensed band using our channel selection algorithm and show how Wi-Fi load and inter-cell interference estimation can jointly be used to select transmission channels for all small cells in the network. Our approaches lead to an increase in user-perceived throughput and spectral efficiency across the entire band when compared to the greedy channel selection. 

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