More Like this

1. Amortized channel divergence for asymptotic quantum channel discrimination

   https://doi.org/10.1007/s11005-020-01297-7  

   Wilde, Mark M.; Berta, Mario; Hirche, Christoph; Kaur, Eneet (June 2020, Letters in Mathematical Physics)
2. Towards Massive MIMO Channel Emulation: Channel Accuracy Versus Implementation Resources

   https://doi.org/10.1109/TVT.2020.2980583  

   Huang, Pengda; Tonnemacher, Matthew Jordan; Du, Yongjiu; Rajan, Dinesh; Camp, Joseph (May 2020, IEEE Transactions on Vehicular Technology)
3. Competitive Channel-Capacity

   https://doi.org/10.1109/ISIT54713.2023.10206801  

   Langberg, Michael; Sabag, Oron (June 2023, IEEE International Symposium on Information Theory (ISIT), 2023.)
4. Turbulent Channel Flow

   https://doi.org/10.7281/T10K26QW  

   Graham, Jason; Lee, MyoungKyu; Malaya, Nicholas; D., Robert Moser; Eyink, Gregory; Meneveau, Charles; Kanov, Kalin; Burns, Randal; Szalay, Alex; staff, IDIES (January 2017, Johns Hopkins Turbulence Databases)

   The turbulent channel flow database is produced from a direct numerical simulation (DNS) of wall bounded flow with periodic boundary conditions in the longitudinal and transverse directions, and no-slip conditions at the top and bottom walls. In the simulation, the Navier-Stokes equations are solved using a wall {normal, velocity {vorticity formulation. Solutions to the governing equations are provided using a Fourier-Galerkin pseudo-spectral method for the longitudinal and transverse directions and seventh-order Basis-splines (B-splines) collocation method in the wall normal direction. De-aliasing is performed using the 3/2-rule [3]. Temporal integration is performed using a low-storage, third-order Runge-Kutta method. Initially, the flow is driven using a constant volume flux control (imposing a bulk channel mean velocity of U = 1) until stationary conditions are reached. Then the control is changed to a constant applied mean pressure gradient forcing term equivalent to the shear stress resulting from the prior steps. Additional iterations are then performed to further achieve statistical stationarity before outputting fields. 

   more » « less
5. Physical Layer Security: Channel Sounding Results for the Multi-Antenna Wiretap Channel

   https://doi.org/10.3390/e25101397  

   Harman, Daniel; Knapp, Karl; Sweat, Tyler; Lundrigan, Philip; Rice, Michael; Harrison, Willie (October 2023, Entropy)

   Many physical-layer security works in the literature rely on purely theoretical work or simulated results to establish the value of physical-layer security in securing communications. We consider the secrecy capacity of a wireless Gaussian wiretap channel using channel sounding measurements to analyze the potential for secure communication in a real-world scenario. A multi-input, multi-output, multi-eavesdropper (MIMOME) system is deployed using orthogonal frequency division multiplexing (OFDM) over an 802.11n wireless network. Channel state information (CSI) measurements were taken in an indoor environment to analyze time-varying scenarios and spatial variations. It is shown that secrecy capacity is highly affected by environmental changes, such as foot traffic, network congestion, and propagation characteristics of the physical environment. We also present a numerical method for calculating MIMOME secrecy capacity in general and comment on the use of OFDM with regard to calculating secrecy capacity. 

   more » « less