Search

Non-orthogonal multiple access (NOMA) has been deemed a promising technology to achieve high spectral efficiency. This paper investigates transmit power optimization for a multi-user multiple-input single-output (MISO) NOMA downlink system endowed with energy harvesting and physical layer security. More specifically, the system is designed to satisfy the data-link security requirements of its legitimate users while charging energy harvesting devices. To achieve that goal, we investigate a joint design on beamforming vectors and artificial noise (AN) covariance matrix. Then, an iterative difference-of-two-convex-function (D.C.) algorithm is proposed by applying the successive convex approximation (SCA) approach. To further reduce the computational complexity, a sub-optimal alternating D.C. (ADC) algorithm is devised by decoupling beamforming and mapping the AN to the null space of legitimate users. In the ADC algorithm, optimal null space and beamforming vectors are alternately solved by convex programming. Furthermore, the convergence of the proposed D.C. and ADC algorithms is derived. Extensive simulation results show that the two proposed AN-aided algorithms significantly reduce the transmit power cost over OMA and NOMA scheme without AN, especially when there are more eavesdroppers. [ABSTRACT FROM AUTHOR]

We present and discuss the use of a high‐dimensional computational method for atmospheric inversions that incorporates the space‐time structure of transport and dispersion errors. In urban environments, transport and dispersion errors are largely the result of our inability to capture the true underlying transport of greenhouse gas (GHG) emissions to observational sites. Motivated by the impact of transport model error on estimates of fluxes of GHGs using in situ tower‐based mole‐fraction observations, we specifically address the need to characterize transport error structures in high‐resolution large‐scale inversion models. We do this using parametric covariance functions combined with shrinkage‐based regularization methods within an Ensemble Transform Kalman Filter inversion setup. We devise a synthetic data experiment to compare the impact of transport and dispersion error component of the model‐data mismatch covariance choices on flux retrievals and study the robustness of the method with respect to fewer observational constraints. We demonstrate the analysis in the context of inferring CO2 fluxes starting with a hypothesized prior in the Washington D.C. /Baltimore area constrained by a synthetic set of tower‐based CO2 measurements within an observing system simulation experiment framework. This study demonstrates the ability of these simple covariance structures to substantially improve the estimation of fluxes over standard covariance models in flux estimation from urban regions. Plain Language Summary: Top‐down inversion approaches use atmospheric observations of greenhouse gases (GHG) to trace back emissions using atmospheric transport and dispersion models. Due to the high‐resolution, complex meteorology and topography of urban‐scale, transport and dispersion models are never accurate and the errors are correlated in space and time. It is therefore important to properly account for that in atmospheric inversions that estimate GHG emissions in urban regions. This study proposes multiple methods to characterize correlated error covariance matrices that demonstrate better performance than ubiquitous choices in a synthetic data experiment. These simple data‐adaptive covariance structures perform better than standard parametric models in both recovering total fluxes and their spatial distributions. Key Points: Urban‐scale transport errors are correlated in space and time, which should be included in atmospheric inversions that estimate greenhouse gas emissionsIn a synthetic data study, multiple methods to characterize correlated errors with model‐data covariance matrices demonstrate a better performanceOverall, dynamically adaptive covariance structures perform better than standard parametric models in both recovering total fluxes and their spatial distributions [ABSTRACT FROM AUTHOR]