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Virtual Reality (VR) applications require high data rate for a high-quality immersive experience, in addition to low latency to avoid dizziness and motion sickness. One of the key wireless VR challenges is providing seamless connectivity and meeting the stringent latency and bandwidth requirements. This work proposes a proactive wireless VR system that utilizes information about the user's future orientation for proactive scheduling and caching. This is achieved by leveraging deep neural networks to predict users' orientation trained on a real dataset. The 360{\deg} scene is then partitioned using an overlapping viewports scheme so that only portions of the scene covered by the users' perceptive field-of-view are streamed. Furthermore, to minimize the backhaul latency, popular viewports are cached at the edge cloud based on spatial popularity profiles. Through extensive simulations, we show that the proposed system provides significant latency and throughput performance improvement, especially in fluctuating channels and heavy load conditions. The proactive scheduling enabled by the combination of machine learning prediction and the proposed viewport scheme reduces the mean latency by more than 80% while achieving successful delivery rate close to 100%., Comment: 6 pages, 5 figures, accepted for presentation in the IEEE Globecom Workshops 2021

We propose a method for learning Markov network structures for continuous data without invoking any assumptions about the distribution of the variables. The method makes use of previous work on a non-parametric estimator for mutual information which is used to create a non-parametric test for multivariate conditional independence. This independence test is then combined with an efficient constraint-based algorithm for learning the graph structure. The performance of the method is evaluated on several synthetic data sets and it is shown to learn considerably more accurate structures than competing methods when the dependencies between the variables involve non-linearities.

In this paper propagation properties of a parallel-plate waveguide with tunable artificial impedance surfaces as sidewalls are studied both analytically and numerically. The impedance surfaces comprise an array of patches over a dielectric slab with embedded metallic vias. The tunability of surfaces is achieved with varactors. Simple design equations for tunable artificial impedance surfaces as well as dispersion equations for the TE and TM modes are presented. The propagation properties are studied in three different regimes: a multi-mode waveguide, a single-mode waveguide, and below-cutoff waveguide. The analytical results are verified with numerical simulations., Comment: 14 pages

This paper introduces simple analytical formulas for the grid impedance of electrically dense arrays of square patches and for the surface impedance of high-impedance surfaces based on the dense arrays of metal strips or square patches over ground planes. Emphasis is on the oblique-incidence excitation. The approach is based on the known analytical models for strip grids combined with the approximate Babinet principle for planar grids located at a dielectric interface. Analytical expressions for the surface impedance and reflection coefficient resulting from our analysis are thoroughly verified by full-wave simulations and compared with available data in open literature for particular cases. The results can be used in the design of various antennas and microwave or millimeter wave devices which use artificial impedance surfaces and artificial magnetic conductors (reflect-array antennas, tunable phase shifters, etc.), as well as for the derivation of accurate higher-order impedance boundary conditions for artificial (high-) impedance surfaces. As an example, the propagation properties of surface waves along the high-impedance surfaces are studied., Comment: 12 pages, 10 figures, submitted to IEEE Transactions on Antennas and Propagation