Optimal Deployments of UAVs With Directional Antennas for a Power-Efficient Coverage

To provide a reliable wireless uplink for users in a given ground area, one can deploy Unmanned Aerial Vehicles (UAVs) as base stations (BSs). In another application, one can use UAVs to collect data from sensors on the ground. For a power-efficient and scalable deployment of such flying BSs, directional antennas can be utilized to efficiently cover arbitrary 2-D ground areas. We consider a large-scale wireless path-loss model with a realistic angle-dependent radiation pattern for the directional antennas. Based on such a model, we determine the optimal 3-D deployment of N UAVs to minimize the average transmit-power consumption of the users in a given target area. The users are assumed to have identical transmitters with ideal omnidirectional antennas and the UAVs have identical directional antennas with given half-power beamwidth (HPBW) and symmetric radiation pattern along the vertical axis. For uniformly distributed ground users, we show that the UAVs have to share a common flight height in an optimal power-efficient deployment. We also derive in closed-form the asymptotic optimal common flight height of $N$ UAVs in terms of the area size, data-rate, bandwidth, HPBW, and path-loss exponent

Deployment Strategies of Multiple Aerial BSs for User Coverage and Power Efficiency Maximization

Unmanned aerial vehicle (UAV) based aerial base stations (BSs) can provide rapid communication services to ground users and are thus promising for future communication systems. In this paper, we consider a scenario where no functional terrestrial BSs are available and the aim is deploying multiple aerial BSs to cover a maximum number of users within a certain target area. To this end, we first propose a naive successive deployment method, which converts the non-convex constraints in the involved optimization into a combination of linear constraints through geometrical relaxation. Then we investigate a deployment method based on K-means clustering. The method divides the target area into K convex subareas, where within each subarea, a mixed integer non-linear problem (MINLP) is solved. An iterative power efficient technique is further proposed to improve coverage probability with reduced power. Finally, we propose a robust technique for compensating the loss of coverage probability in the existence of inaccurate user location information (ULI). Our simulation results show that, the proposed techniques achieve an up to 30% higher coverage probability when users are not distributed uniformly. In addition, the proposed simultaneous deployment techniques, especially the one using iterative algorithm improve power-efficiency by up to 15% compared to the benchmark circle packing theory

SISSO: a compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates

The lack of reliable methods for identifying descriptors - the sets of parameters capturing the underlying mechanisms of a materials property - is one of the key factors hindering efficient materials development. Here, we propose a systematic approach for discovering descriptors for materials properties, within the framework of compressed-sensing based dimensionality reduction. SISSO (sure independence screening and sparsifying operator) tackles immense and correlated features spaces, and converges to the optimal solution from a combination of features relevant to the materials' property of interest. In addition, SISSO gives stable results also with small training sets. The methodology is benchmarked with the quantitative prediction of the ground-state enthalpies of octet binary materials (using ab initio data) and applied to the showcase example of predicting the metal/insulator classification of binaries (with experimental data). Accurate, predictive models are found in both cases. For the metal-insulator classification model, the predictive capability are tested beyond the training data: It rediscovers the available pressure-induced insulator->metal transitions and it allows for the prediction of yet unknown transition candidates, ripe for experimental validation. As a step forward with respect to previous model-identification methods, SISSO can become an effective tool for automatic materials development.Comment: 11 pages, 5 figures, in press in Phys. Rev. Material

Some applications of continuous variable neighbourhood search metaheuristic (mathematical modelling)

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.In the real world, many problems are continuous in nature. In some cases, finding the global solutions for these problems is di±cult. The reason is that the problem's objective function is non convex, nor concave and even not differentiable. Tackling these problems is often computationally too expensive. Although the development in computer technologies are increasing the speed of computations, this often is not adequate, particularly if the size of the problem's instance are large. Applying exact methods on some problems may necessitate their linearisation. Several new ideas using heuristic approaches have been considered particularly since they tackle the problems within reasonable computational time and give an approximate solution. In this thesis, the variable neighbourhood search (VNS) metaheuristic (the framework for building heuristic) has been considered. Two variants of variable neighbourhood search metaheuristic have been developed, continuous variable neighbourhood search and reformulation descent variable neighbourhood search. The GLOB-VNS software (Drazic et al., 2006) hybridises the Microsoft Visual Studio C++ solver with variable neighbourhood search metaheuristics. It has been used as a starting point for this research and then adapted and modified for problems studied in this thesis. In fact, two problems have been considered, censored quantile regression and the circle packing problem. The results of this approach for censored quantile regression outperforms other methods described in the literature, and the near-optimal solutions are obtained in short running computational time. In addition, the reformulation descent variable neighbourhood search variant in solving circle packing problems is developed and the computational results are provided

Wall-Fluid and Liquid-Gas Interfaces of Model Colloid-Polymer Mixtures by Simulation and Theory

We perform a study of the interfacial properties of a model suspension of hard sphere colloids with diameter $\sigma_c$ and non-adsorbing ideal polymer coils with diameter $\sigma_p$. For the mixture in contact with a planar hard wall, we obtain from simulations the wall-fluid interfacial free energy, $\gamma_{wf}$, for size ratios $q=\sigma_p/\sigma_c=0.6$ and 1, using thermodynamic integration, and study the (excess) adsorption of colloids, $\Gamma_c$, and of polymers, $\Gamma_p$, at the hard wall. The interfacial tension of the free liquid-gas interface, $\gamma_{lg}$, is obtained following three different routes in simulations: i) from studying the system size dependence of the interfacial width according to the predictions of capillary wave theory, ii) from the probability distribution of the colloid density at coexistence in the grand canonical ensemble, and iii) for statepoints where the colloidal liquid wets the wall completely, from Young's equation relating $\gamma_{lg}$ to the difference of wall-liquid and wall-gas interfacial tensions, $\gamma_{wl}-\gamma_{wg}$. In addition, we calculate $\gamma_{wf}, \Gamma_c$, and $\Gamma_p$ using density functional theory and a scaled particle theory based on free volume theory. Good agreement is found between the simulation results and those from density functional theory, while the results from scaled particle theory quantitatively deviate but reproduce some essential features. Simulation results for $\gamma_{lg}$ obtained from the three different routes are all in good agreement. Density functional theory predicts $\gamma_{lg}$ with good accuracy for high polymer reservoir packing fractions, but yields deviations from the simulation results close to the critical point.Comment: 23 pages, 10 figures, REVTEX. Fig 5a changed. Final versio

A column of grains in the jamming limit: glassy dynamics in the compaction process

We investigate a stochastic model describing a column of grains in the jamming limit, in the presence of a low vibrational intensity. The key control parameter of the model, $\epsilon$, is a representation of granular shape, related to the reduced void space. Regularity and irregularity in grain shapes, respectively corresponding to rational and irrational values of $\epsilon$, are shown to be centrally important in determining the statics and dynamics of the compaction process.Comment: 29 pages, 14 figures, 1 table. Various minor changes and updates. To appear in EPJ

An Evolutionary Algorithm for solving the Two-Dimensional Irregular Shape Packing Problem combined with the Knapsack Problem

This work presents an evolutionary algorithm to solve a joint problem of the Packing Problem and the Knapsack Problem, where the objective is to place items (with shape, value and weight) in a container (defined by its shape and capacity), maximizing the container's value, without intersections