No abstract

Cognitive radio is a promising technology to improve spectral efficiency. However, the secure performance of a secondary network achieved by using physical layer security techniques is limited by its transmit power and channel fading. In order to tackle this issue, a cognitive unmanned aerial vehicle (UAV) communication network is studied by exploiting the high flexibility of a UAV and the possibility of establishing line-ofsight links. The average secrecy rate of the secondary network is maximized by robustly optimizing the UAV's trajectory and transmit power. Our problem formulation takes into account two practical inaccurate location estimation cases, namely, the worst case and the outage-constrained case. In order to solve those challenging non-convex problems, an iterative algorithm based on S-Procedure is proposed for the worst case while an iterative algorithm based on Bernstein-type inequalities is proposed for the outage-constrained case. The proposed algorithms can obtain effective suboptimal solutions of the corresponding problems. Our simulation results demonstrate that the algorithm under the outage-constrained case can achieve a higher average secrecy rate with a low computational complexity compared to that of the algorithm under the worst case. Moreover, the proposed schemes can improve the secure communication performance significantly compared to other benchmark schemes.

The performance of cellular system significantly depends on its network topology, where the spatial deployment of base stations (BSs) plays a key role in the downlink scenario. Moreover, cellular networks are undergoing a heterogeneous evolution, which introduces unplanned deployment of smaller BSs, thus complicating the performance evaluation even further. In this paper, based on large amount of real BS locations data, we present a comprehensive analysis on the spatial modeling of cellular network structure. Unlike the related works, we divide the BSs into different subsets according to geographical factor (e.g. urban or rural) and functional type (e.g. macrocells or microcells), and perform detailed spatial analysis to each subset. After examining the accuracy of Poisson point process (PPP) in BS locations modeling, we take into account the Gibbs point processes as well as Neyman-Scott point processes and compare their accuracy in view of large-scale modeling test. Finally, we declare the inaccuracy of the PPP model, and reveal the general clustering nature of BSs deployment, which distinctly violates the traditional assumption. This paper carries out a first largescale identification regarding available literatures, and provides more realistic and more general results to contribute to the performance analysis for the forthcoming heterogeneous cellular networks.

We present a flexible general-purpose reservoir simulation framework based on Automatic Differentiation (AD). The new AD-based simulator supports unstructured grids, employs a generalized Multi-Point Flux Approximation (MPFA) for spatial discretization, and uses a multi-level Adaptive Implicit Method (AIM) for time discretization. Given the discrete form of the governing nonlinear residual equations and a declaration of the independent variables, the AD library employs advanced expression templates with block data-structures to automatically generate compact computer code for the Jacobian matrix. Test results indicate that the construction of the Jacobian matrix with MPFA is efficient, and the overhead associated with treating a two-point flux approximation (TPFA) as a special case of MPFA is negligible. Our AIM implementation is designed to facilitate a systematic application of the method to new fluid models and variable formulations. The AD simulator allows for any combination of TPFA (Two-Point Flux Approximation), MPFA, FIM (Fully Implicit Method), and AIM. The generic and modular design is amenable to extension, both in terms of modeling additional flow processes and implementing new numerical methods. The AD-based modeling capability is demonstrated for highly nonlinear compositional problems using challenging large-scale reservoir models that include full-tensor permeability fields and non-orthogonal grids. The behaviors of TPFA and several MPFA schemes are analyzed for both FIM and AIM simulations. The implications of using MPFA and AIM on both the nonlinear and linear solvers are discussed and analyzed.

In order to improve the energy efficiency and resource management of cellular networks, traffic modeling and prediction has been focused in recent years. In this paper, we take advantage of entropy theory to explore the limits of predictability of cellular network traffic based on large amount of traffic dataset gathered from real cellular network in China. By categorizing traffic according to voice, text and data group, we investigate random entropy of each type of traffic, as well as conditional entropy by temporal, spatial and service related information.Our key findings are that (1) traffic can be well predicted by preceding 15 hours traffic, (2) voice traffic has so close similarity to text traffic in the same cell that we can use one of them to predict the other, (3) knowledge of adjacent cells traffic can enhance the predictability of voice and text more than data. Considering the large amount traffic dataset which contains thousands of base stations and billions of records, the impact of dataset pre-processing, quantization and time resolution are also taken into account and are discussed. Moreover, macroscopic view of entropy distribution is presented by geo-location markers.