An Energy-Aware Protocol for Self-Organizing Heterogeneous LTE Systems

This paper studies the problem of self-organizing heterogeneous LTE systems. We propose a model that jointly considers several important characteristics of heterogeneous LTE system, including the usage of orthogonal frequency division multiple access (OFDMA), the frequency-selective fading for each link, the interference among different links, and the different transmission capabilities of different types of base stations. We also consider the cost of energy by taking into account the power consumption, including that for wireless transmission and that for operation, of base stations and the price of energy. Based on this model, we aim to propose a distributed protocol that improves the spectrum efficiency of the system, which is measured in terms of the weighted proportional fairness among the throughputs of clients, and reduces the cost of energy. We identify that there are several important components involved in this problem. We propose distributed strategies for each of these components. Each of the proposed strategies requires small computational and communicational overheads. Moreover, the interactions between components are also considered in the proposed strategies. Hence, these strategies result in a solution that jointly considers all factors of heterogeneous LTE systems. Simulation results also show that our proposed strategies achieve much better performance than existing ones

Nature of the Quantum Phase Transition in Quantum Compass Model

In this work, we show that the quantum compass model on an square lattice can be mapped to a fermionic model with local density interaction. We introduce a mean-field approximation where the most important fluctuations, those perpendicular to the ordering direction, are taken into account exactly. It is found that the quantum phase transition point at $J_x=J_z$ marks a first order phase transition. We also show that the mean field result is robust against the remaining fluctuation corrections up to the second order.Comment: 7 pages, 10 fig

Spectral and optical properties in the antiphase stripe phase of the cuprate superconductors

We investigate the superconducting order parameter, the spectral and optical properties in a stripe model with spin (charge) domain-derived scattering potential $V_{s}$ ($V_{c}$). We show that the charge domain-derived scattering is less effective than the spin scattering on the suppression of superconductivity. For $V_{s}\gg V_{c}$, the spectral weight concentrates on the ($\pi,0$) antinodal region, and a finite energy peak appears in the optical conductivity with the disappearance of the Drude peak. But for $V_{s}\approx V_{c}$, the spectral weight concentrates on the ($\pi/2,\pi/2$) nodal region, and a residual Drude peak exists in the optical conductivity without the finite energy peak. These results consistently account for the divergent observations in the ARPES and optical conductivity experiments in several high-$T_c$ cuprates, and suggest that the "insulating" and "metallic" properties are intrinsic to the stripe state, depending on the relative strength of the spin and charge domain-derived scattering potentials.Comment: 7 pages, 4 figure

Efficient Volumetric Method of Moments for Modeling Plasmonic Thin-Film Solar Cells with Periodic Structures

Metallic nanoparticles (NPs) support localized surface plasmon resonances (LSPRs), which enable to concentrate sunlight at the active layer of solar cells. However, full-wave modeling of the plasmonic solar cells faces great challenges in terms of huge computational workload and bad matrix condition. It is tremendously difficult to accurately and efficiently simulate near-field multiple scattering effects from plasmonic NPs embedded into solar cells. In this work, a preconditioned volume integral equation (VIE) is proposed to model plasmonic organic solar cells (OSCs). The diagonal block preconditioner is applied to different material domains of the device structure. As a result, better convergence and higher computing efficiency are achieved. Moreover, the calculation is further accelerated by two-dimensional periodic Green's functions. Using the proposed method, the dependences of optical absorption on the wavelengths and incident angles are investigated. Angular responses of the plasmonic OSCs show the super-Lambertian absorption on the plasmon resonance but near-Lambertian absorption off the plasmon resonance. The volumetric method of moments and explored physical understanding are of great help to investigate the optical responses of OSCs.Comment: 11 pages, 6 figure

Determining the strange and antistrange quark distributions of the nucleon

The difference between the strange and antistrange quark distributions, \delta s(x)=s(x)-\sbar(x), and the combination of light quark sea and strange quark sea, \Delta (x)=\dbar(x)+\ubar(x)-s(x)-\sbar(x), are originated from non-perturbative processes, and can be calculated using non-perturbative models of the nucleon. We report calculations of $\delta s(x)$ and $\Delta(x)$ using the meson cloud model. Combining our calculations of $\Delta(x)$ with relatively well known light antiquark distributions obtained from global analysis of available experimental data, we estimate the total strange sea distributions of the nucleon.Comment: 4 pages, 3 figures; talk given by F.-G. at QNP0

Asset allocation with multiple analysts’ views: a robust approach

Retail investors often make decisions based on professional analysts’ investment recommendations. Although these recommendations contain up-to-date financial information, they are usually expressed in sophisticated but vague forms. In addition, the quality differs from analyst to analyst and recommendations may even be mutually conflicting. This paper addresses these issues by extending the Black–Litterman (BL) method and developing a multi-analyst portfolio selection method, balanced against any over-optimistic forecasts. Our methods accommodate analysts’ ambiguous investment recommendations and the heterogeneity of data from disparate sources. We prove the validity of our model, using an empirical analysis of around 1000 daily financial newsletters collected from two top 10 Taiwanese brokerage firms over a 2-year period. We conclude that analysts’ views contribute to the investment allocation process and enhance the portfolio performance. We confirm that the degree of investors’ confidence in these views influences the portfolio outcome, thus extending the idea of the BL model and improving the practicality of robust optimisation