Guest Editorial: Nonlinear Optimization of Communication Systems

Linear programming and other classical optimization techniques have found important applications in communication systems for many decades. Recently, there has been a surge in research activities that utilize the latest developments in nonlinear optimization to tackle a much wider scope of work in the analysis and design of communication systems. These activities involve every “layer” of the protocol stack and the principles of layered network architecture itself, and have made intellectual and practical impacts significantly beyond the established frameworks of optimization of communication systems in the early 1990s. These recent results are driven by new demands in the areas of communications and networking, as well as new tools emerging from optimization theory. Such tools include the powerful theories and highly efficient computational algorithms for nonlinear convex optimization, together with global solution methods and relaxation techniques for nonconvex optimization

Influence of unsymmetrical periodicity on extraordinary transmission through periodic arrays of subwavelength holes

Quadrate hole array is explored to study the influence of unsymmetrical periodicity on extraordinary optical transmission through periodic arrays of subwavelength holes. It is found that the transmission efficiency of light and the ratio between transmission efficiencies of horizontal and vertical polarized light can be continuously tuned by rotating the quadrate hole array. We can calculate out the transmission spectra (including the heights and locations of peaks) for any rotation angle $\theta$ with a simple theoretical model.Comment: 6 pages, 5 figure

Division of labor, skill complementarity, and heterophily in socioeconomic networks

Constituents of complex systems interact with each other and self-organize to form complex networks. Empirical results show that the link formation process of many real networks follows either the global principle of popularity or the local principle of similarity or a tradeoff between the two. In particular, it has been shown that in social networks individuals exhibit significant homophily when choosing their collaborators. We demonstrate, however, that in populations in which there is a division of labor, skill complementarity is an important factor in the formation of socioeconomic networks and an individual's choice of collaborators is strongly affected by heterophily. We analyze 124 evolving virtual worlds of a popular "massively multiplayer online role-playing game" (MMORPG) in which people belong to three different professions and are allowed to work and interact with each other in a somewhat realistic manner. We find evidence of heterophily in the formation of collaboration networks, where people prefer to forge social ties with people who have professions different from their own. We then construct an economic model to quantify the heterophily by assuming that individuals in socioeconomic systems choose collaborators that are of maximum utility. The results of model calibration confirm the presence of heterophily. Both empirical analysis and model calibration show that the heterophilous feature is persistent along the evolution of virtual worlds. We also find that the degree of complementarity in virtual societies is positively correlated with their economic output. Our work sheds new light on the scientific research utility of virtual worlds for studying human behaviors in complex socioeconomic systems.Comment: 14 Latex pages + 3 figure

Multifractal detrending moving average cross-correlation analysis

There are a number of situations in which several signals are simultaneously recorded in complex systems, which exhibit long-term power-law cross-correlations. The multifractal detrended cross-correlation analysis (MF-DCCA) approaches can be used to quantify such cross-correlations, such as the MF-DCCA based on detrended fluctuation analysis (MF-X-DFA) method. We develop in this work a class of MF-DCCA algorithms based on the detrending moving average analysis, called MF-X-DMA. The performances of the MF-X-DMA algorithms are compared with the MF-X-DFA method by extensive numerical experiments on pairs of time series generated from bivariate fractional Brownian motions, two-component autoregressive fractionally integrated moving average processes and binomial measures, which have theoretical expressions of the multifractal nature. In all cases, the scaling exponents $h_{xy}$ extracted from the MF-X-DMA and MF-X-DFA algorithms are very close to the theoretical values. For bivariate fractional Brownian motions, the scaling exponent of the cross-correlation is independent of the cross-correlation coefficient between two time series and the MF-X-DFA and centered MF-X-DMA algorithms have comparative performance, which outperform the forward and backward MF-X-DMA algorithms. We apply these algorithms to the return time series of two stock market indexes and to their volatilities. For the returns, the centered MF-X-DMA algorithm gives the best estimates of $h_{xy}(q)$ since its $h_{xy}(2)$ is closest to 0.5 as expected, and the MF-X-DFA algorithm has the second best performance. For the volatilities, the forward and backward MF-X-DMA algorithms give similar results, while the centered MF-X-DMA and the MF-X-DFA algorithms fails to extract rational multifractal nature.Comment: 15 pages, 4 figures, 2 matlab codes for MF-X-DMA and MF-X-DF