Connectivity Analysis of Millimeter-Wave Device-to-Device Networks with Blockage

We consider device-to-device (D2D) communications in millimeter-wave (mm Wave) for the future fifth generation (5G) cellular networks. While the mm Wave systems can support multiple D2D pairs simultaneously through beamforming with highly directional antenna arrays, the mm Wave channel is significantly more susceptible to blockage compared to microwave; mm Wave channel studies indicate that if line-of-sight (LoS) paths are blocked, reliable mm Wave communications may not be achieved for high data-rate applications. Therefore, assuming that an outage occurs in the absence of the LoS path between two wireless devices by obstructions, we focus on connectivity of the mm Wave D2D networks. We consider two types of D2D communications: direct and indirect schemes. The connectivity performances of the two schemes are investigated in terms of (i) the probability to achieve a fully connected network FC and (ii) the average number of reliably connected devices . Through analysis and simulation, we show that, as the network size increases, FC and decrease. Also, FC and decrease, when the blockage parameter increases. Moreover, simulation results indicate that the hybrid direct and indirect scheme can improve both FC and up to about 35% compared to the nonhybrid scheme

An Introduction to Rule-based Modeling of Immune Receptor Signaling

Cells process external and internal signals through chemical interactions. Cells that constitute the immune system (e.g., antigen presenting cell, T-cell, B-cell, mast cell) can have different functions (e.g., adaptive memory, inflammatory response) depending on the type and number of receptor molecules on the cell surface and the specific intracellular signaling pathways activated by those receptors. Explicitly modeling and simulating kinetic interactions between molecules allows us to pose questions about the dynamics of a signaling network under various conditions. However, the application of chemical kinetics to biochemical signaling systems has been limited by the complexity of the systems under consideration. Rule-based modeling (BioNetGen, Kappa, Simmune, PySB) is an approach to address this complexity. In this chapter, by application to the Fc$\varepsilon$RI receptor system, we will explore the origins of complexity in macromolecular interactions, show how rule-based modeling can be used to address complexity, and demonstrate how to build a model in the BioNetGen framework. Open source BioNetGen software and documentation are available at http://bionetgen.org.Comment: 5 figure

Cooperation in the snowdrift game on directed small-world networks under self-questioning and noisy conditions

Cooperation in the evolutionary snowdrift game with a self-questioning updating mechanism is studied on annealed and quenched small-world networks with directed couplings. Around the payoff parameter value $r=0.5$, we find a size-invariant symmetrical cooperation effect. While generally suppressing cooperation for $r>0.5$ payoffs, rewired networks facilitated cooperative behavior for $r<0.5$. Fair amounts of noise were found to break the observed symmetry and further weaken cooperation at relatively large values of $r$. However, in the absence of noise, the self-questioning mechanism recovers symmetrical behavior and elevates altruism even under large-reward conditions. Our results suggest that an updating mechanism of this type is necessary to stabilize cooperation in a spatially structured environment which is otherwise detrimental to cooperative behavior, especially at high cost-to-benefit ratios. Additionally, we employ component and local stability analyses to better understand the nature of the manifested dynamics.Comment: 7 pages, 6 figures, 1 tabl

Advancing functional connectivity research from association to causation

Cognition and behavior emerge from brain network interactions, such that investigating causal interactions should be central to the study of brain function. Approaches that characterize statistical associations among neural time series-functional connectivity (FC) methods-are likely a good starting point for estimating brain network interactions. Yet only a subset of FC methods ('effective connectivity') is explicitly designed to infer causal interactions from statistical associations. Here we incorporate best practices from diverse areas of FC research to illustrate how FC methods can be refined to improve inferences about neural mechanisms, with properties of causal neural interactions as a common ontology to facilitate cumulative progress across FC approaches. We further demonstrate how the most common FC measures (correlation and coherence) reduce the set of likely causal models, facilitating causal inferences despite major limitations. Alternative FC measures are suggested to immediately start improving causal inferences beyond these common FC measures