Distributed Interference-Aware Energy-Efficient Resource Allocation for Device-to-Device Communications Underlaying Cellular Networks

The introduction of device-to-device (D2D) into cellular networks poses many new challenges in the resource allocation design due to the co-channel interference caused by spectrum reuse and limited battery life of user equipments (UEs). In this paper, we propose a distributed interference-aware energy-efficient resource allocation algorithm to maximize each UE's energy efficiency (EE) subject to its specific quality of service (QoS) and maximum transmission power constraints. We model the resource allocation problem as a noncooperative game, in which each player is self-interested and wants to maximize its own EE. The formulated EE maximization problem is a non-convex problem and is transformed into a convex optimization problem by exploiting the properties of the nonlinear fractional programming. An iterative optimization algorithm is proposed and verified through computer simulations.Comment: 6 pages, 3 figures, IEEE GLOBECOM 201

Energy Efficiency and Spectral Efficiency Tradeoff in Device-to-Device (D2D) Communications

In this letter, we investigate the tradeoff between energy efficiency (EE) and spectral efficiency (SE) in device-to-device (D2D) communications underlaying cellular networks with uplink channel reuse. The resource allocation problem is modeled as a noncooperative game, in which each user equipment (UE) is self-interested and wants to maximize its own EE. Given the SE requirement and maximum transmission power constraints, a distributed energy-efficient resource allocation algorithm is proposed by exploiting the properties of the nonlinear fractional programming. The relationships between the EE and SE tradeoff of the proposed algorithm and system parameters are analyzed and verified through computer simulations.Comment: 8 pages, 6 figures, long version paper of IEEE Wireless Communications Letters, accepted for publication. arXiv admin note: text overlap with arXiv:1405.196

Understanding whole-body inter-personal dynamics between two players using neural Granger causality as the explainable AI (XAI)

Background: Simultaneously focusing on intra- and inter-individual body dynamics and elucidating how these affect each other will help understand human inter-personal coordination behavior. However, this association has not been investigated previously owing to difficulties in analyzing complex causal relations among several body components.To address this issue, this study proposes a new analytical framework that attempts to understand the underlying causal structures behind each joint movement of individual baseball players using neural Granger causality (NGC) as the explainable AI. Methods: In the NGC analysis, causal relationships were defined as the size of the weight parameters of the first layer of a machine-learning model trained to predict the future state of a specific time-series variable. To verify the approach in a practical context, we conducted an experiment with 16 pairs of expert baseball pitchers and batters; input datasets with 27 joint resultant velocity data (joints of 13 pitchers and 14 batters) were generated and used for model training.Results: NGC analysis revealed significant causal relations among intra- and inter-individual body components such as the batter's hands having a causal effect from the pitcher's throwing arm. Remarkably, although the causality from the batter's body to pitcher's body is much lower than the reverse, it is significantly correlated with batter performance outcomes. Conclusions: The above results suggest the effectiveness of NGC analysis for understanding whole-body inter-personal coordination dynamics and that of the AI technique as a new approach for analyzing complex human behavior from a different perspective than conventional techniques.Comment: 35 pages (including 6 supporting information), 9 figures, 1 tabl

Zic2 and Zic3 synergistically control neurulation and segmentation of paraxial mesoderm in mouse embryo

AbstractZic family zinc-finger proteins play various roles in animal development. In mice, five Zic genes (Zic1–5) have been reported. Despite the partly overlapping expression profiles of these genes, mouse mutants for each Zic show distinct phenotypes. To uncover possible redundant roles, we characterized Zic2/Zic3 compound mutant mice. Zic2 and Zic3 are both expressed in presomitic mesoderm, forming and newly generated somites with differential spatiotemporal accentuation. Mice heterozygous for the hypomorphic Zic2 allele together with null Zic3 allele generally showed severe malformations of the axial skeleton, including asymmetric or rostro-caudally bridged vertebrae, and reduction of the number of caudal vertebral bones, that are not obvious in single mutants. These defects were preceded by perturbed somitic marker expression, and reduced paraxial mesoderm progenitors in the primitive streak. These results suggest that Zic2 and Zic3 cooperatively control the segmentation of paraxial mesoderm at multiple stages. In addition to the segmentation abnormality, the compound mutant also showed neural tube defects that ran the entire rostro-caudal extent (craniorachischisis), suggesting that neurulation is another developmental process where Zic2 and Zic3 have redundant functions

A Green TDMA Scheduling Algorithm for Prolonging Lifetime in Wireless Sensor Networks

Fast data collection is one of the most important research issues for Wireless Sensor Networks (WSNs). In this paper, a TMDA based energy consumption balancing algorithm is proposed for the general k-hop WSNs, where one data packet is collected in one cycle. The optimal k that achieves the longest network life is obtained through our theoretical analysis. Required time slots, maximum energy consumption and residual network energy are all thoroughly analyzed in this paper. Theoretical analysis and simulation results demonstrate the effectiveness of the proposed algorithm in terms of energy efficiency and time slot scheduling

Big Data Analysis-based Security Situational Awareness for Smart Grid

Advanced communications and data processing technologies bring great benefits to the smart grid. However, cyber-security threats also extend from the information system to the smart grid. The existing security works for smart grid focus on traditional protection and detection methods. However, a lot of threats occur in a very short time and overlooked by exiting security components. These threats usually have huge impacts on smart gird and disturb its normal operation. Moreover, it is too late to take action to defend against the threats once they are detected, and damages could be difficult to repair. To address this issue, this paper proposes a security situational awareness mechanism based on the analysis of big data in the smart grid. Fuzzy cluster based analytical method, game theory and reinforcement learning are integrated seamlessly to perform the security situational analysis for the smart grid. The simulation and experimental results show the advantages of our scheme in terms of high efficiency and low error rate for security situational awareness