Low-Latency Millimeter-Wave Communications: Traffic Dispersion or Network Densification?

This paper investigates two strategies to reduce the communication delay in future wireless networks: traffic dispersion and network densification. A hybrid scheme that combines these two strategies is also considered. The probabilistic delay and effective capacity are used to evaluate performance. For probabilistic delay, the violation probability of delay, i.e., the probability that the delay exceeds a given tolerance level, is characterized in terms of upper bounds, which are derived by applying stochastic network calculus theory. In addition, to characterize the maximum affordable arrival traffic for mmWave systems, the effective capacity, i.e., the service capability with a given quality-of-service (QoS) requirement, is studied. The derived bounds on the probabilistic delay and effective capacity are validated through simulations. These numerical results show that, for a given average system gain, traffic dispersion, network densification, and the hybrid scheme exhibit different potentials to reduce the end-to-end communication delay. For instance, traffic dispersion outperforms network densification, given high average system gain and arrival rate, while it could be the worst option, otherwise. Furthermore, it is revealed that, increasing the number of independent paths and/or relay density is always beneficial, while the performance gain is related to the arrival rate and average system gain, jointly. Therefore, a proper transmission scheme should be selected to optimize the delay performance, according to the given conditions on arrival traffic and system service capability

Kinematic properties of the dual AGN system J0038+4128 based on long-slit spectroscopy

The study of kiloparsec-scale dual active galactic nuclei (AGN) will provide important clues to understand the co-evolution between the host galaxies and their central supermassive black holes undergoing a merging process. We present long-slit spectroscopy of the J0038$+$4128, a kiloparsec-scale dual AGN candidate discovered by Huang et al. recently, using the Yunnan Faint Object Spectrograph and Camera (YFOSC) mounted on Li-Jiang 2.4-m telescope at Yunnan observatories. From the long-slit spectra, we find that the average relative line-of-sight (LOS) velocity between the two nuclei (J0038$+$4128N and J0038$+$4128S) is about 150 km s$^{-1}$. The LOS velocities of the emission lines from the gas ionized by the nuclei activities and of the absorption lines from stars governed by the host galaxies for different regions of the J0038$+$4128 exhibit the same trend. The same velocities trend indicates that the gaseous disks are co-rotating with the stellar disks in this ongoing merge system. We also find several knots/giant HII regions scattered around the two nuclei with strong star formation revealed by the observed line ratios from the spectra. Those regions are also detected clearly in HST $F336W/U$-band and HST $F555W/V$-band images.Comment: 12 pages, 5 figures, 3 tables, Research in Astronomy and Astrophysics accepte

Convolutional Graph-Tensor Net for Graph Data Completion

Graph data completion is a fundamentally important issue as data generally has a graph structure, e.g., social networks, recommendation systems, and the Internet of Things. We consider a graph where each node has a data matrix, represented as a \textit{graph-tensor} by stacking the data matrices in the third dimension. In this paper, we propose a \textit{Convolutional Graph-Tensor Net} (\textit{Conv GT-Net}) for the graph data completion problem, which uses deep neural networks to learn the general transform of graph-tensors. The experimental results on the ego-Facebook data sets show that the proposed \textit{Conv GT-Net} achieves significant improvements on both completion accuracy (50\% higher) and completion speed (3.6x $\sim$ 8.1x faster) over the existing algorithms