40 research outputs found
Joint and Competitive Caching Designs in Large-Scale Multi-Tier Wireless Multicasting Networks
Caching and multicasting are two promising methods to support massive content
delivery in multi-tier wireless networks. In this paper, we consider a random
caching and multicasting scheme with caching distributions in the two tiers as
design parameters, to achieve efficient content dissemination in a two-tier
large-scale cache-enabled wireless multicasting network. First, we derive
tractable expressions for the successful transmission probabilities in the
general region as well as the high SNR and high user density region,
respectively, utilizing tools from stochastic geometry. Then, for the case of a
single operator for the two tiers, we formulate the optimal joint caching
design problem to maximize the successful transmission probability in the
asymptotic region, which is nonconvex in general. By using the block successive
approximate optimization technique, we develop an iterative algorithm, which is
shown to converge to a stationary point. Next, for the case of two different
operators, one for each tier, we formulate the competitive caching design game
where each tier maximizes its successful transmission probability in the
asymptotic region. We show that the game has a unique Nash equilibrium (NE) and
develop an iterative algorithm, which is shown to converge to the NE under a
mild condition. Finally, by numerical simulations, we show that the proposed
designs achieve significant gains over existing schemes.Comment: 30 pages, 6 pages, submitted to IEEE GLOBECOM 2017 and IEEE Trans.
Commo
Submodular Load Clustering with Robust Principal Component Analysis
Traditional load analysis is facing challenges with the new electricity usage
patterns due to demand response as well as increasing deployment of distributed
generations, including photovoltaics (PV), electric vehicles (EV), and energy
storage systems (ESS). At the transmission system, despite of irregular load
behaviors at different areas, highly aggregated load shapes still share similar
characteristics. Load clustering is to discover such intrinsic patterns and
provide useful information to other load applications, such as load forecasting
and load modeling. This paper proposes an efficient submodular load clustering
method for transmission-level load areas. Robust principal component analysis
(R-PCA) firstly decomposes the annual load profiles into low-rank components
and sparse components to extract key features. A novel submodular cluster
center selection technique is then applied to determine the optimal cluster
centers through constructed similarity graph. Following the selection results,
load areas are efficiently assigned to different clusters for further load
analysis and applications. Numerical results obtained from PJM load demonstrate
the effectiveness of the proposed approach.Comment: Accepted by 2019 IEEE PES General Meeting, Atlanta, G
A Brief Discussion on Wide Area Security and Stability Control of Power System Based on Response
At present, with the continuous development of China's social economy, the scale of domestic power system has been further expanded, which also makes the structure of China's power grid system gradually become more complex[1]. Therefore, it is necessary to continuously increase the single unit capacity of power equipment. The purpose is to make the single unit capacity match the operation of power system, so as to improve the operation performance of power system. Besides, it can also increase economic benefits. Based on this, this paper expounds the concept and control mode of power system stability. Then the key technology of wide area security and stability control of power system based on response is analyzed from four aspects. They are wide area dynamic feature information extraction, disturbed trajectory prediction, system stability discrimination and stability control. Finally, the practical application is discussed in detail. It hopes that the power sectors can improve the stability control level of power system wide area security
CREPES: Cooperative RElative Pose Estimation System
Mutual localization plays a crucial role in multi-robot cooperation. CREPES,
a novel system that focuses on six degrees of freedom (DOF) relative pose
estimation for multi-robot systems, is proposed in this paper. CREPES has a
compact hardware design using active infrared (IR) LEDs, an IR fish-eye camera,
an ultra-wideband (UWB) module and an inertial measurement unit (IMU). By
leveraging IR light communication, the system solves data association between
visual detection and UWB ranging. Ranging measurements from the UWB and
directional information from the camera offer relative 3-DOF position
estimation. Combining the mutual relative position with neighbors and the
gravity constraints provided by IMUs, we can estimate the 6-DOF relative pose
from a single frame of sensor measurements. In addition, we design an estimator
based on the error-state Kalman filter (ESKF) to enhance system accuracy and
robustness. When multiple neighbors are available, a Pose Graph Optimization
(PGO) algorithm is applied to further improve system accuracy. We conduct
enormous experiments to demonstrate CREPES' accuracy between robot pairs and a
team of robots, as well as performance under challenging conditions