Can Electromagnetic Information Theory Improve Wireless Systems? A Channel Estimation Example

Electromagnetic information theory (EIT) is an emerging interdisciplinary subject that integrates classical Maxwell electromagnetics and Shannon information theory. The goal of EIT is to uncover the information transmission mechanisms from an electromagnetic (EM) perspective in wireless systems. Existing works on EIT are mainly focused on the analysis of degrees-of-freedom (DoF), system capacity, and characteristics of the electromagnetic channel. However, these works do not clarify how EIT can improve wireless communication systems. To answer this question, in this paper, we provide a novel demonstration of the application of EIT. By integrating EM knowledge into the classical MMSE channel estimator, we observe for the first time that EIT is capable of improving the channel estimation performace. Specifically, the EM knowledge is first encoded into a spatio-temporal correlation function (STCF), which we term as the EM kernel. This EM kernel plays the role of side information to the channel estimator. Since the EM kernel takes the form of Gaussian processes (GP), we propose the EIT-based Gaussian process regression (EIT-GPR) to derive the channel estimations. In addition, since the EM kernel allows parameter tuning, we propose EM kernel learning to fit the EM kernel to channel observations. Simulation results show that the application of EIT to the channel estimator enables it to outperform traditional isotropic MMSE algorithm, thus proving the practical values of EIT.Comment: Electromagnetic information theory (EIT) is an emerging interdisciplinary subject, aiming at providing a unified analytical framework for wireless systems as well as guiding practical system design. This paper answers the question: "How can we improve wireless communication systems via EIT"

Nonlinear behavior of the Chinese SSEC index with a unit root: Evidence from threshold unit root tests

We investigate the behavior of the Shanghai Stock Exchange Composite (SSEC) index for the period from 1990:12 to 2007:06 using an unconstrained two-regime threshold autoregressive (TAR) model with an unit root developed by Caner and Hansen. The method allows us to simultaneously consider non-stationarity and nonlinearity in financial time series. Our finding indicates that the Shanghai stock market exhibits nonlinear behavior with two regimes and has unit roots in both regimes. The important implications of the threshold effect in stock markets are also discussed.Comment: 10 Elsart pages + 5 tables + 1 eps figur

Micro/Nano Gas Sensors: A New Strategy Towards In-Situ Wafer-Level Fabrication of High-Performance Gas Sensing Chips

Nano-structured gas sensing materials, in particular nanoparticles, nanotubes, and nanowires, enable high sensitivity at a ppb level for gas sensors. For practical applications, it is highly desirable to be able to manufacture such gas sensors in batch and at low cost. We present here a strategy of in-situ wafer-level fabrication of the high-performance micro/nano gas sensing chips by naturally integrating microhotplatform (MHP) with nanopore array (NPA). By introducing colloidal crystal template, a wafer-level ordered homogenous SnO_2 NPA is synthesized in-situ on a 4-inch MHP wafer, able to produce thousands of gas sensing units in one batch. The integration of micromachining process and nanofabrication process endues micro/nano gas sensing chips at low cost, high throughput, and with high sensitivity (down to ~20 ppb), fast response time (down to ~1 s), and low power consumption (down to ~30 mW). The proposed strategy of integrating MHP with NPA represents a versatile approach for in-situ wafer-level fabrication of high-performance micro/nano gas sensors for real industrial applications