Hardware Impairments Aware Transceiver Design for Bidirectional Full-Duplex MIMO OFDM Systems

In this paper we address the linear precoding and decoding design problem for a bidirectional orthogonal frequencydivision multiplexing (OFDM) communication system, between two multiple-input multiple-output (MIMO) full-duplex (FD) nodes. The effects of hardware distortion as well as the channel state information error are taken into account. In the first step, we transform the available time-domain characterization of the hardware distortions for FD MIMO transceivers to the frequency domain, via a linear Fourier transformation. As a result, the explicit impact of hardware inaccuracies on the residual selfinterference (RSI) and inter-carrier leakage (ICL) is formulated in relation to the intended transmit/received signals. Afterwards, linear precoding and decoding designs are proposed to enhance the system performance following the minimum-mean-squarederror (MMSE) and sum rate maximization strategies, assuming the availability of perfect or erroneous CSI. The proposed designs are based on the application of alternating optimization over the system parameters, leading to a necessary convergence. Numerical results indicate that the application of a distortionaware design is essential for a system with a high hardware distortion, or for a system with a low thermal noise variance.Comment: Submitted to IEEE for publicatio

Machine Learning for Fluid Mechanics

The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from field measurements, experiments and large-scale simulations at multiple spatiotemporal scales. Machine learning offers a wealth of techniques to extract information from data that could be translated into knowledge about the underlying fluid mechanics. Moreover, machine learning algorithms can augment domain knowledge and automate tasks related to flow control and optimization. This article presents an overview of past history, current developments, and emerging opportunities of machine learning for fluid mechanics. It outlines fundamental machine learning methodologies and discusses their uses for understanding, modeling, optimizing, and controlling fluid flows. The strengths and limitations of these methods are addressed from the perspective of scientific inquiry that considers data as an inherent part of modeling, experimentation, and simulation. Machine learning provides a powerful information processing framework that can enrich, and possibly even transform, current lines of fluid mechanics research and industrial applications.Comment: To appear in the Annual Reviews of Fluid Mechanics, 202

From Social Simulation to Integrative System Design

As the recent financial crisis showed, today there is a strong need to gain "ecological perspective" of all relevant interactions in socio-economic-techno-environmental systems. For this, we suggested to set-up a network of Centers for integrative systems design, which shall be able to run all potentially relevant scenarios, identify causality chains, explore feedback and cascading effects for a number of model variants, and determine the reliability of their implications (given the validity of the underlying models). They will be able to detect possible negative side effect of policy decisions, before they occur. The Centers belonging to this network of Integrative Systems Design Centers would be focused on a particular field, but they would be part of an attempt to eventually cover all relevant areas of society and economy and integrate them within a "Living Earth Simulator". The results of all research activities of such Centers would be turned into informative input for political Decision Arenas. For example, Crisis Observatories (for financial instabilities, shortages of resources, environmental change, conflict, spreading of diseases, etc.) would be connected with such Decision Arenas for the purpose of visualization, in order to make complex interdependencies understandable to scientists, decision-makers, and the general public.Comment: 34 pages, Visioneer White Paper, see http://www.visioneer.ethz.c