scispace - formally typeset
C

Christoph F. Mecklenbrauker

Researcher at Vienna University of Technology

Publications -  343
Citations -  7466

Christoph F. Mecklenbrauker is an academic researcher from Vienna University of Technology. The author has contributed to research in topics: Communication channel & MIMO. The author has an hindex of 39, co-authored 328 publications receiving 6618 citations. Previous affiliations of Christoph F. Mecklenbrauker include Brno University of Technology & University of California, San Diego.

Papers
More filters
Journal ArticleDOI

Time-variant channel estimation using discrete prolate spheroidal sequences

TL;DR: In this article, a low-complexity channel estimator for a multiuser multicarrier code division multiple access (MC-CDMA) downlink in a time-variant frequency-selective channel is proposed and analyzed.
Journal ArticleDOI

Vehicular Channel Characterization and Its Implications for Wireless System Design and Performance

TL;DR: An overview of the existing vehicular channel measurements in a variety of important environments, and the observed channel characteristics (such as delay spreads and Doppler spreads) therein, is provided.
Journal ArticleDOI

A survey on vehicle-to-vehicle propagation channels

TL;DR: In this paper, the authors provide an overview of existing VTV channel measurement campaigns in a variety of important environments, and the channel characteristics (such as delay spreads and Doppler spreads) therein.
Journal ArticleDOI

A geometry-based stochastic MIMO model for vehicle-to-vehicle communications

TL;DR: A new wideband multiple-input-multiple-output (MIMO) model for VTV channels based on extensive MIMO channel measurements performed at 5.2 GHz in highway and rural environments in Lund, Sweden is presented.
Journal ArticleDOI

Delay and Doppler Spreads of Nonstationary Vehicular Channels for Safety-Relevant Scenarios

TL;DR: This paper estimates the LSF from a large set of measurements collected in the DRIVEWAY'09 measurement campaign, which focuses on scenarios for intelligent transportation systems (ITSs) and shows that the distribution of these channel parameters follows a bimodal Gaussian mixture distribution.