Vienna MIMO Testbed
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Vienna MIMO Testbed ¨ Sebastian Caban, Christian Mehlfuhrer, Robert Langwieser, Arpad L. Scholtz, and Markus Rupp Institute of Communications and Radio-Frequency Engineering, Vienna University of Technology, Gusshausstrasse 25/389, 1040 Vienna, Austria Received 2 December 2004; Revised 20 April 2005; Accepted 4 May 2005 While the field of MIMO transmission has been explored over the past decade mainly theoretically, relatively few results exist on how these transmissions perform over realistic, imperfect channels. The reason for this is that measurement equipment is expensive, difficult to obtain, and often inflexible when a multitude of transmission parameters are of interest. This paper presents a flexible testbed developed to examine MIMO algorithms and channel models described in literature by transmitting data at 2.45 GHz through real, physical channels, supporting simultaneously four transmit and four receive antennas. Operation is performed directly from Matlab allowing for a cornucopia of real-world experiments with minimum effort. Examples measuring bit error rates on space-time block codes are provided in the paper. Copyright © 2006 Sebastian Caban et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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MOTIVATION
Investigating the performance of highly sophisticated wireless systems, in particular with multiple transmit and receive antennas, is a difficult task. In most cases, this can only be performed via simulation, which means modeling complex behavior by simpler mathematical descriptions [1]. Matlab simulation [2], with its highly accurate double-precision numerical environment, is on the one hand a perfect tool for the investigation of algorithms. On the other hand many imperfections of the real-world are neglected. In particular, fixedpoint aspects of future products are often underestimated [3] as well as the true physical behavior of the wireless channel which is quite complex and not too well understood. Following the concept of simplification, uncertainties in MIMO decoding algorithms usually do not attract much attention [4]. These uncertainties are mainly simplified assumptions like perfectly known noise levels, additive Gaussian noise, omitted synchronization, linear power amplification, and perfect channel knowledge, which often show too optimistic receiver performance in simulation. 1.1. Motivation for rapid prototyping Prototypes were used in the past to allow for early testing of future products. In particular when new technology was involved, such prototyping was a crucial element in the design path for a new product. The advantage of prototyping was
to reduce the investment risk of the new product in case the new technology would hide unforeseen challenges. Also, one often obtained a flavour of how the new technology needs to be realized and a feeling for how the future product would look like. One could thus present it t
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