326 research outputs found
Multiuser MIMO-OFDM for Next-Generation Wireless Systems
This overview portrays the 40-year evolution of orthogonal frequency division multiplexing (OFDM) research. The amelioration of powerful multicarrier OFDM arrangements with multiple-input multiple-output (MIMO) systems has numerous benefits, which are detailed in this treatise. We continue by highlighting the limitations of conventional detection and channel estimation techniques designed for multiuser MIMO OFDM systems in the so-called rank-deficient scenarios, where the number of users supported or the number of transmit antennas employed exceeds the number of receiver antennas. This is often encountered in practice, unless we limit the number of users granted access in the base station’s or radio port’s coverage area. Following a historical perspective on the associated design problems and their state-of-the-art solutions, the second half of this treatise details a range of classic multiuser detectors (MUDs) designed for MIMO-OFDM systems and characterizes their achievable performance. A further section aims for identifying novel cutting-edge genetic algorithm (GA)-aided detector solutions, which have found numerous applications in wireless communications in recent years. In an effort to stimulate the cross pollination of ideas across the machine learning, optimization, signal processing, and wireless communications research communities, we will review the broadly applicable principles of various GA-assisted optimization techniques, which were recently proposed also for employment inmultiuser MIMO OFDM. In order to stimulate new research, we demonstrate that the family of GA-aided MUDs is capable of achieving a near-optimum performance at the cost of a significantly lower computational complexity than that imposed by their optimum maximum-likelihood (ML) MUD aided counterparts. The paper is concluded by outlining a range of future research options that may find their way into next-generation wireless systems
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
Scaling up MIMO: Opportunities and Challenges with Very Large Arrays
This paper surveys recent advances in the area of very large MIMO systems.
With very large MIMO, we think of systems that use antenna arrays with an
order of magnitude more elements than in systems being built today, say a
hundred antennas or more. Very large MIMO entails an unprecedented number of
antennas simultaneously serving a much smaller number of terminals. The
disparity in number emerges as a desirable operating condition and a practical
one as well. The number of terminals that can be simultaneously served is
limited, not by the number of antennas, but rather by our inability to acquire
channel-state information for an unlimited number of terminals. Larger numbers
of terminals can always be accommodated by combining very large MIMO technology
with conventional time- and frequency-division multiplexing via OFDM. Very
large MIMO arrays is a new research field both in communication theory,
propagation, and electronics and represents a paradigm shift in the way of
thinking both with regards to theory, systems and implementation. The ultimate
vision of very large MIMO systems is that the antenna array would consist of
small active antenna units, plugged into an (optical) fieldbus.Comment: Accepted for publication in the IEEE Signal Processing Magazine,
October 201
Multi-user MIMO wireless communications
Mehrantennensysteme sind auf Grund der erhöhten Bandbreiteneffizienz und
Leistung eine SchlĂĽsselkomponente von Mobilfunksystemen der Zukunft. Diese
ermöglichen das gleichzeitige Senden von mehreren, räumlich getrennten
Datenströmen zu verschiedenen Nutzern. Die zentrale Fragestellung in der Praxis
ist, ob der ursprünglich vorausgesagte Kapazitätsgewinn in realistischen
Szenarios erreicht wird und welche spezifischen Gewinne durch zusätzliche
Antennen und das Ausnutzen von Kanalkenntnis am Sender und Empfänger erzielt
werden, was andererseits einen Zuwachs an Overhead oder nötiger Rechenleistung
bedeutet.
In dieser Arbeit werden neue lineare und nicht-lineare MU-MIMO Precoding-
Verfahren vorgestellt. Der verfolgte Ansatz zur Bestimmung der Precoding-
Matrizen ist allgemein anwendbar und die entstandenen Algorithmen können zur
Optimierung von verschiedenen Kriterien mit beliebig vielen Antennen an der
Mobilstation eingesetzt werden. Das wurde durch die Berechnung der Precoding-
Matrix in zwei Schritten erreicht. Im ersten Schritt wird die Ăśberschneidung der
Zeilenräume minimiert, die durch die effektiven Kanalmatrizen verschiedener
Nutzer aufgespannt werden. Basierend auf mehreren parallelen Einzelnutzer-MIMO-
Kanälen wird im zweiten Schritt die Systemperformanz bezüglich bestimmter
Kriterien optimiert.
Aus der gängigen Literatur ist bereits bekannt, dass für Nutzer mit nur einer
Antenne das MMSE Kriterium beim precoding optimal aber nicht bei Nutzern mit
mehreren Antennen. Deshalb werden in dieser Arbeit zwei neue Mehrnutzer MIMO
Strategien vorgestellt, die vom MSE Kriterium abgeleitet sind, nämlich
sukzessives MMSE und RBD. Bei der sukzessiven Verarbeitung mit einer
entsprechenden Anpassung der Sendeleistungsverteilung kann die volle Diversität
des Systems ausgeschöpft werden. Die Kapazität nähert sich dabei der maximalen
Summenrate des Systems an. Bei gemeinsamer Verarbeitung der MIMO Kanäle wird
unabhängig vom Grad der Mehrnutzerinterferenz die maximale Diversität erreicht.
Die genannten Techniken setzen entweder eine aktuelle oder eine ĂĽber einen
längeren Zeitraum gemittelte Kanalkenntnis voraus. Aus diesem Grund müssen die
Auswirkungen von Kanal-Schätzfehlern und Einflüsse des Transceiver Front-Ends
auf die Verfahren näher untersucht werden.
Für eine weitergehende Abschätzung der Mehrantennensysteme muss die Performanz
des Gesamtsystems untersucht werden, da viele Einflüsse auf die räumliche
Signalverarbeitung bei Betrachtung eines einzelnen Links nicht erkennbar sind.
Es wurde gezeigt, dass mit MIMO Precoding Strategien ein Vielfaches der
Datenrate eines Systems mit nur einer Antenne erzielt werden kann, während der
Overhead durch Pilotsymbole und Steuersignale nur geringfĂĽgig zunimmt.Multiple-input, multiple-output (MIMO) systems are a key component of future
wireless communication systems, because of their promising improvement in terms
of performance and bandwidth efficiency. An important research topic is the
study of multi-user (MU) MIMO systems. Such systems have the potential to
combine the high throughput achievable with MIMO processing with the benefits of
space division multiple access (SDMA). The main question from a practical
standpoint is whether the initially predicted capacity gains can be obtained in
more realistic scenarios and what specific gains result from adding more
antennas and overhead or computational power to obtain channel state information
(CSI) at the transceivers.
In this thesis we introduce new linear and non-linear MU MIMO processing
techniques. The approach used for the design of the precoding matrix is general
and the resulting algorithms can address several optimization criteria with an
arbitrary number of antennas at the user terminals (UTs). This is achieved by
designing the precoding matrices in two steps. In the first step we minimize the
overlap of the row spaces spanned by the effective channel matrices of different
users. In the next step, we optimize the system performance with respect to the
specific optimization criterion assuming a set of parallel single-user MIMO
channels.
As it was previously reported in the literature, minimum mean-squared-error
(MMSE) processing is optimum for single-antenna UTs. However, MMSE suffers from
a performance loss when users are equipped with more than one antenna. The two
MU MIMO processing techniques that result from the two different MSE criteria
that are proposed in this thesis are successive MMSE and regularized block
diagonalization. By iterating the closed form solution with appropriate power
loading we are able to extract the full diversity in the system and empirically
approach the maximum sum-rate capacity in case of high multi-user interference.
Joint processing of MIMO channels yields maximum diversity regardless of the
level of multi-user interference.
As these techniques rely on the fact that there is either instantaneous or long-
term CSI available at the base station to perform precoding and decoding, it was
very important to investigate the influence of the transceiver front-end
imperfections and channel estimation errors on their performance.
For a comprehensive assessment of multi-antenna techniques, it is mandatory to
consider the performance at system level, since many effects of spatial
processing are not tractable at the link level. System level investigations have
shown that MU MIMO precoding techniques provide several times higher data rates
than single-input single-output systems with only slightly increased pilot and
control overhead
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