107 research outputs found
Integer-Forcing Linear Receivers
Linear receivers are often used to reduce the implementation complexity of
multiple-antenna systems. In a traditional linear receiver architecture, the
receive antennas are used to separate out the codewords sent by each transmit
antenna, which can then be decoded individually. Although easy to implement,
this approach can be highly suboptimal when the channel matrix is near
singular. This paper develops a new linear receiver architecture that uses the
receive antennas to create an effective channel matrix with integer-valued
entries. Rather than attempting to recover transmitted codewords directly, the
decoder recovers integer combinations of the codewords according to the entries
of the effective channel matrix. The codewords are all generated using the same
linear code which guarantees that these integer combinations are themselves
codewords. Provided that the effective channel is full rank, these integer
combinations can then be digitally solved for the original codewords. This
paper focuses on the special case where there is no coding across transmit
antennas and no channel state information at the transmitter(s), which
corresponds either to a multi-user uplink scenario or to single-user V-BLAST
encoding. In this setting, the proposed integer-forcing linear receiver
significantly outperforms conventional linear architectures such as the
zero-forcing and linear MMSE receiver. In the high SNR regime, the proposed
receiver attains the optimal diversity-multiplexing tradeoff for the standard
MIMO channel with no coding across transmit antennas. It is further shown that
in an extended MIMO model with interference, the integer-forcing linear
receiver achieves the optimal generalized degrees-of-freedom.Comment: 40 pages, 16 figures, to appear in the IEEE Transactions on
Information Theor
Multi-user MIMO wireless communications
Mehrantennensysteme sind auf Grund der erhöhten Bandbreiteneffizienz und
Leistung eine Schlüsselkomponente 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 Überschneidung 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 über 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 geringfügig 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
MIMO Systems
In recent years, it was realized that the MIMO communication systems seems to be inevitable in accelerated evolution of high data rates applications due to their potential to dramatically increase the spectral efficiency and simultaneously sending individual information to the corresponding users in wireless systems. This book, intends to provide highlights of the current research topics in the field of MIMO system, to offer a snapshot of the recent advances and major issues faced today by the researchers in the MIMO related areas. The book is written by specialists working in universities and research centers all over the world to cover the fundamental principles and main advanced topics on high data rates wireless communications systems over MIMO channels. Moreover, the book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity
On adaptive transmission, signal detection and channel estimation for multiple antenna systems
This research concerns analysis of system capacity, development of adaptive transmission schemes with known channel state information at the transmitter (CSIT) and design of new signal detection and channel estimation schemes with low complexity in some multiple antenna systems. We first analyze the sum-rate capacity of the downlink of a cellular system with multiple transmit antennas and multiple receive antennas assuming perfect CSIT. We evaluate the ergodic sum-rate capacity and show how the sum-rate capacity increases as the number of users and the number of receive antennas increases. We develop upper and lower bounds on the sum-rate capacity and study various adaptive MIMO schemes to achieve, or approach, the sum-rate capacity. Next, we study the minimum outage probability transmission schemes in a multiple-input-single-output (MISO) flat fading channel assuming partial CSIT. Considering two special cases: the mean feedback and the covariance feedback, we derive the optimum spatial transmission directions and show that the associated optimum power allocation scheme, which minimizes the outage probability, is closely related to the target rate and the accuracy of the CSIT. Since CSIT is obtained at the cost of feedback bandwidth, we also consider optimal allocation of bandwidth between the data channel and the feedback channel in order to maximize the average throughput of the data channel in MISO, flat fading, frequency division duplex (FDD) systems. We show that beamforming based on feedback CSI can achieve an average rate larger than the capacity without CSIT under a wide range of mobility conditions. We next study a SAGE-aided List-BLAST detection scheme for MIMO systems which can achieve performance close to that of the maximum-likelihood detector with low complexity. Finally, we apply the EM and SAGE algorithms in channel estimation for OFDM systems with multiple transmit antennas and compare them with a recently proposed least-squares based estimation algorithm. The EM and SAGE algorithms partition the problem of estimating a multi-input channel into independent channel estimation for each transmit-receive antenna pair, therefore avoiding the matrix inversion encountered in the joint least-squares estimation
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