27 research outputs found
Linear Transmit-Receive Strategies for Multi-user MIMO Wireless Communications
Die Notwendigkeit zur Unterdrueckung von Interferenzen auf der einen Seite
und zur Ausnutzung der durch Mehrfachzugriffsverfahren erzielbaren Gewinne
auf der anderen Seite rueckte die raeumlichen Mehrfachzugriffsverfahren
(Space Division Multiple Access, SDMA) in den Fokus der Forschung. Ein
Vertreter der raeumlichen Mehrfachzugriffsverfahren, die lineare
Vorkodierung, fand aufgrund steigender Anzahl an Nutzern und Antennen in
heutigen und zukuenftigen Mobilkommunikationssystemen besondere Beachtung,
da diese Verfahren das Design von Algorithmen zur Vorcodierung
vereinfachen. Aus diesem Grund leistet diese Dissertation einen Beitrag zur
Entwicklung linearer Sende- und Empfangstechniken fuer MIMO-Technologie mit
mehreren Nutzern. Zunaechst stellen wir ein Framework zur Approximation des
Datendurchsatzes in Broadcast-MIMO-Kanaelen mit mehreren Nutzern vor. In
diesem Framework nehmen wir das lineare Vorkodierverfahren regularisierte
Blockdiagonalisierung (RBD) an. Durch den Vergleich von Dirty Paper Coding
(DPC) und linearen Vorkodieralgorithmen (z.B. Zero Forcing (ZF) und
Blockdiagonalisierung (BD)) ist es uns moeglich, untere und obere Schranken
fuer den Unterschied bezueglich Datenraten und bezueglich Leistung zwischen
beiden anzugeben. Im Weiteren entwickeln wir einen Algorithmus fuer
koordiniertes Beamforming (Coordinated Beamforming, CBF), dessen Loesung
sich in geschlossener Form angeben laesst. Dieser CBF-Algorithmus basiert
auf der SeDJoCo-Transformation und loest bisher vorhandene Probleme im
Bereich CBF. Im Anschluss schlagen wir einen iterativen CBF-Algorithmus
namens FlexCoBF (flexible coordinated beamforming) fuer
MIMO-Broadcast-Kanaele mit mehreren Nutzern vor. Im Vergleich mit bis dato
existierenden iterativen CBF-Algorithmen kann als vielversprechendster
Vorteil die freie Wahl der linearen Sende- und Empfangsstrategie
herausgestellt werden. Das heisst, jede existierende Methode der linearen
Vorkodierung kann als Sendestrategie genutzt werden, waehrend die Strategie
zum Empfangsbeamforming frei aus MRC oder MMSE gewaehlt werden darf. Im
Hinblick auf Szenarien, in denen Mobilfunkzellen in Clustern
zusammengefasst sind, erweitern wir FlexCoBF noch weiter. Hier wurde das
Konzept der koordinierten Mehrpunktverbindung (Coordinated Multipoint
(CoMP) transmission) integriert. Zuletzt stellen wir drei Moeglichkeiten
vor, Kanalzustandsinformationen (Channel State Information, CSI) unter
verschiedenen Kanalumstaenden zu erlangen. Die Qualitaet der
Kanalzustandsinformationen hat einen starken Einfluss auf die Guete des
Uebertragungssystems. Die durch unsere neuen Algorithmen erzielten
Verbesserungen haben wir mittels numerischer Simulationen von Summenraten
und Bitfehlerraten belegt.In order to combat interference and exploit large multiplexing gains of the
multi-antenna systems, a particular interest in spatial division multiple
access (SDMA) techniques has emerged. Linear precoding techniques, as one
of the SDMA strategies, have obtained more attention due to the fact that
an increasing number of users and antennas involved into the existing and
future mobile communication systems requires a simplification of the
precoding design. Therefore, this thesis contributes to the design of
linear transmit and receive strategies for multi-user MIMO broadcast
channels in a single cell and clustered multiple cells. First, we present a
throughput approximation framework for multi-user MIMO broadcast channels
employing regularized block diagonalization (RBD) linear precoding.
Comparing dirty paper coding (DPC) and linear precoding algorithms (e.g.,
zero forcing (ZF) and block diagonalization (BD)), we further quantify
lower and upper bounds of the rate and power offset between them as a
function of the system parameters such as the number of users and antennas.
Next, we develop a novel closed-form coordinated beamforming (CBF)
algorithm (i.e., SeDJoCo based closed-form CBF) to solve the existing open
problem of CBF. Our new algorithm can support a MIMO system with an
arbitrary number of users and transmit antennas. Moreover, the application
of our new algorithm is not only for CBF, but also for blind source
separation (BSS), since the same mathematical model has been used in BSS
application.Then, we further propose a new iterative CBF algorithm (i.e.,
flexible coordinated beamforming (FlexCoBF)) for multi-user MIMO broadcast
channels. Compared to the existing iterative CBF algorithms, the most
promising advantage of our new algorithm is that it provides freedom in the
choice of the linear transmit and receive beamforming strategies, i.e., any
existing linear precoding method can be chosen as the transmit strategy and
the receive beamforming strategy can be flexibly chosen from MRC or MMSE
receivers. Considering clustered multiple cell scenarios, we extend the
FlexCoBF algorithm further and introduce the concept of the coordinated
multipoint (CoMP) transmission. Finally, we present three strategies for
channel state information (CSI) acquisition regarding various channel
conditions and channel estimation strategies. The CSI knowledge is required
at the base station in order to implement SDMA techniques. The quality of
the obtained CSI heavily affects the system performance. The performance
enhancement achieved by our new strategies has been demonstrated by
numerical simulation results in terms of the system sum rate and the bit
error rate
Wireless Communications in the Era of Big Data
The rapidly growing wave of wireless data service is pushing against the
boundary of our communication network's processing power. The pervasive and
exponentially increasing data traffic present imminent challenges to all the
aspects of the wireless system design, such as spectrum efficiency, computing
capabilities and fronthaul/backhaul link capacity. In this article, we discuss
the challenges and opportunities in the design of scalable wireless systems to
embrace such a "bigdata" era. On one hand, we review the state-of-the-art
networking architectures and signal processing techniques adaptable for
managing the bigdata traffic in wireless networks. On the other hand, instead
of viewing mobile bigdata as a unwanted burden, we introduce methods to
capitalize from the vast data traffic, for building a bigdata-aware wireless
network with better wireless service quality and new mobile applications. We
highlight several promising future research directions for wireless
communications in the mobile bigdata era.Comment: This article is accepted and to appear in IEEE Communications
Magazin
Advanced optimization algorithms for sensor arrays and multi-antenna communications
Optimization problems arise frequently in sensor array and multi-channel signal processing applications. Often, optimization needs to be performed subject to a matrix constraint. In particular, unitary matrices play a crucial role in communications and sensor array signal processing. They are involved in almost all modern multi-antenna transceiver techniques, as well as sensor array applications in biomedicine, machine learning and vision, astronomy and radars.
In this thesis, algorithms for optimization under unitary matrix constraint stemming from Riemannian geometry are developed. Steepest descent (SD) and conjugate gradient (CG) algorithms operating on the Lie group of unitary matrices are derived. They have the ability to find the optimal solution in a numerically efficient manner and satisfy the constraint accurately. Novel line search methods specially tailored for this type of optimization are also introduced. The proposed approaches exploit the geometrical properties of the constraint space in order to reduce the computational complexity.
Array and multi-channel signal processing techniques are key technologies in wireless communication systems. High capacity and link reliability may be achieved by using multiple transmit and receive antennas. Combining multi-antenna techniques with multicarrier transmission leads to high the spectral efficiency and helps to cope with severe multipath propagation.
The problem of channel equalization in MIMO-OFDM systems is also addressed in this thesis. A blind algorithm that optimizes of a combined criterion in order to be cancel both inter-symbol and co-channel interference is proposed. The algorithm local converge properties are established as well