Sum-Rate Maximization in Two-Way AF MIMO Relaying: Polynomial Time Solutions to a Class of DC Programming Problems

Sum-rate maximization in two-way amplify-and-forward (AF) multiple-input multiple-output (MIMO) relaying belongs to the class of difference-of-convex functions (DC) programming problems. DC programming problems occur as well in other signal processing applications and are typically solved using different modifications of the branch-and-bound method. This method, however, does not have any polynomial time complexity guarantees. In this paper, we show that a class of DC programming problems, to which the sum-rate maximization in two-way MIMO relaying belongs, can be solved very efficiently in polynomial time, and develop two algorithms. The objective function of the problem is represented as a product of quadratic ratios and parameterized so that its convex part (versus the concave part) contains only one (or two) optimization variables. One of the algorithms is called POlynomial-Time DC (POTDC) and is based on semi-definite programming (SDP) relaxation, linearization, and an iterative search over a single parameter. The other algorithm is called RAte-maximization via Generalized EigenvectorS (RAGES) and is based on the generalized eigenvectors method and an iterative search over two (or one, in its approximate version) optimization variables. We also derive an upper-bound for the optimal values of the corresponding optimization problem and show by simulations that this upper-bound can be achieved by both algorithms. The proposed methods for maximizing the sum-rate in the two-way AF MIMO relaying system are shown to be superior to other state-of-the-art algorithms.Comment: 35 pages, 10 figures, Submitted to the IEEE Trans. Signal Processing in Nov. 201

Advanced Algebraic Concepts for Efficient Multi-Channel Signal Processing

Unsere moderne Gesellschaft ist Zeuge eines fundamentalen Wandels in der Art und Weise wie wir mit Technologie interagieren. Geräte werden zunehmend intelligenter - sie verfügen über mehr und mehr Rechenleistung und häufiger über eigene Kommunikationsschnittstellen. Das beginnt bei einfachen Haushaltsgeräten und reicht über Transportmittel bis zu großen überregionalen Systemen wie etwa dem Stromnetz. Die Erfassung, die Verarbeitung und der Austausch digitaler Informationen gewinnt daher immer mehr an Bedeutung. Die Tatsache, dass ein wachsender Anteil der Geräte heutzutage mobil und deshalb batteriebetrieben ist, begründet den Anspruch, digitale Signalverarbeitungsalgorithmen besonders effizient zu gestalten. Dies kommt auch dem Wunsch nach einer Echtzeitverarbeitung der großen anfallenden Datenmengen zugute. Die vorliegende Arbeit demonstriert Methoden zum Finden effizienter algebraischer Lösungen für eine Vielzahl von Anwendungen mehrkanaliger digitaler Signalverarbeitung. Solche Ansätze liefern nicht immer unbedingt die bestmögliche Lösung, kommen dieser jedoch häufig recht nahe und sind gleichzeitig bedeutend einfacher zu beschreiben und umzusetzen. Die einfache Beschreibungsform ermöglicht eine tiefgehende Analyse ihrer Leistungsfähigkeit, was für den Entwurf eines robusten und zuverlässigen Systems unabdingbar ist. Die Tatsache, dass sie nur gebräuchliche algebraische Hilfsmittel benötigen, erlaubt ihre direkte und zügige Umsetzung und den Test unter realen Bedingungen. Diese Grundidee wird anhand von drei verschiedenen Anwendungsgebieten demonstriert. Zunächst wird ein semi-algebraisches Framework zur Berechnung der kanonisch polyadischen (CP) Zerlegung mehrdimensionaler Signale vorgestellt. Dabei handelt es sich um ein sehr grundlegendes Werkzeug der multilinearen Algebra mit einem breiten Anwendungsspektrum von Mobilkommunikation über Chemie bis zur Bildverarbeitung. Verglichen mit existierenden iterativen Lösungsverfahren bietet das neue Framework die Möglichkeit, den Rechenaufwand und damit die Güte der erzielten Lösung zu steuern. Es ist außerdem weniger anfällig gegen eine schlechte Konditionierung der Ausgangsdaten. Das zweite Gebiet, das in der Arbeit besprochen wird, ist die unterraumbasierte hochauflösende Parameterschätzung für mehrdimensionale Signale, mit Anwendungsgebieten im RADAR, der Modellierung von Wellenausbreitung, oder bildgebenden Verfahren in der Medizin. Es wird gezeigt, dass sich derartige mehrdimensionale Signale mit Tensoren darstellen lassen. Dies erlaubt eine natürlichere Beschreibung und eine bessere Ausnutzung ihrer Struktur als das mit Matrizen möglich ist. Basierend auf dieser Idee entwickeln wir eine tensor-basierte Schätzung des Signalraums, welche genutzt werden kann um beliebige existierende Matrix-basierte Verfahren zu verbessern. Dies wird im Anschluss exemplarisch am Beispiel der ESPRIT-artigen Verfahren gezeigt, für die verbesserte Versionen vorgeschlagen werden, die die mehrdimensionale Struktur der Daten (Tensor-ESPRIT), nichzirkuläre Quellsymbole (NC ESPRIT), sowie beides gleichzeitig (NC Tensor-ESPRIT) ausnutzen. Um die endgültige Schätzgenauigkeit objektiv einschätzen zu können wird dann ein Framework für die analytische Beschreibung der Leistungsfähigkeit beliebiger ESPRIT-artiger Algorithmen diskutiert. Verglichen mit existierenden analytischen Ausdrücken ist unser Ansatz allgemeiner, da keine Annahmen über die statistische Verteilung von Nutzsignal und Rauschen benötigt werden und die Anzahl der zur Verfügung stehenden Schnappschüsse beliebig klein sein kann. Dies führt auf vereinfachte Ausdrücke für den mittleren quadratischen Schätzfehler, die Schlussfolgerungen über die Effizienz der Verfahren unter verschiedenen Bedingungen zulassen. Das dritte Anwendungsgebiet ist der bidirektionale Datenaustausch mit Hilfe von Relay-Stationen. Insbesondere liegt hier der Fokus auf Zwei-Wege-Relaying mit Hilfe von Amplify-and-Forward-Relays mit mehreren Antennen, da dieser Ansatz ein besonders gutes Kosten-Nutzen-Verhältnis verspricht. Es wird gezeigt, dass sich die nötige Kanalkenntnis mit einem einfachen algebraischen Tensor-basierten Schätzverfahren gewinnen lässt. Außerdem werden Verfahren zum Finden einer günstigen Relay-Verstärkungs-Strategie diskutiert. Bestehende Ansätze basieren entweder auf komplexen numerischen Optimierungsverfahren oder auf Ad-Hoc-Ansätzen die keine zufriedenstellende Bitfehlerrate oder Summenrate liefern. Deshalb schlagen wir algebraische Ansätze zum Finden der Relayverstärkungsmatrix vor, die von relevanten Systemmetriken inspiriert sind und doch einfach zu berechnen sind. Wir zeigen das algebraische ANOMAX-Verfahren zum Erreichen einer niedrigen Bitfehlerrate und seine Modifikation RR-ANOMAX zum Erreichen einer hohen Summenrate. Für den Spezialfall, in dem die Endgeräte nur eine Antenne verwenden, leiten wir eine semi-algebraische Lösung zum Finden der Summenraten-optimalen Strategie (RAGES) her. Anhand von numerischen Simulationen wird die Leistungsfähigkeit dieser Verfahren bezüglich Bitfehlerrate und erreichbarer Datenrate bewertet und ihre Effektivität gezeigt.Modern society is undergoing a fundamental change in the way we interact with technology. More and more devices are becoming "smart" by gaining advanced computation capabilities and communication interfaces, from household appliances over transportation systems to large-scale networks like the power grid. Recording, processing, and exchanging digital information is thus becoming increasingly important. As a growing share of devices is nowadays mobile and hence battery-powered, a particular interest in efficient digital signal processing techniques emerges. This thesis contributes to this goal by demonstrating methods for finding efficient algebraic solutions to various applications of multi-channel digital signal processing. These may not always result in the best possible system performance. However, they often come close while being significantly simpler to describe and to implement. The simpler description facilitates a thorough analysis of their performance which is crucial to design robust and reliable systems. The fact that they rely on standard algebraic methods only allows their rapid implementation and test under real-world conditions. We demonstrate this concept in three different application areas. First, we present a semi-algebraic framework to compute the Canonical Polyadic (CP) decompositions of multidimensional signals, a very fundamental tool in multilinear algebra with applications ranging from chemistry over communications to image compression. Compared to state-of-the art iterative solutions, our framework offers a flexible control of the complexity-accuracy trade-off and is less sensitive to badly conditioned data. The second application area is multidimensional subspace-based high-resolution parameter estimation with applications in RADAR, wave propagation modeling, or biomedical imaging. We demonstrate that multidimensional signals can be represented by tensors, providing a convenient description and allowing to exploit the multidimensional structure in a better way than using matrices only. Based on this idea, we introduce the tensor-based subspace estimate which can be applied to enhance existing matrix-based parameter estimation schemes significantly. We demonstrate the enhancements by choosing the family of ESPRIT-type algorithms as an example and introducing enhanced versions that exploit the multidimensional structure (Tensor-ESPRIT), non-circular source amplitudes (NC ESPRIT), and both jointly (NC Tensor-ESPRIT). To objectively judge the resulting estimation accuracy, we derive a framework for the analytical performance assessment of arbitrary ESPRIT-type algorithms by virtue of an asymptotical first order perturbation expansion. Our results are more general than existing analytical results since we do not need any assumptions about the distribution of the desired signal and the noise and we do not require the number of samples to be large. At the end, we obtain simplified expressions for the mean square estimation error that provide insights into efficiency of the methods under various conditions. The third application area is bidirectional relay-assisted communications. Due to its particularly low complexity and its efficient use of the radio resources we choose two-way relaying with a MIMO amplify and forward relay. We demonstrate that the required channel knowledge can be obtained by a simple algebraic tensor-based channel estimation scheme. We also discuss the design of the relay amplification matrix in such a setting. Existing approaches are either based on complicated numerical optimization procedures or on ad-hoc solutions that to not perform well in terms of the bit error rate or the sum-rate. Therefore, we propose algebraic solutions that are inspired by these performance metrics and therefore perform well while being easy to compute. For the MIMO case, we introduce the algebraic norm maximizing (ANOMAX) scheme, which achieves a very low bit error rate, and its extension Rank-Restored ANOMAX (RR-ANOMAX) that achieves a sum-rate close to an upper bound. Moreover, for the special case of single antenna terminals we derive the semi-algebraic RAGES scheme which finds the sum-rate optimal relay amplification matrix based on generalized eigenvectors. Numerical simulations evaluate the resulting system performance in terms of bit error rate and system sum rate which demonstrates the effectiveness of the proposed algebraic solutions

Rank-Two Beamforming and Power Allocation in Multicasting Relay Networks

In this paper, we propose a novel single-group multicasting relay beamforming scheme. We assume a source that transmits common messages via multiple amplify-and-forward relays to multiple destinations. To increase the number of degrees of freedom in the beamforming design, the relays process two received signals jointly and transmit the Alamouti space-time block code over two different beams. Furthermore, in contrast to the existing relay multicasting scheme of the literature, we take into account the direct links from the source to the destinations. We aim to maximize the lowest received quality-of-service by choosing the proper relay weights and the ideal distribution of the power resources in the network. To solve the corresponding optimization problem, we propose an iterative algorithm which solves sequences of convex approximations of the original non-convex optimization problem. Simulation results demonstrate significant performance improvements of the proposed methods as compared with the existing relay multicasting scheme of the literature and an algorithm based on the popular semidefinite relaxation technique

Relay-Aided Communication in Large Interference Limited Wireless Networks

In recent years, the number of active wireless devices increases exponentially and it is, therefore, to expect that the interference increases as well. Interference between communication links is the major performance limiting factor in today's communication networks. Hence, the handling of the overall interference in a network is one major challenge in wireless communication networks of the future. If the interference signals are weak in comparison to the useful signal, they can be simply treated as noise. If the interference signals are strong in comparison to the useful signal, they can be reliably decoded and subtracted from the received signal at the receivers. However, in multiuser communication networks, the interference and the useful signal are often of comparable signal strength. The conventional approach to handle these interference signals is to orthogonalize the useful signal and the interference signals using, e.g., time division multiple access (TDMA) or frequency division multiple access (FDMA). In the past few years, instead of orthogonalization, interference alignment (IA) has been developed as an efficient technique to handle interference signals, especially in the high signal to noise ratio (SNR) region. The basic idea of IA is to align multiple interference signals in a particular subspace of reduced dimension at each receiver. The objective is to minimize the signal dimensions occupied by interference at each receiver. In order to perform IA, the receive space is divided into two disjoint subspaces, the useful signal subspace and the interference signal subspace. Each transmitting node designs its transmit filters in such a way that at each receiving node, all interference signals are within the interference subspace and only the useful signal is in the useful subspace. In this thesis, the focus is on large interference limited wireless communication networks. In contrast to the conventional use of relays, for extending the coverage, in this thesis, the relays are used to manipulate the effective end-to-end channel between the transmitters and receivers to perform IA in the network. Since the relays are used to assist the process of IA and not interested in the data streams transmitted by the nodes, amplify-and-forward relays are sufficient to support the process of IA. Therefore, the main focus of this thesis is on amplify-and-forward relays. Throughout this thesis, it is assumed that all nodes and relays are multi-antenna half-duplex devices. When considering large networks, the assumption that all nodes are connected to all relays does not hold due to physical propagation phenomena, e.g., high path loss and shadowing. In such large networks, the distances between different nodes may differ a lot, leading to links of considerably different signal strengths, where sufficiently weak links may be neglected. Hence, large networks are in general partially connected. In this thesis, three important interference-limited relay aided wireless network topologies are investigated, the partially connected relay aided multi-pair pair-wise communication network, the fully connected multi-group multi-way relaying network and the partially connected multi-group multi-way relaying network. For each of these topologies, new algorithms to perform IA are developed in this thesis. First, a large partially connected relay aided pair-wise communication network is considered. The concept of an appropriate partitioning of a partially connected network into subnetworks which are themselves fully connected is introduced. Each of these subnetworks contains a single relay and all nodes being connected to this relay. Some nodes or even communication pairs may be connected to multiple relays. The bidirectional pair-wise communication between the nodes takes place via the intermediate relays, using the two-way relaying protocol. Only relays which are connected to both nodes of a communication pair can serve this pair. Hence, it is assumed that all communication pairs in the entire network are served by at least one relay. The most challenging part of such a partially connected network is the handling of nodes which are connected to multiple relays. Hence, techniques called simultaneous signal alignment (SSA) and simultaneous channel alignment (SCA), are proposed to perform signal alignment (SA) and channel alignment (CA) with multiple relays simultaneously. SA means that all nodes transmit to the relay in such a way that the signals of each communicating pair are pair-wise aligned at the relay. For CA, which is dual to SA, the receive filter of each node is designed such that the effective channels between the relay and both nodes of a communicating pair span the same subspace. A closed-form solution to perform IA in this network topology is obtained and the properness conditions for SSA and SCA are derived. It is shown that local channel state information (CSI) is sufficient to perform IA in partially connected networks, whereas in fully connected relay aided networks, global CSI is required in general. Through simulations, it is shown that the proposed closed-form solution achieves more degrees of freedom (DoF) than the reference algorithms and has better sum-rate performance, especially in the high SNR-region. Especially in large wireless networks, it may happen that not both nodes of a communication pair are connected to the same relays. If a single node of a communication pair is in addition connected to a relay which, therefore, cannot assist the communication, this node receives only interference and no useful signal from this relay. Such a node suffers from inter-subnetwork interference, due to the connection by an inter-subnetwork link to the additional relay. Hence, in this thesis, a closed form algorithm which minimizes the inter-subnetwork interference power in the whole partially connected network is proposed and the properness conditions are derived. The condition under which an interference free-communication can be achieved by the proposed inter-subnetwork interference power minimization algorithm is derived. Further, it is shown that the proposed inter-subnetwork interference power minimization algorithm achieves a higher sum rate in comparison to the considered reference algorithm. Secondly, a fully connected multi-group multi-way relaying networks is considered. In such a network, multiple nodes form a group and each node wants to share its message with all other nodes in its group via an intermediate relay. The group-wise communication between the nodes inside a group takes place via the intermediate relay, using a transmission strategy considering several multiple access (MAC) phases and several multicast (MC) phases, in general. In this thesis, a multicast IA algorithm to handle the interference in such a network is proposed. The idea of the proposed algorithm is that in each of the MC phases, a multiple input multiple output (MIMO) interference multicast channel is created by separating the antennas of the relay into as many clusters as groups in the network. Each of these clusters serves a specific group of nodes and transmits in such a way that the signals transmitted from different clusters are aligned at the receiving nodes of the non-intended multicast groups. It is shown that the minimum required number of antennas at the relay is independent of the number of nodes per group, which is an important property since the number of antennas available at the relay is limited in general. Furthermore, the properness conditions for the proposed multicast IA algorithm are derived. It is shown that the proposed multicast algorithm outperforms a reference algorithm for a broad range of SNR values, while still requiring less antennas at the relay. Finally, a large partially connected multi-group multi-way relay network is considered. In contrast to the fully connected multi-group multi-way relaying network, multiple relays are considered in this partially connected network. Such a partially connected network can be partitioned into subnetworks that are themselves fully connected. Hence, such a partially connected network consists of multiple subnetworks, where each of these contains a single relay and all groups of nodes which are connected to this relay. Each group of nodes may be connected to one or multiple relays. This means that not all groups of nodes are connected to all relays in the network. However, any group is connected to at least one relay which serves this group of nodes. The group-wise exchange of data between the nodes inside a group is performed via the multi-way relaying protocol. The most challenging part of such a partially connected network is the handling of the nodes inside groups which are connected to multiple relays. To overcome this challenge, new techniques called simultaneous group signal alignment (SGSA) and simultaneous group channel alignment (SGCA) are introduced to perform SA and CA in partially connected multi-group multi-way relaying networks. A closed-form IA solution for this network topology is obtained and the properness conditions for the solvability of SGSA and SGCA are derived. It is shown that the proposed IA algorithm outperforms the reference algorithm in terms of sum rate and DoF

Advanced Signal Processing Techniques for Two-Way Relaying Networks and Full-Duplex Communication Systems

Sehr hohe Datenraten und ständig verfügbare Netzabdeckung in zukünftigen drahtlosen Netzwerken erfordern neue Algorithmen auf der physischen Schicht. Die Nutzung von Relais stellt ein vielversprechendes Verfahren dar, da die Netzabdeckung gesteigert werden kann. Zusätzlich steht hierdurch im Vergleich zu Kupfer- oder Glasfaserleitungen eine preiswerte Lösung zur Anbindung an die Netzinfrastruktur zur Verfügung. Traditionelle Einwege-Relais-Techniken (One-Way Relaying [OWR]) nutzen Halbduplex-Verfahren (HD-Verfahren), welche das Übertragungssystem ausbremst und zu spektralen Verlusten führt. Einerseits erlauben es Zweiwege-Relais-Techniken (Two-Way Relaying [TWR]), simultan sowohl an das Relais zu senden als auch von diesem zu empfangen, wodurch im Vergleich zu OWR das Spektrum effizienter genutzt wird. Aus diesem Grunde untersuchen wir Zweiwege-Relais und im Speziellen TWR-Systeme für den Mehrpaar-/Mehrnutzer-Betrieb unter Nutzung von Amplify-and-forward-Relais (AF-Relais). Derartige Szenarien leiden unter Interferenzen zwischen Paaren bzw. zwischen Nutzern. Um diesen Interferenzen Herr zu werden, werden hochentwickelte Signalverarbeitungsalgorithmen – oder in anderen Worten räumliche Mehrfachzugriffsverfahren (Spatial Division Multiple Access [SDMA]) – benötigt. Andererseits kann der spektrale Verlust durch den HD-Betrieb auch kompensiert werden, wenn das Relais im Vollduplexbetrieb arbeitet. Nichtsdestotrotz ist ein FD-Gerät in der Praxis aufgrund starker interner Selbstinterferenz (SI) und begrenztem Dynamikumfang des Tranceivers schwer zu realisieren. Aus diesem Grunde sollten fortschrittliche Verfahren zur SI-Ünterdrückung entwickelt werden. Diese Dissertation trägt diesen beiden Zielen Rechnung, indem optimale und/oder effiziente algebraische Lösungen entwickelt werden, welche verschiedenen Nutzenfunktionen, wie Summenrate und minimale Sendeleistung, maximieren.Im ersten Teil studieren wir zunächst Mehrpaar-TWR-Netzwerke mit einem einzelnen Mehrantennen-AF-Relais. Dieser Anwendungsfall kann auch so betrachtet werden, dass sich mehrere verschiedene Dienstoperatoren Relais und Spektrum teilen, wobei verschiedene Nutzerpaare zu verschiedenen Dienstoperatoren gehören. Aktuelle Ansätzen zielen auf Interferenzunterdrückung ab. Wir schlagen ein auf Projektion basiertes Verfahren zur Trennung mehrerer Dienstoperatoren (projection based separation of multiple operators [ProBaSeMO]) vor. ProBaSeMO ist leicht anpassbar für den Fall, dass jeder Nutzer mehrere Antennen besitzt oder unterschiedliche Systemdesignkriterien angewendet werden müssen. Als Bewertungsmaßstab für ProBaSeMO entwickeln wir optimale Algorithmen zur Maximierung der Summenrate, zur Minimierung der Sendeleistung am Relais oder zur Maximierung des minimalen Signal-zu-Interferenz-und-Rausch-Verhältnisses (Signal to Interference and Noise Ratio [SINR]) am Nutzer. Zur Maximierung der Summenrate wurden spezifische gradientenbasierte Methoden entwickelt, die unabhängig davon sind, ob ein Nutzer mit einer oder mehr Antennen ausgestattet ist. Um im Falle eines „Worst-Case“ immer noch eine polynomielle Laufzeit zu garantieren, entwickelten wir einen Algorithmus mit polynomieller Laufzeit. Dieser ist inspiriert von der „Polynomial Time Difference of Convex Functions“-Methode (POTDC-Methode). Bezüglich der Summenrate des Systems untersuchen wir zuletzt, welche Bedingungen erfüllt sein müssen, um einen Gewinn durch gemeinsames Nutzen zu erhalten. Hiernach untersuchen wir die Maximierung der Summenrate eines Mehrpaar-TWR-Netzwerkes mit mehreren Einantennen-AF-Relais und Einantennen-Nutzern. Das daraus resultierende Problem der Summenraten-Maximierung, gebunden an eine bestimmte Gesamtsendeleistung aller Relais im Netzwerk, ist ähnlich dem des vorangegangenen Szenarios. Dementsprechend kann eine optimale Lösung für das eine Szenario auch für das jeweils andere Szenario genutzt werden. Weiterhin werden basierend auf dem Polynomialzeitalgorithmus global optimale Lösungen entwickelt. Diese Lösungen sind entweder an eine maximale Gesamtsendeleistung aller Relais oder an eine maximale Sendeleistung jedes einzelnen Relais gebunden. Zusätzlich entwickeln wir suboptimale Lösungen, die effizient in ihrer Laufzeit sind und eine Approximation der optimalen Lösung darstellen. Hiernach verlegen wir unser Augenmerk auf ein Mehrpaar-TWR-Netzwerk mit mehreren Mehrantennen-AF-Relais und mehreren Repeatern. Solch ein Szenario ist allgemeiner, da die vorherigen beiden Szenarien als spezielle Realisierungen dieses Szenarios aufgefasst werden können. Das Interferenz-Management in diesem Szenario ist herausfordernder aufgrund der vorhandenen Repeater. Interferenzneutralisierung (IN) stellt eine Lösung dar, um diese Art Interferenz zu handhaben. Im Zuge dessen werden notwendige und ausreichende Bedingungen zur Aufhebung der Interferenz hergeleitet. Weiterhin wird ein Framework entwickelt, dass verschiedene Systemnutzenfunktionen optimiert, wobei IN im jeweiligen Netzwerk vorhanden sein kann oder auch nicht. Dies ist unabhängig davon, ob die Relais einer maximalen Gesamtsendeleistung oder einer individuellen maximalen Sendeleistung unterliegen. Letztendlich entwickeln wir ein Übertragungsverfahren sowie ein Vorkodier- und Dekodierverfahren für Basisstationen (BS) in einem TWR-assistierten Mehrbenutzer-MIMO-Downlink-Kanal. Im Vergleich mit dem Mehrpaar-TWR-Netzwerk leidet dieses Szenario unter Interferenzen zwischen den Kanälen. Wir entwickeln drei suboptimale Algorithmen, welche auf Kanalinversion basieren. ProBaSeMO und „Zero-Forcing Dirty Paper Coding“ (ZFDPC), welche eine geringe Zeitkomplexität aufweisen, schaffen eine Balance zwischen Leistungsfähigkeit und Komplexität. Zusätzlich gibt es jeweils nur geringe Einbrüche in stark beanspruchten Kommunikationssystemen.Im zweiten Teil untersuchen wir Techniken zur SI-Unterdrückung, um den FD-Gewinn in einem Punkt-zu-Punkt-System auszunutzen. Zunächst entwickeln wir ein Übertragungsverfahren, dass auf SI Rücksicht nimmt und die SI-Unterdrückung gegen den Multiplexgewinn abwägt. Die besten Ergebnisse werden durch die perfekte Kenntnis des Kanals erzielt, was praktisch nicht genau der Fall ist. Aus diesem Grund werden Übertragungstechniken für den „Worst Case“ entwickelt, die den Kanalschätzfehlern Rechnung tragen. Diese Fehler werden deterministisch modelliert und durch Ellipsoide beschränkt. In praktischen Szenarien ist der HF-Schaltkreise nicht perfekt. Dies hat Einfluss auf die Verfahren zur SI-Unterdrückung und führt zu einer Restselbstinterferenz. Wir entwickeln effiziente Übertragungstechniken mittels Beamforming, welche auf dem Signal-zu-Verlust-und-Rausch-Verhältnis (signal to leakage plus noise ratio [SLNR]) aufbauen, um Unvollkommenheiten der HF-Schaltkreise auszugleichen. Zusätzlich können alle Designkonzepte auf FD-OWR-Systeme erweitert werden.To enable ultra-high data rate and ubiquitous coverage in future wireless networks, new physical layer techniques are desired. Relaying is a promising technique for future wireless networks since it can boost the coverage and can provide low cost wireless backhauling solutions, as compared to traditional wired backhauling solutions via fiber and copper. Traditional one-way relaying (OWR) techniques suffer from the spectral loss due to the half-duplex (HD) operation at the relay. On one hand, two-way relaying (TWR) allows the communication partners to transmit to and/or receive from the relay simultaneously and thus uses the spectrum more efficiently than OWR. Therefore, we study two-way relays and more specifically multi-pair/multi-user TWR systems with amplify-and-forward (AF) relays. These scenarios suffer from inter-pair or inter-user interference. To deal with the interference, advanced signal processing algorithms, in other words, spatial division multiple access (SDMA) techniques, are desired. On the other hand, if the relay is a full-duplex (FD) relay, the spectral loss due to a HD operation can also be compensated. However, in practice, a FD device is hard to realize due to the strong loop-back self-interference and the limited dynamic range at the transceiver. Thus, advanced self-interference suppression techniques should be developed. This thesis contributes to the two goals by developing optimal and/or efficient algebraic solutions for different scenarios subject to different utility functions of the system, e.g., sum rate maximization and transmit power minimization. In the first part of this thesis, we first study a multi-pair TWR network with a multi-antenna AF relay. This scenario can be also treated as the sharing of the relay and the spectrum among multiple operators assuming that different pairs of users belong to different operators. Existing approaches focus on interference suppression. We propose a projection based separation of multiple operators (ProBaSeMO) scheme, which can be easily extended when each user has multiple antennas or when different system design criteria are applied. To benchmark the ProBaSeMO scheme, we develop optimal relay transmit strategies to maximize the system sum rate, minimize the required transmit power at the relay, or maximize the minimum signal to interference plus noise ratio (SINR) of the users. Specifically for the sum rate maximization problem, gradient based methods are developed regardless whether each user has a single antenna or multiple antennas. To guarantee a worst-case polynomial time solution, we also develop a polynomial time algorithm which has been inspired by the polynomial time difference of convex functions (POTDC) method. Finally, we analyze the conditions for obtaining the sharing gain in terms of the sum rate. Then we study the sum rate maximization problem of a multi-pair TWR network with multiple single antenna AF relays and single antenna users. The resulting sum rate maximization problem, subject to a total transmit power constraint of the relays in the network, yields a similar problem structure as in the previous scenario. Therefore the optimal solution for one scenario can be used for the other. Moreover, a global optimal solution, which is based on the polyblock approach, and several suboptimal solutions, which are more computationally efficient and approximate the optimal solution, are developed when there is a total transmit power constraint of the relays in the network or each relay has its own transmit power constraint. We then shift our focus to a multi-pair TWR network with multiple multi-antenna AF relays and multiple dumb repeaters. This scenario is more general because the previous two scenarios can be seen as special realizations of this scenario. The interference management in this scenario is more challenging due to the existence of the repeaters. Interference neutralization (IN) is a solution for dealing with this kind of interference. Thereby, necessary and sufficient conditions for neutralizing the interference are derived. Moreover, a general framework to optimize different system utility functions in this network with or without IN is developed regardless whether the AF relays in the network have a total transmit power limit or individual transmit power limits. Finally, we develop the relay transmit strategy as well as base station (BS) precoding and decoding schemes for a TWR assisted multi-user MIMO (MU-MIMO) downlink channel. Compared to the multi-pair TWR network, this scenario suffers from the co-channel interference. We develop three suboptimal algorithms which are based on channel inversion, ProBaSeMO and zero-forcing dirty paper coding (ZFDPC), which has a low computational complexity, provides a balance between the performance and the complexity, and suffers only a little when the system is heavily loaded, respectively.In the second part of this thesis, we investigate self-interference (SI) suppression techniques to exploit the FD gain for a point-to-point MIMO system. We first develop SI aware transmit strategies, which provide a balance between the SI suppression and the multiplexing gain of the system. To get the best performance, perfect channel state information (CSI) is needed, which is imperfect in practice. Thus, worst case transmit strategies to combat the imperfect CSI are developed, where the CSI errors are modeled deterministically and bounded by ellipsoids. In real word applications, the RF chain is imperfect. This affects the performance of the SI suppression techniques and thus results in residual SI. We develop efficient transmit beamforming techniques, which are based on the signal to leakage plus noise ratio (SLNR) criterion, to deal with the imperfections in the RF chain. All the proposed design concepts can be extended to FD OWR systems

Wireless MIMO Switching: Weighted Sum Mean Square Error and Sum Rate Optimization

This paper addresses joint transceiver and relay design for a wireless multiple-input-multiple-output (MIMO) switching scheme that enables data exchange among multiple users. Here, a multi-antenna relay linearly precodes the received (uplink) signals from multiple users before forwarding the signal in the downlink, where the purpose of precoding is to let each user receive its desired signal with interference from other users suppressed. The problem of optimizing the precoder based on various design criteria is typically non-convex and difficult to solve. The main contribution of this paper is a unified approach to solve the weighted sum mean square error (MSE) minimization and weighted sum rate maximization problems in MIMO switching. Specifically, an iterative algorithm is proposed for jointly optimizing the relay's precoder and the users' receive filters to minimize the weighted sum MSE. It is also shown that the weighted sum rate maximization problem can be reformulated as an iterated weighted sum MSE minimization problem and can therefore be solved similarly to the case of weighted sum MSE minimization. With properly chosen initial values, the proposed iterative algorithms are asymptotically optimal in both high and low signal-to-noise ratio (SNR) regimes for MIMO switching, either with or without self-interference cancellation (a.k.a., physical-layer network coding). Numerical results show that the optimized MIMO switching scheme based on the proposed algorithms significantly outperforms existing approaches in the literature.Comment: This manuscript is under 2nd review of IEEE Transactions on Information Theor

Design and optimization for wireless-powered networks

Wireless Power Transfer (WPT) opens an emerging area of Wireless-Powered Networks (WPNs). In narrowband WPNs, beamforming is recognized as a key technique for enhancing information and energy transfer. However, in multi-antenna multi-sine WPT systems, not only the beamforming gain but also the rectifier nonlinearity can be exploited by a waveform design to boost the end-to-end power transfer efficiency. This thesis proposes and optimizes novel transmission strategies for two types of WPNs: narrowband autonomous relay networks and multi-antenna multi-sine WPT systems. The thesis starts by proposing a novel Energy Flow-Assisted (EFA) relaying strategy for a one-way multi-antenna Amplify-and-Forward (AF) autonomous relay network. In contrast to state-of-the-art autonomous relaying strategies, the EFA enables the relay to simultaneously harvest power from source information signals and a dedicated Energy Flow (EF) from the destination for forwarding. As a baseline, a Non-EFA (NEFA) strategy, where the relay splits power from the source signals, is also investigated. We optimize relay strategies for EFA and NEFA, so as to maximize the end-to-end rate and gain insights into the benefit of the EF. To transmit multiple data streams, we extend the EFA and the NEFA to a Multiple-Input Multiple-Output (MIMO) relay network. A novel iterative algorithm is developed to jointly optimize source precoders and relay matrices for the EFA and the NEFA, in order to maximize the end-to-end rate. Based on a channel diagonalization method, we also propose less complex EFA and NEFA algorithms. In the study of waveform designs for multi-antenna multi-sine WPT, large-scale designs with many sinewaves and transmit antennas, computationally tractable algorithms and optimal multiuser waveforms remain open challenges. To tackle these issues, we propose efficient waveform optimization algorithms to maximize the multiuser weighted-sum/minimum rectenna DC output voltage, assuming perfect Channel State Information at the Transmitter (CSIT). An optimization framework is developed to derive these waveform algorithms. Relaxing the assumption on CSIT, we propose waveform strategies for multi-antenna multi-sine WPT based on waveform selection (WS) and waveform refinement (WR), respectively. Applying the strategies, an energy transmitter can generate preferred waveforms for WPT from predesigned codebooks of waveform precoders, according to limited feedback from an energy receiver, which carries information on the harvested energy. Although the WR-based strategy is suboptimal for maximizing the average rectenna output voltage, it causes a lower overhead than the WS-based strategy. We propose novel algorithms to optimize the codebooks for the two strategies.Open Acces