DMT Optimality of LR-Aided Linear Decoders for a General Class of Channels, Lattice Designs, and System Models

The work identifies the first general, explicit, and non-random MIMO encoder-decoder structures that guarantee optimality with respect to the diversity-multiplexing tradeoff (DMT), without employing a computationally expensive maximum-likelihood (ML) receiver. Specifically, the work establishes the DMT optimality of a class of regularized lattice decoders, and more importantly the DMT optimality of their lattice-reduction (LR)-aided linear counterparts. The results hold for all channel statistics, for all channel dimensions, and most interestingly, irrespective of the particular lattice-code applied. As a special case, it is established that the LLL-based LR-aided linear implementation of the MMSE-GDFE lattice decoder facilitates DMT optimal decoding of any lattice code at a worst-case complexity that grows at most linearly in the data rate. This represents a fundamental reduction in the decoding complexity when compared to ML decoding whose complexity is generally exponential in rate. The results' generality lends them applicable to a plethora of pertinent communication scenarios such as quasi-static MIMO, MIMO-OFDM, ISI, cooperative-relaying, and MIMO-ARQ channels, in all of which the DMT optimality of the LR-aided linear decoder is guaranteed. The adopted approach yields insight, and motivates further study, into joint transceiver designs with an improved SNR gap to ML decoding.Comment: 16 pages, 1 figure (3 subfigures), submitted to the IEEE Transactions on Information Theor

Interference Alignment for Cognitive Radio Communications and Networks: A Survey

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).Interference alignment (IA) is an innovative wireless transmission strategy that has shown to be a promising technique for achieving optimal capacity scaling of a multiuser interference channel at asymptotically high-signal-to-noise ratio (SNR). Transmitters exploit the availability of multiple signaling dimensions in order to align their mutual interference at the receivers. Most of the research has focused on developing algorithms for determining alignment solutions as well as proving interference alignment’s theoretical ability to achieve the maximum degrees of freedom in a wireless network. Cognitive radio, on the other hand, is a technique used to improve the utilization of the radio spectrum by opportunistically sensing and accessing unused licensed frequency spectrum, without causing harmful interference to the licensed users. With the increased deployment of wireless services, the possibility of detecting unused frequency spectrum becomes diminished. Thus, the concept of introducing interference alignment in cognitive radio has become a very attractive proposition. This paper provides a survey of the implementation of IA in cognitive radio under the main research paradigms, along with a summary and analysis of results under each system model.Peer reviewe

Low-Complexity and Robust Hybrid Beamforming Design for Multi-Antenna Communication Systems

This paper proposes a low-complexity hybrid beamforming design for multi-antenna communication systems. The hybrid beamformer is comprised of a baseband digital beamformer and a constant modulus analog beamformer in the radio frequency (RF) part of the system. As in singular-value-decomposition (SVD)-based beamforming, hybrid beamforming design aims to generate parallel data streams in multi-antenna systems, however, due to the constant modulus constraint of the analog beamformer, the problem cannot be solved similarly. To address this problem, mathematical expressions of the parallel data streams are derived in this paper and desired and interfering signals are specified per stream. The analog beamformers are designed by maximizing the power of desired signal while minimizing the sum-power of interfering signals. Finally, digital beamformers are derived by defining the equivalent channel observed by the transmitter/receiver. Regardless of the number of the antennas or type of channel, the proposed approach can be applied to a wide range of MIMO systems with hybrid structure wherein the number of the antennas is more than the number of the RF chains. In particular, the proposed algorithm is verified for sparse channels that emulate mm-wave transmission as well as rich scattering environments. In order to validate the optimality, the results are compared with those of the state-of-the-art and it is demonstrated that the performance of the proposed method outperforms state-of-the-art techniques, regardless of type of the channel and/or system configuration

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

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

Multi-Antenna Techniques for Next Generation Cellular Communications

Future cellular communications are expected to offer substantial improvements for the pre- existing mobile services with higher data rates and lower latency as well as pioneer new types of applications that must comply with strict demands from a wider range of user types. All of these tasks require utmost efficiency in the use of spectral resources. Deploying multiple antennas introduces an additional signal dimension to wireless data transmissions, which provides a significant alternative solution against the plateauing capacity issue of the limited available spectrum. Multi-antenna techniques and the associated key enabling technologies possess unquestionable potential to play a key role in the evolution of next generation cellular systems. Spectral efficiency can be improved on downlink by concurrently serving multiple users with high-rate data connections on shared resources. In this thesis optimized multi-user multi-input multi-output (MIMO) transmissions are investigated on downlink from both filter design and resource allocation/assignment points of view. Regarding filter design, a joint baseband processing method is proposed specifically for high signal-to-noise ratio (SNR) conditions, where the necessary signaling overhead can be compensated for. Regarding resource scheduling, greedy- and genetic-based algorithms are proposed that demand lower complexity with large number of resource blocks relative to prior implementations. Channel estimation techniques are investigated for massive MIMO technology. In case of channel reciprocity, this thesis proposes an overhead reduction scheme for the signaling of user channel state information (CSI) feedback during a relative antenna calibration. In addition, a multi-cell coordination method is proposed for subspace-based blind estimators on uplink, which can be implicitly translated to downlink CSI in the presence of ideal reciprocity. Regarding non-reciprocal channels, a novel estimation technique is proposed based on reconstructing full downlink CSI from a select number of dominant propagation paths. The proposed method offers drastic compressions in user feedback reports and requires much simpler downlink training processes. Full-duplex technology can provide up to twice the spectral efficiency of conventional resource divisions. This thesis considers a full-duplex two-hop link with a MIMO relay and investigates mitigation techniques against the inherent loop-interference. Spatial-domain suppression schemes are developed for the optimization of full-duplex MIMO relaying in a coverage extension scenario on downlink. The proposed methods are demonstrated to generate data rates that closely approximate their global bounds

Multiuser MIMO Wireless Communications: Optimal and Efficient Schemes For Rate Maximization and Power Minimization

Ph.DDOCTOR OF PHILOSOPH

Optimising Cooperative Spectrum Sensing in Cognitive Radio Networks Using Interference Alignment and Space-Time Coding

In this thesis, the process of optimizing Cooperative Spectrum Sensing in Cognitive Radio has been investigated in fast-fading environments where simulation results have shown that its performance is limited by the Probability of Reporting Errors. By proposing a transmit diversity scheme using Differential space-time block codes (D-STBC) where channel state information (CSI) is not required and regarding multiple pairs of Cognitive Radios (CR’s) with single antennas as a virtual MIMO antenna arrays in multiple clusters, Differential space-time coding is applied for the purpose of decision reporting over Rayleigh channels. Both Hard and Soft combination schemes were investigated at the fusion center to reveal performance advantages for Hard combination schemes due to their minimal bandwidth requirements and simplistic implementation. The simulations results show that this optimization process achieves full transmit diversity, albeit with slight performance degradation in terms of power with improvements in performance when compared to conventional Cooperative Spectrum Sensing over non-ideal reporting channels. Further research carried out in this thesis shows performance deficits of Cooperative Spectrum Sensing due to interference on sensing channels of Cognitive Radio. Interference Alignment (IA) being a revolutionary wireless transmission strategy that reduces the impact of interference seems well suited as a strategy that can be used to optimize the performance of Cooperative Spectrum Sensing. The idea of IA is to coordinate multiple transmitters so that their mutual interference aligns at their receivers, facilitating simple interference cancellation techniques. Since its inception, research efforts have primarily been focused on verifying IA’s ability to achieve the maximum degrees of freedom (an approximation of sum capacity), developing algorithms for determining alignment solutions and designing transmission strategies that relax the need for perfect alignment but yield better performance. With the increased deployment of wireless services, CR’s ability to opportunistically sense and access the unused licensed frequency spectrum, without causing harmful interference to the licensed users becomes increasingly diminished, making the concept of introducing IA in CR a very attractive proposition. For a multiuser multiple-input–multiple-output (MIMO) overlay CR network, a space-time opportunistic IA (ST-OIA) technique has been proposed that allows spectrum sharing between a single primary user (PU) and multiple secondary users (SU) while ensuring zero interference to the PUs. With local CSI available at both the transmitters and receivers of SUs, the PU employs a space-time WF (STWF) algorithm to optimize its transmission and in the process, frees up unused eigenmodes that can be exploited by the SU. STWF achieves higher performance than other WF algorithms at low to moderate signal-to-noise ratio (SNR) regimes, which makes it ideal for implementation in CR networks. The SUs align their transmitted signals in such a way their interference impairs only the PU’s unused eigenmodes. For the multiple SUs to further exploit the benefits of Cooperative Spectrum Sensing, it was shown in this thesis that IA would only work when a set of conditions were met. The first condition ensures that the SUs satisfy a zero interference constraint at the PU’s receiver by designing their post-processing matrices such that they are orthogonal to the received signal from the PU link. The second condition ensures a zero interference constraint at both the PU and SUs receivers i.e. the constraint ensures that no interference from the SU transmitters is present at the output of the post-processing matrices of its unintended receivers. The third condition caters for the multiple SUs scenario to ensure interference from multiple SUs are aligned along unused eigenmodes. The SU system is assumed to employ a time division multiple access (TDMA) system such that the Principle of Reciprocity is employed towards optimizing the SUs transmission rates. Since aligning multiple SU transmissions at the PU is always limited by availability of spatial dimensions as well as typical user loads, the third condition proposes a user selection algorithm by the fusion centre (FC), where the SUs are grouped into clusters based on their numbers (i.e. two SUs per cluster) and their proximity to the FC, so that they can be aligned at each PU-Rx. This converts the cognitive IA problem into an unconstrained standard IA problem for a general cognitive system. Given the fact that the optimal power allocation algorithms used to optimize the SUs transmission rates turns out to be an optimal beamformer with multiple eigenbeams, this work initially proposes combining the diversity gain property of STBC, the zero-forcing function of IA and beamforming to optimize the SUs transmission rates. However, this solution requires availability of CSI, and to eliminate the need for this, this work then combines the D-STBC scheme with optimal IA precoders (consisting of beamforming and zero-forcing) to maximize the SUs data rates

Cooperative Radio Communications for Green Smart Environments

The demand for mobile connectivity is continuously increasing, and by 2020 Mobile and Wireless Communications will serve not only very dense populations of mobile phones and nomadic computers, but also the expected multiplicity of devices and sensors located in machines, vehicles, health systems and city infrastructures. Future Mobile Networks are then faced with many new scenarios and use cases, which will load the networks with different data traffic patterns, in new or shared spectrum bands, creating new specific requirements. This book addresses both the techniques to model, analyse and optimise the radio links and transmission systems in such scenarios, together with the most advanced radio access, resource management and mobile networking technologies. This text summarises the work performed by more than 500 researchers from more than 120 institutions in Europe, America and Asia, from both academia and industries, within the framework of the COST IC1004 Action on "Cooperative Radio Communications for Green and Smart Environments". The book will have appeal to graduates and researchers in the Radio Communications area, and also to engineers working in the Wireless industry. Topics discussed in this book include: • Radio waves propagation phenomena in diverse urban, indoor, vehicular and body environments• Measurements, characterization, and modelling of radio channels beyond 4G networks• Key issues in Vehicle (V2X) communication• Wireless Body Area Networks, including specific Radio Channel Models for WBANs• Energy efficiency and resource management enhancements in Radio Access Networks• Definitions and models for the virtualised and cloud RAN architectures• Advances on feasible indoor localization and tracking techniques• Recent findings and innovations in antenna systems for communications• Physical Layer Network Coding for next generation wireless systems• Methods and techniques for MIMO Over the Air (OTA) testin