Deep Learning Assisted Multiuser MIMO Load Modulated Systems for Enhanced Downlink mmWave Communications

This paper is focused on multiuser load modulation arrays (MU-LMAs) which are attractive due to their low system complexity and reduced cost for millimeter wave (mmWave) multi-input multi-output (MIMO) systems. The existing precoding algorithm for downlink MU-LMA relies on a sub-array structured (SAS) transmitter which may suffer from decreased degrees of freedom and complex system configuration. Furthermore, a conventional LMA codebook with codewords uniformly distributed on a hypersphere may not be channel-adaptive and may lead to increased signal detection complexity. In this paper, we conceive an MU-LMA system employing a full-array structured (FAS) transmitter and propose two algorithms accordingly. The proposed FAS-based system addresses the SAS structural problems and can support larger numbers of users. For LMAimposed constant-power downlink precoding, we propose an FASbased normalized block diagonalization (FAS-NBD) algorithm. However, the forced normalization may result in performance degradation. This degradation, together with the aforementioned codebook design problems, is difficult to solve analytically. This motivates us to propose a Deep Learning-enhanced (FAS-DLNBD) algorithm for adaptive codebook design and codebookindependent decoding. It is shown that the proposed algorithms are robust to imperfect knowledge of channel state information and yield excellent error performance. Moreover, the FAS-DLNBD algorithm enables signal detection with low complexity as the number of bits per codeword increases

Deep Learning Designs for Physical Layer Communications

Wireless communication systems and their underlying technologies have undergone unprecedented advances over the last two decades to assuage the ever-increasing demands for various applications and emerging technologies. However, the traditional signal processing schemes and algorithms for wireless communications cannot handle the upsurging complexity associated with fifth-generation (5G) and beyond communication systems due to network expansion, new emerging technologies, high data rate, and the ever-increasing demands for low latency. This thesis extends the traditional downlink transmission schemes to deep learning-based precoding and detection techniques that are hardware-efficient and of lower complexity than the current state-of-the-art. The thesis focuses on: precoding/beamforming in massive multiple-inputs-multiple-outputs (MIMO), signal detection and lightweight neural network (NN) architectures for precoder and decoder designs. We introduce a learning-based precoder design via constructive interference (CI) that performs the precoding on a symbol-by-symbol basis. Instead of conventionally training a NN without considering the specifics of the optimisation objective, we unfold a power minimisation symbol level precoding (SLP) formulation based on the interior-point-method (IPM) proximal ‘log’ barrier function. Furthermore, we propose a concept of NN compression, where the weights are quantised to lower numerical precision formats based on binary and ternary quantisations. We further introduce a stochastic quantisation technique, where parts of the NN weight matrix are quantised while the remaining is not. Finally, we propose a systematic complexity scaling of deep neural network (DNN) based MIMO detectors. The model uses a fraction of the DNN inputs by scaling their values through weights that follow monotonically non-increasing functions. Furthermore, we investigate performance complexity tradeoffs via regularisation constraints on the layer weights such that, at inference, parts of network layers can be removed with minimal impact on the detection accuracy. Simulation results show that our proposed learning-based techniques offer better complexity-vs-BER (bit-error-rate) and complexity-vs-transmit power performances compared to the state-of-the-art MIMO detection and precoding techniques

Radio Communications

In the last decades the restless evolution of information and communication technologies (ICT) brought to a deep transformation of our habits. The growth of the Internet and the advances in hardware and software implementations modified our way to communicate and to share information. In this book, an overview of the major issues faced today by researchers in the field of radio communications is given through 35 high quality chapters written by specialists working in universities and research centers all over the world. Various aspects will be deeply discussed: channel modeling, beamforming, multiple antennas, cooperative networks, opportunistic scheduling, advanced admission control, handover management, systems performance assessment, routing issues in mobility conditions, localization, web security. Advanced techniques for the radio resource management will be discussed both in single and multiple radio technologies; either in infrastructure, mesh or ad hoc networks

Power Allocation in Wireless Relay Networks

This thesis is mainly concerned with power allocation issues in wireless relay networks where a single or multiple relays assist transmission from a single or multiple sources to a destination. First, a network model with a single source and multiple relays is considered, in which both cases of orthogonal and non--orthogonal relaying are investigated. For the case of orthogonal relaying, two power allocation schemes corresponding to two partial channel state information (CSI) assumptions are proposed. Given the lack of full and perfect CSI, appropriate signal processing at the relays and/or destination is also developed. The performance behavior of the system with power allocation between the source and the relays is also analyzed. For the case of non-orthogonal relaying, it is demonstrated that optimal power allocation is not sufficiently effective. Instead, a relay beamforming scheme is proposed. A comprehensive comparison between the orthogonal relaying with power allocation scheme and the non-orthogonal relaying with beamforming scheme is then carried out, which reveals several interesting conclusions with respect to both error performance and system throughput. In the second part of the thesis, a network model with multiple sources and a single relay is considered. The transmission model is applicable for uplink channels in cellular mobile systems in which multiple mobile terminals communicate with the base station with the help of a single relay station. Single-carrier frequency division multiple access (SC-FDMA) technique with frequency domain equalization is adopted in order to avoid the amplification of the multiple access interference at the relay. Minimizing the transmit power at the relay and optimizing the fairness among the sources in terms of throughput are the two objectives considered in implementing power allocation schemes. The problems are visualized as water-filling and water-discharging models and two optimal power allocation schemes are proposed, accordingly. Finally, the last part of the thesis is extended to a network model with multiple sources and multiple relays. The orthogonal multiple access technique is employed in order to avoid multiple access interference. Proposed is a joint optimal beamforming and power allocation scheme in which an alternative optimization technique is applied to deal with the non-convexity of the power allocation problem. Furthermore, recognizing the high complexity and large overhead information exchange when the number of sources and relays increases, a relay selection scheme is proposed. Since each source is supported by at most one relay, the feedback information from the destination to each relay can be significantly reduced. Using an equal power allocation scheme, relay selection is still an NP-hard combinatorial optimization problem. Nevertheless, the proposed sub-optimal scheme yields a comparable performance with a much lower computational complexity and can be well suited for practical systems

Beyond Massive MIMO : Trade-offs and Opportunities with Large Multi-Antenna Systems

After the commercial emergence of 5G, the research community is already putting its focus on proposing innovative solutions to enable the upcoming 6G. One important lesson put forth by 5G research was that scaling up the conventional multiple-input-multiple-output (MIMO) technology by increasing the number of antennas could be extremely beneficial for effectively multiplexing data streams in the spatial domain. This idea was embodied in massive MIMO, which constitutes one of the major technical advancements included in 5G. Consequently, 6G research efforts have been largely directed towards studying ways to further scale up wireless systems, as can be seen in some of the proposed 6G enabling technologies like large intelligent surface (LIS), cell-free massive MIMO, or even reconfigurable intelligent surface (RIS). This thesis studies the possibilities offered by some of these technologies, as well as the trade-offs that may naturally arise when scaling up such wireless systems.An important part of this thesis deals with decentralized solutions for base station (BS) technologies including a large number of antennas. Already in the initial massive MIMO prototypes, the increased number of BS antennas led to scalability issues due to the high interconnection bandwidths required to send the received signals---as well as the channel state information (CSI)---to a central processing unit (CPU) in charge of the data processing. These issues can only be exacerbated if we consider novel system proposals like LIS, where the number of BS antennas may be increased by an order of magnitude with respect to massive MIMO, or cell-free massive MIMO, where the BS antennas may be located far from each other. We provide a number of decentralized schemes to process the received data while restricting the information that has to be shared with a CPU. We also provide a framework to study architectures with an arbitrary level of decentralization, showing that there exists a direct trade-off between the interconnection bandwidth to a CPU and the complexity of the decentralized processing required for fixed user rates.Another part of this thesis studies RIS-based solutions to enhance the multiplexing performance of wireless communication systems. RIS constitutes one of the most attractive 6G enabling technologies since it provides a cost- and energy-efficient solution to improve the wireless propagation links by generating favorable reflections. We extend the concept of RIS by considering reconfigurable surfaces (RSs) with different processing capabilities, and we show how these surfaces may be employed for achieving perfect spatial multiplexing at reduced processing complexity in general multi-antenna communication settings. We also show that these surfaces can exploit the available degrees of freedom---e.g., due to excess of BS antennas---to embed their own data into the enhanced channel

Advanced signal processing concepts for multi-dimensional communication systems

Die weit verbreitete Nutzung von mobilem Internet und intelligenten Anwendungen hat zu einem explosionsartigen Anstieg des mobilen Datenverkehrs geführt. Mit dem Aufstieg von intelligenten Häusern, intelligenten Gebäuden und intelligenten Städten wächst diese Nachfrage ständig, da zukünftige Kommunikationssysteme die Integration mehrerer Netzwerke erfordern, die verschiedene Sektoren, Domänen und Anwendungen bedienen, wie Multimedia, virtuelle oder erweiterte Realität, Machine-to-Machine (M2M) -Kommunikation / Internet of Things (IoT), Automobilanwendungen und vieles mehr. Daher werden die Kommunikationssysteme zukünftig nicht nur eine drahtlose Verbindung über Gbps bereitstellen müssen, sondern auch andere Anforderungen erfüllen müssen, wie z. B. eine niedrige Latenzzeit und eine massive Maschinentyp-Konnektivität, während die Dienstqualität sichergestellt wird. Ohne bedeutende technologische Fortschritte zur Erhöhung der Systemkapazität wird die bestehende Telekommunikationsinfrastruktur diese mehrdimensionalen Anforderungen nicht unterstützen können. Dies stellt eine wichtige Forderung nach geeigneten Wellenformen und Signalverarbeitungslösungen mit verbesserten spektralen Eigenschaften und erhöhter Flexibilität dar. Aus der Spektrumsperspektive werden zukünftige drahtlose Netzwerke erforderlich sein, um mehrere Funkbänder auszunutzen, wie zum Beispiel niedrigere Frequenzbänder (typischerweise mit Frequenzen unter 10 GHz), mm-Wellenbänder (einige hundert GHz höchstens) und THz-Bänder. Viele alternative Technologien wie Optical Wireless Communication (OWC), dynamische Funksysteme und zellulares Radar sollten ebenfalls untersucht werden, um ihr wahres Potenzial abzuschätzen. Insbesondere bietet OWC ein großes, aber noch nicht genutztes optisches Band im sichtbaren Spektrum, das Licht als Mittel zur Informationsübertragung nutzt. Daher können zukünftige Kommunikationssysteme als zusammengesetzte Hybridnetzwerke angesehen werden, die aus einer Anzahl von verschiedenen drahtlosen Netzwerken bestehen, die auf Funk und optischem Zugang basieren. Auf der anderen Seite ist es eine große Herausforderung, fortschrittliche Signalverarbeitungslösungen für mehrere Bereiche von Kommunikationssystemen zu entwickeln. Diese Arbeit trägt zu diesem Ziel bei, indem sie Methoden für die Suche nach effizienten algebraischen Lösungen für verschiedene Anwendungen der digitalen Mehrkanal-Signalverarbeitung demonstriert. Insbesondere tragen wir zu drei verschiedenen Anwendungsgebieten bei, d.h. Wellenformen, optischen drahtlosen Systemen und mehrdimensionaler Signalverarbeitung. Gegenwärtig ist das Cyclic Prefix Orthogonal Frequency Division Multiplexing (CP-OFDM) die weit verbreitete Multitragetechnik für die meisten Kommunikationssysteme. Um jedoch die CP-OFDM-Nachteile in Bezug auf eine schlechte spektrale Eingrenzung, Robustheit in hoch asynchronen Umgebungen und Unflexibilität der Parameterwahl zu überwinden, wurden viele alternative Wellenformen vorgeschlagen. Solche Mehrfachträgerwellenformen umfassen einen Filter bank Multicarrier (FBMC), ein Generalized Frequency Division Multiplexing (GFDM), einen Universal Filter Multicarrier (UFMC) und ein Unique Word Orthogonal Orthogonal Frequency Division Multiplexing (UW-OFDM). Diese neuen Luftschnittstellenschemata verwenden verschiedene Ansätze, um einige der inhärenten Mängel bei CP-OFDM zu überwinden. Einige dieser Wellenformen wurden gut untersucht, während andere sich noch in den Kinderschuhen befinden. Insbesondere die Integration von Multiple-Input-Multiple-Output (MIMO) -Konzepten mit UW-OFDM und UFMC befindet sich noch in einem frühen Forschungsstadium. Daher schlagen wir im ersten Teil dieser Arbeit neuartige lineare und sukzessive Interferenzunterdrückungstechniken für MIMO UW-OFDM-Systeme vor. Das Design dieser Techniken zielt darauf ab, Empfänger mit einer geringen Rechenkomplexität zu erhalten. Ein weiterer Schwerpunkt ist die Anwendbarkeit von Space-Time Block Codes (STBCs) auf UW-OFDM und UFMC-Wellenformen. Zu diesem Zweck stellen wir neue Techniken zusammen mit Detektionsverfahren vor. Wir vergleichen auch die Leistung dieser Wellenformen mit unseren vorgeschlagenen Techniken mit den anderen Wellenformen des Standes der Technik, die in der Literatur vorgeschlagen wurden. Wir zeigen, dass raumzeitblockierte UW-OFDM-Systeme mit den vorgeschlagenen Methoden nicht nur andere Wellenformen signifikant übertreffen, sondern auch zu Empfängern mit geringer Rechnerkomplexität führen. Der zweite Anwendungsbereich umfasst optische Systeme im sichtbaren Band (390-700 nm), die in Plastic Optical Fibers (POFs), Multimode-Fasern oder OWC-Systemen wie der Kommunikation über Visible Light Communication (VLC) verwendet werden können. VLC kann Lösungen für eine Reihe von Anwendungen anbieten, einschließlich drahtloser lokaler, persönlicher und Körperbereichsnetzwerke (WLAN, WPAN und WBANs), Innenlokalisierung und -navigation, Fahrzeugnetze, U-Bahn- und Unterwassernetze und bietet eine Reihe von Datenraten von wenigen Mbps zu 10 Gbps. VLC nutzt voll sichtbare Light Emitting Diodes (LEDs) für den doppelten Zweck der Beleuchtung und Datenkommunikation bei sehr hohen Geschwindigkeiten. Daher verwenden solche Systeme Intensitätsmodulation und Direct Detection (IM / DD), wodurch gefordert wird, dass das Sendesignal reellwertig und positiv sein sollte. Dies impliziert auch, dass die herkömmlichen Wellenformen, die für die Radio Frequency (RF) Kommunikation ausgelegt sind, nicht direkt verwendet werden können. Zum Beispiel muss eine hermetische Symmetrie auf das CP-OFDM angewendet werden, um ein reellwertiges Signal zu erhalten (oft als Discrete Multitone Transmission (DMT) bezeichnet), das im Gegenzug die Bandbreiteneffizienz verringert. Darüber hinaus begrenzt die LED / LED-Treiberkombination die elektrische Bandbreite. Alle diese Faktoren erfordern die Verwendung spektral effizienter Übertragungsverfahren zusammen mit robusten Entzerrungsschemata, um hohe Datenraten zu erzielen. Deshalb schlagen wir im zweiten Teil der Arbeit Übertragungsverfahren vor, die für solche optischen Systeme am besten geeignet sind. Insbesondere demonstrieren wir die Leistung der PAM-Blockübertragung mit Frequenzbereichsausgleich. Wir zeigen, dass dieses Schema nicht nur leistungsstärker ist, sondern auch alle modernen Verfahren wie CP-DMT-Schemata übertrifft. Wir schlagen auch neue UW-DMT-Schemata vor, die vom UW-OFDM-Konzept abgeleitet sind. Diese Schemata zeigen auch ein sehr überlegenes Bitfehlerverhältnis (BER) -Performance gegenüber den herkömmlichen CP-DMT-Schemata. Der dritte Anwendungsbereich konzentriert sich auf mehrdimensionale Signalverarbeitungstechniken. Bei der Verwendung von MIMO, STBCs, Mehrbenutzerverarbeitung und Mehrträgerwellenformen bei der drahtlosen Kommunikation ist das empfangene Signal mehrdimensional und kann eine multilineare Struktur aufweisen. In diesem Zusammenhang können Signalverarbeitungstechniken, die auf einem Tensor-Modell basieren, gleichzeitig von mehreren Formen von Diversität profitieren, um Mehrbenutzer-Signaltrennung / -entzerrung und Kanalschätzung durchzuführen. Dieser Vorteil ist eine direkte Konsequenz der Eigenschaft der wesentlichen Eindeutigkeit, die für matrixbasierte Ansätze nicht verfügbar ist. Tensor-Zerlegung wie die Higher Order Singular Value Decomposition (HOSVD) und die Canonical Polyadic Decomposition (CPD) werden weithin zur Durchführung dieser Aufgaben empfohlen. Die Leistung dieser Techniken wird oft mit zeitraubenden Monte-Carlo-Versuchen bewertet. Im letzten Teil der Arbeit führen wir eine Störungsanalyse erster Ordnung dieser Tensor-Zerlegungsmethoden durch. Insbesondere führen wir eine analytische Performanceanalyse des Semi-algebraischen Frameworks für approximative Canonical polyadic decompositions Simultaneous matrix diagonalizations (SECSI) durch. Das SECSI-Framework ist ein effizientes Werkzeug zur Berechnung der CPD eines rauscharmen Tensor mit niedrigem Rang. Darüber hinaus werden die erhaltenen analytischen Ausdrücke in Bezug auf die Momente zweiter Ordnung des Rauschens formuliert, so dass abgesehen von einem Mittelwert von Null keine Annahmen über die Rauschstatistik erforderlich sind. Wir zeigen, dass die abgeleiteten analytischen Ergebnisse eine ausgezeichnete Übereinstimmung mit den Monte-Carlo-Simulationen zeigen.The widespread use of mobile internet and smart applications has led to an explosive growth in mobile data traffic. With the rise of smart homes, smart buildings, and smart cities, this demand is ever growing since future communication systems will require the integration of multiple networks serving diverse sectors, domains and applications, such as multimedia, virtual or augmented reality, machine-to-machine (M2M) communication / the Internet of things (IoT), automotive applications, and many more. Therefore, in the future, the communication systems will not only be required to provide Gbps wireless connectivity but also fulfil other requirements such as low latency and massive machine type connectivity while ensuring the quality of service. Without significant technological advances to increase the system capacity, the existing telecommunications infrastructure will be unable to support these multi-dimensional requirements. This poses an important demand for suitable waveforms with improved spectral characteristics and signal processing solutions with an increased flexibility. Moreover, future wireless networks will be required to exploit several frequency bands, such as lower frequency bands (typically with frequencies below 10 GHz), mm-wave bands (few hundred GHz at the most), and THz bands. Many alternative technologies such as optical wireless communication (OWC), dynamic radio systems, and cellular radar should also be investigated to assess their true potential. Especially, OWC offers large but yet unexploited optical band in the visible spectrum that uses light as a means to carry information. Therefore, future communication systems can be seen as composite hybrid networks that consist of a number of different wireless networks based on radio and optical access. On the other hand, it poses a significant challenge to come up with advanced signal processing solutions in multiple areas of communication systems. This thesis contributes to this goal by demonstrating methods for finding efficient algebraic solutions to various applications of multi-channel digital signal processing. In particular, we contribute to three different scientific fields, i.e., waveforms, optical wireless systems, and multi-dimensional signal processing. Currently, cyclic prefix orthogonal frequency division multiplexing (CP-OFDM) is the widely adopted multicarrier technique for most of the communication systems. However, to overcome the CP-OFDM demerits in terms of poor spectral containment, poor robustness in highly asynchronous environments, and inflexibility of parameter choice, and many alternative waveforms have been proposed. Such multicarrier waveforms include filter bank multicarrier (FBMC), generalized frequency division multiplexing (GFDM), universal filter multicarrier (UFMC), and unique word orthogonal frequency division multiplexing (UW-OFDM). These new air interface schemes take different approaches to overcome some of the inherent deficiencies in CP-OFDM. Some of these waveforms have been well investigated while others are still in its infancy. Specifically, the integration of multiple-input multiple-output (MIMO) concepts with UW-OFDM and UFMC is still at an early stage of research. Therefore, in the first part of this thesis, we propose novel linear and successive interference cancellation techniques for MIMO UW-OFDM systems. The design of these techniques is aimed to result in receivers with a low computational complexity. Another focus area is the applicability of space-time block codes (STBCs) to UW-OFDM and UFMC waveforms. For this purpose, we present novel techniques along with detection procedures. We also compare the performance of these waveforms with our proposed techniques to the other state-of-the-art waveforms that has been proposed in the literature. We demonstrate that space-time block coded UW-OFDM systems with the proposed methods not only outperform other waveforms significantly but also results in receivers with a low computational complexity. The second application area comprises of optical systems in the visible band (390-700 nm) that can be utilized in plastic optical fibers (POFs), multimode fibers or OWC systems such as visible light communication (VLC). VLC can provide solutions for a number of applications including wireless local, personal, and body area networks (WLAN, WPAN, and WBANs), indoor localization and navigation, vehicular networks, underground and underwater networks, offering a range of data rates from a few Mbps to 10 Gbps. VLC takes full advantage of visible light emitting diodes (LEDs) for the dual purpose of illumination and data communications at very high speeds. Because of the incoherent nature of the LED sources, such systems employ intensity modulation and direct detection (IM/DD), thus demanding that the transmit signal should be real-valued and positive. This also implies that the conventional waveforms designed for the radio frequency (RF) communication cannot be directly used. For example, a Hermitian symmetry has to be applied to the CP-OFDM spectrum to obtain a real-valued signal (often referred to as discrete multitone transmission (DMT)) that in return reduces the bandwidth efficiency. Moreover, the LED/LED driver combination limits the electrical bandwidth. All these factors require the use of spectrally efficient transmission schemes along with robust equalization schemes to achieve high data rates. Therefore, in the second part of the thesis, we propose transmission schemes that are best suited for such optical systems. Specifically, we demonstrate the performance of PAM block transmission with frequency domain equalization. We show that this scheme is not only more power efficient but also outperforms all of the state-of-the-art schemes such as CP-DMT schemes. We also propose novel UW-DMT schemes that are derived from the UW-OFDM concept. These schemes also show a much superior bit error ratio (BER) performance over the conventional CP-DMT schemes. The third application area focuses on multi-dimensional signal processing techniques. With the use of MIMO, STBCs, multi-user processing, and multicarrier waveforms in wireless communications, the received signal is multidimensional in nature and may exhibit a multilinear structure. In this context, signal processing techniques based on a tensor model can simultaneously benefit from multiple forms of diversity to perform multi-user signal separation/equalization and channel estimation. This advantage is a direct consequence of the essential uniqueness property that is not available for matrix based approaches. Tensor decompositions such as the higher order singular value decomposition (HOSVD) and the canonical polyadic decomposition (CPD) are widely recommended for performing these tasks. The performance of these techniques is often evaluated using time consuming Monte-Carlo trials. In the last part of the thesis, we perform a first-order perturbation analysis of the truncated HOSVD and the Semi-algebraic framework for approximate Canonical polyadic decompositions via Simultaneous matrix diagonalizations (SECSI). The SECSI framework is an efficient tool for the computation of the approximate CPD of a low-rank noise corrupted tensor. Especially, the SECSI framework shows a much improved performance and comparatively low-complexity as compared to the conventional algorithms such as alternative least squares (ALS). Moreover, it also facilitates the implementation on a parallel hardware architecture. The obtained analytical expressions for both algorithms are formulated in terms of the second-order moments of the noise, such that apart from a zero-mean, no assumptions on the noise statistics are required. We demonstrate that the derived analytical results exhibit an excellent match to the Monte-Carlo simulations