Timing and Carrier Synchronization in Wireless Communication Systems: A Survey and Classification of Research in the Last 5 Years

Timing and carrier synchronization is a fundamental requirement for any wireless communication system to work properly. Timing synchronization is the process by which a receiver node determines the correct instants of time at which to sample the incoming signal. Carrier synchronization is the process by which a receiver adapts the frequency and phase of its local carrier oscillator with those of the received signal. In this paper, we survey the literature over the last 5 years (2010–2014) and present a comprehensive literature review and classification of the recent research progress in achieving timing and carrier synchronization in single-input single-output (SISO), multiple-input multiple-output (MIMO), cooperative relaying, and multiuser/multicell interference networks. Considering both single-carrier and multi-carrier communication systems, we survey and categorize the timing and carrier synchronization techniques proposed for the different communication systems focusing on the system model assumptions for synchronization, the synchronization challenges, and the state-of-the-art synchronization solutions and their limitations. Finally, we envision some future research directions

Two-tier channel estimation aided near-capacity MIMO transceivers relying on norm-based joint transmit and receive antenna selection

We propose a norm-based joint transmit and receive antenna selection (NBJTRAS) aided near-capacity multiple-input multiple-output (MIMO) system relying on the assistance of a novel two-tier channel estimation scheme. Specifically, a rough estimate of the full MIMO channel is first generated using a low-complexity, low-training-overhead minimum mean square error based channel estimator, which relies on reusing a modest number of radio frequency (RF) chains. NBJTRAS is then carried out based on this initial full MIMO channel estimate. The NBJTRAS aided MIMO system is capable of significantly outperforming conventional MIMO systems equipped with the same modest number of RF chains, while dispensing with the idealised simplifying assumption of having perfectly known channel state information (CSI). Moreover, the initial subset channel estimate associated with the selected subset MIMO channel matrix is then used for activating a powerful semi-blind joint channel estimation and turbo detector-decoder, in which the channel estimate is refined by a novel block-of-bits selection based soft-decision aided channel estimator (BBSB-SDACE) embedded in the iterative detection and decoding process. The joint channel estimation and turbo detection-decoding scheme operating with the aid of the proposed BBSB-SDACE channel estimator is capable of approaching the performance of the near-capacity maximumlikelihood (ML) turbo transceiver associated with perfect CSI. This is achieved without increasing the complexity of the ML turbo detection and decoding process

A universal space-time architecture for multiple-antenna aided systems

In this tutorial, we first review the family of conventional multiple-antenna techniques, and then we provide a general overview of the recent concept of the powerful Multiple-Input Multiple-Output (MIMO) family based on a universal Space-Time Shift Keying (STSK) philosophy. When appropriately configured, the proposed STSK scheme has the potential of outperforming conventional MIMO arrangements

Tensor-based signal processing with applications to MIMO-ODFM systems and intelligent reflecting surfaces

Der Einsatz von Tensor-Algebra-Techniken in der Signalverarbeitung hat in den letzten zwei Jahrzehnten zugenommen. Anwendungen wie Bildverarbeitung, biomedizinische Signalverarbeitung, radar, maschinelles Lernen, deep Learning und Kommunikation im Allgemeinen verwenden weitgehend tensorbasierte Verarbeitungstechniken zur Wiederherstellung, Schätzung und Klassifizierung von Signalen. Einer der Hauptgründe für den Einsatz der Tensorsignalverarbeitung ist die Ausnutzung der mehrdimensionalen Struktur von Signalen, wobei die Einzigartigkeitseigenschaften der Tensor-Zerlegung profitieren. Bei der drahtlosen Kommunikation beispielsweise können die Signale mehrere "Dimensionen" haben, wie Raum, Zeit, Frequenz, Polarisation, usw. Diese Arbeit ist in zwei Teile gegliedert. Im ersten Teil betrachten wir die Anwendung von Tensor-basierten Algorithmen für multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) Systeme unter Berücksichtigung von Vorhandensein von Phasenrauschenstörungen. In diesem Teil schlagen wir einen zweistufigen tensorbasierten Empfänger für eine gemeinsame Kanal-, Phasenrausch- und Datenschätzung in MIMO-OFDM-Systemen vor. In der ersten Stufe zeigen wir, dass das empfangene Signal auf den Pilotunterträgern als PARAFAC-Tensor dritter Ordnung modelliert werden kann. Auf der Grundlage dieses Modells werden zwei Algorithmen für die Schätzung der Phasen- und Kanalrauschen in den Pilotton vorgeschlagen. In der zweiten Stufe werden die übertragenen Daten geschätzt. Zu diesem Zweck schlagen wir einen Zero Forcing (ZF)-Empfänger vor, der sich die Tensorstruktur des empfangenen Signals auf den Datenträgern zunutze macht, indem er den vorgeschlagenen selektiven Kronecker-Produkt-Operators (SKP) kapitalisiert. Die Simulationsergebnisse zeigen, dass der vorgeschlagene Empfänger sowohl bei der Symbolfehlerrate als auch beim normalisierten mittleren quadratischen Fehler des geschätzten Kanal- und Phasenrauschmatrizen eine bessere Leistung im Vergleich zum Stand der Technik erzielt. Der zweite Teil dieser Arbeit befasst sich mit der Anwendung der Tensormodellierung zur Reduzierung des Kontrollsignalisierungsoverhead in zukünftigen drahtlosen Systemen, die durch intelligent reconfigurable surfaces (IRSs) unterstützt werden. Zu diesem Zweck schlagen wir eine Annäherung an die nahezu optimalen IRS-Phasenverschiebungen vor, die sonst einen prohibitiv hohen Kommunikationsoverhead auf den BS-IRS-Kontrollverbindungen verursachen würde. Die Hauptidee besteht darin, den optimalen Phasenvektor des IRSs, der Hunderte oder Tausende von Elementen haben kann, durch ein Tensormodell mit niedrigem Rang darzustellen. Dies wird erreicht durch Faktorisierung einer tensorisierten Version des IRS-Phasenverschiebungsvektors, wobei jede Komponente als Kronecker-Produkt einer vordefinierten Anzahl von Faktoren mit kleinerer Größe modelliert wird, die durch Tensor Zerlegungsalgorithmen erhaltet werden können. Wir zeigen, dass die vorgeschlagenen Low-Rank-Modelle die Rückkopplungsanforderungen für die BS-IRS-Kontrollverbindungen drastisch reduzieren. Die Simulationsergebnisse zeigen, dass die vorgeschlagene Methode besonders in Szenarien mit einer starken Sichtverbindung attraktiv sind. In diesem Fall wird fast die gleiche spektrale Effizienz erreicht wie in den Fällen mit nahezu optimalen Phasenverschiebungen, jedoch mit einem drastisch reduzierten Kommunikations-Overhead.The use of tensor algebra techniques in signal processing has been growing over the last two decades. Applications like image processing, biomedical signal processing, radar, machine/deep learning, and communications in general, largely employ tensor-based techniques for recovery, estimating, and classifying signals. One of the main reasons for using tensor signal processing is the exploitation of the multidimensional structure of signals, while benefiting from the uniqueness properties of tensor decomposition. For example, in wireless communications, the signals can have several “dimensions", e.g., space, time, frequency, polarization, beamspace, etc. This thesis is divided into two parts, first, in the application of a tensor-based algorithm in multiple-input multiple-output (MIMO)-orthogonal frequency division multiplexing (OFDM) systems with the presence of phase-noise impairments. In this first part, we propose a two-stage tensor-based receiver for a joint channel, phase-noise, and data estimation in MIMO-OFDM systems. In the first stage, we show that the received signal at the pilot subcarriers can be modeled as a third-order PARAFAC tensor. Based on this model, we propose two algorithms for channel and phase-noise estimation at the pilot subcarriers. The second stage consists of data estimation, for which we propose a ZF receiver that capitalizes on the tensor structure of the received signal at the data subcarriers using the proposed SKP operator. Numerical simulations show that the proposed receivers achieves an improved performance compared to the state-of-art receivers in terms of symbol error rate (SER) and normalized mean square error (NMSE) of the estimated channel and phase-noise matrices. The second part of this thesis focuses on the application of tensor modeling to reduce the control signaling overhead in future wireless systems aided by intelligent reconfigurable surfaces (IRS). To this end, we propose a low-rank approximation of the near-optimal IRS phase-shifts, which would incur prohibitively high communication overhead on the BS-IRS controller links. The key idea is to represent the potentially large IRS phase-shift vector using a low-rank tensor model. This is achieved by factorizing a tensorized version of the IRS phase-shift vector, where each component is modeled as the Kronecker product of a predefined number of factors of smaller sizes, which can be obtained via tensor decomposition algorithms. We show that the proposed low-rank models drastically reduce the required feedback requirements associated with the BS-IRS control links. Simulation results indicate that the proposed method is especially attractive in scenarios with a strong line of sight component, in which case nearly the same spectral efficiency is reached as in the cases with near-optimal phase-shifts, but with a drastically reduced communication overhead

Collaborative modulation multiple access for single hop and multihop networks

While the bandwidth available for wireless networks is limited, the world has seen an unprecedented growth in the number of mobile subscribers and an ever increasing demand for high data rates. Therefore efficient utilisation of bandwidth to maximise link spectral efficiency and number of users that can be served simultaneously are primary goals in the design of wireless systems. To achieve these goals, in this thesis, a new non-orthogonal uplink multiple access scheme which combines the functionalities of adaptive modulation and multiple access called collaborative modulation multiple access (CMMA) is proposed. CMMA enables multiple users to access the network simultaneously and share the same bandwidth even when only a single receive antenna is available and in the presence of high channel correlation. Instead of competing for resources, users in CMMA share resources collaboratively by employing unique modulation sets (UMS) that differ in phase, power, and/or mapping structure. These UMS are designed to insure that the received signal formed from the superposition of all users’ signals belongs to a composite QAM constellation (CC) with a rate equal to the sum rate of all users. The CC and its constituent UMSs are designed centrally at the BS to remove ambiguity, maximize the minimum Euclidian distance (dmin) of the CC and insure a minimum BER performance is maintained. Users collaboratively precode their transmitted signal by performing truncated channel inversion and phase rotation using channel state information (CSI ) obtained from a periodic common pilot to insure that their combined signal at the BS belongs to the CC known at the BS which in turn performs a simple joint maximum likelihood detection without the need for CSI. The coherent addition of users’ power enables CMMA to achieve high link spectral efficiency at any time without extra power or bandwidth but on the expense of graceful degradation in BER performance. To improve the BER performance of CMMA while preserving its precoding and detection structure and without the need for pilot-aided channel estimation, a new selective diversity combining scheme called SC-CMMA is proposed. SC-CMMA optimises the overall group performance providing fairness and diversity gain for various users with different transmit powers and channel conditions by selecting a single antenna out of a group of L available antennas that minimises the total transmit power required for precoding at any one time. A detailed study of capacity and BER performance of CMMA and SC-CMMA is carried out under different level of channel correlations which shows that both offer high capacity gain and resilience to channel correlation. SC-CMMA capacity even increase with high channel correlation between users’ channels. CMMA provides a practical solution for implementing the multiple access adder channel (MAAC) in fading environments hence a hybrid approach combining both collaborative coding and modulation referred to as H-CMMA is investigated. H-CMMA divides users into a number of subgroups where users within a subgroup are assigned the same modulation set and different multiple access codes. H-CMMA adjusts the dmin of the received CC by varying the number of subgroups which in turn varies the number of unique constellation points for the same number of users and average total power. Therefore H-CMMA can accommodate many users with different rates while flexibly managing the complexity, rate and BER performance depending on the SNR. Next a new scheme combining CMMA with opportunistic scheduling using only partial CSI at the receiver called CMMA-OS is proposed to combine both the power gain of CMMA and the multiuser diversity gain that arises from users’ channel independence. To avoid the complexity and excessive feedback associated with the dynamic update of the CC, the BS takes into account the independence of users’ channels in the design of the CC and its constituent UMSs but both remain unchanged thereafter. However UMS are no longer associated with users, instead channel gain’s probability density function is divided into regions with identical probability and each UMS is associated with a specific region. This will simplify scheduling as users can initially chose their UMS based on their CSI and the BS will only need to resolve any collision when the channels of two or more users are located at the same region. Finally a high rate cooperative communication scheme, called cooperative modulation (CM) is proposed for cooperative multiuser systems. CM combines the reliability of the cooperative diversity with the high spectral efficiency and multiple access capabilities of CMMA. CM maintains low feedback and high spectral efficiency by restricting relaying to a single route with the best overall channel. Two possible variations of CM are proposed depending on whether CSI available only at the users or just at the BS and the selected relay. The first is referred to Precode, Amplify, and Forward (PAF) while the second one is called Decode, Remap, and Forward (DMF). A new route selection algorithm for DMF based on maximising dmin of random CC is also proposed using a novel fast low-complexity multi-stage sphere based algorithm to calculate the dmin at the relay of random CC that is used for both relay selection and detection

Advanced tensor based signal processing techniques for wireless communication systems and biomedical signal processing

Many observed signals in signal processing applications including wireless communications, biomedical signal processing, image processing, and machine learning are multi-dimensional. Tensors preserve the multi-dimensional structure and provide a natural representation of these signals/data. Moreover, tensors provide often an improved identifiability. Therefore, we benefit from using tensor algebra in the above mentioned applications and many more. In this thesis, we present the benefits of utilizing tensor algebra in two signal processing areas. These include signal processing for MIMO (Multiple-Input Multiple-Output) wireless communication systems and biomedical signal processing. Moreover, we contribute to the theoretical aspects of tensor algebra by deriving new properties and ways of computing tensor decompositions. Often, we only have an element-wise or a slice-wise description of the signal model. This representation of the signal model does not reveal the explicit tensor structure. Therefore, the derivation of all tensor unfoldings is not always obvious. Consequently, exploiting the multi-dimensional structure of these models is not always straightforward. We propose an alternative representation of the element-wise multiplication or the slice-wise multiplication based on the generalized tensor contraction operator. Later in this thesis, we exploit this novel representation and the properties of the contraction operator such that we derive the final tensor models. There exist a number of different tensor decompositions that describe different signal models such as the HOSVD (Higher Order Singular Value Decomposition), the CP/PARAFAC (Canonical Polyadic / PARallel FACtors) decomposition, the BTD (Block Term Decomposition), the PARATUCK2 (PARAfac and TUCker2) decomposition, and the PARAFAC2 (PARAllel FACtors2) decomposition. Among these decompositions, the CP decomposition is most widely spread and used. Therefore, the development of algorithms for the efficient computation of the CP decomposition is important for many applications. The SECSI (Semi-Algebraic framework for approximate CP decomposition via SImultaneaous matrix diagonalization) framework is an efficient and robust tool for the calculation of the approximate low-rank CP decomposition via simultaneous matrix diagonalizations. In this thesis, we present five extensions of the SECSI framework that reduce the computational complexity of the original framework and/or introduce constraints to the factor matrices. Moreover, the PARAFAC2 decomposition and the PARATUCK2 decomposition are usually described using a slice-wise notation that can be expressed in terms of the generalized tensor contraction as proposed in this thesis. We exploit this novel representation to derive explicit tensor models for the PARAFAC2 decomposition and the PARATUCK2 decomposition. Furthermore, we use the PARAFAC2 model to derive an ALS (Alternating Least-Squares) algorithm for the computation of the PARAFAC2 decomposition. Moreover, we exploit the novel contraction properties for element wise and slice-wise multiplications to model MIMO multi-carrier wireless communication systems. We show that this very general model can be used to derive the tensor model of the received signal for MIMO-OFDM (Multiple-Input Multiple-Output - Orthogonal Frequency Division Multiplexing), Khatri-Rao coded MIMO-OFDM, and randomly coded MIMO-OFDM systems. We propose the transmission techniques Khatri-Rao coding and random coding in order to impose an additional tensor structure of the transmit signal tensor that otherwise does not have a particular structure. Moreover, we show that this model can be extended to other multi-carrier techniques such as GFDM (Generalized Frequency Division Multiplexing). Utilizing these models at the receiver side, we design several types for receivers for these systems that outperform the traditional matrix based solutions in terms of the symbol error rate. In the last part of this thesis, we show the benefits of using tensor algebra in biomedical signal processing by jointly decomposing EEG (ElectroEncephaloGraphy) and MEG (MagnetoEncephaloGraphy) signals. EEG and MEG signals are usually acquired simultaneously, and they capture aspects of the same brain activity. Therefore, EEG and MEG signals can be decomposed using coupled tensor decompositions such as the coupled CP decomposition. We exploit the proposed coupled SECSI framework (one of the proposed extensions of the SECSI framework) for the computation of the coupled CP decomposition to first validate and analyze the photic driving effect. Moreover, we validate the effects of scull defects on the measurement EEG and MEG signals by means of a joint EEG-MEG decomposition using the coupled SECSI framework. Both applications show that we benefit from coupled tensor decompositions and the coupled SECSI framework is a very practical tool for the analysis of biomedical data.Zahlreiche messbare Signale in verschiedenen Bereichen der digitalen Signalverarbeitung, z.B. in der drahtlosen Kommunikation, im Mobilfunk, biomedizinischen Anwendungen, der Bild- oder akustischen Signalverarbeitung und dem maschinellen Lernen sind mehrdimensional. Tensoren erhalten die mehrdimensionale Struktur und stellen eine natürliche Darstellung dieser Signale/Daten dar. Darüber hinaus bieten Tensoren oft eine verbesserte Trennbarkeit von enthaltenen Signalkomponenten. Daher profitieren wir von der Verwendung der Tensor-Algebra in den oben genannten Anwendungen und vielen mehr. In dieser Arbeit stellen wir die Vorteile der Nutzung der Tensor-Algebra in zwei Bereichen der Signalverarbeitung vor: drahtlose MIMO (Multiple-Input Multiple-Output) Kommunikationssysteme und biomedizinische Signalverarbeitung. Darüber hinaus tragen wir zu theoretischen Aspekten der Tensor-Algebra bei, indem wir neue Eigenschaften und Berechnungsmethoden für die Tensor-Zerlegung ableiten. Oftmals verfügen wir lediglich über eine elementweise oder ebenenweise Beschreibung des Signalmodells, welche nicht die explizite Tensorstruktur zeigt. Daher ist die Ableitung aller Tensor-Unfoldings nicht offensichtlich, wodurch die multidimensionale Struktur dieser Modelle nicht trivial nutzbar ist. Wir schlagen eine alternative Darstellung der elementweisen Multiplikation oder der ebenenweisen Multiplikation auf der Grundlage des generalisierten Tensor-Kontraktionsoperators vor. Weiterhin nutzen wir diese neuartige Darstellung und deren Eigenschaften zur Ableitung der letztendlichen Tensor-Modelle. Es existieren eine Vielzahl von Tensor-Zerlegungen, die verschiedene Signalmodelle beschreiben, wie die HOSVD (Higher Order Singular Value Decomposition), CP/PARAFAC (Canonical Polyadic/ PARallel FACtors) Zerlegung, die BTD (Block Term Decomposition), die PARATUCK2-(PARAfac und TUCker2) und die PARAFAC2-Zerlegung (PARAllel FACtors2). Dabei ist die CP-Zerlegung am weitesten verbreitet und wird findet in zahlreichen Gebieten Anwendung. Daher ist die Entwicklung von Algorithmen zur effizienten Berechnung der CP-Zerlegung von besonderer Bedeutung. Das SECSI (Semi-Algebraic Framework for approximate CP decomposition via Simultaneaous matrix diagonalization) Framework ist ein effizientes und robustes Werkzeug zur Berechnung der approximierten Low-Rank CP-Zerlegung durch simultane Matrixdiagonalisierung. In dieser Arbeit stellen wir fünf Erweiterungen des SECSI-Frameworks vor, welche die Rechenkomplexität des ursprünglichen Frameworks reduzieren bzw. Einschränkungen für die Faktormatrizen einführen. Darüber hinaus werden die PARAFAC2- und die PARATUCK2-Zerlegung in der Regel mit einer ebenenweisen Notation beschrieben, die sich in Form der allgemeinen Tensor-Kontraktion, wie sie in dieser Arbeit vorgeschlagen wird, ausdrücken lässt. Wir nutzen diese neuartige Darstellung, um explizite Tensormodelle für diese beiden Zerlegungen abzuleiten. Darüber hinaus verwenden wir das PARAFAC2-Modell, um einen ALS-Algorithmus (Alternating Least-Squares) für die Berechnung der PARAFAC2-Zerlegungen abzuleiten. Weiterhin nutzen wir die neuartigen Kontraktionseigenschaften für elementweise und ebenenweise Multiplikationen, um MIMO Multi-Carrier-Mobilfunksysteme zu modellieren. Wir zeigen, dass dieses sehr allgemeine Modell verwendet werden kann, um das Tensor-Modell des empfangenen Signals für MIMO-OFDM- (Multiple- Input Multiple-Output - Orthogonal Frequency Division Multiplexing), Khatri-Rao codierte MIMO-OFDM- und zufällig codierte MIMO-OFDM-Systeme abzuleiten. Wir schlagen die Übertragungstechniken der Khatri-Rao-Kodierung und zufällige Kodierung vor, um eine zusätzliche Tensor-Struktur des Sendesignal-Tensors einzuführen, welcher gewöhnlich keine bestimmte Struktur aufweist. Darüber hinaus zeigen wir, dass dieses Modell auf andere Multi-Carrier-Techniken wie GFDM (Generalized Frequency Division Multiplexing) erweitert werden kann. Unter Verwendung dieser Modelle auf der Empfängerseite entwerfen wir verschiedene Typen von Empfängern für diese Systeme, die die traditionellen matrixbasierten Lösungen in Bezug auf die Symbolfehlerrate übertreffen. Im letzten Teil dieser Arbeit zeigen wir die Vorteile der Verwendung von Tensor-Algebra in der biomedizinischen Signalverarbeitung durch die gemeinsame Zerlegung von EEG-(ElectroEncephaloGraphy) und MEG- (MagnetoEncephaloGraphy) Signalen. Diese werden in der Regel gleichzeitig erfasst, wobei sie gemeinsame Aspekte derselben Gehirnaktivität beschreiben. Daher können EEG- und MEG-Signale mit gekoppelten Tensor-Zerlegungen wie der gekoppelten CP Zerlegung analysiert werden. Wir nutzen das vorgeschlagene gekoppelte SECSI-Framework (eine der vorgeschlagenen Erweiterungen des SECSI-Frameworks) für die Berechnung der gekoppelten CP Zerlegung, um zunächst den photic driving effect zu validieren und zu analysieren. Darüber hinaus validieren wir die Auswirkungen von Schädeldefekten auf die Messsignale von EEG und MEG durch eine gemeinsame EEG-MEG-Zerlegung mit dem gekoppelten SECSI-Framework. Beide Anwendungen zeigen, dass wir von gekoppelten Tensor-Zerlegungen profitieren, wobei die Methoden des gekoppelten SECSI-Frameworks erfolgreich zur Analyse biomedizinischer Daten genutzt werden können