Blind channel estimation and signal retrieving for MIMO relay systems

In this paper, we propose a blind channel estimation and signal retrieving algorithm for two-hop multiple-input multiple-output (MIMO) relay systems. This new algorithm integrates two blind source separation (BSS) methods to estimate the individual channel state information (CSI) of the source-relay and relay-destination links. In particular, a first-order Z-domain precoding technique is developed for the blind estimation of the relay-destination channel matrix, where the signals received at the relay node are pre-processed by a set of precoders before being transmitted to the destination node. With the estimated signals at the relay node, we propose an algorithm based on the constant modulus and signal mutual information properties to estimate the source-relay channel matrix. Compared with training-based MIMO relay channel estimation approaches, the proposed algorithm has a better bandwidth efficiency as no bandwidth is wasted for sending the training sequences. Numerical examples are shown to demonstrate the performance of the proposed algorithm

Superimposed channel training for MIMO relay systems

Based on the knowledge of instantaneous channel state information (CSI), the optimal source and relay pre-coding matrices have been developed recently for multiple-input multiple-output (MIMO) relay communication systems. However, in real communication systems, the instantaneous CSI is unknown and needs to be estimated at the destination node. In this paper, we propose a superimposed channel training method for MIMO relay communication systems. It is shown that to minimize the mean-squared error (MSE) of channel estimation, the optimal training sequence at each node matches the eigenvector matrix of the transmitter correlation matrix of the forward MIMO channel. Then we optimize the power allocation among different streams of the training sequence at the source node and the relay node. Simulation results show that the proposed algorithm leads to a smaller MSE of channel estimation compared with the conventional MIMO relay channel estimation algorithm

An iterative pilot-data-aided estimator for SFBC relay-assisted OFDM-based systems

In this article, we propose and assess an iterative pilot-data-aided channel estimation scheme for space frequency block coding relay-assisted OFDM-based systems. The relay node (RN) employs the equalise-and-forward protocol, and both the base station (BS) and the RN are equipped with antenna arrays, whereas the user terminal (UT) is a single-antenna device. The channel estimation method uses the information carried by pilots and data to improve the estimate of the equivalent channels for the path BS-RN-UT. The mean minimum square error criterion is used in the design of the estimator for both the pilot-based and data-aided iterations. In different scenarios, with only one data iteration, the results show that the proposed scheme requires only half of the pilot density to achieve the same performance of non-data-aided schemes

Transceiver Optimization for Two-Hop AF MIMO Relay Systems With DFE Receiver and Direct Link

In this paper, we consider precoding and receiving matrices optimization for a two-hop amplify-and-forward (AF) multiple-input multiple-output (MIMO) relay system with a decision feedback equalizer (DFE) at the destination node in the presence of the direct source-destination link. By adopting the minimum mean-squared error (MMSE) criterion, we develop two new transceiver design algorithms for such a system. The first one employs an iterative procedure to design the source, relay, feed-forward, and feedback matrices. The second algorithm is a non-iterative suboptimal approach which decomposes the optimization problem into two tractable subproblems and obtains the source and relay precoding matrices by solving the two subproblems sequentially. Simulation results validate the better MSE and bit-error-rate (BER) performance of the proposed algorithms and show that the non-iterative suboptimal method has a negligible performance loss when the ratio of the source node transmission power to the relay node transmission power is small. In addition, the computational complexity analysis suggests that the second algorithm and one iteration of the first algorithm have the same order of complexity. As the first algorithm typically converges within a few iterations, both proposed algorithms exhibit a low complexity order

Transceiver Optimization for Interference MIMO Relay Communication Systems

This thesis focuses on transceivers design for interference MIMO relay systems. Based on the MMSE criterion, several iterative algorithms are proposed to jointly optimize the source, relay, and receiver matrices subjecting to the individual power constraints at the source and the relay nodes. These algorithms have better performance than some existing algorithms and provide a better performance-complexity trade-off which is interesting in practical interference MIMO relay communication systems

Otimização do fronthaul ótico para redes de acesso de rádio (baseadas) em computação em nuvem (CC-RANs)

Doutoramento conjunto (MAP-Tele) em Engenharia Eletrotécnica/TelecomunicaçõesA proliferação de diversos tipos de dispositivos moveis, aplicações e serviços com grande necessidade de largura de banda têm contribuído para o aumento de ligações de banda larga e ao aumento do volume de trafego das redes de telecomunicações moveis. Este aumento exponencial tem posto uma enorme pressão nos mobile operadores de redes móveis (MNOs). Um dos aspetos principais deste recente desenvolvimento, é a necessidade que as redes têm de oferecer baixa complexidade nas ligações, como também baixo consumo energético, muito baixa latência e ao mesmo tempo uma grande capacidade por baixo usto. De maneira a resolver estas questões, os MNOs têm focado a sua atenção na redes de acesso por rádio em nuvem (C-RAN) principalmente devido aos seus benefícios em termos de otimização de performance e relação qualidade preço. O standard para a distribuição de sinais sem fios por um fronthaul C-RAN é o common public radio interface (CPRI). No entanto, ligações óticas baseadas em interfaces CPRI necessitam de uma grande largura de banda. Estes requerimentos podem também ser atingidos com uma implementação em ligação free space optical (FSO) que é um sistema ótico que usa comunicação sem fios. O FSO tem sido uma alternativa muito apelativa aos sistemas de comunicação rádio (RF) pois combinam a flexibilidade e mobilidade das redes RF ao mesmo tempo que permitem a elevada largura de banda permitida pelo sistema ótico. No entanto, as ligações FSO são suscetíveis a alterações atmosféricas que podem prejudicar o desempenho do sistema de comunicação. Estas limitações têm evitado o FSO de ser tornar uma excelente solução para o fronthaul. Uma caracterização precisa do canal e tecnologias mais avançadas são então necessárias para uma implementação pratica de ligações FSO. Nesta tese, vamos estudar uma implementação eficiente para fronthaul baseada em tecnologia á rádio-sobre-FSO (RoFSO). Propomos expressões em forma fechada para mitigação das perdas de propagação e para a estimação da capacidade do canal de maneira a aliviar a complexidade do sistema de comunicação. Simulações numéricas são também apresentadas para formatos de modulação adaptativas. São também considerados esquemas como um sistema hibrido RF/FSO e tecnologias de transmissão apoiadas por retransmissores que ajudam a alivar os requerimentos impostos por um backhaul/fronthaul de C-RAN. Os modelos propostos não só reduzem o esforço computacional, como também têm outros méritos, tais como, uma elevada precisão na estimação do canal e desempenho, baixo requisitos na capacidade de memória e uma rápida e estável operação comparativamente com o estado da arte em sistemas analíticos (PON)-FSO. Este sistema é implementado num recetor em tempo real que é emulado através de uma field-programmable gate array (FPGA) comercial. Permitindo assim um sistema aberto, interoperabilidade, portabilidade e também obedecer a standards de software aberto. Os esquemas híbridos têm a habilidade de suportar diferentes aplicações, serviços e múltiplos operadores a partilharem a mesma infraestrutura de fibra ótica.The proliferation of different mobile devices, bandwidth-intensive applications and services contribute to the increase in the broadband connections and the volume of traffic on the mobile networks. This exponential growth has put considerable pressure on the mobile network operators (MNOs). In principal, there is a need for networks that not only offer low-complexity, low-energy consumption, and extremely low-latency but also high-capacity at relatively low cost. In order to address the demand, MNOs have given significant attention to the cloud radio access network (C-RAN) due to its beneficial features in terms of performance optimization and cost-effectiveness. The de facto standard for distributing wireless signal over the C-RAN fronthaul is the common public radio interface (CPRI). However, optical links based on CPRI interfaces requires large bandwidth. Also, the aforementioned requirements can be realized with the implementation of free space optical (FSO) link, which is an optical wireless system. The FSO is an appealing alternative to the radio frequency (RF) communication system that combines the flexibility and mobility offered by the RF networks with the high-data rates provided by the optical systems. However, the FSO links are susceptible to atmospheric impairments which eventually hinder the system performance. Consequently, these limitations prevent FSO from being an efficient standalone fronthaul solution. So, precise channel characterizations and advanced technologies are required for practical FSO link deployment and operation. In this thesis, we study an efficient fronthaul implementation that is based on radio-on-FSO (RoFSO) technologies. We propose closedform expressions for fading-mitigation and for the estimation of channel capacity so as to alleviate the system complexity. Numerical simulations are presented for adaptive modulation scheme using advanced modulation formats. We also consider schemes like hybrid RF/FSO and relay-assisted transmission technologies that can help in alleviating the stringent requirements by the C-RAN backhaul/fronthaul. The propose models not only reduce the computational requirements/efforts, but also have a number of diverse merits such as high-accuracy, low-memory requirements, fast and stable operation compared to the current state-of-the-art analytical based approaches. In addition to the FSO channel characterization, we present a proof-of-concept experiment in which we study the transmission capabilities of a hybrid passive optical network (PON)-FSO system. This is implemented with the real-time receiver that is emulated by a commercial field-programmable gate array (FPGA). This helps in facilitating an open system and hence enables interoperability, portability, and open software standards. The hybrid schemes have the ability to support different applications, services, and multiple operators over a shared optical fiber infrastructure

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