10 research outputs found

    High Capacity CDMA and Collaborative Techniques

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    The thesis investigates new approaches to increase the user capacity and improve the error performance of Code Division Multiple Access (CDMA) by employing adaptive interference cancellation and collaborative spreading and space diversity techniques. Collaborative Coding Multiple Access (CCMA) is also investigated as a separate technique and combined with CDMA. The advantages and shortcomings of CDMA and CCMA are analysed and new techniques for both the uplink and downlink are proposed and evaluated. Multiple access interference (MAI) problem in the uplink of CDMA is investigated first. The practical issues of multiuser detection (MUD) techniques are reviewed and a novel blind adaptive approach to interference cancellation (IC) is proposed. It exploits the constant modulus (CM) property of digital signals to blindly suppress interference during the despreading process and obtain amplitude estimation with minimum mean squared error for use in cancellation stages. Two new blind adaptive receiver designs employing successive and parallel interference cancellation architectures using the CM algorithm (CMA) referred to as ‘CMA-SIC’ and ‘BA-PIC’, respectively, are presented. These techniques have shown to offer near single user performance for large number of users. It is shown to increase the user capacity by approximately two fold compared with conventional IC receivers. The spectral efficiency analysis of the techniques based on output signal-to interference-and-noise ratio (SINR) also shows significant gain in data rate. Furthermore, an effective and low complexity blind adaptive subcarrier combining (BASC) technique using a simple gradient descent based algorithm is proposed for Multicarrier-CDMA. It suppresses MAI without any knowledge of channel amplitudes and allows large number of users compared with equal gain and maximum ratio combining techniques normally used in practice. New user collaborative schemes are proposed and analysed theoretically and by simulations in different channel conditions to achieve spatial diversity for uplink of CCMA and CDMA. First, a simple transmitter diversity and its equivalent user collaborative diversity techniques for CCMA are designed and analysed. Next, a new user collaborative scheme with successive interference cancellation for uplink of CDMA referred to as collaborative SIC (C-SIC) is investigated to reduce MAI and achieve improved diversity. To further improve the performance of C-SIC under high system loading conditions, Collaborative Blind Adaptive SIC (C-BASIC) scheme is proposed. It is shown to minimize the residual MAI, leading to improved user capacity and a more robust system. It is known that collaborative diversity schemes incur loss in throughput due to the need of orthogonal time/frequency slots for relaying source’s data. To address this problem, finally a novel near-unity-rate scheme also referred to as bandwidth efficient collaborative diversity (BECD) is proposed and evaluated for CDMA. Under this scheme, pairs of users share a single spreading sequence to exchange and forward their data employing a simple superposition or space-time encoding methods. At the receiver collaborative joint detection is performed to separate each paired users’ data. It is shown that the scheme can achieve full diversity gain at no extra bandwidth as inter-user channel SNR becomes high. A novel approach of ‘User Collaboration’ is introduced to increase the user capacity of CDMA for both the downlink and uplink. First, collaborative group spreading technique for the downlink of overloaded CDMA system is introduced. It allows the sharing of the same single spreading sequence for more than one user belonging to the same group. This technique is referred to as Collaborative Spreading CDMA downlink (CS-CDMA-DL). In this technique T-user collaborative coding is used for each group to form a composite codeword signal of the users and then a single orthogonal sequence is used for the group. At each user’s receiver, decoding of composite codeword is carried out to extract the user’s own information while maintaining a high SINR performance. To improve the bit error performance of CS-CDMA-DL in Rayleigh fading conditions, Collaborative Space-time Spreading (C-STS) technique is proposed by combining the collaborative coding multiple access and space-time coding principles. A new scheme for uplink of CDMA using the ‘User Collaboration’ approach, referred to as CS-CDMA-UL is presented next. When users’ channels are independent (uncorrelated), significantly higher user capacity can be achieved by grouping multiple users to share the same spreading sequence and performing MUD on per group basis followed by a low complexity ML decoding at the receiver. This approach has shown to support much higher number of users than the available sequences while also maintaining the low receiver complexity. For improved performance under highly correlated channel conditions, T-user collaborative coding is also investigated within the CS-CDMA-UL system

    Low order channel estimation for CDMA systems

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    New approaches and algorithms are developed for the identification and estimation of low order models that represent multipath channel effects in Code Division Multiple Access (CDMA) communication systems. Based on these parsimonious channel models, low complexity receivers such as RAKE receivers are considered to exploit these propagation effects and enhance the system performance. We consider the scenario where multipath is frequency selective slowly fading and where the channel components including delays and attenuation coefficients are assumed to be constant over one or few signalling intervals. We model the channel as a long FIR-like filter (or a tapped delay line filter) with the number of taps related to the ratio between the channel delay-spread and the chip duration. Due to the high data rate of new CDMA systems, the channel length in terms of the chip duration will be very large. With classical channel estimation techniques this will result in poor estimates of many of the channel parameters where most of them are zero leading to a reduction in the system performance. Unlike classical techniques which estimate directly the channel response given the number of taps or given an estimate of the channel length, the proposed techniques in this work will firstly identify the significant multipath parameters using model selection techniques, then estimate these identified parameters. Statistical tests are proposed to determine whether or not each individual parameter is significant. A low complexity RAKE receiver is then considered based on estimates of these identified parameters only. The level of significance with which we will make this assertion will be controlled based on statistical tests such as multiple hypothesis tests. Frequency and time domain based approaches and model selection techniques are proposed to achieve the above proposed objectives.The frequency domain approach for parsimonious channel estimation results in an efficient implementation of RAKE receivers in DS-CDMA systems. In this approach, we consider a training based strategy and estimate the channel delays and attenuation using the averaged periodogram and modified time delay estimation techniques. We then use model selection techniques such as the sphericity test and multiple hypotheses tests based on F-Statistics to identify the model order and select the significant channel paths. Simulations show that for a pre-defined level of significance, the proposed technique correctly identifies the significant channel parameters and the parsimonious RAKE receiver shows improved statistical as well as computational performance over classical methods. The time domain approach is based on the Bootstrap which is appropriate for the case when the distribution of the test statistics required by the multiple hypothesis tests is unknown. In this approach we also use short training data and model the channel response as an FIR filter with unknown length. Model parameters are then estimated using low complexity algorithms in the time domain. Based on these estimates, bootstrap based multiple hypotheses tests are applied to identify the non-zero coefficients of the FIR filter. Simulation results demonstrate the power of this technique for RAKE receivers in unknown noise environments. Finally we propose adaptive blind channel estimation algorithms for CDMA systems. Using only the spreading code of the user of interest and the received data sequence, four different adaptive blind estimation algorithms are proposed to estimate the impulse response of frequency selective and frequency non-selective fading channels. Also the idea is based on minimum variance receiver techniques. Tracking of a frequency selective varying fading channel is also considered.A blind based hierarchical MDL model selection method is also proposed to select non-zero parameters of the channel response. Simulation results show that the proposed algorithms perform better than previously proposed algorithms. They have lower complexity and have a faster convergence rate. The proposed algorithms can also be applied to the design of adaptive blind channel estimation based RAKE receivers

    Satellite Communications

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    This study is motivated by the need to give the reader a broad view of the developments, key concepts, and technologies related to information society evolution, with a focus on the wireless communications and geoinformation technologies and their role in the environment. Giving perspective, it aims at assisting people active in the industry, the public sector, and Earth science fields as well, by providing a base for their continued work and thinking

    Advanced Techniques for Future Multicarrier Systems

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    Future multicarrier systems face the tough challenge of supporting high data-rate and high-quality services. The main limitation is the frequency-selective nature of the propagation channel that affects the received signal, thus degrading the system performance. OFDM can be envisaged as one of the most promising modulation techniques for future communication systems. It exhibits robustness to ISI even in very dispersive environments and its main characteristic is to take advantage of channel diversity by performing dynamic resource allocation. In a multi-user OFDMA scenario, the challenge is to allocate, on the basis of the channel knowledge, different portions of the available frequency spectrum among the users in the systems. Literature on resource allocation for OFDMA systems mainly focused on single-cell systems, where the objective is to assign subcarriers, power and data-rate for each user according to a predetermined criterion. The problem can be formulated with the goal of either maximizing the system sum-rate subject to a constraint on transmitted power or minimizing the overall power consumption under some predetermined constraints on rate per user. Only recently, literature focuses on resource allocation in multi-cell networks, where the goal is not only to take advantage of frequency and multi-user diversity, but also to mitigate MAI, which represents one of the most limiting factor for such problems. We consider a multi-cell OFDMA system with frequency reuse distance equal to one. Allowing all cells to transmit on the whole bandwidth unveils large potential gains in terms of spectral efficiency in comparison with conventional cellular systems. Such a scenario, however, is often deemed unfeasible because of the strong MAI that negatively affects the system performance. In this dissertation we present a layered architecture that integrates a packet scheduler with an adaptive resource allocator, explicitly designed to take care of the multiple access interference. Each cell performs its resource management in a distributed way without any central controller. Iterative resource allocation assigns radio channels to the users so as to minimize the interference. Packet scheduling guarantees that all users get a fair share of resources regardless of their position in the cell. This scheduler-allocator architecture integrates both goals and is able to self adapt to any traffic and user configuration. An adaptive, distributed load control strategy can reduce the cell load so that the iterative procedure always converges to a stable allocation, regardless of the interference. Numerical results show that the proposed architecture guarantees both high spectral efficiency and throughput fairness among flows. In the second part of this dissertation we deal with FBMC communication systems. FBMC modulation is a valid alternative to conventional OFDM signaling as it presents a set of appealing characteristics, such as robustness to narrowband interferers, more flexibility to allocate groups of subchannels to different users/services, and frequency-domain equalization without any cyclic extension. However, like any other multicarrier modulations, FBMC is strongly affected by residual CFOs that have to be accurately estimated. Unlike previously proposed algorithms, whereby frequency is recovered either relying on known pilot symbols multiplexed with the data stream or exploiting specific properties of the multicarrier signal structure following a blind approach, we present and discuss an algorithm based on the ML principle, which takes advantage both of pilot symbols and also indirectly of data symbols through knowledge and exploitation of their specific modulation format. The algorithm requires the availability of the statistical properties of channel fading up to second-order moments. It is shown that the above approach allows to improve on both frequency acquisition range and estimation accuracy of previously published schemes

    Joint signal detection and channel estimation in rank-deficient MIMO systems

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    L'Ă©volution de la prospĂšre famille des standards 802.11 a encouragĂ© le dĂ©veloppement des technologies appliquĂ©es aux rĂ©seaux locaux sans fil (WLANs). Pour faire face Ă  la toujours croissante nĂ©cessitĂ© de rendre possible les communications Ă  trĂšs haut dĂ©bit, les systĂšmes Ă  antennes multiples (MIMO) sont une solution viable. Ils ont l'avantage d'accroĂźtre le dĂ©bit de transmission sans avoir recours Ă  plus de puissance ou de largeur de bande. Cependant, l'industrie hĂ©site encore Ă  augmenter le nombre d'antennes des portables et des accĂ©soires sans fil. De plus, Ă  l'intĂ©rieur des bĂątiments, la dĂ©ficience de rang de la matrice de canal peut se produire dĂ» Ă  la nature de la dispersion des parcours de propagation, ce phĂ©nomĂšne est aussi occasionnĂ© Ă  l'extĂ©rieur par de longues distances de transmission. Ce projet est motivĂ© par les raisons dĂ©crites antĂ©rieurement, il se veut un Ă©tude sur la viabilitĂ© des transcepteurs sans fil Ă  large bande capables de rĂ©gulariser la dĂ©ficience de rang du canal sans fil. On vise le dĂ©veloppement des techniques capables de sĂ©parer M signaux co-canal, mĂȘme avec une seule antenne et Ă  faire une estimation prĂ©cise du canal. Les solutions dĂ©crites dans ce document cherchent Ă  surmonter les difficultĂ©s posĂ©es par le medium aux transcepteurs sans fil Ă  large bande. Le rĂ©sultat de cette Ă©tude est un algorithme transcepteur appropriĂ© aux systĂšmes MIMO Ă  rang dĂ©ficient

    Location and Map Awareness Technologies in Next Wireless Networks

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    In a future perspective, the need of mapping an unknown indoor environment, of localizing and retrieving information from objects with zero costs and efforts could be satisfied by the adoption of next 5G technologies. Thanks to the mix of mmW and massive arrays technologies, it will be possible to achieve a higher indoor localization accuracy without relying on a dedicated infrastructure for localization but exploiting that designed for communication purposes. Besides users localization and navigation objectives, mapping and thus, the capability of reconstructing indoor scenarios, will be an important field of research with the possibility of sharing environmental information via crowd-sourcing mechanisms between users. Finally, in the Internet of Things vision, it is expected that people, objects and devices will be interconnected to each other with the possibility of exchanging the acquired and estimated data including those regarding objects identification, positioning and mapping contents. To this end, the merge of RFID, WSN and UWB technologies has demonstrated to be a promising solution. Stimulated by this framework, this work describes different technological and signal processing approaches to ameliorate the localization capabilities and the user awareness about the environment. From one side, it has been focused on the study of the localization and mapping capabilities of multi-antenna systems based on 5G technologies considering different technological issues, as for example those related to the existing available massive arrays. From the other side, UWB-RFID systems relying on passive communication schemes have been investigated in terms of localization coverage and by developing different techniques to improve the accuracy even in presence of NLOS conditions

    Advanced Algebraic Concepts for Efficient Multi-Channel Signal Processing

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    ï»żUnsere moderne Gesellschaft ist Zeuge eines fundamentalen Wandels in der Art und Weise wie wir mit Technologie interagieren. GerĂ€te werden zunehmend intelligenter - sie verfĂŒgen ĂŒber mehr und mehr Rechenleistung und hĂ€ufiger ĂŒber eigene Kommunikationsschnittstellen. Das beginnt bei einfachen HaushaltsgerĂ€ten und reicht ĂŒber Transportmittel bis zu großen ĂŒberregionalen 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 GerĂ€te heutzutage mobil und deshalb batteriebetrieben ist, begrĂŒndet 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 fĂŒr eine Vielzahl von Anwendungen mehrkanaliger digitaler Signalverarbeitung. Solche AnsĂ€tze liefern nicht immer unbedingt die bestmögliche Lösung, kommen dieser jedoch hĂ€ufig recht nahe und sind gleichzeitig bedeutend einfacher zu beschreiben und umzusetzen. Die einfache Beschreibungsform ermöglicht eine tiefgehende Analyse ihrer LeistungsfĂ€higkeit, was fĂŒr den Entwurf eines robusten und zuverlĂ€ssigen Systems unabdingbar ist. Die Tatsache, dass sie nur gebrĂ€uchliche algebraische Hilfsmittel benötigen, erlaubt ihre direkte und zĂŒgige Umsetzung und den Test unter realen Bedingungen. Diese Grundidee wird anhand von drei verschiedenen Anwendungsgebieten demonstriert. ZunĂ€chst 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 ĂŒber Chemie bis zur Bildverarbeitung. Verglichen mit existierenden iterativen Lösungsverfahren bietet das neue Framework die Möglichkeit, den Rechenaufwand und damit die GĂŒte der erzielten Lösung zu steuern. Es ist außerdem weniger anfĂ€llig gegen eine schlechte Konditionierung der Ausgangsdaten. Das zweite Gebiet, das in der Arbeit besprochen wird, ist die unterraumbasierte hochauflösende ParameterschĂ€tzung fĂŒr 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 natĂŒrlichere Beschreibung und eine bessere Ausnutzung ihrer Struktur als das mit Matrizen möglich ist. Basierend auf dieser Idee entwickeln wir eine tensor-basierte SchĂ€tzung 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, fĂŒr die verbesserte Versionen vorgeschlagen werden, die die mehrdimensionale Struktur der Daten (Tensor-ESPRIT), nichzirkulĂ€re Quellsymbole (NC ESPRIT), sowie beides gleichzeitig (NC Tensor-ESPRIT) ausnutzen. Um die endgĂŒltige SchĂ€tzgenauigkeit objektiv einschĂ€tzen zu können wird dann ein Framework fĂŒr die analytische Beschreibung der LeistungsfĂ€higkeit beliebiger ESPRIT-artiger Algorithmen diskutiert. Verglichen mit existierenden analytischen AusdrĂŒcken ist unser Ansatz allgemeiner, da keine Annahmen ĂŒber die statistische Verteilung von Nutzsignal und Rauschen benötigt werden und die Anzahl der zur VerfĂŒgung stehenden SchnappschĂŒsse beliebig klein sein kann. Dies fĂŒhrt auf vereinfachte AusdrĂŒcke fĂŒr den mittleren quadratischen SchĂ€tzfehler, die Schlussfolgerungen ĂŒber 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-VerhĂ€ltnis verspricht. Es wird gezeigt, dass sich die nötige Kanalkenntnis mit einem einfachen algebraischen Tensor-basierten SchĂ€tzverfahren gewinnen lĂ€sst. Außerdem werden Verfahren zum Finden einer gĂŒnstigen Relay-VerstĂ€rkungs-Strategie diskutiert. Bestehende AnsĂ€tze basieren entweder auf komplexen numerischen Optimierungsverfahren oder auf Ad-Hoc-AnsĂ€tzen die keine zufriedenstellende Bitfehlerrate oder Summenrate liefern. Deshalb schlagen wir algebraische AnsĂ€tze zum Finden der RelayverstĂ€rkungsmatrix 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. FĂŒr den Spezialfall, in dem die EndgerĂ€te nur eine Antenne verwenden, leiten wir eine semi-algebraische Lösung zum Finden der Summenraten-optimalen Strategie (RAGES) her. Anhand von numerischen Simulationen wird die LeistungsfĂ€higkeit dieser Verfahren bezĂŒglich Bitfehlerrate und erreichbarer Datenrate bewertet und ihre EffektivitĂ€t 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
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