87 research outputs found
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Advanced Techniques for High-Throughput Cellular Communications
The next generation wireless communication systems require ubiquitous high-throughput mobile connectivity under a range of challenging network settings (urban versus rural, high device density, mobility, etc). To improve the performance of the system, the physical layer design is of great importance. The previous research on improving the physical layer properties includes: a) highly directional transmissions that can enhance the throughput and spatial reuse; b) enhanced MIMO that can eliminate
contention, enabling linear increase of capacity with number of antennas; c) mmWave technologies which operate on GHz bandwidth to over substantially higher throughput; d) better cooperative spectrum sharing with cognitive radios; e) better multiple access method which can mitigate multiuser interference and allow more multi-users.
This dissertation addresses several techniques in the physical layer design of the next generation wireless communication systems. In chapter two, an orthogonal frequency division with code division multiple access (OFDM-CDMA) systems is proposed and a polyphase code is used to improve multiple access performance and make the OFDM signal satisfy the peak to average ratio (PAPR) constraint. Chapter three studies the I/Q imbalance for direct down converter. For wideband transmitter and receiver that use direct conversion for I/Q sampling, the I/Q imbalance becomes a critical issue. With higher I/Q imbalance, there will be higher degradation in quadrature amplitude modulation, which degrades the throughput tremendously. Chapter four investigate a problem of spectrum sharing for cognitive wideband communication. An energy-efficient sub-Nyquist sampling algorithm is developed for optimal sampling and spectrum sensing. In chapter ve, we study the channel estimation of millimeter wave full-dimensional MIMO communication. The problem is formulated as an atomic-norm minimization problem and algorithms are derived for the channel estimation in different situations.
In this thesis, mathematical optimization is applied as the main approach to analyze and solve the problems in the physical layer of wireless communication so that the high-throughput is achieved. The algorithms are derived along with the theoretical analysis, which are validated with numerical results
MMV-Based Sequential AoA and AoD Estimation for Millimeter Wave MIMO Channels
The fact that the millimeter-wave (mmWave) multiple-input multiple-output
(MIMO) channel has sparse support in the spatial domain has motivated recent
compressed sensing (CS)-based mmWave channel estimation methods, where the
angles of arrivals (AoAs) and angles of departures (AoDs) are quantized using
angle dictionary matrices. However, the existing CS-based methods usually
obtain the estimation result through one-stage channel sounding that have two
limitations: (i) the requirement of large-dimensional dictionary and (ii)
unresolvable quantization error. These two drawbacks are irreconcilable;
improvement of the one implies deterioration of the other. To address these
challenges, we propose, in this paper, a two-stage method to estimate the AoAs
and AoDs of mmWave channels. In the proposed method, the channel estimation
task is divided into two stages, Stage I and Stage II. Specifically, in Stage
I, the AoAs are estimated by solving a multiple measurement vectors (MMV)
problem. In Stage II, based on the estimated AoAs, the receive sounders are
designed to estimate AoDs. The dimension of the angle dictionary in each stage
can be reduced, which in turn reduces the computational complexity
substantially. We then analyze the successful recovery probability (SRP) of the
proposed method, revealing the superiority of the proposed framework over the
existing one-stage CS-based methods. We further enhance the reconstruction
performance by performing resource allocation between the two stages. We also
overcome the unresolvable quantization error issue present in the prior
techniques by applying the atomic norm minimization method to each stage of the
proposed two-stage approach. The simulation results illustrate the
substantially improved performance with low complexity of the proposed
two-stage method.Comment: Accepted by IEEE Transactions on Communication
Recovery under Side Constraints
This paper addresses sparse signal reconstruction under various types of
structural side constraints with applications in multi-antenna systems. Side
constraints may result from prior information on the measurement system and the
sparse signal structure. They may involve the structure of the sensing matrix,
the structure of the non-zero support values, the temporal structure of the
sparse representationvector, and the nonlinear measurement structure. First, we
demonstrate how a priori information in form of structural side constraints
influence recovery guarantees (null space properties) using L1-minimization.
Furthermore, for constant modulus signals, signals with row-, block- and
rank-sparsity, as well as non-circular signals, we illustrate how structural
prior information can be used to devise efficient algorithms with improved
recovery performance and reduced computational complexity. Finally, we address
the measurement system design for linear and nonlinear measurements of sparse
signals. Moreover, we discuss the linear mixing matrix design based on
coherence minimization. Then we extend our focus to nonlinear measurement
systems where we design parallel optimization algorithms to efficiently compute
stationary points in the sparse phase retrieval problem with and without
dictionary learning
Applications of compressive sensing to direction of arrival estimation
Die SchĂ€tzung der Einfallsrichtungen (Directions of Arrival/DOA) mehrerer ebener Wellenfronten mit Hilfe eines Antennen-Arrays ist eine der prominentesten Fragestellungen im Gebiet der Array-Signalverarbeitung. Das nach wie vor starke Forschungsinteresse in dieser Richtung konzentriert sich vor allem auf die Reduktion des Hardware-Aufwands, im Sinne der KomplexitĂ€t und des Energieverbrauchs der EmpfĂ€nger, bei einem vorgegebenen Grad an Genauigkeit und Robustheit gegen Mehrwegeausbreitung. Diese Dissertation beschĂ€ftigt sich mit der Anwendung von Compressive Sensing (CS) auf das Gebiet der DOA-SchĂ€tzung mit dem Ziel, hiermit die KomplexitĂ€t der EmpfĂ€ngerhardware zu reduzieren und gleichzeitig eine hohe Richtungsauflösung und Robustheit zu erreichen. CS wurde bereits auf das DOA-Problem angewandt unter der Ausnutzung der Tatsache, dass eine Superposition ebener Wellenfronten mit einer winkelabhĂ€ngigen Leistungsdichte korrespondiert, die ĂŒber den Winkel betrachtet sparse ist. Basierend auf der Idee wurden CS-basierte Algorithmen zur DOA-SchĂ€tzung vorgeschlagen, die sich durch eine geringe RechenkomplexitĂ€t, Robustheit gegenĂŒber Quellenkorrelation und FlexibilitĂ€t bezĂŒglich der Wahl der Array-Geometrie auszeichnen. Die Anwendung von CS fĂŒhrt darĂŒber hinaus zu einer erheblichen Reduktion der Hardware-KomplexitĂ€t, da weniger EmpfangskanĂ€le benötigt werden und eine geringere Datenmenge zu verarbeiten und zu speichern ist, ohne dabei wesentliche Informationen zu verlieren. Im ersten Teil der Arbeit wird das Problem des Modellfehlers bei der CS-basierten DOA-SchĂ€tzung mit gitterbehafteten Verfahren untersucht. Ein hĂ€ufig verwendeter Ansatz um das CS-Framework auf das DOA-Problem anzuwenden ist es, den kontinuierlichen Winkel-Parameter zu diskreditieren und damit ein Dictionary endlicher GröĂe zu bilden. Da die tatsĂ€chlichen Winkel fast sicher nicht auf diesem Gitter liegen werden, entsteht dabei ein unvermeidlicher Modellfehler, der sich auf die SchĂ€tzalgorithmen auswirkt. In der Arbeit wird ein analytischer Ansatz gewĂ€hlt, um den Effekt der Gitterfehler auf die rekonstruierten Spektra zu untersuchen. Es wird gezeigt, dass sich die Messung einer Quelle aus beliebiger Richtung sehr gut durch die erwarteten Antworten ihrer beiden Nachbarn auf dem Gitter annĂ€hern lĂ€sst. Darauf basierend wird ein einfaches und effizientes Verfahren vorgeschlagen, den Gitterversatz zu schĂ€tzen. Dieser Ansatz ist anwendbar auf einzelne Quellen oder mehrere, rĂ€umlich gut separierte Quellen. FĂŒr den Fall mehrerer dicht benachbarter Quellen wird ein numerischer Ansatz zur gemeinsamen SchĂ€tzung des Gitterversatzes diskutiert. Im zweiten Teil der Arbeit untersuchen wir das Design kompressiver Antennenarrays fĂŒr die DOA-SchĂ€tzung. Die Kompression im Sinne von Linearkombinationen der Antennensignale, erlaubt es, Arrays mit groĂer Apertur zu entwerfen, die nur wenige EmpfangskanĂ€le benötigen und sich konfigurieren lassen. In der Arbeit wird eine einfache Empfangsarchitektur vorgeschlagen und ein allgemeines Systemmodell diskutiert, welches verschiedene Optionen der tatsĂ€chlichen Hardware-Realisierung dieser Linearkombinationen zulĂ€sst. Im Anschluss wird das Design der Gewichte des analogen Kombinations-Netzwerks untersucht. Numerische Simulationen zeigen die Ăberlegenheit der vorgeschlagenen kompressiven Antennen-Arrays im Vergleich mit dĂŒnn besetzten Arrays der gleichen KomplexitĂ€t sowie kompressiver Arrays mit zufĂ€llig gewĂ€hlten Gewichten. SchlieĂlich werden zwei weitere Anwendungen der vorgeschlagenen AnsĂ€tze diskutiert: CS-basierte VerzögerungsschĂ€tzung und kompressives Channel Sounding. Es wird demonstriert, dass die in beiden Gebieten durch die Anwendung der vorgeschlagenen AnsĂ€tze erhebliche Verbesserungen erzielt werden können.Direction of Arrival (DOA) estimation of plane waves impinging on an array of sensors is one of the most important tasks in array signal processing, which have attracted tremendous research interest over the past several decades. The estimated DOAs are used in various applications like localization of transmitting sources, massive MIMO and 5G Networks, tracking and surveillance in radar, and many others. The major objective in DOA estimation is to develop approaches that allow to reduce the hardware complexity in terms of receiver costs and power consumption, while providing a desired level of estimation accuracy and robustness in the presence of multiple sources and/or multiple paths. Compressive sensing (CS) is a novel sampling methodology merging signal acquisition and compression. It allows for sampling a signal with a rate below the conventional Nyquist bound. In essence, it has been shown that signals can be acquired at sub-Nyquist sampling rates without loss of information provided they possess a sufficiently sparse representation in some domain and that the measurement strategy is suitably chosen. CS has been recently applied to DOA estimation, leveraging the fact that a superposition of planar wavefronts corresponds to a sparse angular power spectrum. This dissertation investigates the application of compressive sensing to the DOA estimation problem with the goal to reduce the hardware complexity and/or achieve a high resolution and a high level of robustness. Many CS-based DOA estimation algorithms have been proposed in recent years showing tremendous advantages with respect to the complexity of the numerical solution while being insensitive to source correlation and allowing arbitrary array geometries. Moreover, CS has also been suggested to be applied in the spatial domain with the main goal to reduce the complexity of the measurement process by using fewer RF chains and storing less measured data without the loss of any significant information. In the first part of the work we investigate the model mismatch problem for CS based DOA estimation algorithms off the grid. To apply the CS framework a very common approach is to construct a finite dictionary by sampling the angular domain with a predefined sampling grid. Therefore, the target locations are almost surely not located exactly on a subset of these grid points. This leads to a model mismatch which deteriorates the performance of the estimators. We take an analytical approach to investigate the effect of such grid offsets on the recovered spectra showing that each off-grid source can be well approximated by the two neighboring points on the grid. We propose a simple and efficient scheme to estimate the grid offset for a single source or multiple well-separated sources. We also discuss a numerical procedure for the joint estimation of the grid offsets of closer sources. In the second part of the thesis we study the design of compressive antenna arrays for DOA estimation that aim to provide a larger aperture with a reduced hardware complexity and allowing reconfigurability, by a linear combination of the antenna outputs to a lower number of receiver channels. We present a basic receiver architecture of such a compressive array and introduce a generic system model that includes different options for the hardware implementation. We then discuss the design of the analog combining network that performs the receiver channel reduction. Our numerical simulations demonstrate the superiority of the proposed optimized compressive arrays compared to the sparse arrays of the same complexity and to compressive arrays with randomly chosen combining kernels. Finally, we consider two other applications of the sparse recovery and compressive arrays. The first application is CS based time delay estimation and the other one is compressive channel sounding. We show that the proposed approaches for sparse recovery off the grid and compressive arrays show significant improvements in the considered applications compared to conventional methods
Technological Evolution from RIS to Holographic MIMO
Multiple-input multiple-output (MIMO) techniques have been widely applied in current cellular networks. To meet the ever-increasing demands on spectral efficiency and network throughput, more and more antennas are equipped at the base station, forming the well-known concept of massive MIMO. However, traditional design with fully digital precoding architecture brings high power consumption and capital expenditure. Cost- and power-efficient solutions are being intensively investigated to address these issues. Among them, both reconfigurable intelligent surface (RIS) and holographic MIMO (HMIMO) stand out. In this chapter, we will focus on the ongoing paradigm shift from RIS to HMIMO, covering both topics in detail. A wide range of closely related topics, e.g., use cases, hardware architectures, channel modeling and estimation, RIS beamforming, HMIMO beamforming, performance analyses of spectral- and energy-efficiency, and challenges and outlook, will be covered to show their potential to be applied in the next-generation wireless networks as well as the rationales for the technological evolution from RIS to holographic MIMO
Terahertz Communications and Sensing for 6G and Beyond: A Comprehensive View
The next-generation wireless technologies, commonly referred to as the sixth
generation (6G), are envisioned to support extreme communications capacity and
in particular disruption in the network sensing capabilities. The terahertz
(THz) band is one potential enabler for those due to the enormous unused
frequency bands and the high spatial resolution enabled by both short
wavelengths and bandwidths. Different from earlier surveys, this paper presents
a comprehensive treatment and technology survey on THz communications and
sensing in terms of the advantages, applications, propagation characterization,
channel modeling, measurement campaigns, antennas, transceiver devices,
beamforming, networking, the integration of communications and sensing, and
experimental testbeds. Starting from the motivation and use cases, we survey
the development and historical perspective of THz communications and sensing
with the anticipated 6G requirements. We explore the radio propagation, channel
modeling, and measurements for THz band. The transceiver requirements,
architectures, technological challenges, and approaches together with means to
compensate for the high propagation losses by appropriate antenna and
beamforming solutions. We survey also several system technologies required by
or beneficial for THz systems. The synergistic design of sensing and
communications is explored with depth. Practical trials, demonstrations, and
experiments are also summarized. The paper gives a holistic view of the current
state of the art and highlights the issues and challenges that are open for
further research towards 6G.Comment: 55 pages, 10 figures, 8 tables, submitted to IEEE Communications
Surveys & Tutorial
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