115 research outputs found
Multi-user MIMO wireless communications
Mehrantennensysteme sind auf Grund der erhöhten Bandbreiteneffizienz und
Leistung eine SchlĂŒsselkomponente von Mobilfunksystemen der Zukunft. Diese
ermöglichen das gleichzeitige Senden von mehreren, rÀumlich getrennten
Datenströmen zu verschiedenen Nutzern. Die zentrale Fragestellung in der Praxis
ist, ob der ursprĂŒnglich vorausgesagte KapazitĂ€tsgewinn in realistischen
Szenarios erreicht wird und welche spezifischen Gewinne durch zusÀtzliche
Antennen und das Ausnutzen von Kanalkenntnis am Sender und EmpfÀnger erzielt
werden, was andererseits einen Zuwachs an Overhead oder nötiger Rechenleistung
bedeutet.
In dieser Arbeit werden neue lineare und nicht-lineare MU-MIMO Precoding-
Verfahren vorgestellt. Der verfolgte Ansatz zur Bestimmung der Precoding-
Matrizen ist allgemein anwendbar und die entstandenen Algorithmen können zur
Optimierung von verschiedenen Kriterien mit beliebig vielen Antennen an der
Mobilstation eingesetzt werden. Das wurde durch die Berechnung der Precoding-
Matrix in zwei Schritten erreicht. Im ersten Schritt wird die Ăberschneidung der
ZeilenrÀume minimiert, die durch die effektiven Kanalmatrizen verschiedener
Nutzer aufgespannt werden. Basierend auf mehreren parallelen Einzelnutzer-MIMO-
KanĂ€len wird im zweiten Schritt die Systemperformanz bezĂŒglich bestimmter
Kriterien optimiert.
Aus der gĂ€ngigen Literatur ist bereits bekannt, dass fĂŒr Nutzer mit nur einer
Antenne das MMSE Kriterium beim precoding optimal aber nicht bei Nutzern mit
mehreren Antennen. Deshalb werden in dieser Arbeit zwei neue Mehrnutzer MIMO
Strategien vorgestellt, die vom MSE Kriterium abgeleitet sind, nÀmlich
sukzessives MMSE und RBD. Bei der sukzessiven Verarbeitung mit einer
entsprechenden Anpassung der Sendeleistungsverteilung kann die volle DiversitÀt
des Systems ausgeschöpft werden. Die KapazitÀt nÀhert sich dabei der maximalen
Summenrate des Systems an. Bei gemeinsamer Verarbeitung der MIMO KanÀle wird
unabhÀngig vom Grad der Mehrnutzerinterferenz die maximale DiversitÀt erreicht.
Die genannten Techniken setzen entweder eine aktuelle oder eine ĂŒber einen
lĂ€ngeren Zeitraum gemittelte Kanalkenntnis voraus. Aus diesem Grund mĂŒssen die
Auswirkungen von Kanal-SchĂ€tzfehlern und EinflĂŒsse des Transceiver Front-Ends
auf die Verfahren nÀher untersucht werden.
FĂŒr eine weitergehende AbschĂ€tzung der Mehrantennensysteme muss die Performanz
des Gesamtsystems untersucht werden, da viele EinflĂŒsse auf die rĂ€umliche
Signalverarbeitung bei Betrachtung eines einzelnen Links nicht erkennbar sind.
Es wurde gezeigt, dass mit MIMO Precoding Strategien ein Vielfaches der
Datenrate eines Systems mit nur einer Antenne erzielt werden kann, wÀhrend der
Overhead durch Pilotsymbole und Steuersignale nur geringfĂŒgig zunimmt.Multiple-input, multiple-output (MIMO) systems are a key component of future
wireless communication systems, because of their promising improvement in terms
of performance and bandwidth efficiency. An important research topic is the
study of multi-user (MU) MIMO systems. Such systems have the potential to
combine the high throughput achievable with MIMO processing with the benefits of
space division multiple access (SDMA). The main question from a practical
standpoint is whether the initially predicted capacity gains can be obtained in
more realistic scenarios and what specific gains result from adding more
antennas and overhead or computational power to obtain channel state information
(CSI) at the transceivers.
In this thesis we introduce new linear and non-linear MU MIMO processing
techniques. The approach used for the design of the precoding matrix is general
and the resulting algorithms can address several optimization criteria with an
arbitrary number of antennas at the user terminals (UTs). This is achieved by
designing the precoding matrices in two steps. In the first step we minimize the
overlap of the row spaces spanned by the effective channel matrices of different
users. In the next step, we optimize the system performance with respect to the
specific optimization criterion assuming a set of parallel single-user MIMO
channels.
As it was previously reported in the literature, minimum mean-squared-error
(MMSE) processing is optimum for single-antenna UTs. However, MMSE suffers from
a performance loss when users are equipped with more than one antenna. The two
MU MIMO processing techniques that result from the two different MSE criteria
that are proposed in this thesis are successive MMSE and regularized block
diagonalization. By iterating the closed form solution with appropriate power
loading we are able to extract the full diversity in the system and empirically
approach the maximum sum-rate capacity in case of high multi-user interference.
Joint processing of MIMO channels yields maximum diversity regardless of the
level of multi-user interference.
As these techniques rely on the fact that there is either instantaneous or long-
term CSI available at the base station to perform precoding and decoding, it was
very important to investigate the influence of the transceiver front-end
imperfections and channel estimation errors on their performance.
For a comprehensive assessment of multi-antenna techniques, it is mandatory to
consider the performance at system level, since many effects of spatial
processing are not tractable at the link level. System level investigations have
shown that MU MIMO precoding techniques provide several times higher data rates
than single-input single-output systems with only slightly increased pilot and
control overhead
MIMO radar spaceâtime adaptive processing using prolate spheroidal wave functions
In the traditional transmitting beamforming radar system, the transmitting antennas send coherent waveforms which form a highly focused beam. In the multiple-input multiple-output (MIMO) radar system, the transmitter sends noncoherent (possibly orthogonal) broad (possibly omnidirectional) waveforms. These waveforms can be extracted at the receiver by a matched filterbank. The extracted signals can be used to obtain more diversity or to improve the spatial resolution for clutter. This paper focuses on spaceâtime adaptive processing (STAP) for MIMO radar systems which improves the spatial resolution for clutter. With a slight modification, STAP methods developed originally for the single-input multiple-output (SIMO) radar (conventional radar) can also be used in MIMO radar. However, in the MIMO radar, the rank of the jammer-and-clutter subspace becomes very large, especially the jammer subspace. It affects both the complexity and the convergence of the STAP algorithm. In this paper, the clutter space and its rank in the MIMO radar are explored. By using the geometry of the problem rather than data, the clutter subspace can be represented using prolate spheroidal wave functions (PSWF). A new STAP algorithm is also proposed. It computes the clutter space using the PSWF and utilizes the block-diagonal property of the jammer covariance matrix. Because of fully utilizing the geometry and the structure of the covariance matrix, the method has very good SINR performance and low computational complexity
Orthogonal frequency division multiplexing multiple-input multiple-output automotive radar with novel signal processing algorithms
Advanced driver assistance systems that actively assist the driver based on environment perception achieved significant advances in recent years. Along with this development, autonomous driving became a major research topic that aims ultimately at development of fully automated, driverless vehicles. Since such applications rely on environment perception, their ever increasing sophistication imposes growing demands on environmental sensors. Specifically, the need for reliable environment sensing necessitates the development of more sophisticated, high-performance radar sensors. A further vital challenge in terms of increased radar interference arises with the growing market penetration of the vehicular radar technology. To address these challenges, in many respects novel approaches and radar concepts are required. As the modulation is one of the key factors determining the radar performance, the research of new modulation schemes for automotive radar becomes essential.
A topic that emerged in the last years is the radar operating with digitally generated waveforms based on orthogonal frequency division multiplexing (OFDM). Initially, the use of OFDM for radar was motivated by the combination of radar with communication via modulation of the radar waveform with communication data. Some subsequent works studied the use of OFDM as a modulation scheme in many different radar applications - from adaptive radar processing to synthetic aperture radar. This suggests that the flexibility provided by OFDM based digital generation of radar waveforms can potentially enable novel radar concepts that are well suited for future automotive radar systems.
This thesis aims to explore the perspectives of OFDM as a modulation scheme for high-performance, robust and adaptive automotive radar. To this end, novel signal processing algorithms and OFDM based radar concepts are introduced in this work. The main focus of the thesis is on high-end automotive radar applications, while the applicability for real time implementation is of primary concern. The first part of this thesis focuses on signal processing algorithms for distance-velocity estimation. As a foundation for the algorithms presented in this thesis, a novel and rigorous signal model for OFDM radar is introduced. Based on this signal model, the limitations of the state-of-the-art OFDM radar signal processing are pointed out. To overcome these limitations, we propose two novel signal processing algorithms that build upon the conventional processing and extend it by more sophisticated modeling of the radar signal. The first method named all-cell Doppler compensation (ACDC) overcomes the Doppler sensitivity problem of OFDM radar. The core idea of this algorithm is the scenario-independent correction of Doppler shifts for the entire measurement signal. Since Doppler effect is a major concern for OFDM radar and influences the radar parametrization, its complete compensation opens new perspectives for OFDM radar. It not only achieves an improved, Doppler-independent performance, it also enables more favorable system parametrization. The second distance-velocity estimation algorithm introduced in this thesis addresses the issue of range and Doppler frequency migration due to the targetâs motion during the measurement. For the conventional radar signal processing, these migration effects set an upper limit on the simultaneously achievable distance and velocity resolution. The proposed method named all-cell migration compensation (ACMC) extends the underlying OFDM radar signal model to account for the target motion. As a result, the effect of migration is compensated implicitly for the entire radar measurement, which leads to an improved distance and velocity resolution. Simulations show the effectiveness of the proposed algorithms in overcoming the two major limitations of the conventional OFDM radar signal processing.
As multiple-input multiple-output (MIMO) radar is a well-established technology for improving the direction-of-arrival (DOA) estimation, the second part of this work studies the multiplexing methods for OFDM radar that enable simultaneous use of multiple transmit antennas for MIMO radar processing. After discussing the drawbacks of known multiplexing methods, we introduce two advanced multiplexing schemes for OFDM-MIMO radar based on non-equidistant interleaving of OFDM subcarriers. These multiplexing approaches exploit the multicarrier structure of OFDM for generation of orthogonal waveforms that enable a simultaneous operation of multiple MIMO channels occupying the same bandwidth. The primary advantage of these methods is that despite multiplexing they maintain all original radar parameters (resolution and unambiguous range in distance and velocity) for each individual MIMO channel. To obtain favorable interleaving patterns with low sidelobes, we propose an optimization approach based on genetic algorithms. Furthermore, to overcome the drawback of increased sidelobes due to subcarrier interleaving, we study the applicability of sparse processing methods for the distance-velocity estimation from measurements of non-equidistantly interleaved OFDM-MIMO radar. We introduce a novel sparsity based frequency estimation algorithm designed for this purpose.
The third topic addressed in this work is the robustness of OFDM radar to interference from other radar sensors. In this part of the work we study the interference robustness of OFDM radar and propose novel interference mitigation techniques. The first interference suppression algorithm we introduce exploits the robustness of OFDM to narrowband interference by dropping subcarriers strongly corrupted by interference from evaluation. To avoid increase of sidelobes due to missing subcarriers, their values are reconstructed from the neighboring ones based on linear prediction methods. As a further measure for increasing the interference robustness in a more universal manner, we propose the extension of OFDM radar with cognitive features. We introduce the general concept of cognitive radar that is capable of adapting to the current spectral situation for avoiding interference. Our work focuses mainly on waveform adaptation techniques; we propose adaptation methods that allow dynamic interference avoidance without affecting adversely the estimation performance.
The final part of this work focuses on prototypical implementation of OFDM-MIMO radar. With the constructed prototype, the feasibility of OFDM for high-performance radar applications is demonstrated. Furthermore, based on this radar prototype the algorithms presented in this thesis are validated experimentally. The measurements confirm the applicability of the proposed algorithms and concepts for real world automotive radar implementations
NOVEL OFDM SYSTEM BASED ON DUAL-TREE COMPLEX WAVELET TRANSFORM
The demand for higher and higher capacity in wireless networks, such as cellular,
mobile and local area network etc, is driving the development of new signaling
techniques with improved spectral and power efficiencies. At all stages of a
transceiver, from the bandwidth efficiency of the modulation schemes through highly
nonlinear power amplifier of the transmitters to the channel sharing between different
users, the problems relating to power usage and spectrum are aplenty. In the coming
future, orthogonal frequency division multiplexing (OFDM) technology promises to
be a ready solution to achieving the high data capacity and better spectral efficiency in
wireless communication systems by virtue of its well-known and desirable
characteristics.
Towards these ends, this dissertation investigates a novel OFDM system based on
dual-tree complex wavelet transform (D
Asynchronous CDMA Systems with Random Spreading-Part II: Design Criteria
Totally asynchronous code-division multiple-access (CDMA) systems are
addressed. In Part I, the fundamental limits of asynchronous CDMA systems are
analyzed in terms of spectral efficiency and SINR at the output of the optimum
linear detector. The focus of Part II is the design of low-complexity
implementations of linear multiuser detectors in systems with many users that
admit a multistage representation, e.g. reduced rank multistage Wiener filters,
polynomial expansion detectors, weighted linear parallel interference
cancellers. The effects of excess bandwidth, chip-pulse shaping, and time delay
distribution on CDMA with suboptimum linear receiver structures are
investigated. Recursive expressions for universal weight design are given. The
performance in terms of SINR is derived in the large-system limit and the
performance improvement over synchronous systems is quantified. The
considerations distinguish between two ways of forming discrete-time
statistics: chip-matched filtering and oversampling
Investigation of pre-detection signal processing of pseudonoise communication signals in the presence of additive white gaussian noise and CW and bursty interference
By comparison to conventional communication systems, spread-spectrum systems are known to be less affected by interference because of their large dimensionality in signal space. Nevertheless, significant performance degradation is experienced when large interference exists in a few or even one signal coordinates. In this case, interference reduction techniques are also known to provide additional processing gain. A novel class of pseudonoise (PN) invariant algorithms is derived to reduce the impact of interference and restore much of the structure of PN signals received in the presence of interference and noise. A PN signal received by a pre-detection signal process (PDSP) implementing a PN invariant algorithm remains unchanged at the output. When an interference waveform is added to the PN signal, most of the DC bias as well as other smooth components of the interference may be significantly reduced at the output of the same PDSP. If n is the longest run in the PN sequence of maximal length N, and Ro is the chip rate, it is shown that the algorithms work well when the interference is sinusoidal with a frequency deviation from the carrier up to Ro/N. At such a low frequency deviation, the processing gain is observed to be relatively high and independent of the phase deviation. As the frequency deviation Increases to nRo/N, the performance of the spread-spectrum system decreases to the level that would have been obtained in the absence of the PDSP
An Analysis of Mutually Dispersive Brown Symbols for Non-Linear Ambiguity Suppression
This thesis significantly advances research towards the implementation of optimal Non-linear Ambiguity Suppression (NLS) waveforms by analyzing the Brown theorem. The Brown theorem is reintroduced with the use of simplified linear algebraic notation. A methodology for Brown symbol design and digitization is provided, and the concept of dispersive gain is introduced. Numerical methods are utilized to design, synthesize, and analyze Brown symbol performance. The theoretical performance in compression and dispersion of Brown symbols is demonstrated and is shown to exhibit significant improvement compared to discrete codes. As a result of this research a process is derived for the design of optimal mutually dispersive symbols for any sized family. In other words, the limitations imposed by conjugate LFM are overcome using NLS waveforms that provide an effective-fold increase in radar unambiguous range. This research effort has taken a theorem from its infancy, validated it analytically, simplified it algebraically, tested it for realizability, and now provides a means for the synthesis and digitization of pulse coded waveforms that generate an N-fold increase in radar effective unambiguous range. Peripherally, this effort has motivated many avenues of future research
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