Transceiver design and system optimization for ultra-wideband communications

This dissertation investigates the potential promises and proposes possible solutions to the challenges of designing transceivers and optimizing system parameters in ultra-wideband (UWB) systems. The goal is to provide guidelines for UWB transceiver implementations under constraints by regulation, existing interference, and channel estimation. New UWB pulse shapes are invented that satisfy the Federal Communications Commission spectral mask. Parameters are designed to possibly implement the proposed pulses. A link budget is quantified based on an accurate frequency-dependent path loss calculation to account for variations across the ultra-wide bandwidth of the signal. Achievable information rates are quantified as a function of transmission distance over additive white Gaussian noise and multipath channels under specific UWB constraints: limited power spectral density, specific modulation formats, and a highly dispersive channel. The effect of self-interference (SI) and inter-symbol interference (ISI) on channel capacity is determined, and modulation formats that mitigate against this effect is identified. Spreading gains of familiar UWB signaling formats are evaluated, and UWB signals are proved to be spread spectrum. Conditions are formulated for trading coding gain with spreading gain with only a small impact on performance. Numerical results are examined to demonstrate that over a frequency-selective channel, the spreading gain may be beneficial in reducing the SI and ISI resulting in higher information rates. A reduced-rank adaptive filtering technique is applied to the problem of interference suppression and optimum combining in UWB communications. The reduced-rank combining method, in particular the eigencanceler, is proposed and compared with a minimum mean square error Rake receiver. Simulation results are evaluated to show that the performance of the proposed method is superior to the minimum mean square error when the correlation matrix is estimated from limited data. Impact of channel estimation on UWB system performance is investigated when path delays and path amplitudes are jointly estimated. Cramér-Rao bound (CRB) expressions for the variance of path delay and amplitude estimates are formulated using maximum likelihood estimation. Using the errors obtained from the CRB, the effective signal-to-noise ratio for UWB Rake receivers employing maximum ratio combining (MRC) is devised in the presence of channel path delay and amplitude errors. An exact expression of the bit error rate (BER) for UWB Rake receivers with MRC is derived with imperfect estimates of channel path delays and amplitudes. Further, this analysis is applied to design optimal transceiver parameters. The BER is used as part of a binary symmetric channel and the achievable information rates are evaluated. The optimum power allocation and number of symbols allocated to the pilot are developed with respect to maximizing the information rate. The optimal signal bandwidth to be used for UWB communications is determined in the presence of imperfect channel state information. The number of multipath components to be collected by Rake receivers is designed to optimize performance with non-ideal channel estimation

Complexity Theory

Computational Complexity Theory is the mathematical study of the intrinsic power and limitations of computational resources like time, space, or randomness. The current workshop focused on recent developments in various sub-areas including arithmetic complexity, Boolean complexity, communication complexity, cryptography, probabilistic proof systems, pseudorandomness, and quantum computation. Many of the developments are related to diverse mathematical fields such as algebraic geometry, combinatorial number theory, probability theory, representation theory, and the theory of error-correcting codes

Investigation of Non-coherent Discrete Target Range Estimation Techniques for High-precision Location

Ranging is an essential and crucial task for radar systems. How to solve the range-detection problem effectively and precisely is massively important. Meanwhile, unambiguity and high resolution are the points of interest as well. Coherent and non-coherent techniques can be applied to achieve range estimation, and both of them have advantages and disadvantages. Coherent estimates offer higher precision but are more vulnerable to noise and clutter and phase wrap errors, particularly in a complex or harsh environment, while the non-coherent approaches are simpler but provide lower precision. With the purpose of mitigating inaccuracy and perturbation in range estimation, miscellaneous techniques are employed to achieve optimally precise detection. Numerous elegant processing solutions stemming from non-coherent estimate are now introduced into the coherent realm, and vice versa. This thesis describes two non-coherent ranging estimate techniques with novel algorithms to mitigate the instinct deficit of non-coherent ranging approaches. One technique is based on peak detection and realised by Kth-order Polynomial Interpolation, while another is based on Z-transform and realised by Most-likelihood Chirp Z-transform. A two-stage approach for the fine ranging estimate is applied to the Discrete Fourier transform domain of both algorithms. An N-point Discrete Fourier transform is implemented to attain a coarse estimation; an accurate process around the point of interest determined in the first stage is conducted. For KPI technique, it interpolates around the peak of Discrete Fourier transform profiles of the chirp signal to achieve accurate interpolation and optimum precision. For Most-likelihood Chirp Z-transform technique, the Chirp Z-transform accurately implements the periodogram where only a narrow band spectrum is processed. Furthermore, the concept of most-likelihood estimator is introduced to combine with Chirp Z-transform to acquire better ranging performance. Cramer-Rao lower bound is presented to evaluate the performance of these two techniques from the perspective of statistical signal processing. Mathematical derivation, simulation modelling, theoretical analysis and experimental validation are conducted to assess technique performance. Further research will be pushed forward to algorithm optimisation and system development of a location system using non-coherent techniques and make a comparison to a coherent approach

Spatial modulation schemes and modem architectures for millimeter wave radio systems

The rapid growth of wireless industry opens the door to several use cases such as internet of things and device-to-device communications, which require boosting the reliability and the spectral efficiency of the wireless access network, while reducing the energy consumption at the terminals. The vast spectrum available in millimeter-wave (mmWave) frequency band is one of the most promising candidates to achieve high-speed communications. However, the propagation of the radio signals at high carrier frequencies suffers from severe path-loss which reduces the coverage area. Fortunately, the small wavelengths of the mmWave signals allow packing a large number of antennas not only at the base station (BS) but also at the user terminal (UT). These massive antenna arrays can be exploited to attain high beamforming and combining gains and overcome the path-loss associated with the mmWave propagation. In conventional (fully digital) multiple-input-multiple-output (MIMO) transceivers, each antenna is connected to a specific radio-frequency (RF) chain and high resolution analog-to-digital-converter. Unfortunately, these devices are expensive and power hungry especially at mmWave frequency band and when operating in large bandwidths. Having this in mind, several MIMO transceiver architectures have been proposed with the purpose of reducing the hardware cost and the energy consumption. Fully connected hybrid analog and digital precoding schemes were proposed in with the aim of replacing some of the conventional RF chains by energy efficient analog devices. These fully connected mapping requires many analog devices that leads to non-negligible energy consumption. Partially connected hybrid architectures have been proposed to improve the energy efficiency of the fully connected transceivers by reducing the number of analog devices. Simplifying the transceiver’s architecture to reduce the power consumption results in a degradation of the attained spectral efficiency. In this PhD dissertation, we propose novel modulation schemes and massive MIMO transceiver design to combat the challenges at the mmWave cellular systems. The structure of the doctoral manuscript can be expressed as In Chapter 1, we introduce the transceiver design challenges at mmWave cellular communications. Then, we illustrate several state of the art architectures and highlight their limitations. After that, we propose scheme that attains high-energy efficiency and spectrum efficiency. In chapter 2, first, we mathematically describe the state of the art of the SM and highlight the main challenges with these schemes when applied at mmWave frequency band. In order to combat these challenges (for example, high cost and high power consumption), we propose novel SM schemes specifically designed for mmWave massive MIMO systems. After that, we explain how these schemes can be exploited in attaining energy efficient UT architecture. Finally, we present the channel model, systems assumptions and the transceiver devices power consumption models. In chapter 3, we consider single user SM system. First, we propose downlink (DL) receive SM (RSM) scheme where the UT can be implemented with single or multiple radio-frequency chains and the BS can be fully digital or hybrid architecture. Moreover, we consider different precoders at the BS and propose low complexity and efficient antenna selection schemes for narrowband and wideband transmissions. After that, we propose joint uplink-downlink SM scheme where we consider RSM in the DL and transmit SM (TSM) in the UL based on energy efficient hybrid UT architecture. In chapter 4, we extend the SM system to the multi-user case. Specifically, we develop joint multi-user power allocation, user selection and antenna selection algorithms for the broadcast and the multiple access channels. Chapter 5 is presented for concluding the thesis and proposing future research directions.Considerando los altos requerimientos de los servicios de nueva generación, las infraestructuras de red actual se han visto obligadas a evolucionar en la forma de manejar los diferentes recursos de red y computación. Con este fin, nuevas tecnologías han surgido para soportar las funcionalidades necesarias para esta evolución, significando también un gran cambio de paradigma en el diseño de arquitecturas para la futura implementación de redes.En este sentido, este documento de tesis doctoral presenta un análisis sobre estas tecnologías, enfocado en el caso de redes inter/intra Data Centre. Por consiguiente, la introducción de tecnologías basadas en redes ópticas ha sido estudiada, con el fin de identificar problemas actuales que puedan llegar a ser solucionados mediante el diseño y aplicación de nuevas técnicas, asimismo como a través del desarrollo o la extensión de los componentes de arquitectura de red.Con este propósito, se han definido una serie de propuestas relacionadas con aspectos cruciales, así como el control de dispositivos ópticos por SDN para habilitar el manejo de redes híbridas, la necesidad de definir un mecanismo de descubrimiento de topologías ópticas capaz de exponer información precisa, y el analizar las brechas existentes para la definición de una arquitectura común en fin de soportar las comunicaciones 5G.Para validar estas propuestas, se han presentado una serie de validaciones experimentales por medio de escenarios de prueba específicos, demostrando los avances en control, orquestación, virtualización y manejo de recursos con el fin de optimizar su utilización. Los resultados expuestos, además de corroborar la correcta operación de los métodos y componentes propuestos, abre el camino hacia nuevas formas de adaptar los actuales despliegues de red respecto a los desafíos definidos en el inicio de una nueva era de las telecomunicaciones.Postprint (published version

Topics in statistical inference for massive data and high-dimensional data

This dissertation consists of three research papers that deal with three different problems in statistics concerning high-volume datasets. The first paper studies the distributed statistical inference for massive data. With the increasing size of the data, computational complexity and feasibility should be taken into consideration for statistical analyses. We investigate the statistical efficiency of the distributed version of a general class of statistics. Distributed bootstrap algorithms are proposed to approximate the distribution of the distributed statistics. These approaches relief the computational burdens of conventional methods while preserving adequate statistical efficiency. The second paper deals with testing the identity and sphericity hypotheses problem regarding high-dimensional covariance matrices, with a focus on improving the power of existing methods. By taking advantage of the sparsity in the underlying covariance matrices, the power improvement is accomplished by utilizing the banding estimator for the covariance matrices, which leads to a significant reduction in the variance of the test statistics. The last paper considers variable selection for high-dimensional data. Distance-based variable importance measures are proposed to rank and select variables with dependence structures being taken into consideration. The importance measures are inspired by the multi-response permutation procedure (MRPP) and the energy distance. A backward selection algorithm is developed to discover important variables and to improve the power of the original MRPP for high-dimensional data