Banco de testes para monitoramento sub-Nyquist de espectro de banda larga

Radioelectric spectrum management is a concern for today’s world, mainly due to the misuse that has been given to this resource through the years, especially on the UHF band. To address this problem, a testbed for sub-Nyquist Wideband Spectrum Monitoring was built, that includes a web interface to remotely measure occupancy of the UHF band. To achieve the above, an RF interface that allows tuning UHF frequencies with an instantaneous bandwidth of 95 MHz was built. Afterwards, a Random Demodulator was connected, and then an embedded system performed sub--Nyquist sampling and spectrum recovery. The embedded system connected to an information system that serves a web page, through which remote users can perform UHF band monitoring. Experimental results showed that spectrum sensing can be achieved by using different algorithms on certain sparse spectra. In addition, the aforementioned web interface allowed simultaneous user connections, in order to perform independent measurements by sharing a hardware subsystem.La gestión del espectro radioeléctrico es una preocupación en la actualidad, hecho derivado, ante todo, del mal uso que se ha dado a este recurso a través de los años, especialmente en la banda de UHF. Para afrontar este problema, se construyó un banco de pruebas para la supervisión del espectro de banda ancha a través de muestreo sub-Nyquist, el cual incluye una interfaz web para medir de forma remota la ocupación de la banda UHF. Para lograr esto, se construyó una interfaz RF que permitiría sintonizar frecuencias UHF con un ancho de banda instantáneo de 95 MHz. Después, se conectó un demodulador aleatorio; y luego, un sistema embebido realizaría el muestreo sub-Nyquist y la recuperación del espectro. Este se conectaría, a su turno, con un sistema de información que sirve una página web, a través de la cual los usuarios remotos pueden realizar la supervisión de la banda de UHF. Los resultados muestran que la detección del espectro se puede lograr mediante diferentes algoritmos en ciertos espectros dispersos. Además, la interfaz web permitió que existiesen conexiones de usuario simultáneas, de tal manera que se realizaran mediciones independientes compartiendo el subsistema de hardware.O gerenciamento do espectro radioelétrico é uma preocupação na atualidade, fato derivado, inicialmente, do mau uso que se tem dado a esse recurso através dos anos, especialmente na banda de UHF. Para enfrentar esse problema, construiu-se um banco de testes para a supervisão do espectro de banda larga por meio de amostragem sub-Nyquist, a qual inclui uma interface web para medir de forma remota a ocupação da banda UHF. Para isso, construiu-se uma interface RF que permitiria sintonizar frequências UHF com uma largura de banda instantânea de 95 MHz. Em seguida, ligou-se um demodulador aleatório; logo, um sistema embebido realizaria a amostragem sub-Nyquist e a recuperação do espectro. Este se ligaria, por sua vez, com um sistema de informação que serve um site, através do qual os usuários remotos podem realizar a supervisão da banda de UHF. Os resultados mostram que a detecção do espectro pode ser conseguida mediante diferentes algoritmos em certos espectros dispersos. Além disso, a interface web permitiu que existissem conexões de usuário simultâneas, de tal maneira que se realizassem medidas independentes compartilhando o subsistema de hardware.&nbsp

Array Architectures and Physical Layer Design for Millimeter-Wave Communications Beyond 5G

Ever increasing demands in mobile data rates have resulted in exploration of millimeter-wave (mmW) frequencies for the next generation (5G) wireless networks. Communications at mmW frequencies is presented with two keys challenges. Firstly, high propagation loss requires base stations (BSs) and user equipment (UEs) to use a large number of antennas and narrow beams to close the link with sufficient received signal power. Consequently, communications using narrow beams create a new challenge in channel estimation and link establishment based on fine angular probing. Current mmW system use analog phased arrays that can probe only one angle at the time which results in high latency during link establishment and channel tracking. It is desirable to design low latency beam training by exploring both physical layer designs and array architectures that could replace current 5G approaches and pave the way to the communications for frequency bands in higher mmW band and sub-THz region where larger antenna arrays and communications bandwidth can be exploited. To this end, we propose a novel signal processing techniques exploiting unique properties of mmW channel, and show both theoretically, in simulation and experiments its advantages over conventional approaches. Secondly, we explore different array architecture design and analyze their trade-offs between spectral efficiency and power consumption and area. For comprehensive comparison, we have developed a methodology for optimal design of system parameters for different array architecture candidates based on the spectral efficiency target, and use these parameters to estimate the array area and power consumption based on the circuits reported in the literature. We show that the hybrid analog and digital architectures have severe scalability concerns in radio frequency signal distribution with increased array size and spatial multiplexing levels, while the fully-digital array architectures have the best performance and power/area trade-offs.The developed approaches are based on a cross-disciplinary research that combines innovation in model based signal processing, machine learning, and radio hardware. This work is the first to apply compressive sensing (CS), a signal processing tool that exploits sparsity of mmW channel model, to accelerate beam training of mmW cellular system. The algorithm is designed to address practical issues including the requirement of cell discovery and synchronization that involves estimation of angular channel together with carrier frequency offset and timing offsets. We have analyzed the algorithm performance in the 5G compliant simulation and showed that an order of magnitude saving is achieved in initial access latency for the desired channel estimation accuracy. Moreover, we are the first to develop and implement a neural network assisted compressive beam alignment to deal with hardware impairments in mmW radios. We have used 60GHz mmW testbed to perform experiments and show that neural networks approach enhances alignment rate compared to CS. To further accelerate beam training, we proposed a novel frequency selective probing beams using the true-time-delay (TTD) analog array architecture. Our approach utilizes different subcarriers to scan different directions, and achieves a single-shot beam alignment, the fastest approach reported to date. Our comprehensive analysis of different array architectures and exploration of emerging architectures enabled us to develop an order of magnitude faster and energy efficient approaches for initial access and channel estimation in mmW systems

Single-pixel, single-photon three-dimensional imaging

The 3D recovery of a scene is a crucial task with many real-life applications such as self-driving vehicles, X-ray tomography and virtual reality. The recent development of time-resolving detectors sensible to single photons allowed the recovery of the 3D information at high frame rate with unprecedented capabilities. Combined with a timing system, single-photon sensitive detectors allow the 3D image recovery by measuring the Time-of-Flight (ToF) of the photons scattered back by the scene with a millimetre depth resolution. Current ToF 3D imaging techniques rely on scanning detection systems or multi-pixel sensor. Here, we discuss an approach to simplify the hardware complexity of the current 3D imaging ToF techniques using a single-pixel, single-photon sensitive detector and computational imaging algorithms. The 3D imaging approaches discussed in this thesis do not require mechanical moving parts as in standard Lidar systems. The single-pixel detector allows to reduce the pixel complexity to a single unit and offers several advantages in terms of size, flexibility, wavelength range and cost. The experimental results demonstrate the 3D image recovery of hidden scenes with a subsecond acquisition time, allowing also non-line-of-sight scenes 3D recovery in real-time. We also introduce the concept of intelligent Lidar, a 3D imaging paradigm based uniquely on the temporal trace of the return photons and a data-driven 3D retrieval algorithm

Arrayed LiDAR signal analysis for automotive applications

Light detection and ranging (LiDAR) is one of the enabling technologies for advanced driver assistance and autonomy. Advances in solid-state photon detector arrays offer the potential of high-performance LiDAR systems but require novel signal processing approaches to fully exploit the dramatic increase in data volume an arrayed detector can provide. This thesis presents two approaches applicable to arrayed solid-state LiDAR. First, a novel block independent sparse depth reconstruction framework is developed, which utilises a random and very sparse illumination scheme to reduce illumination density while improving sampling times, which further remain constant for any array size. Compressive sensing (CS) principles are used to reconstruct depth information from small measurement subsets. The smaller problem size of blocks reduces the reconstruction complexity, improves compressive depth reconstruction performance and enables fast concurrent processing. A feasibility study of a system proposal for this approach demonstrates that the required logic could be practically implemented within detector size constraints. Second, a novel deep learning architecture called LiDARNet is presented to localise surface returns from LiDAR waveforms with high throughput. This single data driven processing approach can unify a wide range of scenarios, making use of a training-by-simulation methodology. This augments real datasets with challenging simulated conditions such as multiple returns and high noise variance, while enabling rapid prototyping of fast data driven processing approaches for arrayed LiDAR systems. Both approaches are fast and practical processing methodologies for arrayed LiDAR systems. These retrieve depth information with excellent depth resolution for wide operating ranges, and are demonstrated on real and simulated data. LiDARNet is a rapid approach to determine surface locations from LiDAR waveforms for efficient point cloud generation, while block sparse depth reconstruction is an efficient method to facilitate high-resolution depth maps at high frame rates with reduced power and memory requirements.Engineering and Physical Sciences Research Council (EPSRC

Structural health monitoring of in-service tunnels

This work presents an overview of some of the most promising technologies for the structural health monitoring (SHM) of in-service tunnels. The common goal of damage or unusual behaviour detection is best pursued by an integrated approach based on the concurrent deployment of multiple technologies. Typically, traditional SHM systems are installed in problematic or special areas of the tunnels, giving information on conditions and helping manage maintenance. However, these methodologies often have the drawbacks of forcing the interruption of traffic for SHM system installation and monitoring only selected portions. Alternative solutions that would make it possible to keep the tunnel in normal operation and/or to analyse the entire infrastructure development through successive and continuous scanning stages, would be beneficial. In this paper, the authors will briefly review some traditional monitoring technologies for tunnels. Furthermore, the work is aimed at identifying alternative solutions, limiting or avoiding traffic interruptions