Compressive channel estimation

In dieser Arbeit untersuchen wir die kompressive Kanalschätzung (KKS), also die Anwendung der Theorie und Methodologie des Compressed Sensing (CS) auf das Problem der Kanalschätzung doppelt selektiver Kanäle in Multicarrier-Systemen. Nach einer kurzen Einführung in die kabellose Kommunikation und einem kleinen Überblick über CS und einigen seiner Varianten betrachten wir die in [1] präsentierte elementare kompressive Kanalschätzmethode. Wir analysieren ihre Leistungsfähigkeit sowie ihre Komplexität, und wir untersuchen die ihr zugrundeliegende Annahme, nämlich die "delay-Doppler sparsity" typischer Kanäle, genauer. Aufbauend auf dieser Analyse stellen wir einige Varianten und Erweiterungen der kompressiven Kanalschätzmethode vor. Zuerst nutzen wir die Tatsache dass typische Kanäle auch als "group sparse" angesehen werden können. Dies ist eine Folge des sogenannten Leck Effekts, welcher die Leistung einer jeden kompressiven Kanalschätzmethode beeinträchtigt und daher eine enorme Herausforderungen für die KKS darstellt. Weiters betrachten wir die Erweiterung der kompressiven Schätzmethode auf Mehrantennensysteme (MIMO). Wir zeigen, dass die einzelnen Querkanäle eines solchen MIMO Systems (in etwa) als "jointly sparse", sogar als "jointly group sparse" angesehen, und daher Methoden des Multichannel CS (MCS) verwendet werden können. Letztens nutzen wir - unter Verwendung der Konzepte des Modified CS (MOD-CS) - die approximative "sequential sparsity" des Kanals zum Kanal-Tracking über mehrere aufeinanderfolgende Symbolblöcke hinweg. Diese Vorgehensweise kann die Leistung zusätzlich steigern, viel wichtiger jedoch, sie kann die Komplexität der Methode reduzieren. Darüber hinaus adaptieren wir die Technik der Basis-Optimierung, welche in [2, 3] vorgestellt wurde, für die verschiedenen Szenarien, und wir präsentieren Simulationsergebnisse, welche die verbesserte Leistung all jener Kanalschätzmethoden demonstrieren, die in dieser Arbeit erklärt werden.In this thesis we investigate compressive channel estimation (CCE), i.e. the application of the theory and methodology of Compressed Sensing (CS) to the problem of estimating doubly selective channels in multicarrier systems. After a brief introduction to wireless communications and a short survey of CS and some of its variations, we review the basic compressive channel estimator that was introduced in [1]. We analyze its performance as well as its computational complexity, and we explore the basic assumption underlying the compressive estimator, namely the delay-Doppler sparsity of typical channels, in more detail. Based on this analysis, we propose several variations and extensions of the conventional compressive channel estimator. First, we make use of the fact that typical channels can be considered group sparse as well. This is due to the so-called leakage effect, which actually impairs the performance of any channel estimator utilizing CS techniques and therefore is one of the main challenges in CCE. Then, we investigate the extension of the compressive estimators to the multi-antenna (MIMO) case. We show that the various cross-channels of a MIMO system can (approximately) be considered jointly sparse, even jointly group sparse, and that therefore the methodology of multichannel CS can be utilized. Last, by using the recently introduced concept of modified CS, we exploit the approximate sequential sparsity of the channel in order to track it over a period of several consecutive symbol blocks. This approach can yield an additional performance gain, but more importantly it can substantially reduce the computational complexity of the method. Additionally, we adapt the basis optimization techniques introduced in [2, 3] to the various settings, and we present simulation results that demonstrate the performance gains that can be achieved by using each of the compressive estimators presented in this thesis

CELLULAR-ENABLED MACHINE TYPE COMMUNICATIONS: RECENT TECHNOLOGIES AND COGNITIVE RADIO APPROACHES

The scarcity of bandwidth has always been the main obstacle for providing reliable high data-rate wireless links, which are in great demand to accommodate nowadays and immediate future wireless applications. In addition, recent reports have showed inefficient usage and under-utilization of the available bandwidth. Cognitive radio (CR) has recently emerged as a promising solution to enhance the spectrum utilization, where it offers the ability for unlicensed users to access the licensed spectrum opportunistically. By allowing opportunistic spectrum access which is the main concept for the interweave network model, the overall spectrum utilization can be improved. This requires cognitive radio networks (CRNs) to consider the spectrum sensing and monitoring as an essential enabling process for the interweave network model. Machine-to-machine (M2M) communication, which is the basic enabler for the Internet-of-Things (IoT), has emerged to be a key element in future networks. Machines are expected to communicate with each other exchanging information and data without human intervention. The ultimate objective of M2M communications is to construct comprehensive connections among all machines distributed over an extensive coverage area. Due to the radical change in the number of users, the network has to carefully utilize the available resources in order to maintain reasonable quality-of-service (QoS). Generally, one of the most important resources in wireless communications is the frequency spectrum. To utilize the frequency spectrum in IoT environment, it can be argued that cognitive radio concept is a possible solution from the cost and performance perspectives. Thus, supporting numerous number of machines is possible by employing dual-mode base stations which can apply cognitive radio concept in addition to the legacy licensed frequency assignment. In this thesis, a detailed review of the state of the art related to the application of spectrum sensing in CR communications is considered. We present the latest advances related to the implementation of the legacy spectrum sensing approaches. We also address the implementation challenges for cognitive radios in the direction of spectrum sensing and monitoring. We propose a novel algorithm to solve the reduced throughput issue due to the scheduled spectrum sensing and monitoring. Further, two new architectures are considered to significantly reduce the power consumption required by the CR to enable wideband sensing. Both systems rely on the 1-bit quantization at the receiver side. The system performance is analytically investigated and simulated. Also, complexity and power consumption are investigated and studied. Furthermore, we address the challenges that are expected from the next generation M2M network as an integral part of the future IoT. This mainly includes the design of low-power low-cost machine with reduced bandwidth. The trade-off between cost, feasibility, and performance are also discussed. Because of the relaxation of the frequency and spatial diversities, in addition, to enabling the extended coverage mode, initial synchronization and cell search have new challenges for cellular-enabled M2M systems. We study conventional solutions with their pros and cons including timing acquisition, cell detection, and frequency offset estimation algorithms. We provide a technique to enhance the performance in the presence of the harsh detection environment for LTE-based machines. Furthermore, we present a frequency tracking algorithm for cellular M2M systems that utilizes the new repetitive feature of the broadcast channel symbols in next generation Long Term Evolution (LTE) systems. In the direction of narrowband IoT support, we propose a cell search and initial synchronization algorithm that utilizes the new set of narrowband synchronization signals. The proposed algorithms have been simulated at very low signal to noise ratios and in different fading environments

Editorial: Emerging Technologies for Ubiquitous and Intelligent Infrastructures

The editorial describe the content of the Special Issue on Emerging Technologies for Ubiquitous and Intelligent Infrastructures, including six papers: “Joint Atomic Norm based Estimation of Sparse Time Dispersive SIMO Channels with Common Support in Pilot Aided OFDM Systems,” “Performance Evaluation of Non-prefiltering vs. Time Reversal prefiltering in distributed and uncoordinated IR-UWB Ad-Hoc networks,” “Analysis of Two-Tier LTE network with Randomized Resource Allocation and Proactive Offloading,” “Energy-Efficient Context Aware Power Management with Asynchronous Protocol for Body Sensor Network,” “Virtual and Oriented WiFi Fingerprinting Indoor Positioning based on Multi- Wall Multi-Floor Propagation Models,” and “Analysis of the Impact of AuthRF and AssRF Attacks on IEEE 802.11e-based Access Point.