SWIFT: A Narrowband-Friendly Cognitive Wideband Network

Wideband technologies in the unlicensed spectrum can satisfy the ever-increasing demands for wireless bandwidth created by emerging rich media applications. The key challenge for such systems, however, is to allow narrowband technologies that share these bands (say, 802.11 a/b/g/n, Zigbee) to achieve their normal performance, without compromising the throughput or range of the wideband network.This paper presents SWIFT, the first system where high-throughput wideband nodes are shown in a working deployment to coexist with unknown narrowband devices, while forming a network of their own. Prior work avoids narrowband devices by operating below the noise level and limiting itself to a single contiguous unused band. While this achieves coexistence, it sacrifices the throughput and operating distance of the wideband device. In contrast, SWIFT creates high throughput wireless links by weaving together non-contiguous unused frequency bands that change as narrowband devices enter or leave the environment. This design principle of cognitive aggregation allows SWIFT to achieve coexistence, while operating at normal power, and thereby obtaining higher throughput and greater operating range. We implement SWIFT on a wideband hardware platform, and evaluate it in the presence of 802.11 devices. In comparison to a baseline that coexists with narrowband devices by operating below their noise level, SWIFT is equally narrowband-friendly but achieves 3.6x-10.5x higher throughput and 6x greater range

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

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

Journal of Telecommunications and Information Technology

kwartalni

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

An Investigation of Orthogonal Wavelet Division Multiplexing Techniques as an Alternative to Orthogonal Frequency Division Multiplex Transmissions and Comparison of Wavelet Families and Their Children

Recently, issues surrounding wireless communications have risen to prominence because of the increase in the popularity of wireless applications. Bandwidth problems, and the difficulty of modulating signals across carriers, represent significant challenges. Every modulation scheme used to date has had limitations, and the use of the Discrete Fourier Transform in OFDM (Orthogonal Frequency Division Multiplex) is no exception. The restriction on further development of OFDM lies primarily within the type of transform it uses in the heart of its system, Fourier transform. OFDM suffers from sensitivity to Peak to Average Power Ratio, carrier frequency offset and wasting some bandwidth to guard successive OFDM symbols. The discovery of the wavelet transform has opened up a number of potential applications from image compression to watermarking and encryption. Very recently, work has been done to investigate the potential of using wavelet transforms within the communication space. This research will further investigate a recently proposed, innovative, modulation technique, Orthogonal Wavelet Division Multiplex, which utilises the wavelet transform opening a new avenue for an alternative modulation scheme with some interesting potential characteristics. Wavelet transform has many families and each of those families has children which each differ in filter length. This research consider comprehensively investigates the new modulation scheme, and proposes multi-level dynamic sub-banding as a tool to adapt variable signal bandwidths. Furthermore, all compactly supported wavelet families and their associated children of those families are investigated and evaluated against each other and compared with OFDM. The linear computational complexity of wavelet transform is less than the logarithmic complexity of Fourier in OFDM. The more important complexity is the operational complexity which is cost effectiveness, such as the time response of the system, the memory consumption and the number of iterative operations required for data processing. Those complexities are investigated for all available compactly supported wavelet families and their children and compared with OFDM. The evaluation reveals which wavelet families perform more effectively than OFDM, and for each wavelet family identifies which family children perform the best. Based on these results, it is concluded that the wavelet modulation scheme has some interesting advantages over OFDM, such as lower complexity and bandwidth conservation of up to 25%, due to the elimination of guard intervals and dynamic bandwidth allocation, which result in better cost effectiveness

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