New Constructions of Zero-Correlation Zone Sequences

In this paper, we propose three classes of systematic approaches for constructing zero correlation zone (ZCZ) sequence families. In most cases, these approaches are capable of generating sequence families that achieve the upper bounds on the family size ($K$) and the ZCZ width ($T$) for a given sequence period ($N$). Our approaches can produce various binary and polyphase ZCZ families with desired parameters $(N,K,T)$ and alphabet size. They also provide additional tradeoffs amongst the above four system parameters and are less constrained by the alphabet size. Furthermore, the constructed families have nested-like property that can be either decomposed or combined to constitute smaller or larger ZCZ sequence sets. We make detailed comparisons with related works and present some extended properties. For each approach, we provide examples to numerically illustrate the proposed construction procedure.Comment: 37 pages, submitted to IEEE Transactions on Information Theor

Sequence Design for Cognitive CDMA Communications under Arbitrary Spectrum Hole Constraint

To support interference-free quasi-synchronous code-division multiple-access (QS-CDMA) communication with low spectral density profile in a cognitive radio (CR) network, it is desirable to design a set of CDMA spreading sequences with zero-correlation zone (ZCZ) property. However, traditional ZCZ sequences (which assume the availability of the entire spectral band) cannot be used because their orthogonality will be destroyed by the spectrum hole constraint in a CR channel. To date, analytical construction of ZCZ CR sequences remains open. Taking advantage of the Kronecker sequence property, a novel family of sequences (called "quasi-ZCZ" CR sequences) which displays zero cross-correlation and near-zero auto-correlation zone property under arbitrary spectrum hole constraint is presented in this paper. Furthermore, a novel algorithm is proposed to jointly optimize the peak-to-average power ratio (PAPR) and the periodic auto-correlations of the proposed quasi-ZCZ CR sequences. Simulations show that they give rise to single-user bit-error-rate performance in CR-CDMA systems which outperform traditional non-contiguous multicarrier CDMA and transform domain communication systems; they also lead to CR-CDMA systems which are more resilient than non-contiguous OFDM systems to spectrum sensing mismatch, due to the wideband spreading.Comment: 13 pages,10 figures,Accepted by IEEE Journal on Selected Areas in Communications (JSAC)--Special Issue:Cognitive Radio Nov, 201

Neural networks for optical channel equalization in high speed communication systems

La demande future de bande passante pour les données dépassera les capacités des systèmes de communication optique actuels, qui approchent de leurs limites en raison des limitations de la bande passante électrique des composants de l’émetteur. L’interférence intersymbole (ISI) due à cette limitation de bande est le principal facteur de dégradation pour atteindre des débits de données élevés. Dans ce mémoire, nous étudions plusieurs techniques de réseaux neuronaux (NN) pour combattre les limites physiques des composants de l’émetteur pilotés à des débits de données élevés et exploitant les formats de modulation avancés avec une détection cohérente. Notre objectif principal avec les NN comme égaliseurs de canaux ISI est de surmonter les limites des récepteurs optimaux conventionnels, en fournissant une complexité évolutive moindre et une solution quasi optimale. Nous proposons une nouvelle architecture bidirectionnelle profonde de mémoire à long terme (BiLSTM), qui est efficace pour atténuer les graves problèmes d’ISI causés par les composants à bande limitée. Pour la première fois, nous démontrons par simulation que notre BiLSTM profonde proposée atteint le même taux d’erreur sur les bits(TEB) qu’un estimateur de séquence à maximum de vraisemblance (MLSE) optimal pour la modulation MDPQ. Les NN étant des modèles pilotés par les données, leurs performances dépendent fortement de la qualité des données d’entrée. Nous démontrons comment les performances du BiLSTM profond réalisable se dégradent avec l’augmentation de l’ordre de modulation. Nous examinons également l’impact de la sévérité de l’ISI et de la longueur de la mémoire du canal sur les performances de la BiLSTM profonde. Nous étudions les performances de divers canaux synthétiques à bande limitée ainsi qu’un canal optique mesuré à 100 Gbaud en utilisant un modulateur photonique au silicium (SiP) de 35 GHz. La gravité ISI de ces canaux est quantifiée grâce à une nouvelle vue graphique des performances basée sur les écarts de performance de base entre les solutions optimales linéaires et non linéaires classiques. Aux ordres QAM supérieurs à la QPSK, nous quantifions l’écart de performance BiLSTM profond par rapport à la MLSE optimale à mesure que la sévérité ISI augmente. Alors qu’elle s’approche des performances optimales de la MLSE à 8QAM et 16QAM avec une pénalité, elle est capable de dépasser largement la solution optimale linéaire à 32QAM. Plus important encore, l’avantage de l’utilisation de modèles d’auto-apprentissage comme les NN est leur capacité à apprendre le canal pendant la formation, alors que la MLSE optimale nécessite des informations précises sur l’état du canal.The future demand for the data bandwidth will surpass the capabilities of current optical communication systems, which are approaching their limits due to the electrical bandwidth limitations of the transmitter components. Inter-symbol interference (ISI) due to this band limitation is the major degradation factor to achieve high data rates. In this thesis, we investigate several neural network (NN) techniques to combat the physical limits of the transmitter components driven at high data rates and exploiting the advanced modulation formats with coherent detection. Our main focus with NNs as ISI channel equalizers is to overcome the limitations of conventional optimal receivers, by providing lower scalable complexity and near optimal solution. We propose a novel deep bidirectional long short-term memory (BiLSTM) architecture, that is effective in mitigating severe ISI caused by bandlimited components. For the first time, we demonstrate via simulation that our proposed deep BiLSTM achieves the same bit error rate (BER) performance as an optimal maximum likelihood sequence estimator (MLSE) for QPSK modulation. The NNs being data-driven models, their performance acutely depends on input data quality. We demonstrate how the achievable deep BiLSTM performance degrades with the increase in modulation order. We also examine the impact of ISI severity and channel memory length on deep BiLSTM performance. We investigate the performances of various synthetic band-limited channels along with a measured optical channel at 100 Gbaud using a 35 GHz silicon photonic(SiP) modulator. The ISI severity of these channels is quantified with a new graphical view of performance based on the baseline performance gaps between conventional linear and nonlinear optimal solutions. At QAM orders above QPSK, we quantify deep BiLSTM performance deviation from the optimal MLSE as ISI severity increases. While deep BiLSTM approaches the optimal MLSE performance at 8QAM and 16QAM with a penalty, it is able to greatly surpass the linear optimal solution at 32QAM. More importantly, the advantage of using self learning models like NNs is their ability to learn the channel during the training, while the optimal MLSE requires accurate channel state information

Laboratory and field trials evaluation of transmit delay Diversity applied to DVB-T/H networks

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The requirements for future DVB-T/H networks demand that broadcasters design and deploy networks that provide ubiquitous reception in challenging indoors and other obstructed situations. It is essential that such networks are designed cost-effectively and with minimized environmental impact. The use of transmit diversity techniques with multiple antennas have long been proposed to improve the performance and capacity of wireless systems. Transmit diversity exploits the scattering effect inherent in the channel by means of transmitting multiple signals in a controlled manner from spatially separated antennas, allowing independently faded signals to arrive at the receiver and improves the chances of decoding a signal of acceptable quality. Transmit diversity can complement receive diversity by adding an additional diversity gain and in situations where receiver diversity is not practical, transmit diversity alone delivers a comparable amount of diversity gain. Transmit Delay Diversity (DD) can be applied to systems employing the DVB standard without receiver equipment modifications. Although transmit DD can provide a gain in NLOS situations, it can introduce degradation in LOS situation. The aim of this thesis is to investigate the effectiveness in real-word applications of novel diversity techniques for broadcast transmitter networks. Tests involved laboratory experiments using a wireless MIMO channel emulator and the deployment of a field measurement campaign dedicated to driving, indoor and rooftop reception. The relationship between the diversity gain, the propagation environment and several parameters such as the transmit antenna separation, the receiver speed and the Forward Error Correction Codes (FEC) configuration are investigated. Results includes the effect of real-word parameter usually not modeled in the software simulation analysis, such as antenna radiation patterns and mutual coupling, scattering vegetation impact, non-Gaussian noise sources and receiver implementation. Moreover, a practical analysis of the effectiveness of experimental techniques to mitigate the loss due to transmit DD loss in rooftop reception is presented. The results of this thesis confirmed, completed and extended the existing predictions with real word measurement results

Strategies for Devising Automatic Signal Recognition Algorithms in a Shared Radio Environment

In an increasingly congested and complex radio environment interference is to be expected, which poses problems for Automatic Signal Recognition (ASR) systems. This thesis explores strategies for improving ASR performance in the presence of interference. The thesis breaks the overall research question down into a number of subquestions and explores each of these in turn. A Phase-symmetric Cross Recurrence Plot is developed and used to show how a radio signal can be manipulated to separate information about the modulation from the information being carried. The Logarithmic Cyclic frequency Domain Profile is introduced to illustrate how a logarithmic representation can be used for analysing mixtures of signals with very different cyclic frequencies. After defining a canonical ASR system architecture, the concepts of an Ideal Feature and Interference Selectivity are introduced and applied to typical features used in ASR processing. Finally it is shown how these algorithmic developments can be combined in a Bayesian chain implementation that can accommodate a wide variety of feature extraction algorithms. It is concluded that future ASR systems will require features that can handle a wide range of signal types with much higher levels of interference selectivity if they are to achieve acceptable performance in shared spectrum bands. Intelligent segmentation is shown to be a requirement for future ASR systems unless features can be developed that have near ideal performance

Communication based loss-of-mains protection method by frequency correlation

Due to the increasing penetration of distributed generation (DGs) in the distribution network in high numbers and proportions, and its conspicuous impact on power system stability. This occurs during a wide system disturbance in the power system, the DGs will start to disconnect from the main source in large proportions. This will further affect the power system stability and causes damages to its components and DGs. This thesis investigates in the reliability, security, and efficiency of satellite and internet communications, specifically for loss of mains (LOM) protection and exploring the strengths, the weaknesses, the feasibility of each type of communications, and the requirements of communication system components. By using communications network to send Phasor Measurement Unit (PMU) data to DGs protection equipment that are connected at remote areas all over UK, the LOM protection can be improved, obtain synchronization, precision, and coordination among power protection components. Satellite communication is chosen as it makes a better communication method when it comes to the installation, construction, urban disruption, time saving, and the installation and annual cost on every participant. However, the high latency issue is approached and solved by making a few changes in the communication protocol format and the data requirements to reduce the effect of latency to a level that can be tolerated. This thesis presents the development of a novel LOM protection method based on communication and frequency correlation. The stability and sensitivity assessment will show that this method is highly secure and reliable. It can also withstand a communication delay of 120ms without causing any nuisance tripping, and have a relay response to LOM operation of a maximum of 1s. The thesis also presents a novel method in time delay estimation that has been developed for power system applications. This method is called the Linear Trajectory Path (LTP) and its performance fulfils the LOM synchronisation requirements by succeeding in determining the time delay between the two data streams within the tolerated estimation error of ±100ms.Due to the increasing penetration of distributed generation (DGs) in the distribution network in high numbers and proportions, and its conspicuous impact on power system stability. This occurs during a wide system disturbance in the power system, the DGs will start to disconnect from the main source in large proportions. This will further affect the power system stability and causes damages to its components and DGs. This thesis investigates in the reliability, security, and efficiency of satellite and internet communications, specifically for loss of mains (LOM) protection and exploring the strengths, the weaknesses, the feasibility of each type of communications, and the requirements of communication system components. By using communications network to send Phasor Measurement Unit (PMU) data to DGs protection equipment that are connected at remote areas all over UK, the LOM protection can be improved, obtain synchronization, precision, and coordination among power protection components. Satellite communication is chosen as it makes a better communication method when it comes to the installation, construction, urban disruption, time saving, and the installation and annual cost on every participant. However, the high latency issue is approached and solved by making a few changes in the communication protocol format and the data requirements to reduce the effect of latency to a level that can be tolerated. This thesis presents the development of a novel LOM protection method based on communication and frequency correlation. The stability and sensitivity assessment will show that this method is highly secure and reliable. It can also withstand a communication delay of 120ms without causing any nuisance tripping, and have a relay response to LOM operation of a maximum of 1s. The thesis also presents a novel method in time delay estimation that has been developed for power system applications. This method is called the Linear Trajectory Path (LTP) and its performance fulfils the LOM synchronisation requirements by succeeding in determining the time delay between the two data streams within the tolerated estimation error of ±100ms

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Efficient Channel Modeling Methods for Mobile Communication Systems

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Advances in Data Mining Knowledge Discovery and Applications

Advances in Data Mining Knowledge Discovery and Applications aims to help data miners, researchers, scholars, and PhD students who wish to apply data mining techniques. The primary contribution of this book is highlighting frontier fields and implementations of the knowledge discovery and data mining. It seems to be same things are repeated again. But in general, same approach and techniques may help us in different fields and expertise areas. This book presents knowledge discovery and data mining applications in two different sections. As known that, data mining covers areas of statistics, machine learning, data management and databases, pattern recognition, artificial intelligence, and other areas. In this book, most of the areas are covered with different data mining applications. The eighteen chapters have been classified in two parts: Knowledge Discovery and Data Mining Applications