Low sidelobe synthesis of dipole arrays by element orientation selection using binary codec genetic algorithm

© 2017 Euraap. Selecting appropriate element orientations can significantly reduce the sidelobe level of the antenna array. In this paper, a binary coded genetic algorithm (BCGA) which selects the element orientations from specified discrete angles, is proposed to reduce the sidelobe level (SLL) of the array. Compared to the conventional GA, the BCGA is much faster in this application. Synthesis results show the effectiveness and efficiency of the proposed method

Practical investigation of Butler matrix application for beamforming with circular antenna arrays

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Cognitive Radio Systems

Cognitive radio is a hot research area for future wireless communications in the recent years. In order to increase the spectrum utilization, cognitive radio makes it possible for unlicensed users to access the spectrum unoccupied by licensed users. Cognitive radio let the equipments more intelligent to communicate with each other in a spectrum-aware manner and provide a new approach for the co-existence of multiple wireless systems. The goal of this book is to provide highlights of the current research topics in the field of cognitive radio systems. The book consists of 17 chapters, addressing various problems in cognitive radio systems

The University Defence Research Collaboration In Signal Processing

This chapter describes the development of algorithms for automatic detection of anomalies from multi-dimensional, undersampled and incomplete datasets. The challenge in this work is to identify and classify behaviours as normal or abnormal, safe or threatening, from an irregular and often heterogeneous sensor network. Many defence and civilian applications can be modelled as complex networks of interconnected nodes with unknown or uncertain spatio-temporal relations. The behavior of such heterogeneous networks can exhibit dynamic properties, reflecting evolution in both network structure (new nodes appearing and existing nodes disappearing), as well as inter-node relations. The UDRC work has addressed not only the detection of anomalies, but also the identification of their nature and their statistical characteristics. Normal patterns and changes in behavior have been incorporated to provide an acceptable balance between true positive rate, false positive rate, performance and computational cost. Data quality measures have been used to ensure the models of normality are not corrupted by unreliable and ambiguous data. The context for the activity of each node in complex networks offers an even more efficient anomaly detection mechanism. This has allowed the development of efficient approaches which not only detect anomalies but which also go on to classify their behaviour

The University Defence Research Collaboration In Signal Processing: 2013-2018

Signal processing is an enabling technology crucial to all areas of defence and security. It is called for whenever humans and autonomous systems are required to interpret data (i.e. the signal) output from sensors. This leads to the production of the intelligence on which military outcomes depend. Signal processing should be timely, accurate and suited to the decisions to be made. When performed well it is critical, battle-winning and probably the most important weapon which you’ve never heard of. With the plethora of sensors and data sources that are emerging in the future network-enabled battlespace, sensing is becoming ubiquitous. This makes signal processing more complicated but also brings great opportunities. The second phase of the University Defence Research Collaboration in Signal Processing was set up to meet these complex problems head-on while taking advantage of the opportunities. Its unique structure combines two multi-disciplinary academic consortia, in which many researchers can approach different aspects of a problem, with baked-in industrial collaboration enabling early commercial exploitation. This phase of the UDRC will have been running for 5 years by the time it completes in March 2018, with remarkable results. This book aims to present those accomplishments and advances in a style accessible to stakeholders, collaborators and exploiters

Machine Learning Meets Communication Networks: Current Trends and Future Challenges

The growing network density and unprecedented increase in network traffic, caused by the massively expanding number of connected devices and online services, require intelligent network operations. Machine Learning (ML) has been applied in this regard in different types of networks and networking technologies to meet the requirements of future communicating devices and services. In this article, we provide a detailed account of current research on the application of ML in communication networks and shed light on future research challenges. Research on the application of ML in communication networks is described in: i) the three layers, i.e., physical, access, and network layers; and ii) novel computing and networking concepts such as Multi-access Edge Computing (MEC), Software Defined Networking (SDN), Network Functions Virtualization (NFV), and a brief overview of ML-based network security. Important future research challenges are identified and presented to help stir further research in key areas in this direction

Signal processing techniques for mobile multimedia systems

Recent trends in wireless communication systems show a significant demand for the delivery of multimedia services and applications over mobile networks - mobile multimedia - like video telephony, multimedia messaging, mobile gaming, interactive and streaming video, etc. However, despite the ongoing development of key communication technologies that support these applications, the communication resources and bandwidth available to wireless/mobile radio systems are often severely limited. It is well known, that these bottlenecks are inherently due to the processing capabilities of mobile transmission systems, and the time-varying nature of wireless channel conditions and propagation environments. Therefore, new ways of processing and transmitting multimedia data over mobile radio channels have become essential which is the principal focus of this thesis. In this work, the performance and suitability of various signal processing techniques and transmission strategies in the application of multimedia data over wireless/mobile radio links are investigated. The proposed transmission systems for multimedia communication employ different data encoding schemes which include source coding in the wavelet domain, transmit diversity coding (space-time coding), and adaptive antenna beamforming (eigenbeamforming). By integrating these techniques into a robust communication system, the quality (SNR, etc) of multimedia signals received on mobile devices is maximised while mitigating the fast fading and multi-path effects of mobile channels. To support the transmission of high data-rate multimedia applications, a well known multi-carrier transmission technology known as Orthogonal Frequency Division Multiplexing (OFDM) has been implemented. As shown in this study, this results in significant performance gains when combined with other signal-processing techniques such as spa ce-time block coding (STBC). To optimise signal transmission, a novel unequal adaptive modulation scheme for the communication of multimedia data over MIMO-OFDM systems has been proposed. In this system, discrete wavelet transform/subband coding is used to compress data into their respective low-frequency and high-frequency components. Unlike traditional methods, however, data representing the low-frequency data are processed and modulated separately as they are more sensitive to the distortion effects of mobile radio channels. To make use of a desirable subchannel state, such that the quality (SNR) of the multimedia data recovered at the receiver is optimized, we employ a lookup matrix-adaptive bit and power allocation (LM-ABPA) algorithm. Apart from improving the spectral efficiency of OFDM, the modified LM-ABPA scheme, sorts and allocates subcarriers with the highest SNR to low-frequency data and the remaining to the least important data. To maintain a target system SNR, the LM-ABPA loading scheme assigns appropriate signal constella tion sizes and transmit power levels (modulation type) across all subcarriers and is adapted to the varying channel conditions such that the average system error-rate (SER/BER) is minimised. When configured for a constant data-rate load, simulation results show significant performance gains over non-adaptive systems. In addition to the above studies, the simulation framework developed in this work is applied to investigate the performance of other signal processing techniques for multimedia communication such as blind channel equalization, and to examine the effectiveness of a secure communication system based on a logistic chaotic generator (LCG) for chaos shift-keying (CSK)

Advanced Trends in Wireless Communications

Physical limitations on wireless communication channels impose huge challenges to reliable communication. Bandwidth limitations, propagation loss, noise and interference make the wireless channel a narrow pipe that does not readily accommodate rapid flow of data. Thus, researches aim to design systems that are suitable to operate in such channels, in order to have high performance quality of service. Also, the mobility of the communication systems requires further investigations to reduce the complexity and the power consumption of the receiver. This book aims to provide highlights of the current research in the field of wireless communications. The subjects discussed are very valuable to communication researchers rather than researchers in the wireless related areas. The book chapters cover a wide range of wireless communication topics