Improved Visible Light Communication Receiver Performance by Leveraging the Spatial Dimension

In wireless communications systems, signals can be transmitted as time (temporal) or spatial variants across 3D space, and in both ways. However, using temporal variant communication channels in high-speed data transmission introduces inter-symbol interference (ISI) which makes the systems unreliable. On the other hand, spatial diversity in signal processing reduces the ISI and improves the system throughput or performance by allowing more signals from different spatial locations at the same time. Therefore, the spatial features or properties of visible light signals can be very useful in designing a reliable visible light communication (VLC) system with higher system throughput and making it more robust against ambient noise and interference. By allowing only the signals of interest, spatial separability in VLC can minimize the noise to a greater extent to improve signal-to-noise ratio (SNR) which can ensure higher data rates (in the order of Gbps-Tbps) in VLC. So, designing a VLC system with spatial diversity is an exciting area to explore and might set the foundation for future VLC system architectures and enable different VLC based applications such as vehicular VLC, multi-VLC, localization, and detection using VLC, etc. This thesis work is motivated by the fundamental challenges in reusing spatial information in VLC systems to increase the system throughput or gain through novel system designing and their prototype implementations

INSTANT MESSAGING SPAM DETECTION IN LONG TERM EVOLUTION NETWORKS

The lack of efficient spam detection modules for packet data communication is resulting to increased threat exposure for the telecommunication network users and the service providers. In this thesis, we propose a novel approach to classify spam at the server side by intercepting packet-data communication among instant messaging applications. Spam detection is performed using machine learning techniques on packet headers and contents (if unencrypted) in two different phases: offline training and online classification. The contribution of this study is threefold. First, it identifies the scope of deploying a spam detection module in a state-of-the-art telecommunication architecture. Secondly, it compares the usefulness of various existing machine learning algorithms in order to intercept and classify data packets in near real-time communication of the instant messengers. Finally, it evaluates the accuracy and classification time of spam detection using our approach in a simulated environment of continuous packet data communication. Our research results are mainly generated by executing instances of a peer-to-peer instant messaging application prototype within a simulated Long Term Evolution (LTE) telecommunication network environment. This prototype is modeled and executed using OPNET network modeling and simulation tools. The research produces considerable knowledge on addressing unsolicited packet monitoring in instant messaging and similar applications

Characterization and Design of Analog Integrated Circuits Exploiting Analog Platforms

Universal Mobile Telecommunication System (UMTS) front end design is challenging because of the need to optimize power while satisfying a very high dynamic range requirement. At the same time, designing analog circuits for automotive applications is very difficult because of the wide temperature range (from -40 to 125 degrees at least) they must tolerate. Dealing with this design problems at the transistor level does not allow to explore efficiently the design space, while using behavioral models does not allow to take into consideration important second-order effects. We present an extension of the platform-based design methodology originally developed for digital systems to the analog domain to conjugate the need of higher levels of abstraction to deal with complexity as well as the one of capturing enough of the actual circuit-level characteristics to deal with second order effects. This methodology is based on the concept of Analog Platform and is very useful both to characterize an analog circuit and to perform a system level optimization. We show how this methodology applied to the UMTS front-end design yields power savings as large as 47% versus an original hand optimized design. Besides, we give details on how to design an RC oscillator for automotive applications and to get its main performances at the aim of characterizing it

Stable and Efficient Sparse Recovery for Machine Learning and Wireless Communication

Recent theoretical study shows that the sparsest solution to an underdetermined linear system is unique, provided the solution vector is sufficiently sparse, and the operator matrix has sufficiently incoherent column vectors. In addition, efficient algorithms have been discovered to find such solutions. This intriguing result opens a new door for many potential applications. In this thesis, we study the design of a class of greedy algorithms that are extremely efficient, e.g., Orthogonal Matching Pursuit (OMP). These greedy algorithms suffer from a stability issue that the greedy selection approach always make locally optimal decisions, thereby easily biasing and mistaking the solutions in particular under data noise. We propose a solution approach that in designing greedy algorithms, new constraints can be devised by leveraging application-specific insights and incorporated into the algorithms. Given that sparse recovery problems by definition are underdetermined, introducing additional constraints can significantly improve the stability of greedy algorithms, yet retain their efficiency.Engineering and Applied Science

A Vision and Framework for the High Altitude Platform Station (HAPS) Networks of the Future

A High Altitude Platform Station (HAPS) is a network node that operates in the stratosphere at an of altitude around 20 km and is instrumental for providing communication services. Precipitated by technological innovations in the areas of autonomous avionics, array antennas, solar panel efficiency levels, and battery energy densities, and fueled by flourishing industry ecosystems, the HAPS has emerged as an indispensable component of next-generations of wireless networks. In this article, we provide a vision and framework for the HAPS networks of the future supported by a comprehensive and state-of-the-art literature review. We highlight the unrealized potential of HAPS systems and elaborate on their unique ability to serve metropolitan areas. The latest advancements and promising technologies in the HAPS energy and payload systems are discussed. The integration of the emerging Reconfigurable Smart Surface (RSS) technology in the communications payload of HAPS systems for providing a cost-effective deployment is proposed. A detailed overview of the radio resource management in HAPS systems is presented along with synergistic physical layer techniques, including Faster-Than-Nyquist (FTN) signaling. Numerous aspects of handoff management in HAPS systems are described. The notable contributions of Artificial Intelligence (AI) in HAPS, including machine learning in the design, topology management, handoff, and resource allocation aspects are emphasized. The extensive overview of the literature we provide is crucial for substantiating our vision that depicts the expected deployment opportunities and challenges in the next 10 years (next-generation networks), as well as in the subsequent 10 years (next-next-generation networks).Comment: To appear in IEEE Communications Surveys & Tutorial

COGNITIVE MULTI-USER FREE SPACE OPTICAL COMMUNICATION

Increasing deployment of terrestrial, aerial, and space-based assets designed with more demanding services and applications is dramatically escalating the need for high capacity, high data-rate, adaptive, and flexible communication networks. Cognitive, multi-user Free Space Optical Communication (FSOC) networks provide a solution to address these challenges. Such FSOC networks can potentially merge automation and intelligence, as well as offer the benefits of optical communication with enhanced bandwidth and data-rate over long communication networks. Extensive research has investigated various designs, techniques, and methods to enable desired FSOC systems. This dissertation reports the investigation and analysis of novel, state-of-the-art methodologies and algorithms for supporting cognitive, multi-user FSOC. This work details an investigation of the ability of diverse Optical-Multiple Access Control (O-MAC) techniques for performing multi-point communication. Independent Component Analysis (ICA) and Non-Orthogonal Multiple Access (NOMA) techniques were experimentally validated, both singularly and in a combined approach, in a high-speed FSOC link. These methods proved to successfully support multi-user FSOC when users share allocation resources (e.g., time, bandwidth, and space, among others). Additionally, transmission and channel parameters that can affect signal reconstruction performance were identified. To introduce cognition and flexibility into the network, the research reported herein details the use of several Machine Learning (ML) algorithms for estimating crucial parameters at the Physical Layer (PHY) of FSOC networks (e.g., number of transmitting users, modulation format, and quality of transmission [QoT]) for automatically supporting and decoding multiple users. In particular, a novel methodology based on a weighted clustering analysis for automatic and blind user discovery is presented in this work. Extensive experimental analysis was conducted under multiple communication scenarios to identify system performance and limitations. Experimental results demonstrated the ability of the proposed techniques to successfully estimate parameters of interest with high accuracy. Finally, this dissertation presents the design and testing of a modular, multiple node, high-speed, real-time Optical Wireless Communication (OWC) testbed, which provides a hardware and software platform for testing proposed methods and for further research development. This dissertation successfully proves the feasibility of cognitive, multi-user FSOC through the developed and presented methodologies, as well as extensive experimental analyses. The main strength of the research outcomes of this work consists of exploiting software solutions (e.g., O-MAC, signal processing, and ML techniques) to intelligently support multiple users into a single optical channel (i.e., same allocation resources). Accordingly, Size, Weight and Power (SWaP) requirement can be reduced while achieving an increased network capacity