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Yes / This paper presents the architectural design, software implementation, the validation and flight trial results of an aeronautical communications system developed within the Seamless Aeronautical Networking through integration of Data links Radios and Antennas (SANDRA) project funded by the European 7th Framework Aeronautics and Transport Programme. Based on Software Defined Radio (SDR) techniques, an Integrated Modular Radio (IMR) platform was developed to accommodate several radio technologies. This can drastically reduce the size, weight and cost in avionics with respect to current radio systems implemented as standalone equipment. In addition, the modular approach ensures the possibility to dynamically reconfigure each radio element to operate on a specific type of radio link. A radio resource management (RRM) framework is developed in the IMR consisting of a communication manager for the resource allocation and management of the different radio links and a radio adaptation manager to ensure protocol convergence through IP. The IMR has been validated though flight trials held at Oberpfaffenhofen, Germany in June 2013. The results presented in the paper validate the flexibility and scalability of the IMR platform and demonstrate seamless service coverage across different airspace domains through interworking between the IMR and other components of the SANDRA network. / European Commission

<p dir="ltr">VANET is formed by vehicles, road units, infrastructure components, and various con- nected objects.It aims mainly to ensure public safety and traffic control. New emerging applications include value-added and user-oriented services. While this technological ad- vancement promises ubiquitous deployment of the VANET, security and privacy challenges must be addressed. Thence, vehicle authentication is a vital process to detect malicious users and prevent them from harming legitimate communications. Hover, the authentication pro- cess uses sensitive information to check the vehicle’s identity. Sharing this information will harm vehicle privacy. In this thesis, we aim to deal with this issues:</p><ul><li>How can we ensure vehicle authentication and avoid sensitive and identity information leaks simultaneously?</li><li>When nodes are asked to provide identity proof, how can we ensure that the shared information is only used by an authorized entity?</li><li>Can we define an effective scheme to distinguish between legitimate and malicious network nodes?This dissertation aims to address the preservation of vehicle private information used within the authentication mechanism in VANET communications.The VANET characteristics are thoroughly presented and analyzed. Security require- ments and challenges are identified. Additionally, we review the proposed authentication techniques and the most well-known security attacks while focusing on the privacy preser- vation need and its challenges.To fulfill, the privacy preservation requirements, we proposed a new solution called Active Bundle AUthentication Solution based on SDN for Vehicular Networks (ABAUS). We intro- duce the Software Defined Networks (SDN) as an authentication infrastructure to guarantee the authenticity of each participant. Furthermore, we enhance the preservation of sensitive data by the use of an active data Bundle (ADB) as a self-protecting security mechanism. It ensures data protection throughout the whole data life cycle. ABAUS defines a dedicated registration protocol to verify and validate the different members of the network.</li></ul><p dir="ltr">first solution focused on legitimate vehicle identification and sensitive data pro- tection. A second scheme is designed to recognize and eliminate malicious users called BEhaviour-based REPutation scheme for privacy preservation in VANET using blockchain technology (BEREP). Dedicated public blockchains are used by a central trust authority to register vehicles and store their behavior evaluation and a trust scoring system allows nodes to evaluate the behavior of their communicators and detect malicious infiltrated users.</p><p dir="ltr">By enhancing sensitive data preservation during the authentication process and detect- ing malicious attempts, our proposed work helps to tackle serious challenges in VANET communications.</p>

This dissertation presents an empirical approach to identifying decisions for adapting cognitive radio parameters with no a priori knowledge of the environment. Cognitively inspired radios, attempt to combine observed metrics of system performance with artificial intelligence decision-making algorithms. Current architectures trend towards hybrid combinations of heuristics, such as genetic algorithms (GA) and experiential methods, such as case-based reasoning (CBR). A weakness in the GA is its reliance on limited mathematical models for estimating bit error rate, packet error rate, throughput, and signal-to-noise ratio. The CBR approach is similarly limited by its dependency on past experiences. Both methods have potential to suffer in environments not previously encountered. In contrast, the statistical methods identify performance estimation models based on exercising defined experimental designs. This represents an experiential decision-making process formed in the present rather than the past. There are three core contributions from this empirical framework: 1) it enables a new approach to decision making based on empirical estimation models of system performance, 2) it provides a systematic method for initializing cognitive engine configuration parameters, and 3) it facilitates deeper understanding of system behavior by quantifying parameter significance, and interaction effects. Ultimately, this understanding enables simplification of system models by identifying insignificant parameters. This dissertation defines an abstract framework that enables application of statistical approaches to cognitive radio systems regardless of its platform or application space. Specifically, it assesses factorial design of experiments and response surface methodology (RSM) to an over-the-air wireless radio link. Results are compared to a benchmark GA cognitive engine. The framework is then used for identifying software-defined radio initialization settings. Taguchi designs, a related statistical method, are implemented to identify initialization settings of a GA. / Ph. D.

Global System for Mobile Communications (GSM) networks are receiving increasing attention in the open-source community. Open-source software allows for deployment of a mobile cellular network with lower costs, more customization, and scalable control. Two popular projects have emerged that offer varying network architectures and allow users to implement a GSM network in different capacities depending on individual needs. Osmocom provides more network control and scalability but requires commercial Base Transceiver Station (BTS) hardware with limited availability and closed source code. OpenBTS provides minimal GSM network functionality with more easily available and open-source hardware; however, it does not allow multi-cellular network configuration. This thesis offers a significant contribution towards a fully open-source GSM network by integrating the two major open-source communities, Osmocom and OpenBTS. Specifically, the Osmo-USRP program provides an inter-layer interface between the different network architectures of two GSM base station projects. Inter-layer primitive messages are processed in a thread multiplexer that manages logical channels across the interface. Downstream flow control is implemented in order to receive data frames on time for transmitting at the appropriate GSM frame number (FN). Uplink measurements, which are necessary for decision making in the Base Station Controller (BSC), are also gathered in the physical layer of Osmo-USRP and reported to Osmocom. Osmo-USRP operation is tested using a Universal Software Radio Peripheral (USRP), a relatively inexpensive and accessible Software-Defined Radio (SDR). Standard GSM events are investigated for single cell and multi-cellular network configurations. These tests include subscriber authentication and encryption, location updating, International Mobile Subscriber Identity (IMSI) attach and detach, Short Message Service (SMS) storage and delivery, voice calls with the full-rate audio codec, and uplink and downlink measurement reporting. While most functionality is successfully tested, inter-cell handover is not currently implemented. Further details on the proposed implementation of program limitations, especially inter-cell handover, are also discussed. / Master of Science

A radio direction finding (DF) system is an antenna array and a receiver arranged in a combination to determine the azimuth angle of a distant emitter. Basically, all DF systems derive the emitter location from an initial determination of the angle-of-arrival (AOA). Radio direction finding techniques have classically been based on multiple-antenna systems employing multiple receivers. Classic techniques such as MUSIC [1][2] and ESPRIT use simultaneous phase information from each antenna to estimate the angle-of-arrival of the signal of interest. In many scenarios (e.g., hand-held systems), however, multiple receivers are impractical. Thus, single channel techniques are of interest, particularly in mobile scenarios. Although the amount of existing research for single channel DF is considerably less than for multi-channel direction finding, single channel direction finding techniques have been previously investigated. Since many of the single channel direction finding techniques are older analog techniques and have been analyzed in previous work, we will investigate a new single channel direction finding technique that takes specific advantage of digital capabilities. Specifically, we propose a phase-based method that uses a bank of Phase-Locked Loops (PLLs) in combination with an eight-element circular array. Our method is similar to the Pseudo-Doppler method in that it samples antennas in a circular array using a commutative switch. In the proposed approach the sampled data is fed to a bank of PLLs which track the phase on each element. The parallel PLLs are implemented in software and their outputs are fed to a signal processing block that estimates the AOA. This thesis presents the details of the new Phase-Locked Loop (PLL) algorithm and compares its performance to existing single channel DF techniques such as the Watson-Watt and the Pseudo-Doppler techniques. We also describe the implementation of the PLL algorithm on a DRS Signal Solutions, Incorporated (DRS-SS) WJ-8629A Software Definable Receiver with Sunrise™ Technology and present measured performance results. / Master of Science

The rise of 5G and beyond systems has fuelled research in merging machine learning with wireless communications to achieve cognitive radios. However, the portability and limited power supply of radio frequency devices limits engineers' ability to combine them with powerful predictive models. This hinders the ability to support advanced 5G applications such as device-to-device (D2D) communication and dynamic spectrum sharing (DSS). This challenge has inspired a wave of research in energy efficient machine learning hardware with low computational and area overhead. In particular, hardware implementations of the delayed feedback reservoir (DFR) model show promising results for meeting these constraints while achieving high accuracy in cognitive radio applications. This thesis answers two research questions surrounding the applicability of FPGA DFR systems for DSS. First, can a DFR network implemented on an FPGA run faster and with lower power than a purely software approach? Second, can the system be implemented efficiently on an edge device running at less than 10 watts? Two systems are proposed that prove FPGA DFRs can achieve these feats: a mixed-signal circuit, followed by a high-level synthesis circuit. The implementations execute up to 58 times faster, and operate at more than 90% lower power than the software models. Furthermore, the lowest recorded average power of 0.130 watts proves that these approaches meet typical edge device constraints. When validated on the NARMA10 benchmark, the systems achieve a normalized error of 0.21 compared to state-of-the-art error values of 0.15. In a DSS task, the systems are able to predict spectrum occupancy with up to 0.87 AUC in high noise, multiple input, multiple output (MIMO) antenna configurations compared to 0.99 AUC in other works. At the end of this thesis, the trade-offs between the approaches are analyzed, and future directions for advancing this study are proposed. / Master of Science / The rise of 5G and beyond systems has fuelled research in merging machine learning with wireless communications to achieve cognitive radios. However, the portability and limited power supply of radio frequency devices limits engineers' ability to combine them with powerful predictive models. This hinders the ability to support advanced 5G and internet-of-things (IoT) applications. This challenge has inspired a wave of research in energy efficient machine learning hardware with low computational and area overhead. In particular, hardware implementations of a low complexity neural network model, called the delayed feedback reservoir, show promising results for meeting these constraints while achieving high accuracy in cognitive radio applications. This thesis answers two research questions surrounding the applicability of field-programmable gate array (FPGA) delayed feedback reservoir systems for wireless communication applications. First, can this network implemented on an FPGA run faster and with lower power than a purely software approach? Second, can the network be implemented efficiently on an edge device running at less than 10 watts? Two systems are proposed that prove the FPGA networks can achieve these feats. The systems demonstrate lower power consumption and latency than the software models. Additionally, the systems maintain high accuracy on traditional neural network benchmarks and wireless communications tasks. The second implementation is further demonstrated in a software-defined radio architecture. At the end of this thesis, the trade-offs between the approaches are analyzed, and future directions for advancing this study are proposed.

The history of Software-Defined Radios (SDRs) epitomizes innovation in wireless communication. Initially serving military needs, SDRs swiftly transitioned to civilian applications, revolutionizing communication. This thesis explores SDR applications such as Spectrum Scanning Systems, Contraband Cellphone Detection, and Human Activity Recognition via Wi-Fi signals. SDRs empower Spectrum Scanning Systems to monitor and analyze radio frequencies, optimizing spectrum allocation for seamless wireless communication. In Contraband Cellphone Detection, SDRs identify unauthorized signals in restricted areas, bolstering security efforts by thwarting illicit cellphone usage. Human Activity Recognition utilizes Raspberry Pi 3B+ to track movement patterns via Wi-Fi signals, offering insights across various sectors. Additionally, the thesis conducts a comparative analysis of Wi-Fi-based Human Activity Recognition and Radar for accuracy assessment. SDRs continue to drive innovation, enhancing wireless communication and security in diverse domains, from defense to healthcare and beyond.

The objective of this dissertation is to better understand the impact of the range extension and interference effects of opportunistic large arrays (OLAs), in the context of cooperative routing in multi-hop ad hoc networks. OLAs are a type of concurrent cooperative transmission (CCT), in which the number and location of nodes that will participate in a particular CCT cannot be known a priori. The motivation of this research is that the previous CCT research simplifies or neglects significant issues that impact the CCT-based network performance. Therefore, to develop and design more efficient and realistic OLA-based protocols, we clarify and examine through experimentation and analysis the simplified or neglected characteristics of CCT, which should be considered in the network-level system design. The main contributions of this research are (i) intra-flow interference analysis and throughput optimization in both disk- and strip-shaped networks, for multi-packet OLA transmission, (ii) CCT link modeling focusing on path-loss disparity and link asymmetry, (iii) demonstration of CCT range-extension and OLA-based routing using a software-defined radio (SDR) test-bed, (iv) a new OLA-based routing protocol with practical error control algorithm. In the throughput optimization in presence of the intra-channel interference, we analyze the feasibility condition of spatially pipelined OLA transmissions using the same channel and present numerical results with various system parameters. In the CCT link model, we provide the impact of path-loss disparity that are inherent in a virtual multiple-input-single-output (VMISO) link and propose an approximate model to calculate outage rates in high signal-to-noise-ratio (SNR) regime. Moreover, we present why link asymmetry is relatively more severe in CCT compared to single-input-single-output (SISO) links. The experimental studies show actual measurement values of the CCT range extension and realistic performance evaluation of OLA-based routing. Lastly, OLA with primary route set-up (OLA-PRISE) is proposed with a practical route recovery technique.