With the reality that diverse air interfaces and dissimilar access networks coexist, accompanied by the trend that dynamic spectrum access (DSA) is allowed and will be gradually employed, cognition and cooperation form a promising framework to achieve the ideality of seamless ubiquitous connectivity in future communication networks. In this dissertation, the cognitive gateway (CG), conceived as a special cognitive radio (CR) node, is proposed and designed to facilitate universal interoperability among incompatible waveforms. A proof-of-concept prototype is built and tested. Located in places where various communication nodes and diverse access networks coexist, the CG can be easily set up and works like a network server with differentiated service (Diffserv) architecture to provide automatic traffic relaying and link establishment. The author extracts a scalable '“source-CG-destination“ snapshot from the entire network and investigates the key enabling technologies for such a snapshot. The CG features provide universal interoperability, which is enabled by a generic waveform representation format and the reconfigurable software defined radio platform. According to the trend of an all IP-based solution for future communication systems, the term “waveform“ in this dissertation has been defined as a protocol stack specification suite. The author gives a generic waveform representation format based on the five-layer TCP/IP protocol stack architecture. This format can represent the waveforms used by Ethernet, WiFi, cellular system, P25, cognitive radios etc. A significant advantage of CG over other interoperability solutions lies in its autonomy, which is supported by appropriate signaling processes and automatic waveform identification. The service process in a CG is usually initiated by the users who send requests via their own waveforms. These requests are transmitted during the signaling procedures. The complete operating procedure of a CG is depicted as a waveform-oriented cognition loop, which is primarily executed by the waveform identifier, scenario analyzer, central controller, and waveform converter together. The author details the service process initialized by a primary user (e.g. legacy public safety radio) and that initialized by a secondary user (e.g. CR), and describes the signaling procedures between CG and clients for the accomplishment of CG discovery, user registration and un-registration, link establishment, communication resumption, service termination, route discovery, etc. From the waveforms conveyed during the signaling procedures, the waveform identifier extracts the parameters that can be used for a CG to identify the source waveform and the destination waveform. These parameters are called “waveform indicators.“ The author analyzes the four types of waveforms of interest and outlines the waveform indicators for different types of communication initiators. In particular, a multi-layer waveform identifier is designed for a CG to extract the waveform indicators from the signaling messages. For the physical layer signal recognition, a Universal Classification Synchronization (UCS) system has been invented. UCS is conceived as a self-contained system which can detect, classify, synchronize with a received signal and provide all parameters needed for physical layer demodulation without prior information from the transmitter. Currently, it can accommodate the modulations including AM, FM, FSK, MPSK, QAM and OFDM. The design and implementation details of a UCS have been presented. The designed system has been verified by over-the-air (OTA) experiments and its performance has been evaluated by theoretical analysis and software simulation. UCS can be ported to different platforms and can be applied for various scenarios. An underlying assumption for UCS is that the target signal is transmitted continually. However, it is not the case for a CG since the detection objects of a CG are signaling messages. In order to ensure higher recognition accuracy, signaling efficiency, and lower signaling overhead, the author addresses the key issues for signaling scheme design and their dependence on waveform identification strategy. In a CG, waveform transformation (WT) is the last step of the link establishment process. The resources required for transformation of waveform pairs, together with the application priority, constitute the major factors that determine the link control and scheduling scheme in a CG. The author sorts different WT into five categories and describes the details of implementing the four typical types of WT (including physical layer analog – analog gateway, up to link layer digital – digital gateway, up-to-network-layer digital gateway, and Voice over IP (VoIP) – an up to transport layer gateway) in a practical CG prototype. The issues that include resource management and link scheduling have also been addressed. This dissertation presents a CG prototype implemented on the basis of GNU Radio plus multiple USRPs. In particular, the service process of a CG is modeled as a two-stage tandem queue, where the waveform identifier queues at the first stage can be described as M/D/1/1 models and the waveform converter queue at the second stage can be described as G/M/K/K model. Based on these models, the author derives the theoretical block probability and throughput of a CG. Although the “source-CG-destination” snapshot considers only neighboring nodes which are one-hop away from the CG, it is scalable to form larger networks. CG can work in either ad-hoc or infrastructure mode. Utilizing its capabilities, CG nodes can be placed in different network architectures/topologies to provide auxiliary connectivity. Multi-hop cooperative relaying via CGs will be an interesting research topic deserving further investigation. / Ph. D.

This dissertation provides the theory, design, and implementation of a cognitive engine, the enabling technology of cognitive radio. A cognitive radio is a wireless communications device capable of sensing the environment and making decisions on how to use the available radio resources to enable communications with a certain quality of service. The cognitive engine, the intelligent system behind the cognitive radio, combines sensing, learning, and optimization algorithms to control and adapt the radio system from the physical layer and up the communication stack. The cognitive engine presented here provides a general framework to build and test cognitive engine algorithms and components such as sensing technology, optimization routines, and learning algorithms. The cognitive engine platform allows easy development of new components and algorithms to enhance the cognitive radio capabilities. It is shown in this dissertation that the platform can easily be used on a simulation system and then moved to a real radio system. The dissertation includes discussions of both theory and implementation of the cognitive engine. The need for and implementation of all of the cognitive components is strongly featured as well as the specific issues related to the development of algorithms for cognitive radio behavior. The discussion of the theory focuses largely on developing the optimization space to intelligently and successfully design waveforms for particular quality of service needs under given environmental conditions. The analysis develops the problem into a multi-objective optimization process to optimize and trade-of of services between objectives that measure performance, such as bit error rate, data rate, and power consumption. The discussion of the multi-objective optimization provides the foundation for the analysis of radio systems in this respect, and through this, methods and considerations for future developments. The theoretical work also investigates the use of learning to enhance the cognitive engine's capabilities through feed-back, learning, and knowledge representation. The results of this work include the analysis of cognitive radio design and implementation and the functional cognitive engine that is shown to work in both simulation and on-line experiments. Throughout, examples and explanations of building and interfacing cognitive components to the cognitive engine enable the use and extension of the cognitive engine for future work. / Ph. D.

Software Defined Networking (SDN) has gained popularity and attractiveness in the past years’ thanks to its dynamic and programmable nature. The possibility to decouple the data plane and control plane allows for the implementation of Internet networks in an innovative way. Thanks to its ease in changing flow rules in network switches, SDN allows network resources optimization. In the case of critical applications, an essential aspect is to ensure connectivity on the network even in case of link failures. Even when a failure causes an interruption of connectivity, the challenge also stays in recovering as fast as possible. Nonetheless, the SDN controller should have the policy to decide which pairs of end-hosts to disable connectivity when there is a shortage of resources to keep the most important connections active. In this thesis, we developed a proactive-reactive SDN controller coded in Python that copes with restoring end-hosts connectivity as fast as possible. The controller prioritizes the couples of end-hosts that need connectivity based on their importance. During a shortage of network resources, the connectivity of pairs of end-hosts with low importance is disabled, and the connectivity between the most important couples can be ensured. We tested our solution with a reactive-only SDN controller and a proactive-reactive SDN controller that does not consider any prioritization order between end-hosts connectivity. Both the benchmark SDN controllers were developed in the thesis. Experiments were run on the same network topology, with the same couple of endhosts involved. The comparison between the proactive-reactive and reactive-only controllers showed the first one to be faster in restoring the connectivity after a failure. It saves time restoring the connectivity and has fewer packets lost under certain conditions in the relationship between the switch-to-switch and the switchto-controller transmission delay. The comparison between the proactive-reactive iii controller and the controller with no prioritization confirms that without an ordered queue of priorities, it may be the most important couple of end-hosts to lose connectivity in case of shortages of network resources. To simulate a realistic scenario, the project considers the case study of electric power transmission networks using SDN. In particular, the focus is on reconnecting Phasor Measurement Unit (PMU)s to the power grid to ensure system observability. During our experiments, we adopted the typical measurement transmission frequency used by PMUs (50Hz). The SDN switches are deployed with P4, and the SDN controller is coded in Python. Furthermore, it exploits P4Runtime to communicate with the switches in run-time. / Software Defined Networking (SDN) har vunnit popularitet och attraktionskraft under de senaste åren tack vare sin dynamiska och programmerbara natur. Möjligheten att frikoppla dataplanet från kontrollplanet gör det möjligt att genomföra Internetnät på ett innovativt sätt. Tack vare att det är lätt att ändra flödesreglerna i nätverksväxlar gör SDN det möjligt att optimera nätverksresurserna. När det gäller kritiska tillämpningar är en viktig aspekt att säkerställa konnektiviteten i nätet även vid länkfel. Även när ett fel orsakar ett avbrott i konnektiviteten är utmaningen också att återhämta sig så snabbt som möjligt. Trots detta bör SDNstyrenheten ha en policy för att avgöra vilka par av slutvärdar som ska inaktivera anslutningen när det råder brist på resurser för att hålla de viktigaste anslutningarna aktiva. I den här avhandlingen har vi utvecklat en proaktiv-reaktiv SDN-styrenhet kodad i Python som klarar av att återställa slutvärdarnas anslutning så snabbt som möjligt. Styrenheten prioriterar paren av slutvärdar som behöver anslutning utifrån deras betydelse. Vid brist på nätverksresurser inaktiveras anslutningen för par av slutvärdar med låg betydelse, och anslutningen mellan de viktigaste paren kan säkerställas. Vi testade vår lösning med en enbart reaktiv SDN-styrenhet och en proaktiv-reaktiv SDN-styrenhet som inte tar hänsyn till någon prioriteringsordning mellan slutvärdarnas konnektivitet. Båda riktmärkeskontrollerna SDN utvecklades i avhandlingen. Experimenten genomfördes på samma nätverkstopologi med samma antal slutvärdar. Jämförelsen mellan den proaktivt-reaktiva och den enbart reaktiva kontrollören visade att den förstnämnda kontrollören var snabbare när det gäller att återställa anslutningen efter ett fel. Den sparar tid för att återställa anslutningen och har färre förlorade paket under vissa förhållanden i förhållandet mellan överföringsfördröjningen från switch till switch och från switch till styrenhet. Jämförelsen mellan den proaktiva-reaktiva styrenheten och v styrenheten utan prioritering bekräftar att utan en ordnad kö av prioriteringar kan det vara det viktigaste paret av slutvärdar som förlorar konnektiviteten vid brist på nätverksresurser. För att simulera ett realistiskt scenario används SDN i projektet som fallstudie för elöverföringsnät. Fokus ligger särskilt på att återansluta Phasor Measurement Unit (PMU)s till elnätet för att säkerställa systemets observerbarhet. Under våra experiment antog vi den typiska överföringsfrekvensen för mätningar som används av PMUs (50Hz). SDN-växlarna installeras med P4, och SDN-styrenheten är kodad i Python. Dessutom utnyttjas P4Runtime för att kommunicera med växlarna i körtid.

Due to advances in digital computing much of the baseband signal processing of a communication system has moved into the digital domain from the analog domain. Within the domain of digital communication systems, Software Defined Radios (SDRs) allow for majority of the signal processing tasks to be implemented in reconfigurable digital hardware. However this comes at a cost of higher power and resource requirements. Therefore, highly efficient custom hardware implementations for SDRs are needed to make SDRs feasible for practical use. Efficient custom hardware motivates the use of fixed point arithmetic in the implementation of Digital Signal Processing (DSP) algorithms. This conversion to finite precision arithmetic introduces quantization noise in the system, which significantly affects the performance metrics of the system. As a result, characterizing quantization noise and its effects within a DSP system is an important challenge that needs to be addressed. Current models to do so significantly over-estimate the quantization effects, resulting in an over-allocation of hardware resources to implement a system. Polynomial Chaos Expansion (PCE) is a method that is currently gaining attention in modelling uncertainty in engineering systems. Although it has been used to analyze quantization effects in DSP systems, previous investigations have been limited to simple examples. The purpose of this thesis is to therefore introduce new techniques that allow the application of PCE to be scaled up to larger DSP blocks with many noise sources. Additionally, the thesis introduces design space exploration algorithms that leverage the accuracy of PCE simulations to estimate bitwidths for fixed point implementations of DSP systems. The advantages of using PCE over current modelling techniques will be presented though its application to case studies relevant to practice. These case studies include Sine Generators, Infinite Impulse Response (IIR) filters, Finite Impulse Response (FIR) filters, FM demodulators and Phase Locked Loops (PLLs). / Thesis / Master of Applied Science (MASc)

Digital Selective Calling (DSC) is a vital maritime communications and safety system, enabling ships in distress to alert nearby vessels and coast guard stations of their emergency. While DSC is suitable for calling, its technical format is substandard from a cybersecurity perspective. Specifically, this work aims to demonstrate that Very High Frequency (VHF) DSC distress calls can be spoofed using Software Defined Radio (SDR). A VHF DSC distress call encoder and VHF DSC SDR signal constructor were developed. The forged distress call was transmitted using various techniques to two different DSC decoder programs, as well as to the maritime VHF transceiver ICOM IC-M510. It was shown that all of the targeted DSC decoders were susceptible to spoofing. This thesis concludes that VHF DSC distress calls can be spoofed using SDR, and infers that the DSC system as a whole has inherent security vulnerabilities that need to be addressed to assure the safety of future seafaring.

The onset of quantum computing threatens commonly used schemes for information secrecy across wireless communication channels, particularly key-based data-level encryption. This calls for secrecy schemes that can provide everlasting secrecy resistant to increased computational power of an adversary. One novel physical layer scheme proposes that an intended receiver capable of performing analog cancellation of a known key-based interference would hold a significant advantage in recovering small underlying messages versus an eavesdropper performing cancellation after analog-to-digital conversion. This advantage holds even in the event that an eavesdropper can recover and use the original key in their digital cancellation. Inspired by this scheme, a flexible software-defined radio receiver design capable of maintaining analog cancellation ratios consistently over 40 dB, reaching up to and over 50 dB, is implemented in this thesis. Maintaining this analog cancellation requires very precise time-frequency synchronization along with accurate modeling and simulation of the channel effects on the interference. The key sources of synchronization error preventing this test bed from achieving and maintaining perfect interference cancellation, sub-sample period timing errors and limited radio frequency stability, are explored for possible improvements. To further prove robustness of the implemented secrecy scheme, the testbed is shown to operate with both phase-shift keying and frequency-modulated waveforms. Differences in the synchronization algorithm used for the two waveforms are highlighted. Interference cancellation performance is measured for increasing interference bandwidth and shown to decrease with such. The implications this testbed has on security approaches based on intentional interference employed to confuse eavesdroppers is approached from the framework proposed in the motivating everlasting secrecy scheme. Using analog cancellation levels from the hardware testbed, it is calculated that secrecy rates up to 2.3 bits/symbol are gained by receivers (intended or not) performing interference cancellation in analog rather than on a digital signal processor. Inspired by the positive gains in secrecy over systems not performing analog cancellation prior to signal reception, a novel secrecy scheme that focuses on the advantage an analog canceller holds in receiver amplifier compression is proposed here. The adversary amplifier is assumed to perform linear cancellation after the interference has passed through their nonlinear amplifier. This is accomplished by deriving the distribution of the interference residual after undergoing an inverse tangent transfer function and perfect linear cancellation. Parameters of this scheme are fit for the radios and cancellation ratios observed in the testbed, resulting in a secrecy gain of 0.95 bits/symbol. The model shows that larger message powers can still be kept secure for the achieved levels of cancellation, thus providing an even greater secrecy gain with increased message transmission power.

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The concepts of a digital twin and extended reality have recently emerged, which require a massive amount of sensor data to be transmitted with low latency and high reliability. For low-latency communications, joint source-channel coding (JSCC) is an attractive method for error correction coding and compared to highly complex digital systems that are currently in use. I propose the use of complex-valued and quaternionic neural networks (QNN) to decode JSCC codes, where the complex-valued neural networks show a significant improvement over real-valued networks and the QNNs have an exceptionally high performance. Furthermore, I propose mapping encoded JSCC code words to the baseband of the frequency domain in order to enable time/frequency synchronization as well as to mitigate fading using robust estimation theory. Additionally, I perform robust statistical signal processing on the high-dimensional JSCC code showing significant noise immunity with drastic performance improvements at low signal-to-noise ratio (SNR) levels. The performance of the proposed JSCC codes is within 5 dB of the optimal performance theoretically achievable and outperforms the maximum likelihood decoder at low SNR while exhibiting the smallest possible latency. I designed a Bayesian minimum mean square error estimator for decoding high-dimensional JSCC codes achieving 99.96% accuracy. With the recent introduction of electromagnetic reconfigurable intelligent surfaces (RIS), a paradigm shift is currently taking place in the world of wireless communications. These new technologies have enabled the inclusion of the wireless channel as part of the optimization process. In order to decode polarization-space modulated RIS reflections, robust polarization state decoders are proposed using the Weiszfeld algorithm and an generalized Huber M-estimator. Additionally, QNNs are trained and evaluated for the recovery of the polarization state. Furthermore, I propose a novel 64-ary signal constellation based on scaled and shifted Eisenstein integers and generated using media-based modulation with a RIS. The waveform is received using an antenna array and decoded with complex-valued convolutional neural networks. I employ the circular cross-correlation function and a-priori knowledge of the phase angle distribution of the constellation to blindly resolve phase offsets between the transmitter and the receiver without the need for pilots or reference signals. Furthermore, the channel attenuation is determined using statistical methods exploiting that the constellation has a particular distribution of magnitudes. After resolving the phase and magnitude ambiguities, the noise power of the channel can also be estimated. Finally, I tune an Sq-estimator to robustly decode the Eisenstein waveform. / Doctor of Philosophy / This dissertation covers three novel wireless communications methods; analog coding, communications using the electromagnetic polarization and communications with a novel signal constellation. The concepts of a digital twin and extended reality have recently emerged, which require a massive amount of sensor data to be transmitted with low latency and high reliability. Contemporary digital communication systems are highly complex with high reliability at the expense of high latency. In order to reduce the complexity and hence latency, I propose to use an analog coding scheme that directly maps the sensor data to the wireless channel. Furthermore, I propose the use of neural networks for decoding at the receiver, hence using the name neural receiver. I employ various data types in the neural receivers hence leveraging the mathematical structure of the data in order to achieve exceptionally high performance. Another key contribution here is the mapping of the analog codes to the frequency domain enabling time and frequency synchronization. I also utilize robust estimation theory to significantly improve the performance and reliability of the coding scheme. With the recent introduction of electromagnetic reconfigurable intelligent surfaces (RIS), a paradigm shift is currently taking place in the world of wireless communications. These new technologies have enabled the inclusion of the wireless channel as part of the optimization process. Therefore, I propose to use the polarization state of the electromagnetic wave to convey information over the channel, where the polarization is determined using a RIS. As with the analog codes, I also extensively employ various methods of robust estimation to improve the performance of the recovery of the polarization at the receiver. Finally, I propose a novel communications signal constellation generated by a RIS that allows for equal probability of error at the receiver. Traditional communication systems utilize reference symbols for synchronization. In this work, I utilize statistical methods and the known distributions of the properties of the transmitted signal to synchronize without reference symbols. This is referred to as blind channel estimation. The reliability of the third communications method is enhanced using a state-of-the-art robust estimation method.