Unmanned aerial vehicles (UAVs) attract increasing attention for various wireless network applications by using UAVs' reliable line-of-sight (LoS) paths in air-ground connections and their flexible placement and movement. As such, the wireless network architecture is becoming three-dimensional (3D), incorporating terrestrial and aerial network nodes, which is more dynamic than the traditional terrestrial communications network. Despite the UAVs' advantages of high LoS path probability and flexible mobility, the challenges of UAV communications need to be considered in the design of integrated air-ground networks, such as spectrum sharing, air-ground interference management, energy-efficient and cost-effective UAV-assisted communications. On the other hand, in wireless networks, users request not only reliable communication services but also execute computation-intensive and latency-sensitive tasks. As one of the enabling technologies in wireless networks, edge computing is proposed to offload users' computation tasks to edge servers to reduce users' latency and energy consumption. However, this requires efficient utilization of both communication resources and computation resources. Furthermore, integrating UAVs into edge computing networks brings many benefits, such as enhancing offloading ability and extending offloading coverage region. This dissertation makes a series of fundamental contributions to UAVs and edge computing in wireless networks that include: 1) Reliable UAV communications, 2) Efficient edge computing schemes, and 3) Integration of UAV and edge computing. In the first contribution, we investigate UAV spectrum access and UAV swarm-enabled aerial reconfigurable intelligent surface (SARIS) for achieving reliable UAV communications. On the one hand, we study a 3D spectrum sharing between device-to-device (D2D) and UAVs communications. Specifically, UAVs perform spatial spectrum sensing to opportunistically access the licensed channels occupied by the D2D communications of ground users. The results show that UAVs' optimal spatial spectrum sensing radius can be obtained given specific network parameters. On the other hand, we study the beamforming and placement design for SARIS networks in downlink transmissions. We consider that the direct links between the ground base station (BS) and mobile users are blocked due to obstacles in the urban environment. SARIS assists the BS in reflecting the signals to randomly distributed mobile users. The results show that the proposed SARIS network significantly improves the weighted sum-rate for ground users, and the placement design plays an essential role in the overall system performance. In the second contribution, we develop a joint communication and computation resource allocation scheme for vehicular edge computing (VEC) systems. The full channel state information (CSI) in VEC systems is not always available at roadside units (RSUs). The channel varies fast due to vehicles' mobility, and it is pretty challenging to estimate CSI and feed back the RSUs for processing the VEC algorithms. To address the above problem, we introduce a large-scale CSI-based partial computation offloading scheme for VEC systems. Using deep learning and optimization tools, we minimize the users' energy consumption while guaranteeing their offloading latency and outage constraints. The results demonstrate that the introduced resource allocation scheme can significantly reduce the total energy consumption of users compared with other computation offloading schemes. In the third contribution, we present novel frameworks for integrating UAVs to edge computing networks to achieve improved computing performance. We study mobile edge computing (MEC) in air-ground integrated wireless networks, including ground computational access points (GCAPs), UAVs, and user equipment (UE), where UAVs and GCAPs cooperatively provide computation resources for UEs. The resource allocation algorithm is developed based on the block coordinate descent method by optimizing the subproblems of users' association, power control, bandwidth allocation, computation capacity allocation, and UAV placement. The results show the advantages of the introduced iterative algorithm regarding the reduced total energy consumption of UEs. Finally, we highlight directions for future works to advance the research presented in this dissertation and discuss its broader impact across the wireless networks industry and standard-making. / Doctor of Philosophy / The fifth-generation (5G) cellular network aims to achieve a high data rate by having greater bandwidth, deploying denser networks, and multiplying the antenna links' capacity. However, the current wireless cellular networks are fixed on the ground and thus pose many disadvantages. Moreover, the improved system performance comes at the cost of increased capital expenditures and operating expenses in wireless networks due to the enormous energy consumption at base stations (BS) and user equipment (UE). More spectrum and energy-efficient yet cost-effective technologies need to be developed in next-generation wireless networks, i.e., beyond-5G or sixth-generation (6G) networks. Recently, unmanned aerial vehicle (UAV) has attracted significant attention in wireless communications. Due to UAVs' agility and mobility, UAVs can be quickly deployed to support reliable communications, resorting to its line-of-sight-dominated connections in the air-ground channels. However, the sufficient available spectrum for extensive UAV communications is scarce, and the co-channel interference in air-air and air-ground connections need to be considered in the design of UAV networks. In addition to users' communication requests, users also need to execute intensive computation tasks with specific latency requirements. As such, edge computing has been proposed to integrate wireless communications and computing by offloading users' computation tasks to edge servers in proximity, reducing users' computation energy consumption and latency. Besides, integrating UAVs into edge computing networks makes efficient offloading schemes by leveraging the advantages of UAV communications. This dissertation makes several contributions that enhance UAV communications and edge computing systems performance, respectively, and present novel frameworks for UAV-assisted three-dimensional (3D) edge computing systems. This dissertation addresses the fundamental challenges in UAV communications, including spectrum sharing, interference management, UAV 3D placement, and beamforming, allowing broadband, wide-scale, cost-effective, and reliable wireless connectivity. Furthermore, this dissertation focuses on the energy-efficient vehicular edge computing systems and mobile edge computing systems, where the UAVs are applied to achieve 3D edge computing systems. To this end, various mathematical frameworks and efficient joint communication and computation resource allocation algorithms are proposed to design, analyze, optimize, and deploy UAV and edge computing systems. The results show that the proposed air-ground integrated networks can deliver spectrum-and-energy-efficient yet cost-effective wireless services, thus providing ubiquitous wireless connectivity and green computation offloading in the future beyond-5G or 6G wireless networks.

A major component of future communication systems is vehicle-to-vehicle (V2V) communications, in which vehicles along roadways transfer information directly among themselves and with roadside infrastructure. Despite its numerous potential advantages, V2V communication suffers from one inherent shortcoming: the stochastic and time-varying nature of the node distributions in a vehicular ad hoc network (VANET) often leads to loss of connectivity and lower coverage. One possible way to improve this coverage is to allow the vehicular nodes to connect to the more reliable cellular network, especially in cases of loss of connectivity in the vehicular network. In this thesis, we analyze this possibility of boosting performance of VANETs, especially their node coverage, by taking assistance from the cellular network. The spatial locations of the vehicular nodes in a VANET exhibit a unique characteristic: they always lie on roadways, which are predominantly linear but are irregularly placed on a two dimensional plane. While there has been a signifcant work on modeling wireless networks using random spatial models, most of it uses homogeneous planar Poisson Point Process (PPP) to maintain tractability, which is clearly not applicable to VANETs. Therefore, to accurately capture the spatial distribution of vehicles in a VANET, we model the roads using the so called Poisson Line Process and then place vehicles randomly on each road according to a one-dimensional homogeneous PPP. As is usually the case, the locations of the cellular base stations are modeled by a planar two-dimensional PPP. Therefore, in this thesis, we propose a new two-tier model for cellular-assisted VANETs, where the cellular base stations form a planar PPP and the vehicular nodes form a one-dimensional PPP on roads modeled as undirected lines according to a Poisson Line Process. The key contribution of this thesis is the stochastic geometric analysis of a maximum power-based cellular-assisted VANET scheme, in which a vehicle receives information from either the nearest vehicle or the nearest cellular base station, based on the received power. We have characterized the network interference and obtained expressions for coverage probability in this cellular-assisted VANET, and successfully demonstrated that using this switching technique can provide a significant improvement in coverage and thus provide better vehicular network performance in the future. In addition, this thesis also analyzes two threshold-distance based schemes which trade off network coverage for a reduction in additional cellular network load; notably, these schemes also outperform traditional vehicular networks that do not use any cellular assistance. Thus, this thesis mathematically validates the possibility of improving VANET performance using cellular networks. / Master of Science

Third generation (3G) mobile communication systems will provide services supporting high-speed data network and multimedia applications in addition to voice applications. The Smart antenna technique is one of the leading technologies that helps to meet the requirement by such services to radio network capacity. Resource management schemes such as power control, handoff and channel reservation/assignment are also essential for providing the seamless services with high quality. Smart antenna techniques will help to enhance the capability of resource management through more efficient and flexible use of resources. In this thesis, adaptive array and switched beam antenna techniques are compared in terms of algorithm, performance, complexity and hardware requirements. Based on these comparisons, sub-optimal code gate algorithm are most likely the suitable algorithms for next generation code division multiple access (CDMA) systems due to its good performances, robustness, and low complexity. A multi-cell CDMA simulator is developed for investigating the gain from smart antenna techniques in both bit error rate (BER) performance improvement and enhancement to resource management schemes. Our study shows that smart antenna techniques can significantly improve the performance of the system and help to build more powerful and flexible resource management schemes. With eight array elements, the system capacity can be increased by a factor of four. Power control command rates can be reduced through the tradeoff with the interference reduction by smart antennas. Smart antennas will also reduce handover failure rates and further increase the system capacity by reducing the resources reserved for soft handover. / Master of Science

Afflicted heavily by spectrum congestion, the unpredictable, dynamic conditions of the radio frequency (RF) spectrum has increasingly become a major obstacle for devices today. More specifically, a significant threat existing within this kind of environment is interference caused by collisions, which is increasingly unavoidable in an overcrowded spectrum. Thus, these devices require a way to avoid such events. Cognitive radios (CR) were proposed as a solution through its transmission adaptability and decision-making capabilities within a radio. Through spectrum sensing, CRs are able to capture the current condition of the RF spectrum and based on its decision-making strategy, interpret these results to make an informed decision on what to do next to optimize its own communication. With the emergence of artificial intelligence, one such decision-making strategy CRs can utilize is Reinforcement Learning (RL). Unlike standard adaptive radios, CRs equipped with RL can predict the conditions of the RF spectrum, and using these predictions, understand what it must do in the future to operate optimally. Recognizing the usefulness of RL in hard-to-predict environments, such as the RF spectrum, research of RL within CRs have become more popular over the past decade, especially for interference mitigation. However, the existing literature neglects to confront the possible limitations that pose a threat to the proper implementation of RL in RF systems. Therefore, this thesis is motivated to investigate what limitations in real-time communication systems can hinder the performance of RL, and as a result of these limitations, emphasize the considerations that should be a focus in the design and implementation of radio frequency reinforcement learning (RFRL) systems. The effects of latency, power, wireless channel impairments, different transmission protocols, and different spectrum sensing detectors are among the possible limitations simulated and analyzed within this work that are not typically considered within simulation-based prior art. To perform this investigation, a representative real-time OFDM transmit/receive chain is implemented within the GNU Radio framework. The system, operating over-the-air through USRPs, leverages reinforcement learning, e.g. Q-Learning, in order to avoid interference with other spectrum users. Performance analysis of this representative system provides a systematic approach for helping to predict limiting factors within an implemented real-time system and thus, aim to provide guidance on how to design these systems with these practical limitations in mind. / M.S. / Because the space in which communication signals travel is congested with activity, collisions among signals, called interference, is becoming more of a problem in wireless communications. Therefore, to avoid such an occurrence, intelligent radios are used to adapt communication devices to operate optimally within this congested space. With the emergence of artificial intelligence, where devices can learn on their own how to adapt, one such way an intelligent radio can dynamically adapt to the congestion is through Reinforcement Learning (RL), which enables prediction of signal activity within the communication space over time. Intelligent radios equipped with RL learn through trial-and-error how to operate optimally within the communication space to avoid places within the communication space that are busy and congested. Recognizing the usefulness of RL in hard-to-predict environments, research of RL within intelligent radios have become more popular over the past decade, especially for navigating a communication space that is congested and where collisions are common. However, existing literature neglects to confront the possible limitations that pose a threat to the proper implementation of RL in communication systems. Therefore, this thesis is motivated to investigate what limitations in real-world communication systems can hinder the performance of RL, and as a result of these limitations, emphasize the considerations that should be a focus in the design and implementation of communication systems equipped with RL. Effects, such as delays in the system, differences in how the signal operates, and how the signal is affected while it is traveling, are among the possible limitations simulated and analyzed within this work that are not typically considered within prior art. To perform this investigation, a modern representative communication system was implemented within software-enabled radios. The system leverages reinforcement learning in order to avoid collisions with other signals in the communication space. Performance analysis of this representative system provides a systematic approach for helping to predict limiting factors within an implemented real-world communication system with RL and thus, aim to provide guidance on how to design these systems with these practical limitations in mind.

Thesis ( M.Sc. (Computer Science)) -- University of Limpopo, 2019 / Cognitive radio network, which enables dynamic spectrum access, addresses the shortage of radio spectrum caused by ever-increasing wireless technology. This allows efficient utilisation of underutilised licenced spectrum by allowing cognitive radios to opportunistically make use of available licenced spectrum. Cognitive radios (CR), also known as secondary users, must constantly sense the spectrum band to avoid interfering with the transmission of the licenced users, known as primary users. Cognitive radios must cooperate in sensing the spectrum environment to avoid environmental issues that can affect the spectrum sensing. However, cooperative spectrum sensing is vulnerable to Byzantine attacks where selfish CR falsify the spectrum reports. Hence, there is a need to design and implement a defence mechanism that will thwart the Byzantine attacks and guarantee correct available spectrum access decisions. The use of extreme studentized deviate (ESD) test together with consensus algorithms are proposed in this study to combat the results of the availability of Byzantine attack in a cognitive radio network. The ESD test was used to detect and isolate falsified reports from selfish cognitive radios during the information sharing phase. The consensus algorithm was used to combine sensing reports at each time k to arrive at a consensus value which will be used to decide the spectrum availability. The proposed scheme, known extreme studentized cooperative consensus spectrum sensing (ESCCSS), was implemented in an ad hoc cognitive radio networks environment where the use of a data fusion centre (DFC) is not required. Cognitive radios make their own data fusion and make the final decision about the availability of the spectrum on their sensed reports and reports from their neighbouring nodes without any assistance from the fusion centre. MATLAB was used to implement and simulate the proposed scheme. We compared our scheme with Attack-Proof Cooperative Spectrum Sensing to check its effectiveness in combating the effect of byzantine attack.

Master of Science / Department of Electrical and Computer Engineering / Steven Warren / The continuous and autonomous real-time monitoring of cattle state of health can provide major benefits for the U.S. livestock industry and lead to a higher quality beef product. Complete real-time monitoring could not only lead to earlier detection of disease in individual animals and reduce the spread of disease to a larger herd, but it could ultimately reduce the cost and frequency of on-site veterinary consultations. This thesis details a wearable device that is mounted on cattle to collect data from a network of internal and external sensors. In addition to the basic data collection, this thesis will describe the infrastructure to communicate these data sets to a central database for permanent storage and future analysis. Physiological, ambient environment, and physical activity data are acquired by the various sensors to give a good indication of the state of health of an animal wearing the device. The communication of data from internal sensors to an external wearable receiver is of particular interest since tissue is not an ideal medium for radio-frequency data transmission. Past research has attempted to use such links with little success due to large signal attenuation at high frequencies and a package that becomes much too large to be usable at low frequencies. As a result, a wireless communications method employing magnetic inductance at relatively low frequencies over short distances is described here.

Thesis (MSc)--Stellenbosch University, 2012. / ENGLISH ABSTRACT: This thesis explores two key aspects of wireless ad hoc networks. The first aspect concerns the topic of stimulating cooperation between nodes in a wireless ad hoc network. The functionality of a wireless ad hoc network depends entirely on the willingness of nodes to relay messages on behalf of other nodes. Network functionality depends on ensuring cooperation between nodes, so that each node benefits from continued participation in the network. This suggests an important question: how can cooperation among individual nodes be managed to improve overall wireless ad hoc network performance? The second aspect explored in this thesis concerns the concept of optimal resource utilisation. Wireless ad hoc networks are characterised by limited bandwidth and energy resources, which facilitates deployment in situations in which traditional infrastructure based networks are not practical. This suggests another important question: how can the use of the limited energy and bandwidth resources of wireless ad hoc networks be optimised? This research relies on the concept of a credit-based market economy. Nodes in simulated ad hoc networks use credits to pay for the cost of sending their own traffic and earn credits by forwarding traffic on behalf of other nodes. We show that a credit-based market economy approach can be employed to stimulate and regulate cooperation between nodes in a wireless ad hoc network. We show that this approach can be implemented in a simple decentralised manner and that it has several variants depending on which node is considered to be paying for the service, what the price of each service should be and how we route packets around the network using information derived from the credit-based economy. This thesis demonstrates that several variants of a credit-based scheme can be implemented in a packet based simulator and that these variants result in the stable operation of the network and improve the overall performance. The credit-based mechanisms also show significant improvement to network performance in resource constrained conditions and represent an effective means for optimising limited energy and bandwidth resource. The effectiveness of the credit-based mechanisms increases as the load on the networks increases. / AFRIKAANSE OPSOMMING: Hierdie tesis ondersoek twee belangrike aspekte van draadlose ad hoc-netwerke. Die eerste aspek het betrekking op die onderwerp van 'n stimulerende samewerking tussen die nodusse in 'n draadlose ad hoc-netwerk. Dat die network funksioneer hang daarvan af om samewerking tussen die nodusse te verseker sodat elke nodus voordeel put uit voortgesette deelname in die netwerk. Dit dui op 'n belangrike vraag: Hoe kan die samewerking tussen die individuele nodusse bestuur word om die prestasie van 'n draadlose ad hoc-netwerk te verbeter? Die tweede aspek wat in hierdie tesis ondersoek word, behels die konsep van optimale hulpbronbenutting. Draadlose ad hoc-netwerke word gekenmerk deur beperkte bandwydte- en energie-hulpbronne, wat ontplooiing bewerkstellig in situasies waar tradisionele infrastruktuur-gebaseerde netwerke nie practise moontlik is nie. Dit dui op 'n ander belangrike vraag: Hoe kan die gebruik van die beperkte energie- en bandwydte-hulpbronne van draadlose ad hoc-netwerke optimaal bestuur word? Hierdie navorsing berus op die konsep van 'n krediet-gebaseerde markekonomie. Nodusse in gesimuleerde ad hoc-netwerke gebruik krediete om te betaal vir die versending van hul eie verkeer en nodusse verdien krediete deur die verkeer van ander nodusse aan te stuur. Ons wys dat die benadering van 'n krediet-gebaseerde markekonomie gebruik kan word om die samewerking tussen die nodusse in 'n draadlose ad hoc-netwerk te stimuleer en te reguleer. Ons wys dat hierdie benadering geïmplementeer kan word op 'n eenvoudige gedesentraliseerde wyse. Ons ondersoek verskeie variasies van die benadering, na gelang van watter nodus oorweeg word om vir die diens te betaal, wat die prys van elke diens moet wees en hoe inligting afgelei van die krediet-gebaseerde ekonomie gebruik kan word om pakkies in die netwerk te roeteer. Hierdie tesis toon dat verskeie variante van 'n krediet-gebaseerde skema geïmplementeer kan word in 'n netwerksimulator en dat hierdie variante die stabiele bedryf van en algehele verbetering in die prestasie van die network tot gevolg het. Die krediet-gebaseerde meganismes toon 'n beduidende verbetering in hulpbronbenutting en netwerkprestasie in omgewings met beperkte hulpbronne en verteenwoordig 'n doeltreffende manier om die beperkte energie- en bandwydte-hulpbronne optimaal te benut. Laastens, die doeltreffendheid van die krediet-gebaseerde meganismes word verhoog as die las op die netwerke word verhoog.

We investigate spectral-efficient design and develop novel schemes to improve spectral efficiency of the modern wireless communications networks. Nowadays, more and more spectrum resources are required to support various high-data-rate applications while spectrum resources are limited. Moreover, static allocation and exclusive access in current spectrum assignment policy caused a lot of licensed spectrum bands to be underutilized. To deal with the problem, cognitive radio (CR) has been developed, which allows unlicensed/secondary users to transmit with licensed/primary users as long as the former ones do not generate intolerable interference to the latter ones. The coexistence of users and networks requires careful and dynamic planning to mitigate interference. Otherwise, the network performance will be severely undermined. We study both spectrum sensing and spectrum access techniques and propose several transmit schemes for different types of cognitive ratio networks, including spectrum overlay and spectrum underlay systems. The proposed algorithms can improve spectral efficiency of the networks efficiently and have potentials to be used in future wireless communications networks.

The spectrum has become one of the most important and scarce resources for future wireless communications. However, the current static spectrum policy cannot meet the increasing demands for spectrum access. To improve spectrum efficiency, dynamic spectrum access (DSA) attempts to allocate the spectrum to users in an intelligent manner. Cognitive radio (CR) is an enabling technology for DSA, and can maximize spectrum utilization by introducing unlicensed or secondary users (SUs) to the primary system. The key component of DSA is dynamic spectrum sharing (DSS), which is responsible for providing efficient and fair spectrum allocation or scheduling solutions among licensed or primary users (PUs) and SUs. This thesis focuses on the design of efficient DSS schemes for the future wireless communication networks. Firstly, based on the coordinated DSS model, this thesis proposes a heterogeneous-prioritized spectrum sharing policy for coordinated dynamic spectrum access networks. Secondly, based on the uncoordinated DSS model, a novel partial spectrum sharing strategy and the cross-layer optimization method have been proposed to achieve efficient spectrum sharing between two licensed networks. Then, a hybrid strategy which combines the overlay and underlay schemes is proposed under uncoordinated DSS model. The proposed analytical methods can provide efficient and accurate modeling to predict the behaviors of the PUs and SUs in DSS systems. This thesis presents the performance prediction of the proposed novel DSS schemes that achieve efficient spectrum sharing for coordinated and uncoordinated future wireless networks.

Automatic modulation classification detects the modulation type of received communication signals. It has important applications in military scenarios to facilitate jamming, intelligence, surveillance, and threat analysis. The renewed interest from civilian scenes has been fuelled by the development of intelligent communications systems such as cognitive radio and software defined radio. More specifically, it is complementary to adaptive modulation and coding where a modulation can be deployed from a set of candidates according to the channel condition and system specification for improved spectrum efficiency and link reliability. In this research, we started by improving some existing methods for higher classification accuracy but lower complexity. Machine learning techniques such as k-nearest neighbour and support vector machine have been adopted for simplified decision making using known features. Logistic regression, genetic algorithm and genetic programming have been incorporated for improved classification performance through feature selection and combination. We have also developed a new distribution test based classifier which is tailored for modulation classification with the inspiration from Kolmogorov-Smirnov test. The proposed classifier is shown to have improved accuracy and robustness over the standard distribution test. For blind classification in imperfect channels, we developed the combination of minimum distance centroid estimator and non-parametric likelihood function for blind modulation classification without the prior knowledge on channel noise. The centroid estimator provides joint estimation of channel gain and carrier phase o set where both can be compensated in the following nonparametric likelihood function. The non-parametric likelihood function, in the meantime, provide likelihood evaluation without a specifically assumed noise model. The combination has shown to have higher robustness when different noise types are considered. To push modulation classification techniques into a more timely setting, we also developed the principle for blind classification in MIMO systems. The classification is achieved through expectation maximization channel estimation and likelihood based classification. Early results have shown bright prospect for the method while more work is needed to further optimize the method and to provide a more thorough validation.