Resource allocation in modern wireless networks is constrained by increasingly stringent real-time requirements. Such real-time requirements typically come from, among others, the short coherence time on a wireless channel, the small time resolution for resource allocation in OFDM-based radio frame structure, or the low-latency requirements from delay-sensitive applications. An optimal resource allocation solution is useful only if it can be determined and applied to the network entities within its expected time. For today's wireless networks such as 5G NR, such expected time (or real-time requirement) can be as low as 1 ms or even 100 μs. Most of the existing resource optimization solutions to wireless networks do not explicitly take real-time requirement as a constraint when developing solutions. In fact, the mainstream of research works relies on the asymptotic complexity analysis for designing solution algorithms. Asymptotic complexity analysis is only concerned with the growth of its computational complexity as the input size increases (as in the big-O notation). It cannot capture the real-time requirement that is measured in wall-clock time. As a result, existing approaches such as exact or approximate optimization techniques from operations research are usually not useful in wireless networks in the field. Similarly, many problem-specific heuristic solutions with polynomial-time asymptotic complexities may suffer from a similar fate, if their complexities are not tested in actual wall-clock time. To address the limitations of existing approaches, this dissertation presents novel real- time solution designs to two types of optimization problems in wireless networks: i) problems that have closed-form mathematical models, and ii) problems that cannot be modeled in closed-form. For the first type of problems, we propose a novel approach that consists of (i) problem decomposition, which breaks an original optimization problem into a large number of small and independent sub-problems, (ii) search intensification, which identifies the most promising problem sub-space and selects a small set of sub-problems to match the available GPU processing cores, and (iii) GPU-based large-scale parallel processing, which solves the selected sub-problems in parallel and finds a near-optimal solution to the original problem. The efficacy of this approach has been illustrated by our solutions to the following two problems. • Real-Time Scheduling to Achieve Fair LTE/Wi-Fi Coexistence: We investigate a resource optimization problem for the fair coexistence between LTE and Wi-Fi in the unlicensed spectrum. The real-time requirement for finding the optimal channel division and LTE resource allocation solution is on 1 ms time scale. This problem involves the optimal division of transmission time for LTE and Wi-Fi across multi- ple unlicensed bands, and the resource allocation among LTE users within the LTE's "ON" periods. We formulate this optimization problem as a mixed-integer linear pro- gram and prove its NP-hardness. Then by exploiting the unique problem structure, we propose a real-time solution design that is based on problem decomposition and GPU-based parallel processing techniques. Results from an implementation on the NVIDIA GPU/CUDA platform demonstrate that the proposed solution can achieve near-optimal objective and meet the 1 ms timing requirement in 4G LTE. • An Ultrafast GPU-based Proportional Fair Scheduler for 5G NR: We study the popular proportional-fair (PF) scheduling problem in a 5G NR environment. The real-time requirement for determining the optimal (with respect to the PF objective) resource allocation and MCS selection solution is 125 μs (under 5G numerology 3). In this problem, we need to allocate frequency-time resource blocks on an operating channel and assign modulation and coding scheme (MCS) for each active user in the cell. We present GPF+ — a GPU based real-time PF scheduler. With GPF+, the original PF optimization problem is decomposed into a large number of small and in- dependent sub-problems. We then employ a cross-entropy based search intensification technique to identify the most promising problem sub-space and select a small set of sub-problems to fit into a GPU. After solving the selected sub-problems in parallel using GPU cores, we find the best sub-problem solution and use it as the final scheduling solution. Evaluation results show that GPF+ is able to provide near-optimal PF performance in a 5G cell while meeting the 125 μs real-time requirement. For the second type of problems, where there is no closed-form mathematical formulation, we propose to employ model-free deep learning (DL) or deep reinforcement learning (DRL) techniques along with judicious consideration of timing requirement throughout the design. Under DL/DRL, we employ deep function approximators (neural networks) to learn the unknown objective function of an optimization problem, approximate an optimal algorithm to find resource allocation solutions, or discover important mapping functions related to the resource optimization. To meet the real-time requirement, we propose to augment DL or DRL methods with optimization techniques at the input or output of the deep function approximators to reduce their complexities and computational time. Under this approach, we study the following two problems: • A DRL-based Approach to Dynamic eMBB/URLLC Multiplexing in 5G NR: We study the problem of dynamic multiplexing of eMBB and URLLC on the same channel through preemptive resource puncturing. The real-time requirement for determining the optimal URLLC puncturing solution is 1 ms (under 5G numerology 0). A major challenge in solving this problem is that it cannot be modeled using closed-form mathematical expressions. To address this issue, we develop a model-free DRL approach which employs a deep neural network to learn an optimal algorithm to allocate the URLLC puncturing over the operating channel, with the objective of minimizing the adverse impact from URLLC traffic on eMBB. Our contributions include a novel learning method that exploits the intrinsic properties of the URLLC puncturing optimization problem to achieve a fast and stable learning convergence, and a mechanism to ensure feasibility of the deep neural network's output puncturing solution. Experimental results demonstrate that our DRL-based solution significantly outperforms state-of-the-art algorithms proposed in the literature and meets the 1 ms real-time requirement for dynamic multiplexing. • A DL-based Link Adaptation for eMBB/URLLC Multiplexing in 5G NR: We investigate MCS selection for eMBB traffic under the impact of URLLC preemptive puncturing. The real-time requirement for determining the optimal MCSs for all eMBB transmissions scheduled in a transmission interval is 125 μs (under 5G numerology 3). The objective is to have eMBB meet a given block-error rate (BLER) target under the adverse impact of URLLC puncturing. Since this problem cannot be mathematically modeled in closed-form, we proposed a DL-based solution design that uses a deep neural network to learn and predict the BLERs of a transmission under each MCS level. Then based on the BLER predictions, an optimal MCS can be found for each transmission that can achieve the BLER target. To meet the 5G real-time requirement, we implement this design through a hybrid CPU and GPU architecture to minimize the execution time. Extensive experimental results show that our design can select optimal MCS under the impact of preemptive puncturing and meet the 125 μs timing requirement. / Doctor of Philosophy / In modern wireless networks such as 4G LTE and 5G NR, the optimal allocation of radio resources must be performed within a real-time requirement of 1 ms or even 100 μs time scale. Such a real-time requirement comes from the physical properties of wireless channels, the short time resolution for resource allocation defined in the wireless communication standards, and the low-latency requirement from delay-sensitive applications. Real-time requirement, although necessary for wireless networks in the field, has hardly been considered as a key constraint for solution design in the research community. Existing solutions in the literature mostly consider theoretical computational complexities, rather than actual computation time as measured by wall clock. To address the limitations of existing approaches, this dissertation presents real-time solution designs to two types of optimization problems in wireless networks: i) problems that have mathematical models, and ii) problems that cannot be modeled mathematically. For the first type of problems, we propose a novel approach that consists of (i) problem decomposition, (ii) search intensification, and (iii) GPU-based large-scale parallel processing techniques. The efficacy of this approach has been illustrated by our solutions to the following two problems. • Real-Time Scheduling to Achieve Fair LTE/Wi-Fi Coexistence: We investigate a resource optimization problem for the fair coexistence between LTE and Wi-Fi users in the same (unlicensed) spectrum. The real-time requirement for finding the optimal LTE resource allocation solution is on 1 ms time scale. • An Ultrafast GPU-based Proportional Fair Scheduler for 5G NR: We study the popular proportional-fair (PF) scheduling problem in a 5G NR environment. The real-time requirement for determining the optimal resource allocation and modulation and coding scheme (MCS) for each user is 125 μs. For the second type of problems, where there is no mathematical formulation, we propose to employ model-free deep learning (DL) or deep reinforcement learning (DRL) techniques along with judicious consideration of timing requirement throughout the design. Under this approach, we study the following two problems: • A DRL-based Approach to Dynamic eMBB/URLLC Multiplexing in 5G NR: We study the problem of dynamic multiplexing of eMBB and URLLC on the same channel through preemptive resource puncturing. The real-time requirement for determining the optimal URLLC puncturing solution is 1 ms. • A DL-based Link Adaptation for eMBB/URLLC Multiplexing in 5G NR: We investigate MCS selection for eMBB traffic under the impact of URLLC preemptive puncturing. The real-time requirement for determining the optimal MCSs for all eMBB transmissions scheduled in a transmission interval is 125 μs.

With increasing mobile data demand there is a push towards heterogeneous networks. Small-scale operators (SSOs) of WLANs are becoming more prevalent, while Mobile Network Operators (MNOs) seek an outlet for their customers' data usage. These conditions prompt the need for an effective relationship between the two parties for the purpose of offloading cellular data traffic to WLANs in a way that is economically beneficial to all involved. This thesis presents a model of such a relationship, in which the SSO sets a strategic offloading price per subscriber and several MNOs can choose how many subscribers they want to offload in order to minimize their costs. We determine the optimal offloading price, identify how the SSO incorporates its own network's quality of service (QoS) into its price decision, and examine the way in which the MNOs' cost structures affect their ability to offload. This model can be applied by both MNOs and SSOs to make informed network deployment decisions, even before engaging in an offloading relationship. / Master of Science

Wireless network virtualization is a promising avenue of research for next-generation 5G cellular networks. This work investigates the problem of selecting base stations to construct virtual networks for a set of service providers, and adaptive slicing of the resources between the service providers to satisfy service provider demands. A two-stage stochastic optimization framework is introduced to solve this problem, and two methods are presented for approximating the stochastic model. The first method uses a sampling approach applied to the deterministic equivalent program of the stochastic model. The second method uses a genetic algorithm for base station selection and adaptively slicing via a single-stage linear optimization problem. A number of scenarios are simulated using a log-normal model designed to emulate demand from real world cellular networks. Simulations indicate that the first approach can provide a reasonably tight solution, but is constrained as the time expense grows exponentially with the number of parameters. The second approach provides a significant improvement in run time with the introduction of marginal error. / Master of Science / 5G, the next generation cellular network standard, promises to provide significant improvements over current generation standards. For 5G to be successful, this must be accompanied by similarly significant efficiency improvements. Wireless network virtualization is a promising technology that has been shown to improve the cost efficiency of current generation cellular networks. By abstracting the physical resource—such as cell tower base stations— from the use of the resource, virtual resources are formed. This work investigates the problem of selecting virtual resources (e.g., base stations) to construct virtual wireless networks with minimal cost and slicing the selected resources to individual networks to optimally satisfy individual network demands. This problem is framed in a stochastic optimization framework and two approaches are presented for approximation. The first approach converts the framework into a deterministic equivalent and reduces it to a tractable form. The second approach uses a genetic algorithm to approximate resource selection. Approaches are simulated and evaluated utilizing a demand model constructed to emulate the statistics of an observed real world urban network. Simulations indicate that the first approach can provide a reasonably tight solution with significant time expense, and that the second approach provides a solution in significantly less time with the introduction of marginal error.

Wireless local area networks (WLAN) are beginning to play a much larger role in corporate network environments and are already very popular for home networking applications. This increase in accessibility has created large security holes for hackers and thieves to abuse, which is finally being addressed by stronger security methods such as advanced encryption algorithms and efficient authentication processes. However, these security methods often hamper network performance unbeknownst to engineers and users. This research examines the effects of Wired Equivalent Privacy (WEP), Temporal Key Integrity Protocol (TKIP), and Counter Mode/CBC-MAC Protocol (CCMP) encryption algorithms on throughput rates for IEEE 802.11 networks as well as the authentication times for Lightweight Extensible Authentication Protocol (LEAP) and Protected Extensible Authentication Protocol (PEAP). The research shows that today's wireless hardware is capable of reducing overhead of even the most advanced encryption schemes to less than five percent of the total bandwidth. / Master of Science

No description available.

In recent years, Multi-hop Wireless Networks (MHWNs) have experienced an explosion of deployment due to the increasing demand for continuous connectivity regardless of the physical location. Internet predominant transport protocols, i.e. Transmission Control Protocol (TCP), face performance degradation in MHWNs because of the high loss and link failure rates. Several solutions have been proposed which are based on network state estimation or use information from MAC layer (called metrics) in a cross-layer manner to better comprehend the network state. The first part of this thesis provides a survey and comprehensive definition of common metrics from Physical, MAC, Network and Transport layers and thus provides a multi-criteria and hierarchical classification. After that, the effectiveness in reflecting network information of MAC metrics is also investigated in a systematic way by simulating various network situations and measuring the MAC metrics. Thus, the good MAC metric for congestion control which is coupled with the network contention level and the medium induced losses will be found out. From the results of the effectiveness study, new rate control schemes for transport protocols are proposed which adapt efficiently the source bit rate depending on the network condition provided by some MAC metrics. Through an extensive set of simulations, the performance of the proposed rate control schemes in MHWNs is investigated thoroughly with several network situations.

Cognitive radios are currently presented as the solution to the ever-increasing spectrum shortage problem. However, their increased capabilities over traditional radios introduce a new dimension of security threats. Cooperative Spectrum Sensing (CSS) has been proposed as a means to protect cognitive radio networks from the well known security threats: Primary User Emulation (PUE) and Spectrum Sensing Data Falsification (SSDF). I demonstrate a new threat to trust-based CSS protocols, called the Rogue Signal Framing (RSF) intrusion. Rogue signals can be exploited to create the illusion of malicious sensors which leads to the framing of innocent sensors and consequently, their removal from the shared spectrum sensing. Ultimately, with fewer sensors working together, the spectrum sensing is less robust for making correct spectrum access decisions. The simulation experiments illustrate the impact of RSF intrusions which, in severe cases, shows roughly 40\% of sensors removed. To mitigate the RSF intrusion's damage to the network's trust, I introduce a new defense based on community detection from analyzing the network's Received Signal Strength (RSS) diversity. Tests show a 95\% damage reduction in terms of removed sensors from the shared spectrum sensing, thus retaining the benefits of CSS protocols.

In this thesis, we present novel software technology for the analysis of wireless networks, an emerging area of computer science. To address the widely acknowledged lack of formal foundations in this field, probabilistic model checking, a formal method for verification and performance analysis, is used. Contrary to test and simulation, it systematically explores the full state space and therefore allows reasoning about all possible behaviours of a system. This thesis contributes to design, modelling, and analysis of ad-hoc networks and randomised distributed coordination protocols. First, we present a new hybrid approach that effectively combines probabilistic model checking and state-of-the-art models from the simulation community in order to improve the reliability of design and analysis of wireless sensor networks and their protocols. We describe algorithms for the automated generation of models for both analysis methods and their implementation in a tool. Second, we study spatial properties of wireless sensor networks, mainly with respect to Quality of Service and energy properties. Third, we investigate the contention resolution protocol of the networking standard ZigBee. We build a generic stochastic model for this protocol and analyse Quality of Service and energy properties of it. Furthermore, we assess the applicability of different interference models. Fourth, we explore slot allocation protocols, which serve as a bandwidth allocation mechanism for ad-hoc networks. We build a generic model for this class of protocols, study real-world protocols, and optimise protocol parameters with respect to Quality of Service and energy constraints. We combine this with the novel formalisms for wireless communication and interference models, and finally we optimise local (node) and global (network) routing policies. This is the first application of probabilistic model checking both to protocols of the ZigBee standard and protocols for slot allocation.

The aim of this diploma thesis is to analyze, design and create the architecture of the WSageNt system web interface. The main focus of the system will be traffic monitoring and topology control of the network. The work describes basic technologies, design principles and implementation methods.

Le cadre de cette thèse est l'étude des systèmes contrôlés en réseau sans fil (SCRSF) qui utilise la technologie IEEE 802.15.4. Le premier objectif est d'étudier la pertinence de l'utilisation du réseau de type IEEE 802.15.4 pour les SCRSF puis de proposer et d'évaluer des mécanismes pour garantir la Qualité de Service (QdS) offerte par le réseau au système contrôlé. Nous analysons l'utilisation des slots temporels réservés (GTS) dans le cadre des SCRSF et les contraintes qui en découlent. De plus, nous proposons des mécanismes de gestion de la QdS avec priorité aussi bien pour le mode avec balise que pour le mode sans balise du protocole IEEE 802.15.4. Ces propositions ont été validées par des simulations et une partie de manière analytique. Notre deuxième objectif est de concevoir, d'une manière conjointe, les SCRSF pour pouvoir régler en ligne la QdS offerte par le réseau en fonction de la Qualité de Contrôle (QdC) du système contrôlé. Nous proposons des protocoles d'adaptation en ligne de la QdS du réseau qui prennent en compte la QdC du système contrôlé. Ces protocoles ont été validés par simulations et une implémentation réelle de chacun d'eux est proposée / In this thesis, we study wireless networked control systems (WNCS) which use the IEEE 802.15.4 technology. The first objective is to study the pertinence of the use of the IEEE 802.15.4 for the WNCS, then to propose and evaluate QoS management mechanisms which guarantee the Quality of Service (QoS) offered by network to the controlled system. We analyse the use of the guaranteed temporel slots (GTS) for WNCS and in which conditions it is possible. We propose QoS management mechanisms with priority for both the beacon enabled mode and the non-beacon enabled mode of the IEEE 802.15.4 protocol. These proposals are validated through simulations and partially with analytical approach. The second objective is to design the WNCS so that the QoS offered by the network is adated online depending on the Quality of Control (QoC) on the controlled system. We propose QoS online adaptation protocols which take as parameter the QoC of the system. These protocols are validated through simulations and a realistic implementation of them is proposed