Analytical Performance Evaluation of Cooperative and Multi-Radio Concepts in Emerging Wireless Networks

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Proximity as a Service via Cellular Network-Assisted Mobile Device-to-Device

PhD ThesisThe research progress of communication has brought a lot of novel technologies to meet the multi-dimensional demands such as pervasive connection, low delay and high bandwidth. Device-to-Device (D2D) communication is a way to no longer treat the User Equipment (UEs) as a terminal, but rather as a part of the network for service provisioning. This thesis decouples UEs into service providers (helpers) and service requesters. By collaboration among proximal devices, with the coordination of cellular networks, some local tasks can be achieved, such as coverage extension, computation o oading, mobile crowdsourcing and mobile crowdsensing. This thesis proposes a generic framework Proximity as a Service (PaaS) for increasing the coverage with demands of service continuity. As one of the use cases, the optimal helper selection algorithm of PaaS for increasing the service coverage with demands of service continuity is called ContAct based Proximity (CAP). Mainly, fruitful contact information (e.g., contact duration, frequency, and interval) is captured, and is used to handle ubiquitous proximal services through the optimal selection of helpers. The nature of PaaS is evaluated under the Helsinki city scenario, with movement model of Points Of Interest (POI) and with critical factors in uencing the service demands (e.g., success ratio, disruption duration and frequency). Simulation results show the advantage of CAP, in both success ratio and continuity of the service (outputs). Based on this perspective, metrics such as service success ratio and continuity as a service evaluation of the PaaS are evaluated using the statistical theory of the Design Of Experiments (DOE). DOE is used as there are many dimensions to the state space (access tolerance, selected helper number, helper access limit, and transmit range) that can in uence the results. A key contribution of this work is that it brings rigorous statistical experiment design methods into the research into mobile computing. Results further reveal the influence of four factors (inputs), e.g., service tolerance, number of helpers allocated, the number of concurrent devices supported by each helper and transmit range. Based on this perspective, metrics such as service success ratio and continuity are evaluated using DOE. The results show that transmit range is the most dominant factor. The number of selected helpers is the second most dominant factor. Since di erent factors have di erent regression levels, a uni ed 4 level full factorial experiment and a cubic multiple regression analysis have been carried out. All the interactions and the corresponding coe cients have been found. This work is the rst one to evaluate LTE-Direct and WiFi-Direct in an opportunistic proximity service. The contribution of the results for industry is to guide how many users need to cooperate to enable mobile computing and for academia. This reveals the facts that: 1, in some cases, the improvement of spectrum e ciency brought by D2D is not important; 2, nodal density and the resources used in D2D air-interfaces are important in the eld of mobile computing. This work built a methodology to study the D2D networks with a di erent perspective (PaaS)

Design and analysis of LTE and wi-fi schemes for communications of massive machine devices

Existing communication technologies are designed with speciÿc use cases in mind, however, ex-tending these use cases usually throw up interesting challenges. For example, extending the use of existing cellular networks to emerging applications such as Internet of Things (IoT) devices throws up the challenge of handling massive number of devices. In this thesis, we are motivated to investigate existing schemes used in LTE and Wi-Fi for supporting massive machine devices and improve on observed performance gaps by designing new ones that outperform the former. This thesis investigates the existing random access protocol in LTE and proposes three schemes to combat massive device access challenge. The ÿrst is a root index reuse and allocation scheme which uses link budget calculations in extracting a safe distance for preamble reuse under vari-able cell size and also proposes an index allocation algorithm. Secondly, a dynamic subframe optimization scheme that combats the challenge from an optimisation solution perspective. Thirdly, the use of small cells for random access. Simulation and numerical analysis shows performance improvements against existing schemes in terms of throughput, access delay and probability of collision. In some cases, over 20% increase in performance was observed. The proposed schemes provide quicker and more guaranteed opportunities for machine devices to communicate. Also, in Wi-Fi networks, adaptation of the transmission rates to the dynamic channel condi-tions is a major challenge. Two algorithms were proposed to combat this. The ÿrst makes use of contextual information to determine the network state and respond appropriately whilst the second samples candidate transmission modes and uses the e˛ective throughput to make a deci-sion. The proposed algorithms were compared to several existing rate adaptation algorithms by simulations and under various system and channel conÿgurations. They show signiÿcant per-formance improvements, in terms of throughput, thus, conÿrming their suitability for dynamic channel conditions

Cognitive networking for next generation of cellular communication systems

This thesis presents a comprehensive study of cognitive networking for cellular networks with contributions that enable them to be more dynamic, agile, and efficient. To achieve this, machine learning (ML) algorithms, a subset of artificial intelligence, are employed to bring such cognition to cellular networks. More specifically, three major branches of ML, namely supervised, unsupervised, and reinforcement learning (RL), are utilised for various purposes: unsupervised learning is used for data clustering, while supervised learning is employed for predictions on future behaviours of networks/users. RL, on the other hand, is utilised for optimisation purposes due to its inherent characteristics of adaptability and requiring minimal knowledge of the environment. Energy optimisation, capacity enhancement, and spectrum access are identified as primary design challenges for cellular networks given that they are envisioned to play crucial roles for 5G and beyond due to the increased demand in the number of connected devices as well as data rates. Each design challenge and its corresponding proposed solution are discussed thoroughly in separate chapters. Regarding energy optimisation, a user-side energy consumption is investigated by considering Internet of things (IoT) networks. An RL based intelligent model, which jointly optimises the wireless connection type and data processing entity, is proposed. In particular, a Q-learning algorithm is developed, through which the energy consumption of an IoT device is minimised while keeping the requirement of the applications--in terms of response time and security--satisfied. The proposed methodology manages to result in 0% normalised joint cost--where all the considered metrics are combined--while the benchmarks performed 54.84% on average. Next, the energy consumption of radio access networks (RANs) is targeted, and a traffic-aware cell switching algorithm is designed to reduce the energy consumption of a RAN without compromising on the user quality-of-service (QoS). The proposed technique employs a SARSA algorithm with value function approximation, since the conventional RL methods struggle with solving problems with huge state spaces. The results reveal that up to 52% gain on the total energy consumption is achieved with the proposed technique, and the gain is observed to reduce when the scenario becomes more realistic. On the other hand, capacity enhancement is studied from two different perspectives, namely mobility management and unmanned aerial vehicle (UAV) assistance. Towards that end, a predictive handover (HO) mechanism is designed for mobility management in cellular networks by identifying two major issues of Markov chains based HO predictions. First, revisits--which are defined as a situation whereby a user visits the same cell more than once within the same day--are diagnosed as causing similar transition probabilities, which in turn increases the likelihood of making incorrect predictions. This problem is addressed with a structural change; i.e., rather than storing 2-D transition matrix, it is proposed to store 3-D one that also includes HO orders. The obtained results show that 3-D transition matrix is capable of reducing the HO signalling cost by up to 25.37%, which is observed to drop with increasing randomness level in the data set. Second, making a HO prediction with insufficient criteria is identified as another issue with the conventional Markov chains based predictors. Thus, a prediction confidence level is derived, such that there should be a lower bound to perform HO predictions, which are not always advantageous owing to the HO signalling cost incurred from incorrect predictions. The outcomes of the simulations confirm that the derived confidence level mechanism helps in improving the prediction accuracy by up to 8.23%. Furthermore, still considering capacity enhancement, a UAV assisted cellular networking is considered, and an unsupervised learning-based UAV positioning algorithm is presented. A comprehensive analysis is conducted on the impacts of the overlapping footprints of multiple UAVs, which are controlled by their altitudes. The developed k-means clustering based UAV positioning approach is shown to reduce the number of users in outage by up to 80.47% when compared to the benchmark symmetric deployment. Lastly, a QoS-aware dynamic spectrum access approach is developed in order to tackle challenges related to spectrum access, wherein all the aforementioned types of ML methods are employed. More specifically, by leveraging future traffic load predictions of radio access technologies (RATs) and Q-learning algorithm, a novel proactive spectrum sensing technique is introduced. As such, two different sensing strategies are developed; the first one focuses solely on sensing latency reduction, while the second one jointly optimises sensing latency and user requirements. In particular, the proposed Q-learning algorithm takes the future load predictions of the RATs and the requirements of secondary users--in terms of mobility and bandwidth--as inputs and directs the users to the spectrum of the optimum RAT to perform sensing. The strategy to be employed can be selected based on the needs of the applications, such that if the latency is the only concern, the first strategy should be selected due to the fact that the second strategy is computationally more demanding. However, by employing the second strategy, sensing latency is reduced while satisfying other user requirements. The simulation results demonstrate that, compared to random sensing, the first strategy decays the sensing latency by 85.25%, while the second strategy enhances the full-satisfaction rate, where both mobility and bandwidth requirements of the user are simultaneously satisfied, by 95.7%. Therefore, as it can be observed, three key design challenges of the next generation of cellular networks are identified and addressed via the concept of cognitive networking, providing a utilitarian tool for mobile network operators to plug into their systems. The proposed solutions can be generalised to various network scenarios owing to the sophisticated ML implementations, which renders the solutions both practical and sustainable

Queueing-Theoretic End-to-End Latency Modeling of Future Wireless Networks

The fifth generation (5G) of mobile communication networks is envisioned to enable a variety of novel applications. These applications demand requirements from the network, which are diverse and challenging. Consequently, the mobile network has to be not only capable to meet the demands of one of these applications, but also be flexible enough that it can be tailored to different needs of various services. Among these new applications, there are use cases that require low latency as well as an ultra-high reliability, e.g., to ensure unobstructed production in factory automation or road safety for (autonomous) transportation. In these domains, the requirements are crucial, since violating them may lead to financial or even human damage. Hence, an ultra-low probability of failure is necessary. Based on this, two major questions arise that are the motivation for this thesis. First, how can ultra-low failure probabilities be evaluated, since experiments or simulations would require a tremendous number of runs and, thus, turn out to be infeasible. Second, given a network that can be configured differently for different applications through the concept of network slicing, which performance can be expected by different parameters and what is their optimal choice, particularly in the presence of other applications. In this thesis, both questions shall be answered by appropriate mathematical modeling of the radio interface and the radio access network. Thereby the aim is to find the distribution of the (end-to-end) latency, allowing to extract stochastic measures such as the mean, the variance, but also ultra-high percentiles at the distribution tail. The percentile analysis eventually leads to the desired evaluation of worst-case scenarios at ultra-low probabilities. Therefore, the mathematical tool of queuing theory is utilized to study video streaming performance and one or multiple (low-latency) applications. One of the key contributions is the development of a numeric algorithm to obtain the latency of general queuing systems for homogeneous as well as for prioritized heterogeneous traffic. This provides the foundation for analyzing and improving end-to-end latency for applications with known traffic distributions in arbitrary network topologies and consisting of one or multiple network slices.Es wird erwartet, dass die fünfte Mobilfunkgeneration (5G) eine Reihe neuartiger Anwendungen ermöglichen wird. Allerdings stellen diese Anwendungen sowohl sehr unterschiedliche als auch überaus herausfordernde Anforderungen an das Netzwerk. Folglich muss das mobile Netz nicht nur die Voraussetzungen einer einzelnen Anwendungen erfüllen, sondern auch flexibel genug sein, um an die Vorgaben unterschiedlicher Dienste angepasst werden zu können. Ein Teil der neuen Anwendungen erfordert hochzuverlässige Kommunikation mit niedriger Latenz, um beispielsweise unterbrechungsfreie Produktion in der Fabrikautomatisierung oder Sicherheit im (autonomen) Straßenverkehr zu gewährleisten. In diesen Bereichen ist die Erfüllung der gestellten Anforderungen besonders kritisch, da eine Verletzung finanzielle oder sogar personelle Schäden nach sich ziehen könnte. Eine extrem niedrige Ausfallwahrscheinlichkeit ist daher von größter Wichtigkeit. Daraus ergeben sich zwei wesentliche Fragestellungen, welche diese Arbeit motivieren. Erstens, wie können extrem niedrige Ausfallwahrscheinlichkeiten evaluiert werden. Ihr Nachweis durch Experimente oder Simulationen würde eine extrem große Anzahl an Durchläufen benötigen und sich daher als nicht realisierbar herausstellen. Zweitens, welche Performanz ist für ein gegebenes Netzwerk durch unterschiedliche Konfigurationen zu erwarten und wie kann die optimale Konfiguration gewählt werden. Diese Frage ist insbesondere dann interessant, wenn mehrere Anwendungen gleichzeitig bedient werden und durch sogenanntes Slicing für jeden Dienst unterschiedliche Konfigurationen möglich sind. In dieser Arbeit werden beide Fragen durch geeignete mathematische Modellierung der Funkschnittstelle sowie des Funkzugangsnetzes (Radio Access Network) adressiert. Mithilfe der Warteschlangentheorie soll die stochastische Verteilung der (Ende-zu-Ende-) Latenz bestimmt werden. Dies liefert unterschiedliche stochastische Metriken, wie den Erwartungswert, die Varianz und insbesondere extrem hohe Perzentile am oberen Rand der Verteilung. Letztere geben schließlich Aufschluss über die gesuchten schlimmsten Fälle, die mit sehr geringer Wahrscheinlichkeit eintreten können. In der Arbeit werden Videostreaming und ein oder mehrere niedriglatente Anwendungen untersucht. Zu den wichtigsten Beiträgen zählt dabei die Entwicklung einer numerischen Methode, um die Latenz in allgemeinen Warteschlangensystemen für homogenen sowie für priorisierten heterogenen Datenverkehr zu bestimmen. Dies legt die Grundlage für die Analyse und Verbesserung von Ende-zu-Ende-Latenz für Anwendungen mit bekannten Verkehrsverteilungen in beliebigen Netzwerktopologien mit ein oder mehreren Slices

Machine Learning-based Orchestration Solutions for Future Slicing-Enabled Mobile Networks

The fifth generation mobile networks (5G) will incorporate novel technologies such as network programmability and virtualization enabled by Software-Defined Networking (SDN) and Network Function Virtualization (NFV) paradigms, which have recently attracted major interest from both academic and industrial stakeholders. Building on these concepts, Network Slicing raised as the main driver of a novel business model where mobile operators may open, i.e., “slice”, their infrastructure to new business players and offer independent, isolated and self-contained sets of network functions and physical/virtual resources tailored to specific services requirements. While Network Slicing has the potential to increase the revenue sources of service providers, it involves a number of technical challenges that must be carefully addressed. End-to-end (E2E) network slices encompass time and spectrum resources in the radio access network (RAN), transport resources on the fronthauling/backhauling links, and computing and storage resources at core and edge data centers. Additionally, the vertical service requirements’ heterogeneity (e.g., high throughput, low latency, high reliability) exacerbates the need for novel orchestration solutions able to manage end-to-end network slice resources across different domains, while satisfying stringent service level agreements and specific traffic requirements. An end-to-end network slicing orchestration solution shall i) admit network slice requests such that the overall system revenues are maximized, ii) provide the required resources across different network domains to fulfill the Service Level Agreements (SLAs) iii) dynamically adapt the resource allocation based on the real-time traffic load, endusers’ mobility and instantaneous wireless channel statistics. Certainly, a mobile network represents a fast-changing scenario characterized by complex spatio-temporal relationship connecting end-users’ traffic demand with social activities and economy. Legacy models that aim at providing dynamic resource allocation based on traditional traffic demand forecasting techniques fail to capture these important aspects. To close this gap, machine learning-aided solutions are quickly arising as promising technologies to sustain, in a scalable manner, the set of operations required by the network slicing context. How to implement such resource allocation schemes among slices, while trying to make the most efficient use of the networking resources composing the mobile infrastructure, are key problems underlying the network slicing paradigm, which will be addressed in this thesis